NZ617009B2 - Methods of disease activity profiling for personalized therapy management - Google Patents

Abstract

method for personalized therapeutic management of a disease in order to optimize therapy and/or monitor therapeutic efficacy is disclosed. An array of one or a plurality of biomarkers is measured at one or more time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment. l healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment.

Description

METHODS OF DISEASE TY PROFILING FOR PERSONALIZED THERAPY MANAGEMENT CROSS-REFERENCES TO RELATED ATIONS This application claims priority to US. Provisional Patent ation No. 61/484,607, filed May 10, 2011, US. Provisional Patent Application No. 61/505,026, filed July 6, 2011, US. ional Application No. 61/553,909, filed r 31, 2011, US.

Provisional Application No. 61/566,509, filed December 2, 2011, and US. Provisional Application No. 61/636,575, filed April 20, 2012, the disclosures of which are hereby incorporated by reference in their entirety for all purposes.

BACKGROUND OF THE ION Inflammatory bowel disease (IBD) which includes Crohn’s disease (CD) and ulverative colitis (UC) is a chronic idiopathic inflammatory disorder affecting the gatrointestine tract. Disease progression of CD and UC es repeated episodes of inflammation and ulceration of the ine, leading to complications requiring hospitalization, surgery and escalation of therapy (Peyrin-Biroulet et al., Am. J.

Gastroenterol,. 105: 289-297 (2010); Langholz E., Dan. Med. Bil/1., 46: 400-415 (1999)).

Current treatments such as anti-tumor necrosis factor-alpha (TNF-(x) biologics (e.g., infliximab (IFX), cept, adalimumab (ADL) and certolizumab pegol), thiopurine drugs (e.g., azathioprine (AZA), 6-mercaptopurin (6-MP)), anti-inflammatory drugs (e.g., mesalazine), and steroids (e. g., corticosteroids) have been shown to reduce disease activity.

In some clinical trials of CD, mucosal healing which is described as the absence of intestinal ulcers, was induced in patients on combination therapy of corticosteroids, IFX and ADL.

Furthermore, MH was maintained in patients receiving IFX.

Other s have shown that mucosal healing can be a hallmark of suppression of bowel inflammation and predict erm disease remission (Froslie et al., Gastroenterology, 133: 412-422 (2007); Baert et (1]., Gastroenterology, (2010)). Long-term mucosal healing has been associated with a decreased risk of colectomy and colorectal cancer in UC patients, a decreased need for corticosteroid treatment in CD patients, and possibly a decreased need for alization (Dave et al., Gastroenterology & Hepatology, 8(1): 29-38 (2012)).

The International Organization for the Study of Inflammatory Bowel Disease ed defining mucosal healing in UC as the absence of friability, blood, erosions an dulcers in all visualized segments of gut mucosa (D’Haens et al,. Gastroenterology, 132: 763-786 (2007)). MH in CD was proposed to be the absence of ulcers. The gold standard for measurement of s disease activity is the Crohn’s Disease Endoscopic Index of Severity (CDEIS). This disease index score is established from several variables such as superficial and deep ulceration, ulcerated and nonulcerated stenosis, and surface area of ulcerated and disease segments. A simplified version of the index is the Simple Endoscopic Score for Crohn’s Disease, which takes into account disease variables including ulcer size, ulcerated surface, affected surface and presence of narrowing. Both indices evaluate clinical symptoms of CD, yet fail to measure the underlying cause of disease (e.g., inflammation) or resolution of disease (e.g., mucosal healing). A measurement of l healing can be performed to assess disease induction as well as disease progression and resolution.

[0005] The process of mucosal healing begins with bleeding (e.g., degradation of the endothelial layers of the blood vessels) and inflammation, then progresses to cell and tissue proliferation, and finally tissue remodeling. At the inflammation stage, inflammatory markers and anti-inflammatory s, such as, but not limited to, IL-1, IL-2, IL-6, IL-14, IL-17, TGF and TNF are expressed. During remodeling, tissue repair and remodeling growth factors, such as, but not limited to, AREG, EREG, HBEGF , HGF, NRG1-4, BTC, EGF, IGF, TGF-, VEGFs, FGFs, and TWEAK are sed. Repair of the intestinal epithelium requires multiple signal transduction pathways which are necessary for cell survival, proliferation, and migration. We have identified novel markers of mucosal healing that are predictive of the risk of disease relapse and disease remission. A measurement of mucosal g can be used to periodically assess disease status in patients receiving a y regimen. [0005a] Any discussion of the prior art hout the specification should in no way be ered as an admission that such prior art is widely known or forms part of common l knowledge in the field.

[0006] Mucosal g is lly assessed by endoscopy. gh the invasive procedure is considered to be low-risk, its cost and patient fort and compliance remain obstacles to nt, regular opies to assess mucosal healing. There is an unmet need in the art for non-invasive methods of determining mucosal healing in a patient.

There is a need in the art for methods of therapeutic management of diseases such as autoimmune disorders using an individualized approach to optimize therapy and monitor efficacy. The methods need to include ing disease course and clinical parameters such as phamacokinetics, disease activity s, e burden, and l status. [0007a] It is an object of the present invention to overcome or ameliorate at least one of the disadvantages of the prior art, or to provide a useful alternative.

BRIEF SUMMARY OF THE INVENTION [0007b] According to a first aspect, the present invention provides a non-invasive method for measuring mucosal healing in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen, the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample ed from the individual; (b) comparing the levels of an array of mucosal healing markers in the individual to that of a control to compute the mucosal healing index of the individual, wherein the l healing index comprises a representation of the extent of mucosal healing; and (c) determining whether the dual undergoing mucosal healing should maintain the y regimen. [0007c] According to a second aspect, the present invention provides a method for monitoring therapeutic efficiency in an individual with inflammatory bowel disease (IBD) receiving therapy, the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample ed from the individual at a plurality of time points over the course of y with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers ined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; (d) determining whether the therapy is appropriate for the individual to e l g. [0007d] According to a third aspect, the present invention provides a method for selecting a therapy regimen for an individual with inflammatory bowel disease (IBD), the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample obtained from the individual at a plurality of time points over the course of therapy, the individual receiving a therapeutic antibody; (b) applying a tical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s l healing index to that of a control; (d) selecting an appropriate therapy regimen for the individual, wherein the therapy regimen promotes mucosal healing. [0007e] According to a fourth aspect, the present ion provides a method for reducing or zing the risk of surgery in an individual diagnosed with inflammatory bowel disease (IBD) being administered a therapy regimen, said method comprising: (a) ing an array of mucosal healing markers at a ity of time points over the course of therapy with a therapeutic antibody in samples obtained from an individual; (b) generating the individual’s l healing index comprising a representation of the presence and/or concentration levels of each of the markers over time; (c) comparing the individual’s mucosal healing index to that of a control; (d) selecting an riate therapy n to reduce or minimize the risk of surgery. [0007f] Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise”, “comprising”, and the like are to be construed in an - 3a - ive sense as opposed to an exclusive or exhaustive sense; that is to say, in the sense of “including, but not limited to”.

The present invention provides methods for personalized therapeutic ment of a disease in order to optimize therapy and/or r therapeutic efficacy. In ular, the t invention comprises measuring an array of one or a ity of mucosal healing biomarkers at one or a plurality of time points over the course of therapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing ty, and/or monitoring the efficacy of therapeutic treatment. In some embodiments, the therapy is an anti-TNF therapy, an immunosuppressive agent, a corticosteroid, a drug that targets a different mechanism, a nutrition therapy and combinations thereof. In certain instances, the anti-TNF therapy is a TNF inhibitor (e.g., anti-TNF drug, anti-TNFα antibody) for the treatment of a TNFαmediated e or disorder.

TNFα has been implicated in inflammatory diseases, autoimmune diseases, viral, ial and parasitic infections, ancies, and/or neurodegenerative diseases and is a useful target for specific biological therapy in diseases, such as toid arthritis and Crohn’s disease. TNF inhibitors such as anti-TNFα antibodies are an important class of therapeutics. In some embodiments, the methods of the present invention advantageously improve therapeutic management of patients with a TNFα- mediated disease or disorder by zing therapy and/or monitoring therapeutic efficacy to anti-TNF drugs such as anti-TNFα therapeutic antibodies.

As such, in a further aspect, the present invention provides a non-invasive method for measuring mucosal healing in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen, the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample from the individual; (b) ing the levels of an array of mucosal healing markers in the individual to that of a control to compute the mucosal healing index of the dual, wherein the mucosal healing index comprises a representation of the extent of mucosal healing; and - 3b - (c) determining whether the individual oing mucosal healing should maintain the therapy regimen. - 3c - As such, in one , the present invention provides a method for monitoring therapeutic efficiency in an individual with IBD receiving therapy, the method comprising: (a) measuring levels of an array of mucosal healing markers in a sample from the indiVidual at a plurality of time points over the course of therapy with a therapeutic (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the indiVidual’s mucosal healing index to that of a control; and (d) determining r the therapy is appropriate for the indiVidual to e mucosal g.

In another aspect, the present invention es a method for selecting a therapy regimen in an indiVidual with IBD, the method comprising: (a) measuring levels of an array of mucosal healing markers in a sample from the indiVidual at a plurality of time points over the course of therapy, the indiVidual receiVing a therapeutic dy; (b) applying a statistical algorithm to the level of the one or more markers ined in step (a) to generate a mucosal healing index; (c) comparing the indiVidual’s mucosal healing index to that of a control; and (d) selecting an appropriate therapy regimen for the indiVidual n the therapy regimen es mucosal healing As such, in another aspect, the present invention provides a method for reducing or minimizing the risk of surgery in an indiVidual diagnosed with IBD being administered a therapy regimen, the method comprising: (a) measuring an array of mucosal healing markers at a plurality of time points over the course of therapy with a eutic antibody; (b) generating the indiVidual’s mucosal healing index comprising a representation of the presence and/or concentration levels of each of the s over time; (c) comparing the indiVidual’s mucosal healing index to that of a control, and (d) selecting an appropriate therapy regimen for to reduce or minimize the risk of surgery.

As such, in another aspect, the present invention provides a method for selecting a therapy n to promote mucosal healing in an indiVidual diagnosed with IBD, the method comprising: (a) measuring levels of a panel of mucosal healing markers at time point to to generate a mucosal healing index at to; (b) measuring levels of a panel of l g markers at time point t1 to generate a mucosal healing index at t1; (c) comparing the change in the mucosal healing index from to to t1; and (d) selecting the therapy n for the individual to promote mucosal healing.

As such, in one aspect, the present ion provides a non-invasive method for measuring mucosal healing in an individual diagnosed with Crohn’s disease ing an anti-TNF therapy regimen, the method sing: (a) ing the levels of an array of mucosal healing markers in a sample from the individual; (b) comparing the levels of an array of mucosal g s in the individual to that of a control to compute the mucosal healing index of the individual, wherein the mucosal healing index comprises a representation of the extent of mucosal healing; and (c) determining whether the individual undergoing mucosal healing should maintain the anti-TNF therapy regimen.

As such, in another aspect, the present invention provides a method for monitoring therapeutic efficiency in an individual with Crohn’s disease receiving anti-TNF therapy, the method comprising: (a) measuring levels of an array of mucosal healing markers in a sample from the individual at a plurality of time points over the course of y with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; and (d) ining whether the anti-TNF therapy is appropriate for the individual to promote mucosal healing.

[0017] As such, in another aspect, the present invention provides a method for selecting an anti-TNF therapy regimen in an individual with Crohn’s e, the method comprising: (a) measuring levels of an array of l healing markers in a sample from the individual at a plurality of time points over the course of therapy, the individual receiving a therapeutic antibody; (b) ng a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the indiVidual’s l healing index to that of a control; and (d) selecting an appropriate anti-TNF therapy regimen for the individual n the anti-TNF therapy promotes l healing.

As such, in another aspect, the present invention es a method for reducing or minimizing the risk of surgery in an individual diagnosed with Crohn’s disease being administered an anti-TNF antibody therapy n, the method comprising: (a) measuring an array of l healing markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) generating the indiVidual’s mucosal healing index sing a representation of the presence and/or concentration levels of each of the markers over time; (c) comparing the indiVidual’s mucosal healing index to that of a control, and (d) selecting an appropriate anti-TNF antibody y regimen for to reduce or minimize the risk of surgery.

As such, in another aspect, the present invention provides a method for selecting an anti-TNF antibody therapy regimen to promote mucosal healing in an indiVidual diagnosed with Crohn’s disease, the method comprising: (a) measuring levels of a panel of mucosal healing markers at time point to to te a mucosal healing index at to; (b) measuring levels of a panel of mucosal healing s at time point t1 to generate a mucosal healing index at t1; (c) comparing the change in the mucosal healing index from to to t1; and (d) selecting the anti-TNF antibody therapy regimen for the dual to promote mucosal healing.

In some embodiments, the disease is a intestinal disease or an autoimmune disease. In certain ces, the subject has Crohn’s disease (CD) or rheumatoid arthritis (RA). In other embodiments, the therapeutic antibody is an anti-TNFOL antibody. In some ments, the anti-TNFOL antibody is a member selected from the group consisting of REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRATM (adalimumab), CIMZIA® (certolizumab pegol), and combinations thereof. In preferred embodiments, the subject is a human.

In some embodiments, the array of markers ses a mucosal healing marker. In some embodiments, the mucosal marker comprises AREG, EREG, HB-EGF, HGF, NRGl, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.

On other embodiments, the array of markers further comprises a member selected from the group consisting of an anti-TNFu antibody, an anti-drug antibody (ADA), an inflammatory marker, an nflammatory marker, a tissue repair marker (e.g., a growth factor), and combinations thereof. In n instances, the anti-TNFu antibody is a member selected from the group consisting of REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRATM (adalimumab), CIMZIA® (certolizumab pegol), and combinations thereof In certain other instances, the anti-drug dy (ADA) is a member selected from the group consisting of a human himeric antibody (HACA), a human anti-humanized antibody (HAHA), a human ouse antibody , and combinations thereof. In yet other instances, the inflammatory marker is a member selected from the group consisting of GM- CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-u, sTNF R11, and combinations thereof In further instances, the anti-inflammatory marker is a member selected from the group consisting of IL-12p70, IL- 1 0, and combinations f.

In n ments, the array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, ll, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more markers. In some embodiments, the markers are measured in a biological sample selected from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from a site of inflammation such as a portion of the gastrointestinal tract or synovial ).

In certain embodiments, the plurality of time points comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points.

In some instances, the first time point in the plurality of time points is prior to the course of therapy with the therapeutic antibody. In other instances, the first time point in the plurality of time points is during the course of therapy with the therapeutic antibody. As non-limiting examples, each of the markers can be measured prior to therapy with a therapeutic antibody and/or during the course of therapy at one or more (e.g., a ity) of the following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc. 2012/037375 In some embodiments, selecting an appropriate y comprises maintaining, increasing, or decreasing a subsequent dose of the course of y for the subject. In other embodiments, the method further comprises determining a ent course of therapy for the subject. In certain instances, the different course of therapy comprises treatment with a different anti-TNFu antibody. In other instances, the different course of therapy comprises the current course of therapy along with another therapeutic agent, such as, but not limited to an anti-TNF therapy, an immunosuppressive agent, a corticosteroid, a drug that targets a different mechanism, a nutrition therapy and other combination treatments.

In some embodiments, selecting an appropriate therapy comprises selecting an appropriate therapy for initial treatment. In some ces, the therapy comprises an anti- TNFoc antibody therapy.

In certain embodiments, the methods disclosed herein can be used as confirmation that a proposed new drug or therapeutic is the same as or is sufficiently similar to an approved drug product, such that the proposed new drug can be used as a “biosimilar” therapeutic. For example, if the ed new drug has only a slightly different e activity profile compared to the branded drug product, this would be apparent using the methods disclosed herein. If the proposed new drug has a significantly ent disease activity profile compared to the branded drug product, then the new drug would not be ilar. Advantageously, the methods disclosed herein can be used in clinical trials of proposed new drugs in order to assess the effective eutic efficacy or value of the drug.

Accordingly, in some aspects, the methods of the invention provide ation useful for guiding treatment decisions for patients receiving or about to receive anti-TNF drug therapy, e.g., by selecting an appropriate anti-TNF therapy for initial treatment, by determining when or how to adjust or modify (e.g., se or decrease) the subsequent dose of an anti-TNF drug, by determining when or how to combine an anti-TNF drug (e.g., at an initial, increased, decreased, or same dose) with one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA), and/or by ining when or how to change the current course of therapy (e.g., switch to a different anti-TNF drug or to a drug that s a different mechanism such as an IL-6 receptor-inhibiting monoclonal antibody, anti-integrin molecule (e.g., Tysabri, Vedaluzamab), JAK-2 inhibitor, and tyrosine kinase inhibitor, or to a nutritition therapy (e.g., special ydrate diet)).

In other embodiments, the s of the present invention can be used to predict responsiveness to a TNFOL inhibitor, especially to an anti-TNFOL antibody in a subject having 2012/037375 an autoimmune disorder (e.g., rheumatoid arthritis, Crohn’s Disease, ulcerative colitis and the like). In this method, by assaying the subject for the correct or eutic dose of anti- TNFOL antibody, z'.e., the therapeutic concentration level, it is possible to predict r the individual will be responsive to the therapy.

In another embodiment, the present invention provides s for monitoring IBD (e.g., Crohn’s disease and ulcerative colitis) in a subject having the IBD disorder, wherein the method comprises assaying the subject for the correct or therapeutic dose of NFu dy, z'.e., the therapeutic concentration level, over time. In this manner, it is possible to t whether the individual will be responsive to the therapy over the given time period.

[0031] Other objects, features, and advantages of the present invention will be apparent to one of skill in the art from the ing detailed description and figures.

BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 shows a personalized IBD activity profile as described in Example 1.

Figure 2A show various patient infliximab concentrations as a function of treatment time. Figure 2B shows patient ranks over a course of treatment with events (infliximab falling below a threshold concentration) noted.

Figure 3A show various patient HACA (ATI) concentrations as a flinction of treatment time. Figure 3B shows patient ranks over a course of treatment with events (HACA detection or appearance) noted.

[0035] Figure 4A illustrates an association between the presence ofATI and the level of IFX in patient samples. Samples with no detectable level of ATI had a significantly higher IFX median concentration, ed to sample with detectable ATI. Figure 4B illustrates that the presence of ATI correlates with higher CDAI. Figure 4C shows that rent immunosuppressant therapy (e.g, MTX) is more likely to suppress the presence of ATI.

[0036] Figure 5A shows that ts with ATI are more likely to develop a poor response to treatment. Figure 5B illustrates that the inflammatory marker CRP is associated with sed levels of ATI.

Figure 6 illustrates that the protein levels of an array of one or more inflammatory and tissue repair s correlate to the formation of antibodies to IFX.

[0038] Figure 7A illustrates that an array of inflammatory markers can be used to establish an inflammatory index what correlates with the presence ofATI and/or disease progression.

Figure 7B shows the relationship between the P11 and IFX concentrations in samples with ATI present. Figure 7C illustrates that an ary PRO Inflammatory Index correlates with levels of IFX (p<0.0001 and R2 = -0. 129) in patient samples of the COMMIT study.

Figure 8A illustrates the correlation between Crohn’s Disease ty Index (CDAI) score and the concentration of infliximab in serum in a number of patients in clinical study #1. Figure 8B shows that the presence of IFX in a sample correlated with a higher CDAI.

Figure 9A illustrates the association between IFX concentration and the presence of antidrug antibodies to inflixamab in samples analyzed. Figure 9B illustrates that a high concentration ofATI can lead to neutralizing antibodies and undetectable levels of IFX.

Figure 9C rates that an ATI positive sample ined at an early time point leads to a higher CDAI at a later time point, compared to the lower CDAI level from an ATI negative sample. “Vl” = Visit 1; “V3” = Visit 3. Figure 9D rates that in clinical study #1, patients had lower odds of developing ATI if receiving a combination therapy of infliximab and an immunosuppressant agent (6.g. MTX and AZA).

Figure 10A shows that correlation between IFX concentration and the presence of ATI in samples of clinical study #2A. Figure 103 illustrates the relationship between ISA y and the presence ofATI in the study. Figure 10C illustrates the relationship between CRP concentrations and the presence ofATI (ATI and/or neutralizing ATI). Figure 10D illustrates the relationship between loss of responsiveness to IFX therapy and the presence of ATI in the study.

Figure 11 illustrates that levels of ATI and neutralizing antibodies can be determined over time in a series of samples from various patients.

Figure 12A illustrates the comparison of CRP levels to the presence of IFX.

Figure IZB illustrates the onship between the presence of ATI and the infusion reaction.

Figure 12C illustrates the relationship n IFX concentration and the presence ofATI in clinical study #2B. Figure 12D rates the correlation between the presence of ATI and the awal of ISA therapy at a specific, given date.

Figure 13A illustrates the relationship between ATI and the atory marker CRP. Our analysis showed that the odds of experiencing a loss of response to IFX was higher in patients determined to be ATI ve at any time point. Figure 133 illustrates the correlation between the presence of ATI at any time point and responsiveness to IFX treatment. Figure 13C shows that loss of response can be related to an increase in CRP.

Figure 13D illustrates the association between the presence of IFX and CRP levels.

Figure 14A shows that lower IFX levels are associated with the presence ofATI in clinical study #2C. Figure 14B shows that lower IFX levels are associated with the presence ofATI in clinical study #3. Figure 14C illustrates that the same correlation between IFX levels and ATI was also present in the study data, follow-up study and in the pharmacokinetics study.

Figure 15A illustrates the relationship between ATI levels and IFX. It was determined that samples with high concentration ATI are neutralizing on IFX and thus, IFX concentration was determined to be 0 . Figure 153 illustrates an association between ADL tration and the presence ofATA in patient samples.

Figure 16A describes the details of an exemplary PRO Inflammatory Index.

Figure 163 illustrates that there is no obvious relationship between the P11 and the concentration ofADL in an array of samples with ADL alone or in combination with other drugs.

Figure 17 shows a plot of the P11 scores for patients receiving Humira and Humira in ation with other drug such as Remicade, Cimzia, Asathioprine and Methotrexate.

Figure 18 shows details of methods for ed patient ment of CD and/or

[0050] Figure 19 shows the effect of the TNF-oc pathway and related ys on different cell types, cellular mechanisms and e (e.g., Crohn’s Disease (CD), rheumatoid arthritis (RA) and Psoriasis (Ps)).

Figure 20 illustrates an exemplary CEER multiplex growth factor array.

Figures 21A-G illustrate multiplexed growth factor profiling of patient samples using CEER growth factor arrays.

Figure 22 illustrates the association between CRP levels and the growth factor index score in determining disease remission.

Figure 23 illustrates embodiments of the t invention to assist in developing alized t treatment for an IBD patient with mild, moderate, or severe disease activity. 2012/037375 Figure 24 illustrates a treatment paradigm to personalize patient treatment.

Monitoring of disease burden and mucosal healing can assist in determining treatment selection, dose selection, and initial drug response.

Figure 25 shows the ROC analysis of CRP and IFX trough thresholds.

Figure 26 shows the relationship of CRP, serum IFX concentration and ATI at sequential time points. Figure 26A shows presence of IFX and ATI in the pair’s first data point and CRP in the subsequent measurements. Figure 26B shows CRP levels, IFX serum concentration and ATI status at sequential time points for a sample. In this sample CRP levels are lowest when the patient is ATI- and has a serum IFX concentration higher than threshold.

Figure 27 shows that there was no association between IFX levels higher than threshold and CRP in ATI+ patients. Yet, in ATI- patients CRP levels were significantly higher in patients with IFX levels less than threshold (3 ug/ml).

DETAILED DESCRIPTION OF THE INVENTION 1. Introduction The present invention provides methods for ing mucosal healing in patients with IBD, CD and/or UC. In ular, the present invention es methods of measuring mucosal healing markers wherein the markers are indicative of intestinal tissue , and disease resolution or ion.

[0060] The present invention is advantageous because it addresses and overcomes current limitations associated with monitoring mucosal g in patients with IBD (e.g., Crohn’s e and ulcerative colitis). The present invention provides non-invasive methods for monitoring mucosal healing patients receiving anti-TNF therapy. In addition, the present ion provides methods of predicting therapeutic response, risk of relapse, and risk of surgery in patients with IBD (e.g., s disease and ulcerative colitis). In particular, the s of the present ion find utility for selecting an appropriate anti-TNF therapy for initial treatment, for determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-TNF drug to optimize therapeutic efficacy and/or to reduce toxicity, for determining when or how to combine an anti-TNF drug (e.g., at an initial, increased, sed, or same dose) with one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA), and/or for ining when or how to change the current course of therapy (e.g, switch to a different anti-TNF drug or to a drug that targets a different mechanism). The present invention also provides methods for selecting an appropriate therapy for patients diagnosed with CD, n the y promotes mucosal healing. 11. Definitions As used herein, the following terms have the meanings ascribed to them unless specified otherwise.

The phrase “mucosal healing index” includes an empirically derived index that is based upon an analysis of a plurality of mucosal healing markers. In one aspect, the concentration of markers or their measured concentration values are transformed into an index by an algorithm resident on a computer. In certain aspects, the index is a synthetic or human d output, score, or cut off value(s), which expresses the biological data in numerical terms. The index can be used to determine or make or aid in making a clinical decision. A l healing index can be measured multiple times over the course of time.

In one aspect, the algorithm can be trained with known samples and thereafter validated with samples ofknown identity.

[0063] The phrase al healing index control” es a l healing index derived from a y individual, or an individual who has progressed from a disease state to a healthy state. Alternatively, the control can be an index enting a time course of a more diseased state to a less disease state or to a healthy state.

The phrase “determining the course of therapy” and the like includes the use of an empirically derived index, score or analysis to select for example, selecting a dose of drug, selecting an appropriate drug, or a course or length of therapy, a therapy regimen, or maintenance of an ng drug or dose. In certain aspects, a derived or measured index can be used to ine the course of y.

The terms “TNF inhibitor”, “TNF-(X inhibitor” and “TNFOL inhibitor” as used herein are intended to encompass agents including ns, antibodies, antibody fragments, fusion proteins (6.g. , Ig fiasion proteins or PC fusion proteins), alent binding proteins (e.g., DVD Ig), small molecule TNF-0L antagonists and similar naturally- or nonnaturally-occurring molecules, and/or recombinant and/or engineered forms thereof, that, directly or indirectly, inhibits TNF or activity, such as by inhibiting interaction of TNF-0L with a cell surface receptor for TNF-0L, inhibiting TNF-0L protein production, inhibiting TNF-0L gene expression, inhibiting TNFOL secretion from cells, inhibiting TNF-0L receptor signaling or any other means resulting in decreased TNF-0L ty in a subject. The term “TNFOL inhibitor” preferably includes agents which interfere with TNF-0L activity. Examples of TNF-0L inhibitors include etanercept (ENBRELT'V', Amgen), infliximab (REMICADET'V', Johnson and Johnson), human anti-TNF monoclonal dy umab (D2E7/HUMIRAT'V', Abbott tories), CDP 571 ech), and CDP 870 (Celltech), as well as other compounds which inhibit TNF-0L activity, such that when administered to a subject suffering from or at risk of suffering from a disorder in which TNF-0L activity is detrimental (e.g., RA), the disorder is treated.

The term “predicting responsiveness to a TNFu inhibitor”, as used herein, is intended to refer to an ability to assess the likelihood that treatment of a subject with a TNF tor will or will not be effective in (e.g, provide a measurable benefit to) the subject. In ular, such an y to assess the likelihood that treatment will or will not be effective typically is exercised after treatment has begun, and an indicator of effectiveness (e.g., an indicator of able benefit) has been observed in the subject. Particularly preferred TNFu inhibitors are biologic agents that have been approved by the FDA for use in humans in the treatment of rheumatoid arthritis, which agents include adalimumab AT'V'), infliximab (REMICADET'V') and etanercept (ENBRELT'V'), most preferably adalimumab (HUMIRATM).

The term “course of therapy” includes any therapeutic approach taken to relieve or prevent one or more symptoms associated with a ediated disease or disorder. The term encompasses administering any compound, drug, procedure, and/or regimen useful for improving the health of an individual with a TNFu-mediated disease or disorder and includes any of the therapeutic agents described herein. One skilled in the art will appreciate that either the course of therapy or the dose of the current course of y can be changed (e.g., increased or decreased) based upon the presence or concentration level of TNF, anti-TNF drug, and/or anti-drug antibody using the methods of the present invention.

The term “immunosuppressive agent” includes any substance capable of producing an immunosuppressive , e.g, the prevention or diminution of the immune response, as by irradiation or by administration of drugs such as anti-metabolites, anti-lymphocyte sera, antibodies, etc. Examples of suitable immunosuppressive agents include, without tion, thiopurine drugs such as azathioprine (AZA) and metabolites thereof; etabolites such as rexate (MTX); sirolimus (rapamycin); temsirolimus; imus; tacrolimus (FK- 506); FK-778; anti-lymphocyte globulin antibodies, anti-thymocyte globulin antibodies, anti- CD3 antibodies, anti-CD4 antibodies, and antibody-toxin conjugates; cyclosporine; mycophenolate; mizoribine monophosphate; scoparone; glatiramer acetate; metabolites WO 54987 thereof; pharmaceutically acceptable salts thereof; derivatives thereof; prodrugs thereof; and combinations thereof.

The term “thiopurine drug” includes azathioprine (AZA), 6-mercaptopurine (6-MP), or any metabolite thereof that has therapeutic cy and includes, t limitation, 6- thioguanine (6-TG), 6-methylmercaptopurine riboside, 6-thioinosine nucleotides (e.g., 6- thioinosine monophosphate, 6-thioinosine diphosphate, 6-thioinosine triphosphate), 6- thioguanine nucleotides (e.g., 6-thioguanosine monophosphate, 6-thioguanosine diphosphate, 6-thioguanosine sphate), 6-thioxanthosine nucleotides (e.g., 6-thioxanthosine monophosphate, 6-thioxanthosine diphosphate, 6-thioxanthosine triphosphate), derivatives thereof, analogues thereof, and ations f.

The term “sample” as used herein includes any biological specimen ed from a t. Samples include, without limitation, whole blood, plasma, serum, red blood cells, white blood cells (e.g., peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells), ductal lavage fluid, nipple aspirate, lymph (e.g., disseminated tumor cells of the lymph node), bone marrow aspirate, saliva, urine, stool (z'.e., , sputum, bronchial lavage fluid, tears, fine needle aspirate (e.g, ted by random periareolar fine needle aspiration), any other bodily fluid, a tissue sample such as a biopsy of a site of inflammation (e.g., needle biopsy), and cellular extracts thereof. In some embodiments, the sample is whole blood or a fractional ent thereof such as plasma, serum, or a cell pellet. In other embodiments, the sample is obtained by isolating PBMCs and/or PMN cells using any technique known in the art. In yet other embodiments, the sample is a tissue biopsy, e.g., from a site of inflammation such as a portion of the gastrointestinal tract or synovial .

The term “Crohn’s Disease Activity Index” or “CDAI” includes a research tool used to quantify the symptoms of patients with Crohn’s disease (CD). The CDAI is generally used to define response or remission of CD. The CDAI consists of eight factors, each summed after adjustment with a weighting factor. The components of the CDAI and ing factors are the following: Clinical or laboratory variable Wefzightingactor Number of liquid or soft stools each day for seven days x 2 Abdominal pain (graded from 0-3 on ty) each day for seven days x 5 General well being, subjectively assessed from 0 (well) to 4 (terrible) each day for seven days Presence of complications* x 20 Taking Lomitil or opiates for diarrhea X 30 Presence of an abdominal mass (0 as none, 2 as questionable, 5 as definite) x 10 Hematocrit of <0.47 in men and <0.42 in women x 6 Percentage ion from rd weight x 1 One point each is added for each set of complications: 0 the presence ofjoint pains (arthralgia) or frank arthritis; 0 inflammation of the iris or uveitis; . presence of erythema nodosum, pyoderma nosum, or aphthous ulcers; . anal fissures, fistulae or abscesses; 0 other fistulae; and/or . fever during the previous week.

Remission of Crohn’s disease is typically defined as a fall in the CDAI of less than 150 points. Severe disease is typically defined as a value of greater than 450 points. In certain aspects, response to a particular medication in a s disease patient is defined as a fall of the CDAI of r than 70 points.

The terms “mucosal injury” or “mucosal damage” include the formation of macroscopically visible mucosal lesions in the ines detectable during endoscopy, granuloma formation and tion of the muscularis layer at the microscopic tissue level, epithelial apoptosis and infiltration of activated inflammatory and lymphocytic cells at the cellular level, increased lial permeability at a sub-cellular level, and gap on tion at a molecular level. In IBD such as s disease, the intestinal epithelium is damaged by the inflammatory environment, which results in the formation of refractory ulcers and lesions.

The term “mucosal healing” refers to restoration of normal mucosal appearance of a previously inflamed region, and complete absence of tion and inflammation at the endoscopic and microscopic levels. Mucosal healing includes repair and restoration of the mucosa and muscularis layers. It can also include neuronal and , submucosa, lymphangiogenic elements of the intestinal wall.

The term “nutrition-based therapy ” es butyrate, probiotics (e.g., VSL#3, E. coli Nissle l9 1 7, bacterium bacillus polyfermenticus), vitamins, proteins, macromolecules, and/or chemicals that promote mucosal healing such as growth and turnover of intestinal mucosa. 111. Description of the ments The present invention es methods for personalized therapeutic management of a disease in order to optimize therapy and/or r therapeutic efficacy. In particular, the t invention comprises measuring an array of one or a ity of mucosal healing biomarkers at one or a plurality of time points over the course oftherapy with a therapeutic agent to determine a mucosal healing index for selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment. In certain instances, the therapeutic agent is a TNFOL inhibitor for the treatment of a TNFu-mediated disease or disorder. In some embodiments, the methods of the present invention advantageously improve therapeutic management of patients with a TNFu-mediated disease or disorder by optimizing therapy and/or monitoring therapeutic efficacy to anti-TNF drugs such as anti-TNFOL therapeutic antibodies.

As such, in one aspect, the present invention provides a method for alized therapeutic management of a disease in order to optimize therapy or monitor therapeutic efficacy in a subject, the method comprising: (a) measuring an array of mucosal healing markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) generating the subject’s mucosal healing index comprising a representation of the ce and/or concentration levels of each of the markers over time; (c) comparing the subject’s mucosal healing index to that of a l; and (d) selecting an appropriate therapy for the subject, to thereby achieve personalized therapeutic management of the disease in the subject.

As such, in r , the present invention provides a method for personalized therapeutic management of a disease in order to select therapy in a subject, the method comprising: (a) ing an array of mucosal healing markers; (b) generating the t’s mucosal healing index comprising a representation of the presence and/or concentration levels of each of the markers; (c) comparing the subject’s mucosal healing index to that of a control; and (d) selecting an riate y for the subject, to thereby achieve alized therapeutic management of the disease in the subject.

As such, in one aspect, the present invention provides a method for optimizing y in a subject, the method comprising: WO 54987 (a) measuring an array of mucosal healing markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more s determined in step (a) to generate a mucosal healing index; (c) comparing the t’s mucosal healing index to that of a control; and (d) determining a subsequence dose of the course of therapy for the subject or r a different course of therapy should be administered to the subject based upon the mucosal healing index.

As such, in one aspect, the present invention provides a method for ing therapy in a subject, the method comprising: (a) measuring an array of mucosal healing markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the subject’s mucosal healing index to that of a control; and (d) selecting an appropriate course of therapy for the t based upon the mucosal g index.

As such, in another aspect, the present invention provides a method for reducing the risk of surgery in a subject diagnosed with IBD (e.g., Crohn’s disease) being administered a therapy regimen (e.g., an anti-TNF antibody therapy regimen), the method sing: (a) ing an array of mucosal healing markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the subject’s mucosal healing index to that of a control; and (d) determining whether the therapy regimen is reducing the t’s risk of surgery.

As such, in one aspect, the present invention provides a method for monitoring therapeutic efficiency in a subject receiving y (e.g., anti-TNF therapy), the method sing: (a) measuring an array of mucosal g markers at a plurality of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers ined in step (a) to generate a mucosal healing index; (c) comparing the subject’s mucosal healing index to that of a control; and (d) determining whether the current course of therapy is appropriate for the subject based upon the mucosal healing index.

In some embodiments, the disease is a gastrointestinal disease or an autoimmune disease. In certain instances, the subject has inflammatory bowel disease (IBD, e.g., Crohn’s disease (CD) or ulcerative colitis (UC)). In other instances, the t has rheumatoid arthritis (RA). In preferred embodiments, the subject is a human.

In some embodiments, the therapy is selected from the group comprising an anti- TNF therapy, an immunosuppressive agent, a corticosteroid, a drug that targets a ent mechanism, a nutrition therapy or combinations thereof. In certain instances, the anti-TNF y is a TNF inhibitor (e.g., anti-TNF drug, anti-TNFu dy).

In other embodiments, the anti-TNF therapy is an anti-TNFu antibody. In some embodiments, the anti-TNFOL antibody is a member selected from the group consisting of REMICADETM (infliximab), T'V' rcept), HUMIRAT'V' (adalimumab), CIMZIA® (certolizumab pegol), and combinations thereof In preferred embodiments, the subject is a human.

In some embodiments, the therapy is an immunosuppressive agent. Non-limiting examples of immunosuppressive agents include thiopurine drugs such as azathioprine (AZA), 6-mercaptopurine (6-MP), and/or any metabolite thereof that has therapeutic efficacy and es, Without limitation, 6-thioguanine (6-TG), 6-methylmercaptopurine de, 6- thioinosine nucleotides (e.g., 6-thioinosine monophosphate, 6-thioinosine diphosphate, 6- thioinosine triphosphate), 6-thioguanine tides (e.g, 6-thioguanosine monophosphate, 6-thioguanosine diphosphate, guanosine triphosphate), 6-thioxanthosine nucleotides (e.g., 6-thioxanthosine osphate, 6-thioxanthosine diphosphate, 6-thioxanthosine triphosphate), derivatives thereof, analogues thereof, and combinations thereof; anti- metabolites such as methotrexate (MTX); sirolimus (rapamycin); temsirolimus; everolimus; imus 6); FK-778; ymphocyte globulin antibodies, anti-thymocyte globulin antibodies, anti-CD3 antibodies, anti-CD4 dies, and antibody-toxin conjugates; cyclosporine; mycophenolate; mizoribine monophosphate; one; glatiramer acetate; lites thereof; pharmaceutically acceptable salts thereof; derivatives thereof; prodrugs thereof; and combinations thereof.

In other embodiments, the therapy is a corticosteroid. In yet other embodiments, the therapy is a drug that targets a different mechanism (e.g., a mechanism that is not mediated by the TNFoc pathway). Non-limiting examples of a drug that targets a different mechanism include IL-6 receptor inhibiting onal dies, anti-integrin molecules (e.g., zumab (Tysabri), vedoluzamab), JAK-2 inhibitors, tyrosine kinase inhibitors, and combinations thereof.

In other embodiments, the y is a nutrition therapy. In particular embodiments, the nutrition therapy is a special carbohydrate diet. Special carbohydrate diet (SCD) is a strict grain-free, lactose-free, and e-free nutritional regimen that was designed to reduce the symptoms of IBD such as Crohn’s disease and ulcerative colitis. It has been shown that SCD can promote and/or maintain mucosal healing in patients with IBD (e.g., Crohn’s disease or ulcerative colitis). lly, SCD restricts the use of complex carbohydrates and eliminates refined sugar, grains and starch from the diet. It has been bed that the microvilli of patients with IBD lack the ability to break down specific types of complex carbohydrates, resulting in the overgrowth of l bacteria and irritation of the gut mucosa. It has been recommended that SCD is a therapy for IBD (e.g., s disease or ulcerative colitis) because it enables the gut to undergo l healing.

In some embodiments, the array of markers comprises a mucosal healing marker. In some embodiments, the mucosal marker comprises AREG, EREG, HB-EGF, HGF, NRGl, NRGZ, NRG3, NRG4, BTC, EGF, IGF, TGF-Oc, VEGF-A, VEGF-B, VEGF-C, , FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.

[0090] In other embodiments, the array of markers further comprises a member selected from the group consisting of an anti-TNFu antibody, an rug antibody (ADA), an inflammatory marker, an anti-inflammatory marker, a tissue repair marker (e.g., a growth factor), and ations thereof. In certain instances, the anti-TNFu antibody is a member selected from the group consisting of REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRATM (adalimumab), CIMZIA® (certolizumab pegol), and combinations f In certain other instances, the anti-drug antibody (ADA) is a member selected from the group consisting of a human anti-chimeric antibody (HACA), a human anti-humanized antibody (HAHA), a human anti-mouse antibody , and combinations thereof. In yet other instances, the inflammatory marker is a member selected from the group consisting of GM- CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-u, sTNF R11, and combinations thereof In further instances, the anti-inflammatory marker is a member selected from the group ting of IL- 12p70, IL- 1 0, and combinations thereof.

In certain embodiments, the array comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more s. In some embodiments, the markers are ed in a biological sample selected from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy (e.g., from a site of inflammation such as a portion of the gastrointestinal tract or al ).

In certain embodiments, the plurality of time points comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time .

In some instances, the first time point in the plurality of time points is prior to the course of therapy with the therapeutic antibody. In other instances, the first time point in the plurality of time points is during the course of therapy with the therapeutic antibody. As non-limiting examples, each of the markers can be measured prior to therapy with a therapeutic antibody and/or during the course of y at one or more (e.g., a plurality) of the following weeks: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 80, 90, 100, etc.

In fiarther embodiments, the method for assessing or measuring mucosal healing further comprises comparing the determined level of the mucosal healing marker t in a sample to an index value or cutoff value or reference value or threshold value, wherein the level of the mucosal healing marker above or below that value is predictive or indicative of an increased or higher likelihood of the subject either oing mucosal healing or not undergoing mucosal healing. One skilled in the art will understand that the index value or cutoff value or reference value or threshold value is in units such as mg/ml, ug/ml, ng/ml, pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest that is being measured.

In some embodiments, the mucosal healing index includes an empirically derived index that is based upon an is of a plurality of mucosal healing markers. In one aspect, the concentration of markers or their measured concentration values are transformed into an index by an algorithm resident on a computer. In certain aspects, the index is a tic or human derived output, score, or cut off value(s), which expresses the biological data in numerical terms. The index can be used to determine or make or aid in making a clinical decision. A mucosal healing index can be measured multiple times over the course of time.

In one aspect, the algorithm can be trained with known samples and thereafter validated with s ofknown identity.

In some embodiments, the mucosal healing index control is a mucosal healing index d from a y individual, or an individual who has progressed from a e state to a healthy state. Alternatively, the control can be an index representing a time course of a more diseased state or healthy to disease.

In some embodiments, the methods of ining the course of therapy and the like include the use of an empirically derived index, score or analysis to select for example, ing a dose of drug, selecting an appropriate drug, or a course or length of therapy, a therapy regimen, or maintenance of an existing drug or dose. In certain aspects, a derived or measured index can be used to determine the course of therapy.

[0097] In some embodiments, mucosal healing can be assessed or monitored by endoscopy.

Non-limiting examples of endoscopy include video capsule endoscopy (capsule endoscopy), disposable endoscopy, and 3D endoscopy. In other embodiment, the mucosal healing index is red or confirmed by endoscopy.

In some embodiments, ing an appropriate therapy comprises maintaining, increasing, or decreasing a subsequent dose of the course of therapy for the subject. In other embodiments, the method further comprises determining a different course of therapy for the subject. In certain instances, the different course of therapy comprises treatment with a different anti-TNFu dy. In other instances, the different course of therapy comprises the current course of therapy along with another therapeutic agent, such as, but not d to, an immunosuppressive agent, a corticosteroid, a drug that targets a different mechanism, nutrition therapy, and combinations thereof).

In some embodiments, selecting an riate therapy comprises selecting an appropriate y for initial treatment. In some instances, the therapy comprises an anti- TNFOL dy therapy.

[0100] In certain embodiments, the s disclosed herein can be used as confirmation that a proposed new drug or therapeutic is the same as or is iently similar to an approved drug product, such that the proposed new drug can be used as a “biosimilar” therapeutic. For e, if the proposed new drug has only a slightly different disease activity profile compared to the branded drug product, this would be apparent using the methods disclosed herein. If the proposed new drug has a significantly different disease activity profile ed to the branded drug product, then the new drug would not be biosimilar. Advantageously, the methods disclosed herein can be used in clinical trials of proposed new drugs in order to assess the effective therapeutic value of the drug.

Accordingly, in some aspects, the methods of the invention provide information useful for guiding treatment decisions for ts receiving or about to receive anti-TNF drug therapy, e.g., by selecting an appropriate anti-TNF therapy for initial treatment, by determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-TNF drug, by determining when or how to combine an anti-TNF drug (e.g., at an initial, sed, sed, or same dose) with one or more immunosuppressive agents such as rexate (MTX) or azathioprine (AZA), and/or by ining when or how to change the current course of therapy (e.g., switch to a different NF drug or to a drug that targets a different mechanism such as an IL-6 or-inhibiting monoclonal antibody, anti-integrin molecule (e.g., Tysabri, Vedaluzamab), JAK-2 tor, and ne kinase tor, or to a nutritition therapy (e.g., special carbohydrate diet)).

In other embodiments, the methods of the present invention can be used to predict responsiveness to a TNFu inhibitor, especially to an anti-TNFu antibody in a subject having an autoimmune disorder (e.g., rheumatoid arthritis, Crohn’s Disease, ulcerative colitis and the like). In this method, by assaying the subject for the correct or therapeutic dose of anti- TNFOL antibody, l'.e., the therapeutic tration level, it is possible to predict whether the dual will be responsive to the y.

In another embodiment, the present invention provides methods for monitoring IBD (e.g., Crohn’s disease and ulcerative s) in a subject having the IBD disorder, wherein the method comprises assaying the subject for the correct or therapeutic dose of anti-TNFu antibody, z'.e., the therapeutic concentration level, over time. In this manner, it is possible to predict whether the individual will be responsive to the therapy over the given time period.

In certain embodiments, step (a) comprises determining the presence and/or level of at least two, three, four, five, six, seven, eight, nine, ten, fifteen, twenty, thirty, forty, fifty, or more markers in the sample.

In other embodiments, the algorithm comprises a ng tical classifier system. In some instances, the learning statistical classifier system is selected from the group consisting of a random forest, classification and regression tree, boosted tree, neural network, support vector machine, general uared automatic interaction detector model, interactive tree, multiadaptive sion spline, machine learning classifier, and combinations thereof.

In certain instances, the statistical algorithm comprises a single learning statistical classifier system. In certain other instances, the statistical algorithm comprises a combination of at least two learning statistical classifier systems. In some instances, the at least two learning statistical classifier systems are d in tandem. Non-limiting examples of statistical algorithms and analysis suitable for use in the invention are described in International Application No. PCT/U8201 1/056777, filed October 18, 2011, the disclosure of which is hereby incorporated by reference in its entirety for all es.

In other ments, step (b) further comprises applying a statistical algorithm to the ce and/or level of one or more mucosal g markers determined at an earlier time during the course of therapy to generate an earlier mucosal healing index. In some instances, the earlier mucosal healing index is compared to the mucosal healing index generated in step (b) to determine a subsequent dose of the course of therapy or whether a different course of therapy should be administered. In certain embodiments, the subsequent dose of the course of therapy is increased, decreased, or maintained based upon mucosal healing index generated in step (b). In some instances, the different course of therapy comprises a different anti-TNFu antibody. In other instances, the different course of therapy comprises the current course of therapy along with an suppressive agent.

[0107] In some embodiments, step (b) further comprises ng a statistical algorithm to the presence and/or level of one or more of the mucosal healing markers ined at an r time to generate an earlier disease activity/severity index. In certain instances, the mucosal healing index is compared to the mucosal healing index generated in step (b) to predict the course of the TNF-mediated disease or disorder.

[0108] In some embodiments, the method r comprises sending the results from the selection or determination of step (d) to a clinician. In other embodiments, step (d) ses selecting an initial course of therapy for the subject.

Once the diagnosis or prognosis of a subject receiving anti-TNF drug therapy has been determined or the hood of response to an anti-TNF drug has been predicted in a subject diagnosed with a disease and disorder in which TNF has been implicated in the pathophysiology, e.g., but not limited to, shock, sepsis, ions, autoimmune diseases, RA, Crohn’s disease, transplant rejection and graft-versus-host disease, according to the methods described herein, the present ion may fiarther comprise recommending a course of therapy based upon the diagnosis, prognosis, or prediction. In n instances, the present invention may further comprise administering to a subject a therapeutically effective amount of an NFOL drug useful for treating one or more symptoms associated with the TNF- mediated e or disorder. For therapeutic applications, the anti-TNF drug can be administered alone or co-administered in combination with one or more additional anti-TNF drugs and/or one or more drugs that reduce the side-effects associated with the anti-TNF drug (e.g., an immunosuppressive agent). As such, the t ion advantageously enables a clinician to practice “personalized medicine” by g treatment decisions and informing therapy selection and optimization for anti-TNFOL drugs such that the right drug is given to the right patient at the right time.

The present invention is advantageous because it ses and overcomes current limitations associated with the stration of anti-TNF drugs such as infliximab, in part, by providing ation useful for guiding treatment decisions for those patients receiving or about to receive anti-TNF drug therapy. In particular, the methods of the present invention find utility for selecting an appropriate NF therapy for initial treatment, for determining when or how to adjust or modify (e.g., increase or decrease) the uent dose of an anti- TNF drug to optimize therapeutic efficacy and/or to reduce toxicity, for ining when or how to combine an anti-TNF drug (e.g, at an initial, increased, decreased, or same dose) with one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA), and/or for determining when or how to change the current course of therapy (e.g., switch to a different anti-TNF drug or to a drug that targets a different mechanism).

Accordingly, the present invention is particularly useful in the following methods of improving patient management by guiding treatment decisions: 1. Crohn’s disease prognostics: Treat patients most likely to benefit from y 2. Anti-therapeutic antibody monitoring (ATM) + Biomarker-based disease activity profiling 3. ATM sub-stratification 4. ATM with pharmacokinetic modeling . Monitor response and t risk of relapse: a. Avoid chronic maintenance therapy in patients with low risk of recurrence b. Markers of mucosal healing c. Therapy selection: Whether to e or not to combine anti-TNF drug therapy with an immunosuppressive agent such as MTX or AZA 6. Patient selection for ics.

[0112] In some embodiments, the present invention es a method for measuring an inflammatory index for Crohn’s e management for an individual to optimize therapy, and predict response to the anti-TNF therapeutic, the method comprising: (a) chromatographically measuring anti-TNF therapeutics and autoantibodies in a sample from the individual to determine their concentration levels; (b) chromatographically measuring anti- TNF therapeutics and tibodies in a sample from the individual to ine their concentration levels; (c) comparing the measured values to an efficacy scale to optimize therapy, and predict se to the anti-TNF therapeutic.

In some embodiments, the present invention provides a method for predicting the likelihood the concentration of an anti-TNF therapeutic during the course of treatment will fall below a threshold value, the method comprising: (a) measuring a panel of markers selected from the group consisting of 1) GM- CSF; 2) IL-2; 3) TNF-0L; 4) sTNFRII; and 5) the disease being ed in the small intestine; and (b) predicting the likelihood the concentration of an NFOL therapeutic will fall below the old based upon the concentration of the markers.

For the purpose of illustration only, Example 5 shows an exemplary embodiment of the present invention In particular, a method of predicting the likelihood the concentration of an anti-TNF treatment will fall below a threshold value.

In some embodiments, the present invention provides a method for predicting the likelihood the concentration of an NF eutic during the course of treatment will fall below a threshold value, the method comprising: (a) measuring a panel of markers selected from the group consisting of l) GM-CSF; 2) IL-2; 3) TNF-0L; 4) sTNFRII; and 5) the e being situated in the small intestine; and (b) predicting the hood the concentration of an anti-TNF eutic will fall below the threshold based upon the concentration of the markers.

In other embodiments, the present invention provides a method for predicting the likelihood that anti-drug antibodies will occur in an individual on anti-TNF therapy, the method comprising: (a) measuring a panel of markers selected from the group consisting of t EGF, VEGF, IL-8, CRP and VCAM-l; and (b) predicting the likelihood that anti-drug antibodies will occur in an individual on anti-TNF y based on the concentration of marker levels.

For the purpose of illustration only, Example 4 is an ary embodiment of the present invention and demonstrates the detectin of anti-drug antibodies to infliximab (ATI).

WO 54987 2012/037375 In other embodiments, the present invention provides a method for monitoring an infliximab ent regimen, the method comprising: (a) measuring infliximab and ug antibodies to infliximab (ATI); (b) measuring inflammatory markers CRP, SAA, ICAM, VCAM; (c) measuring tissue repair marker VEGF; and (d) correlating the measurements to therapeutic efficacy.

For the purpose of illustration only, Example 5 is an exemplary embodiment of the present invention and shows a method of monitoring an IFX treatment regimen.

In other embodiments, the present invention provides a method for determining whether an individual is a candidate for combination therapy n said individual is administered infliximab, the method comprising: (a) measuring for the presence or absence of ATI in said individual; and (b) stering an immunosuppressant (e.g., MTX) is the individual has significant levels of ATI.

[0121] In yet other embodiments, the method also es measuring the concentration level of CRP which is indicative of the presence of ATI. For the purpose of illustration only, Examples 6 and 7 show that the presence and absence ofATI are predictive of ders and non-responders of Remicade therapy. Examples 6 and 7 are exemplary embodiments.

In yet other embodiments, the present invention provides a method for monitoring Crohn’s disease activity, the method comprising: (a) determining an inflammatory index comprising the measurement of a panel of markers sing VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml; and (b) comparing the index to an efficacy scale to monitor and mange disease.

[0123] For the purpose of illustration only, Example 9 is an ary embodiment and shows use of the inflammatory index.

In particular embodiments, the present invention provides s for determining the threshold of an anti-TNF drug such as IFX that can best discriminate disease activity as measured by C-reactive protein (CRP) levels. For the purpose of illustration only, Example 12 shows that IFX dichotomized at a old of 3 ug/ml can be differentiated by CRP. In certain instances, random IFX < 3 and IFX Z 3 ug/ml serum samples have higher CRP in IFX < 3 ug/ml at a 74 % rate (ROC AUC). Example 12 also shows that in ATI+ samples pairs, no significant difference in CRP between IFX groups (above and below 3 ug/ml) was observed.

In particular, CRP levels were generally higher in ATI+ sample pairs, and CRP levels were higher in IFX < 3 ug/ml for ATI— samples. Regression confirmed that CRP was positively related to ATI and negatively related to IFX. As such, the interaction corresponds to a CRP- IFX relationship that differs between ATI+ and ATI—.

IV. Mucosal Healing Index The methods of the present invention comprise monitoring therapy response and predicting risk of relapse. In some embodiments, the methods include detecting, measuring and/or determining the presence and/or levels of markers of mucosal healing.

[0126] The gut mucosa plays a key role in barrier defense in addition to nutrient digestion, tion and metabolism. The dynamic ses of intestinal epithelial cell proliferation, migration, and apoptosis are highly affected by general nutritional status, route of feeding, and adequacy of specific nutrients in the diet. However, with inflammatory es of the gut, l cell impairment can result in mucosal injury or , thereby resulting in enhanced permeability to macromolecules, increased ial translocation from the lumen, and stimulation of epithelial cell sis.

Mucosal injury is a multi-faceted physiological process ng macroscopic to molecular levels. l injury includes the formation of macroscopically visible mucosal lesions detectable during endoscopy, granuloma formation and disruption of the muscularis layer at the microscopic tissue level, epithelial apoptosis and infiltration of ted inflammatory and lymphocytic cells at the cellular level, increased epithelial permeability at a sub-cellular level, and gap junction disruption at a molecular level.

Mucosal injury is likely initiated by a combination of endogenous and environmental factors. At first stage, it is believed that food-derived compounds, viral and bacterial-derived factors, as well as host-derived factors, may cause epithelial cell destruction and activation of innate and adaptive immunity. d mucosa is initially infiltrated by diverse inflammatory cells consisting of neutrophils, eosinophils, mast cells, atory monocytes, ted macrophages and dendritic cells. Specific adaptive immune responses toward the intestinal flora are generated leading to the later recruitment of ted B cells, CD4+ and CD8+ T cells to the d mucosa. Neutrophils secrete elastase which can result in extracellular matrix degradation of the epithelium. Likewise, T cells, macrophages and intestinal fibroblasts express inflammatory factors such as IL-l, IL-2, IL-6, IL-l4, IL-l7, TGFB and TNFoc that lead to extracellular matrix degradation, epithelial , endothelial 2012/037375 activation, and/or fibrosis stricture formation. Non-limiting examples of markers of mucosal injury include matrix metalloproteases (MMPs) and markers of oxidative stress (e.g., iNOS, reactive oxygen metabolites).

A. Array of Mucosal Healing Markers A y of mucosal markers including growth factors are particularly useful in the s of the present invention for personalized therapeutic management by selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or more therapeutic agents such as biologics (e.g., anti-TNF drugs). In ular embodiments, the methods described herein utilize the determination of a mucosal healing index based upon one or more (a plurality of) mucosal healing markers such as growth factors (e.g., alone or in combination with biomarkers from other ries) to aid or assist in predicting e course, selecting an appropriate anti-TNF drug therapy, zing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-TNF drug.

[0130] As such, in certain embodiments, the determination of the presence and/or level of one or more growth factors in a sample is useful in the present invention. As used , the term “growth factor” includes any of a variety of peptides, polypeptides, or proteins that are capable of ating cellular proliferation and/or cellular differentiation.

In some embodiments, mucosal healing markers include, but are not limited to, growth factors, inflammatory s, cellular adhesion markers, cytokines, antiinflammatory markers, matrix metalloproteinases, oxidative stress markers, and/or stress response markers.

In some embodiments, mucosal healing markers include growth s. Non- limiting examples of growth s include amphiregulin , epiregulin (EREG), heparin binding epidermal growth factor (HB-EGF), hepatocye growth factor (HGF), heregulin-[31 (HRG) and isoforms, neuregulins (NRGl, NRG2, NRG3, NRG4), betacellulin (BTC), epidermal growth factor (EGF), insulin growth factor-1 (IGF-l), transforming growth factor (TGF), platelet-derived growth factor (PDGF), vascular endothelial growth s (VEGF-A, VEGF-B, VEGF-C, VEGF-D), stem cell factor (SCF), platelet derived growth factor (PDGF), soluble fms-like ne kinase 1 ), placenta growth factor (PIGF, PLGF or PGF), last growth factors (FGFl, FGF2, FGF7, FGF9), and combinations thereof. In other embodiments, mucosal g markers also include pigment epithelium- derived factor (PEDF, also known as SERPINFl), endothelin-1 (ET-1), keratinocyte growth factor (KGF; also known as FGF7), bone morphogenetic proteins (e.g., MPlS), platelet-derived growth factor (PDGF), nerve growth factor (NGF), B-nerve growth factor (BNGF ), neurotrophic s (e.g., brain-derived rophic factor (BDNF), rophin 3 (NT3), neurotrophin 4 (NT4), eta), growth differentiation factor-9 (GDP-9), granulocytecolony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GMCSF ), myostatin (GDP-8), erythropoietin (EPO), thrombopoietin (TPO), and combinations thereof.

In other embodiments, mucosal healing markers also include cytokines. Non- limiting examples of cytokines that can be used to establish a mucosal healing index include bFGF, TNF-oc, IL-lO, IL-12(p70), IL-l[3, IL-2, IL-6, GM-CSF, IL-l3, IFN—y, TGF-Bl, TGF- [32, 3, and combinations thereof. Non-limiting examples of cellular adhesion markers include SAA, CRP, ICAM, VCAM, and combinations thereof. Non-limiting examples of anti-inflammatory markers include IL- 12p70, IL-10, and combinations thereof In some embodiments, mucosal g markers include markers specific to the gastrointestinal tract including inflammatory markers and serology markers as described herein. miting examples include antibodies to ial antigens such as, e.g., OmpC, flagellins (cBir—l, Fla-A, Fla-X, etc), 12, and others (pANCA, ASCA, eta); eutrophil antibodies, anti-Saccharomyces cerevz’sz’ae antibodies, and anti-microbiol antibodies.

The determination of markers of oxidative stress in a sample is also useful in the present invention. Non-limiting examples of markers of oxidative stress include those that are protein-based or DNA-based, which can be detected by measuring protein ion and DNA fragmentation, tively. Other examples of markers of oxidative stress include organic compounds such as ialdehyde.

Oxidative stress represents an imbalance between the production and manifestation of reactive oxygen species and a biological system’s ability to readily detoxify the reactive intermediates or to repair the resulting damage. Disturbances in the normal redox state of tissues can cause toxic effects through the production of peroxides and free radicals that damage all components of the cell, including proteins, lipids, and DNA. Some reactive oxidative species can even act as gers through a phenomenon called redox signaling.

[0137] In n ments, derivatives of ve oxidative metabolites (DROMs), ratios of oxidized to reduced glutathione (Eh GSH), and/or ratios of oxidized to reduced cysteine (Eh CySH) can be used to quantify oxidative stress. See, e. g., Neuman et al., Clin.

Chem, 53:1652-1657 (2007). Oxidative modifications of highly reactive cysteine residues in proteins such as tyrosine phosphatases and thioredoxin-related proteins can also be ed or measured using a technique such as, e.g., mass spectrometry (MS). See, e.g., Naito et al., Anti-Aging Medicine, 7 (5):36-44 (2010). Other markers of ive stress e proteinbound acrolein as described, e.g., in Uchida et al., PNAS, 95 (9) 4882-4887 (1998), the free oxygen radical test (FORT), which reflects levels of c hydroperoxides, and the redox potential of the reduced glutathione/glutathione disulfide couple, (Eh) GSH/GSSG. See, e. g., Abramson et al., Atherosclerosis, 178(1):115-21 (2005).

In some embodiments, matrix metalloproteinases (MMPs) include members of a family of Zn2+-dependent extracellular matrix (ECM) degrading endopeptidases that are able to degrade all types ofECM proteins. miting examples ofMMPs include MMP-l, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, , MMP-13, MTl-MMP-l, and combinations thereof. It has been shown that MMP-3 and MMP-9 are associated with mucosal injury and f1stulae in CD patients (Baugh et al., Gastroenterology, 117: 814-822, (1999); Bailey et al., J. Clin. ., 47: 113-116 (1994)). In some embodiments, stress response markers include markers of oxidative stress, such as reactive oxygen species (ROS), superoxide dismutase (SOD), catalase (CAT), and glutathione, and markers of endoplasmic reticulum (ER) stress. Non-limiting examples of markers of oxidative stress include those that are n-based or DNA-based, which can be detected by measuring protein oxidation and DNA fragmentation, tively. In other embodiments, mucosal healing markers further include markers of oxidative DNA and/or protein damage. Non-limiting examples of ER stress markers include markers of unfolded protein response (e.g., ATF6, HSPA5, PDIA4, XBPl, IREl, PERK, EIF2A, GADD34, GRP-78, phosphoylated JNK, caspase-12, caspase-3, and ations thereof).

The human amphiregulin (AREG) polypeptide sequence is set forth in, e.g., k Accession Nos. NP_001648.1 and XP_001 1256841. The human AREG mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_001657.2 and XM_001125684.3. One skilled in the art will appreciate that AREG is also known as AR, colorectum cell-derived growth factor, CRDGF, SDGF, and AREGB.

The human epiregulin (EREG) polypeptide sequence is set forth in, e.g., Genbank ion No. NP_001423. 1. The human EREG mRNA (coding) sequence is set forth in, e. g., Genbank ion No. NM_001432.2. One skilled in the art will appreciate that EREG is also known as EPR.

The human heparin—binding EGF-like growth factor (HB-EGF) polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_001936. 1. The human HB-EGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. 945.2. One skilled in the art will appreciate that HB-EGF is also known as diphtheria toxin receptor, DT- R, HBEGF, DTR, DTS, and HEGFL.

The human hepatocyte growth factor (HGF) ptide sequence is set forth in, e.g., Genbank Accession Nos. NP_000592.3, NP_00101093 1 . 1, 010932. 1, NP_001010933. 1, and NP_001010934.1. The human HGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_000601.4, 010931.1, NM_001010932. 1, NM_001010933.1 and NM_001010934. 1. One skilled in the art will appreciate that HGF is also known as r , SF, HPTA and hepatopoietin-A. One of skill will also appreciate that HGF includes to all m variants.

The human neuregulin-1 (NRG1) polypeptide sequence is set forth in, e.g., Genbank ion Nos., NP_001153467.1, NP_001153471.1, NP_001153473.1, NP_001153477.1, NP_039250.2, NP_039251.2, NP_039252.2, NP_039253.1, NP_039254.1, NP_039256.2, and NP_039258. 1. The human NRGl mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001159995.1, NM_001159999.1, NM_001160001.1, NM_001160005.1, NM_013956.3, NM_013957.3, NM_013958.3, 959.3, NM_013960.3, NM_013962.2, and NM_013964.3. One skilled in the art will appreciate that NRGl is also known as GGF, HGL, HRGA, NDF, SMDF, ARIA, acetylcholine receptor-inducing ty, breast cancer cell differentiation factor p45, glial growth factor, heregulin, HRG, neu differentiation factor, and y and motor neuron-derived factor. One of skill will also appreciate that NRGl includes to all isoform variants.

The human neuregulin-2 (NRG2) polypeptide sequence is set forth in, e.g., Genbank Accession Nos. NP_001171864.1, NP_004874.1, NP_053584.1, NP_053585.1 and NP_053586.1. The human NRG2 mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. 184935.1, NM_004883.2, NM_013981.3, NM_013982.2 and NM_013983.2. One skilled in the art will appreciate that NRG2 is also known as NTAK, neural- and thymus-derived tor for ERBB kinases, DON-1, and divergent of neuregulin-l. One of skill will also appreciate that NRG2 includes to all isoform variants.

The human neuregulin-3 (NRG3) polypeptide sequence is set forth in, e.g., Genbank Accession Nos. NP_001010848.2 and NP_001 1594451. The human NRG3 mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_001010848.3 and NM_001165973.1.. One skilled in the art will appreciate that NRG2 es to all isoform variants.

The human neuregulin-4 (NRG4) polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_612640.1. The human NRG4 mRNA g) ce is set forth in, e. g., Genbank Accession No. NM_138573.3. One skilled in the art will appreciate that NRG4 includes to all isoform variants.

The human betacellulin (BTC) polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_001720.1. The human BTC mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001729.2. One skilled in the art will iate that BTC includes to all isoform ts.

The human epidermal growth factor (EGF) polypeptide sequence is set forth in, e.g., Genbank Accession Nos. NP_001954.2 and NP_001 1716021. The human EGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_001963.4 and NM_001178131.1.. One skilled in the art will appreciate that EGF is also known as beta- urogastrone, urogastrone, URG, and HOMG4.

The human insulin-like growth factor (IGF) polypeptide ce is set forth in, e. g., Genbank Accession Nos. NP_000609.1 and NP_001104755.1. The human IGF mRNA (coding) sequence is set forth in, e.g., k Accession No. NM_000618.3 and NM_001111285.1. One d in the art will appreciate that IGF includes to all isoform variants. One skilled in the art will also appreciate that IGF is also known as mechano growth factor, MGF and somatomedin-C.

The human transforming growth factor alpha (TGF-0c) polypeptide ce is set forth in, e.g., k Accession Nos. NP_003227.1 and NP_001093161.1. The human TGF-0c mRNA (coding) ce is set forth in, e.g., Genbank Accession Nos.

NM_003236.3 and NM_001099691.2. One skilled in the art will appreciate that TGF-0c includes to all isoform variants. One d in the art will also appreciate that TGF-0c is also known as EGF-like TGF, ETGF, and TGF type 1. [0 1 5 1] The human vascular endothelial growth factor (VEGF-A) polypeptide sequence is set forth in, e.g. Genbank Accession Nos. NP_001020537, NP_00102053 8, NP_001020539, NP_001020540, NP_001020541, NP_001028928, and NP_003367. The human VEGF-A mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001025366, NM_001025367, NM_001025368, NM_001025369, NM_001025370, NM_001033756, and NM_003376. One d in the art will appreciate that VEGF-A is also known as VPF, VEGFA, VEGF, and MGC70609. . One skilled in the art will appreciate that VEGF-A includes to all isoform variants.

The human vascular elial growth factor B) polypeptide ce is set forth in, e.g., k Accession Nos. NP_001230662, and NP_003368. The human VEGF-B mRNA (coding) sequence is set forth in, e.g., Genbank ion Nos.

NM_001243733 and NM_003377. One skilled in the art will appreciate that VEGF-B is also known as VEGFB, VEGF-related factor, and VRF. One skilled in the art will appreciate that VEGF-B includes to all isoform variants.

[0153] The human vascular endothelial growth factor (VEGF-C) polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_005420. The human VEGF-C mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_005429. One skilled in the art will appreciate that VEGF-C is also known as Flt4 ligand, Flt4-L, VRP and vascular endothelial growth factor-realted protein. One skilled in the art will appreciate that VEGF-C includes to all isoform variants.

The human fibroblast growth factor 1 (FGF1) ptide sequence is set forth in, e. g., Genbank Accession Nos. NP_000791, NP_001138364, NP_001138406, NP_001138407, 138407, NP_149127, and NP_149128. The human FGF1 mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_000800, NM_001144892, 144934, NM_001144934, NM_001144935, NM_033136 and NM_033137. One skilled in the art will appreciate that FGF1 is also known as FGFA, FGF- 1, acidic fibroblast growth factor, aFGF, endothelial cell growth factor, ECGF, heparin- g growth factor 1, and HB-EGFl. One skilled in the art will appreciate that FGF1 includes to all isoform variants.

[0155] The human basic fibroblast growth factor (bFGF) polypeptide sequence is set forth in, e. g., Genbank Accession No. NP_001997.5. The human bFGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_002006.4. One skilled in the art will iate that bFGF is also known as FGF2, FGFB, and HBGF-Z.

The human fibroblast growth factor 7 (FGF7) polypeptide sequence is set forth in, e. g., k Accession No. NP_002000. 1. The human FGF7 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_002009.3. One skilled in the art will appreciate that FGF7 is also known as FGF-7, HBGF-7 and keratinocyte growth factor.

The human fibroblast growth factor 9 (FGF9) polypeptide ce is set forth in, e. g., k Accession No. NP_002001.l. The human FGF9 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_002010.2. One d in the art will appreciate that FGF9 is also known as FGF-9, GAF, and HBGF-9.

The human TNF-related weak inducer of apoptosis (TWEAK) polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_003800. l. The human TWEAK mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_003 809.2. One skilled in the art will appreciate that TWEAK is also known as TNFl2, APO3 , APO3L, DR3LG, and UNQlSl/PRO207..

[0159] In certain instances, the presence or level of a particular mucosal healing marker such as a growth factor is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay. In n other instances, the presence or level of a particular growth factor is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. In an ary embodiment, the presence or level of a particular growth factor is detected using a multiplexed array, such as a Collaborative Enzyme Enhanced Reactive ImmunoAssay (CEER), also known as the Collaborative Proximity Immunoassay (COPIA). CEER is described in the following patent nts which are herein incorporated by reference in their entirety for all purposes: PCT Publication No. WO 2008/036802; PCT Publication No. ; PCT Publication No. WO 2009/108637; PCT Publication No. ; PCT Publication No. WO 2011/008990; and PCT Application No. , filed October 20, 2010.

Suitable ELISA kits for determining the presence or level of a growth factor in a serum, plasma, saliva, or urine sample are available from, e.g., Antigenix America Inc. (Huntington Station, NY), Promega (Madison, WI), R&D Systems, Inc. (Minneapolis, MN), Invitrogen (Camarillo, CA), CHEMICON International, Inc. (Temecula, CA), Neogen Corp. (Lexington, KY), PeproTech (Rocky Hill, NJ), Alpco Diagnostics (Salem, NH), Pierce Biotechnology, Inc. ord, IL), and/or Abazyme (Needham, MA).

In ular ments, at least one or a plurality (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, ll, l2, l3, l4 ,lS ,l6,l7,18,l9, 20, or 21, such as, e.g., apanel or an array) ofthe following growth factor markers can be detected (e.g., alone or in combination with kers from other categories) to aid or assist in predicting disease course, and/or to improve the accuracy of selecting therapy, zing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to NF drug therapy: AREG, EREG, HB-EGF, HGF,NRG1,NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGFl, FGF2, FGF7, FGF9, TWEAK and combinations thereof.

B. Mucosal Healing Index In certain aspects, the present invention provides an algorithmic-based analysis of one or a plurality of(e.g., 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15,16,17,18,19, 20, 21, or more) l healing markers to e the accuracy of selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to anti- TNFOL drug therapy.

A single statistical algorithm or a combination of two or more statistical algorithms described herein can then be d to the presence or concentration level of the mucosal healing markers detected, ed, or determined in the sample to thereby select therapy, optimize therapy, reduce toxicity, or monitor the efficacy of therapeutic treatment with an anti-TNFOL drug. As such, the s of the invention find utility in determining patient ment by ining patient immune status.

[0163] In some embodiments, the statistical algorithm comprises a ng statistical classifier . In some instances, the learning statistical classifier system is ed from the group consisting of a random forest, classification and regression tree, boosted tree, neural network, support vector e, general chi-squared automatic interaction detector model, interactive tree, multiadaptive regression spline, machine learning fier, and combinations f. In certain instances, the statistical algorithm comprises a single learning statistical classifier system. In other embodiments, the statistical thm comprises a combination of at least two learning statistical classifier systems. In some instances, the at least two ng statistical classifier systems are applied in tandem. Non- limiting examples of statistical algorithms and analysis le for use in the invention are described in International Application No. , filed October 18, 2011, the disclosure of which is hereby incorporated by reference in its entirety for all purposes.

Preferably, mucosal healing index is an empirically derived experimentally ed index of values. In some instances, the index of values is transformed from an array of control measurements that were experimentally determined. In one aspect, the concentration of markers or their measured concentration values are ormed into an index by an algorithm resident on a computer. In certain aspects, the index is a synthetic or human derived output, score, or cut off value(s), which expresses the biological data in numerical terms. The index can be used to determine or make or aid in making a clinical decision. A mucosal healing index can be measured multiple times over the course of time. In one , the algorithm can be trained with known samples and thereafter validated with samples ofknown identity.

In filrther embodiments, the method for ing or measuring mucosal healing further comprises comparing the determined level of the mucosal healing marker present in a sample to an index value or cutoff value or reference value or threshold value, wherein the level of the mucosal healing marker above or below that value is predictive or indicative of an increased or higher hood of the subject either undergoing mucosal healing or not undergoing mucosal healing. One skilled in the art will understand that the index value or cutoff value or reference value or threshold value is in units such as mg/ml, ug/ml, ng/ml, pg/ml, fg/ml, EU/ml, or U/ml depending on the marker of interest that is being measured.

In some embodiments, the mucosal g index control is a l healing index derived from a healthy dual, or an individual who has progressed from a disease state to a healthy state. Alternatively, the control can be an index representing a time course of a more diseased state or healthy to disease.

In some embodiments, the methods of determining the course of therapy and the like include the use of an empirically derived index, score or analysis to select for example, selecting a dose of drug, selecting an appropriate drug, or a course or length of therapy, a therapy regimen, or nance of an existing drug or dose. In certain aspects, a d or measured index can be used to determine the course of therapy. tanding the clinical course of disease will enable physicians to make better ed treatment decisions for their inflammatory disease patients (e.g., IBD, Crohn’s disease or ulcerative colitis) and may help to direct new drug development in the future. The ideal mucosal healing marker(s) for use in the mucosal healing index described herein should be able to identify individuals at risk for the disease and should be disease-specific. er, mucosal healing (s) should be able to detect disease activity and monitor the effect of treatment; and should have a predictive value towards relapse or ence of the disease. Predicting disease course, however, has now been expanded beyond just disease recurrence, but perhaps more antly to include predictors of disease complications including surgery. The present invention is particularly advantageous because it provides indicators of mucosal healing and enables a prediction of the risk of relapse in those patients in remission. In addition, the l g markers and mucosal g index of present invention have enormous implications for patient management as well as therapeutic decision-making and would aid or assist in directing the appropriate therapy to those patients who would most likely benefit from it and avoid the expense and ial toxicity of chronic maintenance y in those who have a low risk of recurrence. 1. Disease Activity Profile As described herein, the disease activity profile (DAP) of the present invention can advantageously be used in methods for personalized therapeutic management of a disease in order to optimize therapy and/or monitor therapeutic efficacy. In certain embodiments, the methods of the invention can improve the accuracy of ing therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to anti-TNF drug therapy. In ular embodiments, the DAP is determined by measuring an array of one or a plurality of(e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, or more) markers at a plurality of time points over the course of therapy with a therapeutic antibody (e.g., anti-TNF drug) to determine a DAP, wherein the DAP comprises a representation of the concentration level of each marker over time. In certain ments, the DAP may comprise a representation of the ce or absence, concentration (e.g., expression) level, activation (e.g, phosphorylation) level, and/or ty value (e.g., change in slope of the level of a particular marker) of each marker over time. As such, the methods of the present invention find utility in ining patient management by ining patient immune status.

[0170] In certain instances, a single statistical algorithm or a combination of two or more statistical algorithms can be applied to the concentration level of each marker over the course of therapy or to the DAP itself.

Understanding the clinical course of disease enables physicians to make better ed treatment decisions for their atory disease ts (e.g., IBD (e.g., Crohn’s disease), rheumatoid arthritis (RA), others) and helps to direct new drug development. The ideal biomarker(s) for use in the disease activity profile described herein is able to fy individuals at risk for the disease and is disease-specific. Moreover, the biomarker(s) are able to detect disease activity and monitor the effect of treatment; and have a predictive value towards relapse or recurrence of the disease. Predicting disease course, however, has now been expanded beyond just disease recurrence, but more importantly to include predictors of disease cations including surgery. The present invention is particularly advantageous e it provides indicators of disease activity and/or severity and enables a prediction of the risk of relapse in those patients in ion. In addition, the biomarkers and disease WO 54987 activity profile of the t invention have enormous implications for patient management, as well as therapeutic decision-making, and aid or assist in directing the riate therapy to patients who most likely will benefit from it and avoid the expense and potential toxicity of chronic maintenance therapy in those who have a low risk of recurrence.

As a non-limiting e, the disease actiVity profile (DAP) in one ment comprises detecting, measuring, or determining the presence, level (concentration (e.g, total) and/or activation (e.g., phosphorylation)), or genotype of one or more specific biomarkers in one or more of the following categories of kers: (1) Drug levels (e.g., NF drug levels); (2) Anti-drug antibody (ADA) levels (e.g., level of autoantibody to an anti-TNF drug); (3) Inflammatory s; (4) Anti-inflammatory markers; and/or (5) Tissue repair markers.

Non-limiting examples of additional and/or alternative markers in which the presence, level (concentration (e.g, total) and/or activation (e.g, phosphorylation)), or genotype can be measured include: (6) Serology (e.g., immune markers); (7) Markers of oxidative stress; (8) Cell e receptors (e.g., CD64, others); (9) Signaling pathways; (10) kel, or the elimination rate constant of a drug such as a therapeutic antibody (e.g., infliximab); and/or (1 1) Other markers (e.g., genetic markers such as inflammatory pathway genes).

A. Anti-TNF Drug Levels & Anti-Drug Antibody (ADA) Levels

[0174] In some embodiments, the disease actiVity profile (DAP) comprises determining the presence and/or level of anti-TNF drug (e.g., level of free anti-TNFu therapeutic antibody such as infliximab) and/or anti-drug antibody (ADA) (e.g., level of tibody to the anti- TNF drug such as HACA) in a patient sample (e.g., a serum sample from a patient on anti- TNF drug therapy) at multiple time points, e.g, before, during, and/or after the course of therapy.

In particular embodiments, the ce and/or level of anti-TNF drug and/or ADA is determined with a homogeneous mobility shift assay using size exclusion chromatography.

This , which is described in PCT Application No. PCT/USZO l 0/054125, filed October 26, 2010, the disclosure of which is hereby incorporated by reference in its entirety for all purposes, is particularly advantageous for measuring the presence or level of TNFu inhibitors as well as autoantibodies (e.g, HACA, HAHA, etc.) that are generated against them.

In one embodiment, the method for detecting the presence of an anti-TNFu antibody in a sample comprises: (a) contacting labeled TNFu with a sample having or suspected of having an anti- TNFu antibody to form a labeled complex with the anti-TNFOL antibody; (b) subjecting the d complex to size exclusion chromatography to separate the labeled complex; and (c) detecting the labeled complex, thereby detecting the anti-TNFu antibody.

In certain instances, the s are ally useful for the following anti-TNFu antibodies: REMICADETM (infliximab), ENBRELT'V' (etanercept), HUMIRAT'V' (adalimumab), and ® lizumab pegol).

Tumor necrosis factor 0L (TNFu) is a cytokine involved in systemic inflammation and is a member of a group of cytokines that stimulate the acute phase reaction. The primary role of TNFu is in the regulation of immune cells. TNFu is also able to induce apoptotic cell death, to induce inflammation, and to inhibit tumorigenesis and viral replication. TNF is primarily produced as a 212-amino acid-long type II transmembrane protein ed in stable homotrimers.

[0179] The terms “TNF”, “TNFu,” and L,” as used herein, are intended to include a human ne that exists as a 17 kDa secreted form and a 26 kDa membrane associated form, the biologically active form of which is composed of a trimer of noncovalently bound 17 kDa molecules. The structure of TNF-0L is described further in, for example, Jones, et al. (1989) Nature, 5-228. The term TNF-0L is intended to include human, a recombinant human TNF-0L (rhTNF-(x), or at least about 80% identity to the human TNFu protein. Human TNFOL consists of a 35 amino acid (aa) cytoplasmic domain, a 21 aa transmembrane t, and a 177 aa extracellular domain (ECD) (Pennica, D. et al. (1984) Nature 4). Within the ECD, human TNFu shares 97% aa sequence identity with rhesus and 71% 92% with bovine, canine, cotton rat, equine, feline, mouse, porcine, and rat TNFu. TNFu can be ed by standard inant expression methods or purchased commercially (R & D Systems, Catalog No. 210-TA, Minneapolis, Minn.).

In certain instances, after the TNF 0L antibody is detected, the TNF 0L antibody is measured using a standard curve.

In another embodiment, the method for ing an autoantibody to an anti-TNFOL antibody in a sample comprises: (a) contacting d anti-TNFOL antibody with the sample to form a labeled complex with the autoantibody; (b) subjecting the labeled complex to size exclusion chromatography to separate the labeled complex; and (c) detecting the labeled complex, y detecting the tibody.

In n instances, the autoantibodies include human anti-chimeric antibodies , human anti-humanized antibodies (HAHA), and human anti-mouse antibodies (HAMA).

Non-limiting es of other methods for determining the presence and/or level of anti-TNF drug and/or anti-drug antibodies (ADA) include enzyme-linked immunosorbent assays (ELISAs) such as bridging ELISAs. For example, the Infliximab ELISA from s Biotek Laboratories detects free infliximab in serum and plasma samples, and the HACA ELISA from PeaceHealth Laboratories detects HACA in serum samples.

B. Inflammatory Markers Although disease course of an inflammatory disease is typically measured in terms of inflammatory activity by noninvasive tests using white blood cell count, this method has a low specificity and shows limited correlation with disease activity.

[0185] As such, in certain embodiments, a variety of inflammatory markers, including biochemical markers, serological markers, protein markers, genetic markers, and/or other clinical or echographic characteristics, are particularly useful in the methods of the present invention for personalized therapeutic management by selecting therapy, optimizing y, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or more eutic agents such as biologics (e.g, NF drugs). In particular embodiments, the methods described herein utilize the ination of a disease activity profile (DAP) based upon one or more (a ity of) inflammatory s (e.g., alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-TNF drug.

Non-limiting examples of atory markers include cytokines, chemokines, acute phase proteins, cellular adhesion molecules, S100 proteins, and/or other inflammatory markers. In red embodiments, the inflammatory markers comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, or more cytokines. In one particular embodiment, the cytokines are at least 1, 2, 3, 4, 5, 6, 7, or all 8 ofthe following: GM-CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, and sTNF R11. 1. Cytokines and Chemokines The determination of the presence or level of at least one cytokine or chemokine in a sample is particularly useful in the t invention. As used herein, the term “cytokine” includes any of a variety of polypeptides or proteins secreted by immune cells that regulate a range of immune system fianctions and encompasses small cytokines such as chemokines.

The term “cytokine” also includes adipocytokines, which comprise a group of cytokines secreted by adipocytes that fianction, for example, in the regulation of body weight, poiesis, angiogenesis, wound healing, insulin resistance, the immune response, and the inflammatory response.

In certain embodiments, the presence or level of at least one ne including, but not limited to, granulocyte-macrophage colony-stimulating factor (GM-CSF), IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, soluble tumor necrosis -0L receptor II (sTNF RII), TNF-related weak inducer of apoptosis (TWEAK), rotegerin (OPG), IFN—u, IFN-B, IL-lu, IL-1 receptor antagonist (IL-lra), IL-4, IL-5, soluble IL-6 receptor R), IL-7, IL-9, IL-l2, IL- l3, IL-lS, IL-l7, IL-23, and IL-27 is determined in a sample. [0 1 89] In certain other embodiments, the ce or level of at least one chemokine such as, for e, CXCLl/GRO l/GROOL, CXCL2/GRO2, CXCL3/GRO3, CXCL4/PF-4, CXCLS/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG, CXCL l O/IP- l 0, CXCLl l/I-TAC, CXCL l2/SDF- l, CXCL l 3/BCA-l, CXCL l4/BRAK, CXCL l 5, CXCL16, CXCL l 7/DMC, CCLl, CCL2/MCP- l, CCL3/MIP-10L, CCL4/MIP-l B, ANTES, CCL6/C l 0, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL l 0, CCLl l/Eotaxin, CCL l 5, CCL l 3/MCP-4, CCL l4/HCC- l, CCL l S/MIP-S, CCL l 6/LEC, CCL l , CCL18/MIP- 4, CCL l 9/MIP-3 B, CCL2O/MIP-30L, CCL2 l/SLC, CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK, CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CLl, CL2, and CX3CL1 is ined in a sample. In certain further embodiments, the presence or level of at least one adipocytokine including, but not limited to, leptin, adiponectin, resistin, active or total plasminogen activator inhibitor-1 (PAL 1 ), visfatin, and retinol binding protein 4 (RBP4) is determined in a sample. Preferably, the ce or level of GM-CSF, IFN-y, IL-1 [3, IL-2, IL-6, IL-8, TNF-u, sTNF RII, and/or other cytokines or chemokines is determined.

In certain instances, the presence or level of a particular ne or chemokine is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay. In certain other instances, the presence or level of a particular cytokine or chemokine is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable ELISA kits for determining the presence or level of a cytokine or chemokine of interest in a serum, plasma, saliva, or urine sample are available from, e.g., R&D Systems, Inc. (Minneapolis, MN), Neogen Corp. (Lexington, KY), Alpco stics (Salem, NH), Assay Designs, Inc.

(Ann Arbor, MI), BD Biosciences ngen (San Diego, CA), Invitrogen illo, CA), Calbiochem (San Diego, CA), CHEMICON International, Inc. (Temecula, CA), Antigenix America Inc. (Huntington Station, NY), QIAGEN Inc. (Valencia, CA), d Laboratories, Inc. (Hercules, CA), and/or Bender MedSystems Inc. (Burlingame, CA).

[0191] The human IL-6 polypeptide ce is set forth in, e.g., Genbank Accession No.

NP_00059l. The human IL-6 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000600. One d in the art will appreciate that IL-6 is also known as eron beta 2 (IFNB2), HGF, HSF, and BSF2.

The human IL-lB polypeptide sequence is set forth in, e.g., Genbank Accession No.

NP_000567. The human IL-lB mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000576. One skilled in the art will appreciate that IL-lB is also known as ILlF2 and IL-lbeta.

The human IL-8 ptide sequence is set forth in, e.g., Genbank Accession No.

NP_000575 (SEQ ID NO: 1). The human IL-8 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_0005 84 (SEQ ID NO:2). One skilled in the art will appreciate that IL-8 is also known as CXCL8, K60, NAF, GCPl, LECT, LUCT, NAPl, 3-lOC, GCP-l, LYNAP, MDNCF, MONAP, NAP-l, SCYB8, TSG-l, AMCF-I, and b-ENAP.

The human TWEAK ptide ce is set forth in, e.g., k Accession Nos. NP_003800 and AAC5 1923. The human TWEAK mRNA (coding) sequence is set forth in, e.g., Genbank Accession Nos. NM_003 809 and BC104420. One skilled in the art will appreciate that TWEAK is also known as tumor necrosis factor ligand superfamily member 12 (TNFSF12), APO3 ligand (APO3L), CD255, DR3 ligand, growth factor- inducible l4 (Fnl4) ligand, and UNQ18 l/PROZO7. 2. Acute Phase Proteins The determination of the presence or level of one or more acute-phase proteins in a sample is also useful in the present invention. Acute-phase proteins are a class of proteins whose plasma concentrations increase ive acute-phase proteins) or decrease (negative phase proteins) in response to inflammation. This response is called the acute-phase reaction (also called acute-phase response). Examples of positive acute-phase proteins include, but are not d to, C-reactive protein (CRP), D-dimer protein, mannose-binding protein, alpha l-antitrypsin, alpha l-antichymotrypsin, alpha 2-macr0globulin, f1brin0gen, prothrombin, factor VIII, von Willebrand factor, plasminogen, complement factors, ferritin, serum amyloid P component, serum amyloid A (SAA), orosomucoid (alpha 1-acid glycoprotein, AGP), ceruloplasmin, haptoglobin, and combinations thereof. Non-limiting examples of negative acute-phase proteins include albumin, transferrin, transthyretin, transcortin, retinol-binding protein, and combinations thereof Preferably, the presence or level of CRP and/or SAA is determined.

[0196] In certain ces, the presence or level of a particular acute-phase protein is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an cation-based assay. In certain other instances, the ce or level of a particular acute-phase protein is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. For example, a sandwich colorimetric ELISA assay ble from Alpco Diagnostics (Salem, NH) can be used to determine the level of CRP in a serum, plasma, urine, or stool sample. rly, an ELISA kit ble from Biomeda Corporation (Foster City, CA) can be used to detect CRP levels in a sample. Other methods for determining CRP levels in a sample are described in, e.g, US. Patent Nos. 6,838,250 and 6,406,862; and US. Patent Publication Nos. 20060024682 and 19410. Additional methods for determining CRP levels include, e.g., immunoturbidimetry assays, rapid immunodiffusion assays, and visual ination . Suitable ELISA kits for determining the ce or level of SAA in a sample such as serum, plasma, saliva, urine, or stool are ble from, e.g., Antigenix America Inc.

(Huntington Station, NY), Abazyme (Needham, MA), USCN Life (Missouri City, TX), and/or U.S. Biological (Swampscott, MA).

C-reactive protein (CRP) is a protein found in the blood in response to inflammation (an acute-phase protein). CRP is typically produced by the liver and by fat cells (adipocytes).

It is a member of the pentraxin family of proteins. The human CRP polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_000558. The human CRP mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000567. One skilled in the art will appreciate that CRP is also known as PTXl, MGC88244, and MGCl49895.

Serum amyloid A (SAA) proteins are a family of apolipoproteins associated with high-density lipoprotein (HDL) in plasma. Different isoforms of SAA are expressed constitutively (constitutive SAAs) at different levels or in response to inflammatory stimuli (acute phase SAAs). These ns are predominantly produced by the liver. The conservation of these ns hout invertebrates and vertebrates suggests SAAs play a highly essential role in all animals. Acute phase serum amyloid A proteins (A-SAAs) are secreted during the acute phase of inflammation. The human SAA polypeptide ce is set forth in, e.g., Genbank Accession No. NP_000322. The human SAA mRNA (coding) ce is set forth in, e.g., Genbank Accession No. NM_00033 1. One skilled in the art will appreciate that SAA is also known as PIG4, , MGCl l 1216, and SAAl. 3. Cellular Adhesion Molecules (IgSF CAMs) The determination of the presence or level of one or more immunoglobulin superfamily ar adhesion molecules in a sample is also useful in the t invention.

As used herein, the term “immunoglobulin superfamily cellular adhesion molecule” (IgSF CAM) includes any of a variety of polypeptides or proteins located on the surface of a cell that have one or more immunoglobulin-like fold domains, and which on in intercellular adhesion and/or signal transduction. In many cases, IgSF CAMs are transmembrane proteins.

Non-limiting examples of IgSF CAMs include Neural Cell on les (NCAMs; e.g., NCAM-lZO, NCAM-lZS, NCAM-l40, 45, 80, NCAM-185, eta), Intercellular Adhesion Molecules (ICAMs, e.g. and , ICAM-l, ICAM-2, ICAM-3, ICAM-4, ICAM-S), Vascular Cell Adhesion Molecule-l (VCAM-l), Platelet-Endothelial Cell Adhesion Molecule-l (PECAM-l), Ll Cell Adhesion Molecule (LlCAM), cell adhesion molecule with homology to LlCAM (close homolog of Ll) (CHLl), sialic acid binding Ig- like lectins (SIGLECs; e.g., -l, SIGLEC-Z, SIGLEC-3, SIGLEC-4, eta), Nectins (e.g., Nectin-l, Nectin-2, Nectin-3, etc), and Nectin-like molecules (e.g., Necl-l, Neel-2, Neel-3, Necl-4, and Necl-S). ably, the presence or level of ICAM-l and/or VCAM-l is determined.

[0200] ICAM-l is a transmembrane cellular adhesion protein that is continuously present in low concentrations in the membranes of leukocytes and endothelial cells. Upon cytokine stimulation, the concentrations y increase. ICAM-l can be induced by IL-l and TNFOL and is expressed by the vascular endothelium, macrophages, and lymphocytes. In IBD, proinflammatory cytokines cause inflammation by lating expression of on molecules such as ICAM-l and VCAM-l. The increased expression of adhesion molecules recruit more lymphocytes to the infected tissue, resulting in tissue inflammation (see, Goke et al., J., Gastroenterol., 32:480 ; and Rijcken et al., Gut, 51:529 (2002)). ICAM-l is encoded by the intercellular adhesion molecule 1 gene ; Entrez GeneID:3383; Genbank Accession No. NM_000201) and is produced after processing of the intercellular adhesion molecule 1 precursor polypeptide (Genbank Accession No. NP_000192).

VCAM-l is a transmembrane cellular adhesion protein that mediates the adhesion of lymphocytes, monocytes, eosinophils, and basophils to vascular endothelium.

Upregulation ofVCAM-1 in endothelial cells by cytokines occurs as a result of sed gene transcription (e.g., in response to Tumor necrosis factor-alpha (TNFu) and Interleukin-l (IL-1)). VCAM-l is encoded by the vascular cell adhesion molecule 1 gene (VCAMl; Entrez GeneID:74l2) and is ed after differential splicing of the transcript (Genbank ion No. NM_001078 (variant 1) or 682 (variant 2)), and processing of the sor polypeptide splice isoform (Genbank Accession No. NP_001069 (isoform a) or NP_542413 (isoform b)).

In certain instances, the presence or level of an IgSF CAM is detected at the level of mRNA sion with an assay such as, for example, a hybridization assay or an amplification-based assay. In certain other ces, the presence or level of an IgSF CAM is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable antibodies and/or ELISA kits for determining the presence or level of ICAM-l and/or VCAM-l in a sample such as a tissue sample, biopsy, serum, , saliva, urine, or stool are available from, e.g., Invitrogen illo, CA), Santa Cruz Biotechnology, Inc. (Santa Cruz, CA), and/or Abcam Inc.

(Cambridge, MA). 4. S100 Proteins The determination of the presence or level of at least one S100 protein in a sample is also useful in the present invention. As used herein, the term “S100 n” includes any member of a family of low molecular mass acidic proteins characterized by cell-type-speciflc expression and the presence of 2 EF-hand calcium-binding domains. There are at least 21 different types of S100 proteins in humans. The name is derived from the fact that S100 proteins are 100% soluble in ammonium sulfate at neutral pH. Most S100 proteins are homodimeric, consisting of two identical polypeptides held together by non-covalent bonds. gh S100 proteins are structurally similar to calmodulin, they differ in that they are cell- specif1c, expressed in particular cells at different levels ing on nmental factors.

S-100 proteins are normally present in cells derived from the neural crest (e.g., Schwann cells, melanocytes, glial cells), chondrocytes, adipocytes, myoepithelial cells, macrophages, Langerhans cells, dendritic cells, and keratinocytes. S100 proteins have been implicated in a variety of ellular and extracellular fianctions such as the regulation of protein phosphorylation, transcription factors, Ca2+ homeostasis, the dynamics of cytoskeleton constituents, enzyme activities, cell growth and entiation, and the inflammatory I'GSpOIlSG.

[0204] Calgranulin is an S100 protein that is expressed in le cell types, including renal epithelial cells and neutrophils, and are abundant in infiltrating monocytes and ocytes under conditions of chronic inflammation. Examples of calgranulins include, without limitation, calgranulin A (also known as SlOOA8 or MRP-8), calgranulin B (also known as SlOOA9 or MRP-l4), and calgranulin C (also known as SlOOA12).

[0205] In certain instances, the ce or level of a particular S100 protein is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay. In certain other instances, the presence or level of a particular S100 protein is detected at the level of n expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. Suitable ELISA kits for determining the presence or level of an S100 protein such as nulin A (SlOOA8), calgranulin B (SlOOA9), or calgranulin C (SlOOA12) in a serum, plasma, or urine sample are available from, e.g., Peninsula Laboratories Inc. (San Carlos, CA) and Hycult biotechnology b.v. (Uden, The Netherlands).

Calprotectin, the complex of SlOOA8 and SlOOA9, is a calcium- and zinc-binding protein in the cytosol of neutrophils, tes, and keratinocytes. Calprotectin is a major protein in neutrophilic granulocytes and macrophages and accounts for as much as 60% of the total protein in the cytosol fraction in these cells. It is therefore a surrogate marker of neutrophil turnover. Its tration in stool correlates with the intensity of neutrophil ration of the intestinal mucosa and with the severity of inflammation. In some instances, calprotectin can be measured with an ELISA using small (50-100 mg) fecal samples (see, e.g., Johne et al., ScandJ Gastroenter01., 36:291-296 (2001)).

. Other Inflammatory Markers The ination of the presence or level of lactoferrin in a sample is also useful in the present invention. In certain instances, the presence or level of lactoferrin is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay. In certain other instances, the presence or level of lactoferrin is detected at the level of n expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. A errin ELISA kit available from Calbiochem (San Diego, CA) can be used to detect human lactoferrin in a plasma, urine, bronchoalveolar lavage, or cerebrospinal fluid sample. Similarly, an ELISA kit available from US. Biological (Swampscott, MA) can be used to determine the level of lactoferrin in a plasma sample. US.

Patent Publication No. 20040137536 bes an ELISA assay for determining the presence of elevated lactoferrin levels in a stool sample. Likewise, US. Patent ation No. 20040033537 describes an ELISA assay for determining the concentration of endogenous lactoferrin in a stool, mucus, or bile sample. In some embodiments, then presence or level of actoferrin antibodies can be ed in a sample using, e.g., lactoferrin protein or a fragment thereof.

The determination of the ce or level of one or more pyruvate kinase isozymes such as Ml-PK and M2-PK in a sample is also useful in the present invention. In certain instances, the presence or level of Ml-PK and/or M2-PK is detected at the level ofmRNA expression with an assay such as, for example, a hybridization assay or an amplification- based assay. In certain other instances, the presence or level of Ml-PK and/or M2-PK is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay. Pyruvate kinase isozymes Ml/M2 are also known as pyruvate kinase muscle isozyme (PKM), pyruvate kinase type K, cytosolic thyroid hormone- binding protein ), thyroid hormone-binding n 1 ), or opa-interacting n 3 (OIP3).

In fithher embodiments, the determination ofthe presence or level of one or more growth factors in a sample is also useful in the present invention. Non-limiting examples of growth factors include transforming growth factors (TGF) such as TGF-(x, TGF-B, TGF-B2, TGF-B3, etc, which are described in detail below. 6. Exemplary Set of Inflammatory s In particular embodiments, at least one or a plurality (e.g., two, three, four, five, six, seven, or all eight, such as, e.g., a panel or an array) of the following inflammatory markers WO 54987 can be detected (e.g, alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, and/or to improve the accuracy of selecting therapy, optimizing therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment to anti-TNF drug therapy: (1) GM-CSF; (2) IFN-y; (3) IL-lB; (4) IL-2; (5) IL-6; (6) IL-8; (7) TNF-u; and (8) sTNF RII.

C. Anti-Inflammatory Markers In certain embodiments, a variety of anti-inflammatory markers are particularly useful in the methods of the t invention for personalized therapeutic management by selecting therapy, optimizing therapy, reducing toxicity, and/or ring the efficacy of therapeutic treatment with one or more therapeutic agents such as biologics (e.g., anti-TNF drugs). In particular embodiments, the s described herein utilize the determination of a disease activity profile (DAP) based upon one or more (a ity of) anti-inflammatory s (e.g., alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti- TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-TNF drug.

Non-limiting examples of nflammatory markers include IL-12p70 and IL-10.

In preferred embodiments, the presence and/or concentration levels of both IL-12p70 and IL- are determined.

[0213] In certain instances, the presence or level of a particular nflammatory marker is detected at the level ofmRNA sion with an assay such as, for example, a hybridization assay or an amplification-based assay. In certain other instances, the presence or level of a particular anti-inflammatory marker is detected at the level of protein expression using, for example, an immunoassay (e.g., ELISA) or an immunohistochemical assay.

[0214] The human IL-12p70 polypeptide is a heterodimer made up of two subunits of IL- 12 proteins: one is 40kDa (IL-12p40) and one is 35kDa p35). Suitable ELISA kits for determining the ce or level of IL-12p70 in a serum, plasma, saliva, or urine sample are available from, e.g., Gen-Probe Diaclone SAS (France), Abazyme (Needham, MA), BD Biosciences Pharmingen (San Diego, CA), Cell Sciences n, MA), ience (San Diego, CA), Invitrogen (Camarillo, CA), R&D Systems, Inc. (Minneapolis, MN), and Thermo Scientific Pierce Protein Research ts (Rockford, IL).

The human IL-lO polypeptide is an anti-inflammatory cytokine that is also known as human ne synthesis inhibitory factor (CSIF). Suitable ELISA kits for determining the ce or level of IL-l2p70 in a serum, , saliva, or urine sample are available from, e.g., Antigenix America Inc. (Huntington Station, NY), BD Biosciences Pharmingen (San Diego, CA), Cell Sciences (Canton, MA), eBioscience (San Diego, CA), Gen-Probe Diaclone SAS e), Invitrogen (Camarillo, CA), R&D s, Inc. (Minneapolis, MN), and Thermo Scientific Pierce Protein Research Products (Rockford, IL).

D. Serology e Markers) The determination of serological or immune s such as autoantibodies in a sample (e.g., serum sample) is also useful in the present invention. Antibodies against anti- inflammatory les such as IL-lO, TGF-B, and others might suppress the body’s ability to control inflammation and the presence or level of these antibodies in the t indicates the use of powerful immunosuppressive medications such as anti-TNF drugs. Mucosal healing might result in a decrease in the antibody titre of antibodies to bacterial antigens such as, e.g., OmpC, flagellins (cBir—l, Fla-A, Fla-X, etc), 12, and others (pANCA, ASCA, etc.) As such, in certain aspects, the methods described herein utilize the determination of a disease activity profile (DAP) based upon one or more (a plurality of) serological or immune markers (e.g., alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, selecting an appropriate anti-TNF drug y, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic ent with an anti-TNF drug.

[0218] Non-limiting examples of gical immune markers suitable for use in the present invention include anti-neutrophil antibodies, anti-Saccharomyces cerevisiae antibodies, and/or other anti-microbial antibodies. 1. Anti-Neutrophil Antibodies The determination ofANCA levels and/or the presence or absence ofpANCA in a sample is useful in the methods of the present invention. As used herein, the term “anti- neutrophil cytoplasmic antibody” or “ANCA” includes dies directed to cytoplasmic and/or nuclear components of neutrophils. ANCA activity can be divided into several broad categories based upon the ANCA ng n in neutrophils: (l) cytoplasmic neutrophil staining without perinuclear highlighting (cANCA); (2) perinuclear staining around the outside edge of the nucleus (pANCA); (3) perinuclear staining around the inside edge of the nucleus (NSNA); and (4) diffuse staining with speckling across the entire neutrophil ). In certain ces, pANCA staining is sensitive to DNase ent. The term ANCA encompasses all varieties of anti-neutrophil reactivity, including, but not limited to, cANCA, pANCA, NSNA, and SAPPA. Similarly, the term ANCA asses all immunoglobulin isotypes including, t limitation, immunoglobulin A and G.

ANCA levels in a sample from an dual can be determined, for example, using an immunoassay such as an enzyme-linked immunosorbent assay (ELISA) with alcohol-fixed neutrophils. The presence or absence of a particular category ofANCA such as pANCA can be determined, for example, using an immunohistochemical assay such as an ct fluorescent antibody (IFA) assay. Preferably, the presence or absence ofpANCA in a sample is determined using an imrnunofiuorescence assay with DNase-treated, fixed neutrophils. In addition to fixed neutrophils, antigens specific for ANCA that are suitable for determining ANCA levels include, without limitation, unpurified or lly purified neutrophil extracts; purified proteins, protein fragments, or tic es such as histone H1 or ANCA- reactive fragments thereof (see, e.g., US. Patent No. 6,074,835); histone e antigens, porin antigens, Bacteroides antigens, or ANCA-reactive fragments thereof (see, e.g., US.

Patent No. 6,033,864); secretory vesicle antigens or ANCA-reactive fragments thereof (see, e. g., US. Patent Application No. 08/804,106); and anti-ANCA idiotypic antibodies. One skilled in the art will appreciate that the use of additional ns specific for ANCA is within the scope of the present invention. 2. Anti-Saccharomyces cerevisiae Antibodies The determination ofASCA (e.g., ASCA-IgA and/or ASCA-IgG) levels in a sample is useful in the present invention. As used herein, the term “anti-Saccharomyces ’sz’ae immunoglobulin A” or “ASCA-IgA” includes antibodies of the immunoglobulin A isotype that react specifically with S. cerevisiae. Similarly, the term Saccharomyces cerevisiae immunoglobulin G” or “ASCA-IgG” includes antibodies of the immunoglobulin G isotype that react specifically with S. cerevisiae.

[0222] The determination of whether a sample is positive for ASCA-IgA or ASCA-IgG is made using an antigen c for ASCA. Such an antigen can be any antigen or mixture of antigens that is bound specifically by ASCA-IgA and/or ASCA-IgG. Although ASCA antibodies were initially characterized by their ability to bind S. cerevisiae, those of skill in the art will understand that an antigen that is bound specifically by ASCA can be ed from S. cerevisiae or from a variety of other sources so long as the antigen is capable of binding specifically to ASCA antibodies. Accordingly, exemplary sources of an antigen specific for ASCA, which can be used to determine the levels ofASCA-IgA and/or ASCA- IgG in a , include, t limitation, whole killed yeast cells such as Saccharomyces or Candida cells; yeast cell wall mannan such as phosphopeptidomannan (PPM); oligosachharides such as oligomannosides; neoglycolipids; anti-ASCA idiotypic antibodies; and the like. Different species and strains of yeast, such as S. cerevisiae strain Su1, Su2, CBS 1315, or BM 156, or Candida albicans strain VW32, are suitable for use as an antigen specific for ASCA-IgA and/or ASCA-IgG. Purified and synthetic antigens specific for ASCA are also suitable for use in ining the levels ofASCA-IgA and/or ASCA-IgG in a sample. Examples of purified antigens include, without limitation, purified oligosaccharide antigens such as oligomannosides. Examples of synthetic antigens e, without limitation, synthetic oligomannosides such as those described in US. Patent Publication No. 05060, e.g., D-Man [3(1-2) D-Man [3(1-2) D-Man [3(1-2) D-Man-OR, D-Man 0L(1-2) D-Man 0L(1-2) D-Man 0L(1-2) D-Man-OR, and D-Man 0L(1-3) D-Man 0L(1-2) D-Man 0L(1-2) D- Man-OR, wherein R is a hydrogen atom, a C1 to C20 alkyl, or an optionally d connector group .

Preparations of yeast cell wall mannans, e.g., PPM, can be used in determining the levels ofASCA-IgA and/or gG in a sample. Such water-soluble surface antigens can be prepared by any riate extraction que known in the art, including, for example, by autoclaving, or can be obtained commercially (see, e. g., Lindberg et al., Gut, 33:909-913 (1992)). The acid-stable fraction of PPM is also useful in the present invention d et al., Clin. Diag. Lab. Imman01., 3:219-226 ). An exemplary PPM that is useful in determining ASCA levels in a sample is derived from S. avaram strain ATCC #38926.

Purified oligosaccharide ns such as oligomannosides can also be useful in determining the levels ofASCA-IgA and/or ASCA-IgG in a sample. The purified oligomannoside antigens are preferably ted into neoglycolipids as described in, for example, Faille et al., Eur. J. Microbiol. Infect. Dis., 11:438-446 (1992). One skilled in the art tands that the reactivity of such an oligomannoside antigen with ASCA can be optimized by varying the mannosyl chain length (Frosh et al. , Proc Natl. Acad. Sci. USA, 82: 1 194-1 198 (1985)); the anomeric ration (Fukazawa et al. In “Immunology of Fungal Disease,” E. Kurstak (ed.), Marcel Dekker Inc., New York, pp. 37-62 (1989); awa et al., Microbiol. Immanol., 34:825-840 (1990); Poulain et al., Eur. J. Clin.

Microbiol., 52 (1993); Shibata et al., Arch. Biochem. Biophys, 243:338-348 (1985); Trinel et al., Infect. Imman, 60:3845-3851 (1992)); or the position of the linkage (Kikuchi et al., Planta, 190:525-535 (1993)). le oligomannosides for use in the methods of the present invention e, without limitation, an oligomannoside having the mannotetraose Man(1-3) Man(1-2) Man(1- 2) Man. Such an oligomannoside can be purified from PPM as described in, e.g., Faille et al., supra. An exemplary colipid specific for ASCA can be constructed by ing the oligomannoside from its respective PPM and subsequently coupling the released oligomannoside to 4-hexadecylaniline or the like. 3. Anti-Microbial Antibodies The determination of mpC antibody levels in a sample is also useful in the present ion. As used herein, the term “anti-outer membrane protein C antibody” or “anti-OmpC dy” es antibodies ed to a bacterial outer membrane porin as described in, e. g., PCT Patent ation No. W0 01/89361. The term “outer membrane protein C” or “OmpC” refers to a bacterial porin that is immunoreactive with an anti-OmpC The level of anti-OmpC antibody present in a sample from an individual can be determined using an OmpC protein or a fragment thereof such as an immunoreactive fragment thereof. le OmpC antigens useful in determining anti-OmpC antibody levels in a sample include, without tion, an OmpC protein, an OmpC polypeptide having substantially the same amino acid sequence as the OmpC protein, or a fragment thereof such as an immunoreactive fragment thereof. As used herein, an OmpC polypeptide generally describes polypeptides having an amino acid sequence with r than about 50% identity, preferably greater than about 60% identity, more preferably greater than about 70% identity, still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with an OmpC protein, with the amino acid identity determined using a sequence alignment program such as CLUSTALW. Such antigens can be prepared, for example, by purif1cation from enteric ia such as E. 0011', by recombinant expression of a nucleic acid such as Genbank Accession No. K0054l, by synthetic means such as solution or solid phase peptide synthesis, or by using phage display.

The determination of anti-I2 antibody levels in a sample is also useful in the present invention. As used herein, the term “anti-I2 antibody” includes antibodies directed to a microbial antigen sharing homology to bacterial transcriptional tors as described in, e.g., US. Patent No. 6,309,643. The term “12” refers to a microbial antigen that is immunoreactive with an anti-I2 antibody. The microbial 12 protein is a ptide of 100 amino acids sharing some similarity weak homology with the predicted protein 4 from C. pasteurianum, Rv3557c from Mycobacterium tuberculosis, and a transcriptional regulator from ex aeolicus. The nucleic acid and protein sequences for the 12 protein are described in, e. g., US. Patent No. 6,309,643.

The level of 2 antibody present in a sample from an individual can be determined using an 12 protein or a fragment thereof such as an immunoreactive fragment thereof. Suitable 12 antigens useful in determining anti-12 antibody levels in a sample include, without limitation, an 12 protein, an 12 polypeptide having substantially the same amino acid ce as the 12 protein, or a fragment thereof such as an immunoreactive fragment thereof Such 12 polypeptides t greater sequence rity to the 12 protein than to the C. pasteurianum protein 4 and include isotype variants and homologs f. As used herein, an 12 polypeptide generally describes polypeptides having an amino acid sequence with greater than about 50% identity, preferably greater than about 60% identity, more preferably greater than about 70% identity, still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a naturally-occurring 12 protein, with the amino acid identity determined using a sequence alignment program such as CLUSTALW. Such 12 antigens can be prepared, for example, by purification from es, by recombinant expression of a nucleic acid encoding an 12 antigen, by synthetic means such as solution or solid phase peptide synthesis, or by using phage display.

The determination of anti-flagellin antibody levels in a sample is also useful in the present invention. As used herein, the term “anti-flagellin antibody” es antibodies directed to a protein component of bacterial flagella as bed in, e.g., PCT Patent Publication No. WO 03/053220 and US. Patent Publication No. 2004004393 1. The term “flagellin” refers to a bacterial flagellum protein that is immunoreactive with an anti-flagellin antibody. Microbial flagellins are proteins found in bacterial flagellum that arrange themselves in a hollow cylinder to form the filament.

The level of anti-flagellin antibody present in a sample from an individual can be determined using a flagellin n or a fragment thereof such as an immunoreactive fragment thereof. Suitable flagellin antigens useful in determining anti-flagellin antibody levels in a sample e, without limitation, a flagellin protein such as Cbir-l flagellin, flagellin X, flagellin A, flagellin B, fragments f, and combinations thereof, a flagellin ptide having ntially the same amino acid sequence as the flagellin protein, or a fragment thereof such as an immunoreactive fragment thereof. As used herein, a flagellin polypeptide generally describes polypeptides having an amino acid sequence with greater than about 50% ty, preferably greater than about 60% ty, more ably greater than about 70% identity, still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a naturally-occurring flagellin protein, with the amino acid ty determined using a sequence alignment program such as CLUSTALW. Such flagellin antigens can be prepared, e.g., by purification from bacterium such as Helicobacter Bill's, Helicobacter mustelae, Helicobacter pylori, Butyrz’vz’brz’o fibrisolvens, and bacterium found in the cecum, by recombinant expression of a nucleic acid encoding a flagellin antigen, by synthetic means such as solution or solid phase peptide sis, or by using phage display.

E. Cell Surface Receptors

[0232] The determination of cell surface receptors in a sample is also useful in the present ion. The half-life of anti-TNF drugs such as de and Humira is significantly decreased in patients with a high level of ation. CD64, the high-affinity receptor for immunoglobulin (Ig) G1 and IgG3, is predominantly expressed by mononuclear phagocytes.

Resting polymorphonuclear (PMN) cells scarcely express CD64, but the expression of this marker is upregulated by interferon and granulocyte-colony-stimulating factor acting on d precursors in the bone marrow. Crosslinking of CD64 with IgG complexes exerts a number of cellular responses, including the internalization of immune complexes by endocytosis, phagocytosis of zed les, degranulation, activation of the oxidative burst, and the release of cytokines.

[0233] As such, in certain aspects, the methods described herein utilize the determination of a disease activity profile (DAP) based upon one or more (a plurality of) cell surface receptors such as CD64 (e.g., alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, ing an appropriate anti-TNF drug therapy, optimizing anti-TNF drug y, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the cy of therapeutic treatment with an anti-TNF drug.

F. Signaling Pathways The determination of ing pathways in a sample is also useful in the present invention. Polymorphonuclear (PMN) cell activation, ed by infltration into the intestinal mucosa (synovium for RA) and migration across the crypt epithelium is regarded as a key feature of IBD. It has been estimated by fecal indium-l l l-labeled leukocyte excretion that migration ofPMN cells from the ation to the diseased section of the intestine is increased by 10-fold or more in IBD patients. Thus, measuring activation ofPMN cells from blood or tissue inflammation by measuring signaling pathways using an assay such as the gollaborative Enzyme Enhanced Reactive ImmunoAssay (CEER) described herein is an ideal way to understand inflammatory disease.

As such, in certain aspects, the methods described herein utilize the determination of a disease activity profile (DAP) based upon one or more (a plurality of) signal transduction molecules in one or more signaling pathways (e.g., alone or in combination with biomarkers from other categories) to aid or assist in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti- TNF drug. In preferred embodiments, the total (e.g., expression) level and/or activation (e.g., orylation) level of one or more signal transduction molecules in one or more signaling pathways is measured.

The term “signal uction molecule” or “signal transducer” includes proteins and other molecules that carry out the process by which a cell converts an extracellular signal or stimulus into a response, typically ing ordered sequences of biochemical reactions inside the cell. Examples of signal transduction molecules include, but are not limited to, receptor tyrosine kinases such as EGFR (e.g, EGFR/HERl/ErbBl, HER2/Neu/ErbB2, rbB3, HER4/ErbB4), /FLTl, /FLKl/KDR, VEGFR3/FLT4, FLT3/FLK2, PDGFR (e.g., PDGFRA, PDGFRB), c-KIT/SCFR, INSR (insulin receptor), IGF-IR, , IRR (insulin receptor-related or), CSF-lR, FGFR l-4, HGFR 1-2, CCK4, TRK A-C, c-MET, RON, EPHA 1-8, EPHB 1-6, AXL, MER, TYRO3, TIE 1-2, TEK, RYK, DDR 1-2, RET, c-ROS, erin, LTK (leukocyte tyrosine kinase), ALK (anaplastic lymphoma kinase), ROR 1-2, MUSK, AATYK 1-3, and RTK 106; truncated forms of receptor tyrosine s such as truncated HER2 receptors with missing amino- terminal extracellular s (e.g, p95ErbB2 (p95m), pl 10, p95c, p95n, eta), truncated cMET receptors with missing amino-terminal ellular domains, and ted HER3 receptors with missing amino-terminal extracellular domains; receptor tyrosine kinase dimers (e.g., p95HER2/HER3; p95HER2/HER2; truncated HER3 or with HERl , HER2, HER3, or HER4; HER2/HER2; HER3/HER3; ER3; HERl/HERZ; HERl/HER3; HER2/HER4; HER3/HER4; eta); non-receptor tyrosine kinases such as BCR—ABL, Src, Frk, Btk, Csk, Abl, Zap70, Fes/Fps, Fak, Jak, Ack, and LIMK; tyrosine kinase signaling cascade components such as AKT (e.g, AKTl, AKTZ, AKT3), MEK(MAP2K1), ERK2 (MAPKl), ERKl (MAPK3), PI3K (e.g., PIK3CA (pl 10), PIK3Rl (p85)), PDKl, PDK2, phosphatase and tensin homolog (PTEN), SGK3, 4E-BPl, P7OS6K (e.g., p70 S6 kinase splice variant alpha 1), protein tyrosine atases (e.g., PTPlB, PTPNl3, BDPl, eta), RAF, PLA2, MEKK, JNKK, JNK, p38, Shc (p66), Ras (e.g., K-Ras, N—Ras, H-Ras), Rho, Racl, Cdc42, PLC, PKC, p53, cyclin D1, STATl, STAT3, phosphatidylinositol 4,5-bisphosphate (PIP2), phosphatidylinositol trisphosphate (PIP3), mTOR, BAD, p21, p27, ROCK, 1P3, TSP-l, NOS, GSK-3B, RSK 1-3, JNK, c-Jun, Rb, CREB, Ki67, paxillin, NF-kB, and IKK; nuclear hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), en receptor, glucocorticoid receptor, mineralocorticoid receptor, vitamin A receptor, vitamin D receptor, retinoid receptor, d hormone receptor, and orphan receptors; nuclear receptor coactivators and repressors such as ed in breast cancer-l (AIBl) and r receptor corepressor l (NCOR), respectively; and combinations f.

[0237] The term “activation state” refers to whether a particular signal transduction molecule is activated. Similarly, the term “activation level” refers to what extent a particular signal transduction le is activated. The tion state typically corresponds to the phosphorylation, ubiquitination, and/or complexation status of one or more signal transduction molecules. miting examples of activation states (listed in parentheses) e: HERl/EGFR (EGFRvIII, phosphorylated (p-) EGFR, hc, tinated (u-) EGFR, p-EGFRvIII); ErbB2 (p-ErbB2, p95HER2 (truncated ErbB2), p-p95HER2, ErbB2:Shc, ErbB2:PI3K, ErbB2:EGFR, ErbB2:ErbB3, ErbB2:ErbB4); ErbB3 (p-ErbB3, truncated ErbB3, ErbB3:PI3K, p-ErbB3:PI3K, ErbB3:Shc); ErbB4 (p-ErbB4, ErbB4:Shc); c- MET (p-c-MET, truncated c-MET, c-Met:HGF complex); AKTl (p-AKTl); AKT2 (p- AKT2); AKT3 (p-AKT3); PTEN (p-PTEN); P7086K (p-P7OS6K); MEK (p-MEK); ERKl (p-ERKl); ERK2 (p-ERK2); PDKl (p-PDKl); PDK2 2); SGK3 3); 4E-BPl (p-4E-BPl); PIK3Rl (p-PIK3Rl); c-KIT IT); ER (p-ER); IGF-lR (p-IGF-lR, IGF- lR:IRS, IRS:PI3K, p-IRS, IGF-lR:PI3K); INSR (p-INSR); FLT3 (p-FLT3); HGFRl (p- HGFRl); HGFR2 (p-HGFR2); RET (p-RET); PDGFRA (p-PDGFRA); PDGFRB (p- PDGFRB); VEGFRl (p-VEGFRl, VEGFRl :PLCy, VEGFRl :Src); VEGFR2 (p-VEGFR2, VEGFR2:PLCy, VEGFR2:Src, VEGFR2:heparin sulphate, VEGFR2:VE-cadherin); VEGFR3 (p-VEGFR3); FGFRl (p-FGFRl); FGFR2 (p-FGFR2); FGFR3 R3); FGFR4 (p-FGFR4); TIEl (p-TIEl); TIE2 (p-TIE2); EPHA A); EPHB (p-EPHB); GSK-3B (p-GSK-3B); NF-kB (p-NF-kB, NF-kB-IkB alpha complex and others), IkB (p-IkB, p-P65:IkB); IKK (phospho IKK); BAD (p-BAD, BAD:l43); mTOR (p-mTOR); Rsk-l (p- Rsk-l); Jnk (p-Jnk); P38 (p-P38); STATl (p-STATl); STAT3 (p-STAT3); FAK (p-FAK); RB (p-RB); Ki67; p53 (p-p53); CREB (p-CREB); c-Jun (p-c-Jun); c-Src (p-c-Src); paxillin (p-paxillin); GRB2 (p-GRB2), Shc (p-Shc), Ras (p-Ras), GABl (p-GABl), SHP2 (p-SHP2), WO 54987 GRB2 (p-GRBZ), CRKL (p-CRKL), PLCy y), PKC (e.g., p-PKCOL, p-PKCB, p- PKCS), adducin (p-adducin), RBl (p-RBl), and PYK2 (p-PYKZ).

The following tables e additional examples of signal transduction molecules for which total levels and/or activation (e.g., phosphorylation) levels can be determined in a sample (e.g., alone or in combination with kers from other categories) to aid or assist in predicting disease course, selecting an appropriate anti-TNF drug therapy, optimizing anti- TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, or monitoring the efficacy of therapeutic treatment with an anti-TNF drug. \\\\\\\\\\\\\\\\\x w\KW\ §\\\\\\\\\\\> \\§§ The Collaborative Enzyme Enhanced Reactive ImmunoAssay , also known as the Collaborative Proximity Immunoassay (COPIA), is described in the following patent documents which are herein incorporated by reference in their entirety for all purposes: PCT ation No. ; PCT Publication No. WO 12140; PCT Publication No. ; PCT Publication No. WO 32723; PCT Publication No. WO 2011/008990; and PCT Application No. PCT/U820 l 0/053386, filed r 20, 2010.

G. Elimination Rate Constant In certain embodiments, a marker for the disease activity profile (DAP) is kel, or the elimination rate constant of an antibody such as an anti-TNF antibody (e.g., infliximab). The determination of an elimination rate constant such as kel is particularly useful in the methods of the invention for personalized therapeutic management by selecting therapy, optimizing y, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or more therapeutic agents such as biologics (e.g, anti-TNF drugs).

[0241] In certain instances, a differential equation can be used to model drug elimination from the patient. In certain instances, a two-compartment PK model can be used. In this instance, the equation for the drug in the central compartment ing intravenous bolus stration is: :--‘- ----- 2211.12; 212 . X1 22.; . X2 The kel 0 X1 term describes elimination of the drug from the l compartment, while the k12 0 X1 and k21 0 X2 terms describe the distribution of drug between the central and peripheral tments.

H. Genetic Markers The ination of the presence or absence of allelic variants (e.g., SNPs) in one or more genetic markers in a sample (e.g., alone or in combination with biomarkers from other categories) is also useful in the methods of the present invention to aid or assist in predicting disease , selecting an appropriate anti-TNF drug therapy, optimizing anti- TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, or monitoring the efficacy of therapeutic treatment with an anti-TNF drug.

Non-limiting examples of genetic markers include, but are not limited to, any of the atory pathway genes and corresponding SNPs that can be genotyped as set forth in Table l (e.g., a NOD2/CARD15 gene, an L23 pathway gene, eta). Preferably, the ce or absence of at least one allelic variant, e.g, a single nucleotide polymorphism (SNP), in the NOD2/CARD15 gene and/or one or more genes in the ILl2/IL23 pathway is determined. See, e. g., Barrett et al., Nat. Genet., 40:955-62 (2008) and Wang et al., Amer. J.

Hum. Genet., 84:399-405 (2009).

Table 1 Gene EXP NUDE“ {R7022 — 31:?x 13 36555-544 NUDE {LCfi'EfiER} »— SNPIL‘ 132001434 i NOSE {:Sifiilfiinsfiifi — SNPI 3‘.

AImfiLi (13:039.; 1L23R E3 E 1 Q} mugs.

NDD2.,-’¢1A1RDI 5 13.339.

ATGMLI 3'18T ’1 PTGERL‘E ERG? “\F‘wfl 5 PTPNE 13TPNE3 KATE C1 1MES {1+ HERE: NIUC' 1 g} OQVHHS $128353? STATE 1's F441 66 {€051.13 B"Du'12_., $1.136in HLA-DPBE, PUB ii} 3's 1 30034:“.54 CCLE, can $5991 865$ LYRNH aEI‘E‘TIQR SLCEEJES 1‘5- F3=C§§SZF 111 SPLAP “1-1:“ 2012/037375 IL12RB} 1L1? EELS I {BEL} CD385 C524? LDQE IT. 13R } CCTR‘E ExiikPK 1i 4 1 SEEirTfiQS IL} E? ExiéPEK-fi STATL‘E TL 3 EA TYRE ETV5 1‘59 E:$34: fi-‘EAPKS rs 1769"MS IRGM 31% 13:91 SE? IRiER-i 1x43"334? 'IRGM 1'3 1 8-00 3 1 3 IRGM :31 i 34 {.3 ...“ TL13TH?ST'l 5 1&1: -' '13 3 0-9 TL 1 AT\T3T1 5 1336.4 T S 183 TL 1 iT‘fl:SF] 5 1x423“3'39 FTNEE 1:324'T55f3T C‘CRfic 151450573 Carma, ,53333,-PTGER4 siflssm'eEm 53:133.:‘PTE3ER4 53) 1 SSTJ‘TGER4 I'TLN I Hm: refilflfifi‘fi) ITLNE 3'51 $534383 WO 54987 133183963 C110: '30 N‘xua.”.n.

LRRL:LES-i555} 9 \ _.

A . . . n34? $132993} “408603 TLR4 $39221) 13‘9“???“ K94 {SEEMS} EFL-RE} 31R? RTE: Km 1‘ B 1311534383 IKE]? 1‘s} iififiééfli is: imfifi Ix'Ql"SQ—l EIiZTL‘EflCTL? 12's.; “£59 1 3 {1:1 KTEQSLG ELI-'25[.2 m4 3.9 9311:1534 EEMI BEER-i1 @1303»; E4341 33} :GLII RG73: 5'} ’7". f" ",5 ‘3‘ Mag“)-.. ‘ GL1": {Qi 119 ray-r} WW"? I r}.- ‘f-IJEH'SLL t5 mm:T‘$$35C?l‘-’T}‘\ mlfii‘ififl 3-333153393; :1;if} MASH 18(10i‘!-‘~’flt‘ 31333813 3333 III-2'6 . 4.11-11 35.12.3333 ; :; 33,3313 1131 545 13.35; ;:;;333:33313 33133. 13131612335? ILI-{EELIQ 111$le HEB-R.

{LEE-R; ILEEER U—I 112BR. 325103 We“? “.7 BTL; 3 814m 333:3sz MEPI 113m MEPI FREE; I :3 PUFS I {} PM\TF1 Eli: 1'3 I 3 I "23*?" REQTI 8&3 331333333: LEW- BLT}LAMB I {231331031333 31333 2123-3 31-814 331 -Bp—V‘ 333133191343 33.1 3.3111413 IL 1. 81-191? LE’RE‘av‘Ecl (DELHI.- l TNPRfiFEcB TNFRSFIIB TNTRSEHE minim-mm nmmm Additional SNPs useful in the present invention include, e.g, 62, rs9286879, rsl 1584383, rs7746082, rsl456893, r5155 1398, rsl7582416, rs3764l47, rs1736135, rs4807569, rs7758080, and rs8098673. See, e.g., Barrett et al., Nat. , 40:955-62 (2008).

In particular embodiments, the presence or absence of one or more mutations in one or more of the ing genetic markers is determined: inflammatory pathway genes, e.g, the ce or e of t alleles (e.g, SNPs) in one or more inflammatory markers such as, e. g., ARD15 (e. g., SNP 8, SNP 12, and/or SNP 13 described in US Patent No. 7,592,437), ATG16L1 (e. g., the rs2241880 (T300A) SNP bed in Lakatos et al., Digestive and Liver Disease, 40 (2008) 867-873), IL23R (e.g., the rs11209026 (R381Q) SNP described in Lakatos et al.), the human leukocyte antigen (HLA) genes and/or ne genes described in, e. g., Gasche et al. (Eur. J. Gastroenterology & Hepatology, (2003) 15:599- 606), and the DLG5 and/or OCTN genes from the IBD5 locus. 1. NOD2/CARD15 The determination of the presence or absence of allelic variants such as SNPs in the NOD2/CARD15 gene is particularly useful in the present invention. As used herein, the term “NOD2/CARD15 variant” or “NOD2 variant” includes a nucleotide sequence of a NOD2 gene containing one or more changes as compared to the wild-type NOD2 gene or an amino acid sequence of a NOD2 polypeptide containing one or more changes as compared to the wild-type NOD2 polypeptide sequence. NOD2, also known as CARD15, has been localized to the IBD1 locus on chromosome 16 and identified by positional-cloning (Hugot et al., Nature, 411:599-603 ) as well as a positional candidate gene strategy (Ogura et al., Nature, 411:603-606 (2001); Hampe et al., Lancet, 357: 928 (2001)). The IBD1 locus has a high oint e score (MLS) for inflammatory bowel disease (MLS=5.7 at marker D16S411 in 16q12). See, e.g., Cho et al., Inflamm. Bowel Dis., 3:186-190 (1997); Akolkar et al., Am. J. Gastroenterol., 96: 1 127-1 132 (2001); Ohmen et al., Ham. M01. Genet., 5: 1679-1683 (1996); Parkes et al., Lancet, 348: 1588 (1996); Cavanaugh et al., Ann. Hum.

Genet., 62:291-8 (1998); Brant et al., Gastr0enter010gy, 115 : 1056-1061 (1998); Curran et al., Gastr0enter010gy, 115:1066-1071 (1998); Hampe et al. , Am. J. Hum. Genet. , 64:808-816 (1999); and Annese et al., Eur. J. Hum. Genet., 7:567-573 (1999).

The mRNA (coding) and polypeptide sequences of human NOD2 are set forth in, e. g., Genbank Accession Nos. NM_022162 and NP_071445, respectively. In addition, the complete ce of human chromosome 16 clone RP11-327F22, which includes NOD2, is set forth in, e.g., k Accession No. AC007728. Furthermore, the sequence ofNOD2 from other species can be found in the GenBank database.

The NOD2 protein contains terminal caspase recruitment domains (CARDS), which can activate NF-kappa B (NF-kB), and l carboxy-terminal leucine-rich repeat domains (Ogura et al., J. Biol. Chem, 276:4812-4818 (2001)). NOD2 has structural homology with the apoptosis regulator Apaf-1/CED-4 and a class of plant disease resistant gene products (Ogura et al., supra). Similar to plant disease resistant gene ts, NOD2 has an amino-terminal effector domain, a nucleotide-binding domain and leucine rich repeats (LRRs). Wild-type NOD2 activates nuclear factor NF-kappa B, making it responsive to bacterial lipopolysaccharides (LPS; Ogura et al., supra; Inohara et al., J. Biol. Chem, 51-2554 (2001). NOD2 can function as an intercellular receptor for LPS, with the leucine rich repeats required for responsiveness.

Variations at three single nucleotide polymorphisms in the coding region ofNOD2 have been previously described. These three SNPs, designated R702W (“SNP 8”), G908R (“SNP 12”), and 1007fs (“SNP 13”), are located in the carboxy-terminal region of the NOD2 gene (Hugot et al., . A fiarther description of SNP 8, SNP 12, and SNP 13, as well as additional SNPs in the NOD2 gene suitable for use in the invention, can be found in, e.g., US. Patent Nos. 6,835,815; 6,858,391; and 7,592,437; and US. Patent Publication Nos. 20030190639, 20050054021, and 72180.

In some embodiments, a NOD2 variant is located in a coding region of the NOD2 locus, for example, within a region encoding l leucine-rich repeats in the carboxy- terminal portion of the NOD2 polypeptide. Such NOD2 variants located in the e-rich repeat region ofNOD2 include, t limitation, R702W (“SNP 8”) and G908R (“SNP 12”). A NOD2 variant useful in the invention can also encode a NOD2 polypeptide with reduced ability to activate NF-kappa B as compared to NF-kappa B activation by a wild-type NOD2 polypeptide. As a non-limiting example, the NOD2 variant 1007fs (“SNP 13”) results in a truncated NOD2 polypeptide which has reduced ability to induce pa B in response to LPS stimulation (Ogura et al., Nature, 411:603-606 (2001)).

A NOD2 variant useful in the invention can be, for example, R702W, G908R, or 1007fs. R702W, G908R, and 1007fs are located within the coding region . In one embodiment, a method of the invention is practiced with the R702W NOD2 variant. As used herein, the term “R702W” includes a single nucleotide polymorphism within exon 4 of the NOD2 gene, which occurs within a triplet encoding amino acid 702 of the NOD2 protein.

The wild-type NOD2 allele contains a cytosine (c) residue at position 138,991 of the AC007728 sequence, which occurs within a triplet encoding an arginine at amino acid702.

The R702W NOD2 variant ns a thymine (t) e at position 138,991 of the 2012/037375 AC007728 sequence, resulting in an arginine (R) to tryptophan (W) tution at amino acid 702 of the NOD2 protein. Accordingly, this NOD2 variant is denoted “R702W” or “702W” and can also be denoted ” based on the earlier ing system of Hugot et al., supra. In addition, the R702W variant is also known as the “SNP 8” allele or a “2” allele at SNP 8. The NCBI SNP ID number for R702W or SNP 8 is rs2066844. The presence of the R702W NOD2 variant and other NOD2 variants can be conveniently detected, for example, by allelic discrimination assays or sequence analysis.

A method of the invention can also be practiced with the G908R NOD2 variant. As used herein, the term “G908R” includes a single nucleotide polymorphism within exon 8 of the NOD2 gene, which occurs within a triplet encoding amino acid 908 of the NOD2 n.

Amino acid 908 is located within the leucine rich repeat region of the NOD2 gene. The wild- type NOD2 allele ns a guanine (g) residue at position 7 of the AC007728 sequence, which occurs within a triplet encoding glycine at amino acid 908. The G908R NOD2 variant contains a cytosine (c) residue at position 128,377 of the AC007728 sequence, resulting in a glycine (G) to arginine (R) substitution at amino acid 908 of the NOD2 protein.

Accordingly, this NOD2 variant is denoted “G908R” or “908R” and can also be denoted “G88 1R” based on the earlier numbering system of Hugot et al., supra. In addition, the G908R variant is also known as the “SNP 12” allele or a “2” allele at SNP 12. The NCBI SNP ID number for G908R SNP 12 is 845.

[0254] A method of the invention can also be practiced with the 1007fs NOD2 variant.

This variant is an insertion of a single nucleotide that results in a frame shift in the tenth leucine-rich repeat of the NOD2 n and is followed by a premature stop codon. The resulting truncation of the NOD2 protein appears to prevent activation ofNF-kappaB in response to bacterial lipopolysaccharides (Ogura et al., supra). As used herein, the term “1007fs” includes a single nucleotide polymorphism within exon 11 of the NOD2 gene, which occurs in a triplet encoding amino acid 1007 of the NOD2 protein. The 1007fs t contains a cytosine which has been added at position 9 of the AC007728 sequence, resulting in a frame shift mutation at amino acid 1007. Accordingly, this NOD2 variant is denoted “1007fs” and can also be denoted “3020insC” or ” based on the earlier numbering system of Hugot et al., supra. In on, the 1007fs NOD2 variant is also known as the “SNP 13” allele or a “2” allele at SNP 13. The NCBI SNP ID number for 1007fs or SNP 13 is rs2066847.

One skilled in the art recognizes that a particular NOD2 variant allele or other polymorphic allele can be conveniently defined, for example, in comparison to a Centre d’Etude du Polymorphisme Humain (CEPH) reference individual such as the dual designated 1347-02 (Dib et al., Nature, 380: 152-154 (1996)), using cially available reference DNA obtained, for example, from PE Biosystems (Foster City, CA). In addition, specific information on SNPs can be ed from the dbSNP of the National Center for Biotechnology Information (NCBI).

A NOD2 variant can also be located in a non-coding region of the NOD2 locus.

Non-coding regions include, for example, intron sequences as well as 5’ and 3’ untranslated sequences. A non-limiting example of a NOD2 t allele located in a non-coding region of the NOD2 gene is the JWl variant, which is bed in Sugimura et al., Am. J. Hum.

Genet., 72:509-518 (2003) and US. Patent Publication No. 20070072180. Examples of NOD2 variant alleles located in the 3 ’ untranslated region of the NOD2 gene include, without limitation, the JW15 and JW16 t alleles, which are described in US. Patent ation No. 20070072180. Examples ofNOD2 variant alleles located in the 5’ slated region (e.g., promoter region) of the NOD2 gene include, without limitation, the JW17 and JW18 variant alleles, which are described in US. Patent Publication No. 20070072180.

As used herein, the term “JWl variant allele” includes a genetic variation at nucleotide 158 of ening sequence 8 (intron 8) of the NOD2 gene. In relation to the AC007728 sequence, the JWl variant allele is located at position 128,143. The genetic variation at nucleotide 158 of intron 8 can be, but is not limited to, a single nucleotide substitution, multiple nucleotide substitutions, or a deletion or ion of one or more nucleotides. The ype sequence of intron 8 has a cytosine at position 158. As non- limiting examples, a JWl variant allele can have a cytosine (c) to adenine (a), cytosine (c) to guanine (g), or cytosine (c) to thymine (t) substitution at nucleotide 158 of intron 8. In one ment, the JWl variant allele is a change from a cytosine (c) to a thymine (t) at nucleotide 158 ofNOD2 intron 8.

The term “JW15 variant allele” includes a genetic variation in the 3’ untranslated region ofNOD2 at nucleotide position 118,790 of the AC007728 sequence. The genetic variation at nucleotide 118,790 can be, but is not limited to, a single nucleotide substitution, multiple nucleotide substitutions, or a deletion or insertion of one or more nucleotides. The wild-type sequence has an e (a) at position 118,790. As non-limiting es, a JW15 variant allele can have an adenine (a) to cytosine (c), adenine (a) to guanine (g), or e (a) to thymine (t) tution at nucleotide 118,790. In one embodiment, the JW15 variant allele is a change from an adenine (a) to a cytosine (c) at nucleotide 118,790.

As used herein, the term “JW16 t allele” includes a genetic variation in the 3’ untranslated region ofNOD2 at nucleotide position 118,031 of the 28 sequence. The genetic variation at nucleotide 118,031 can be, but is not limited to, a single nucleotide tution, multiple nucleotide substitutions, or a on or insertion of one or more nucleotides. The wild-type sequence has a guanine (g) at position 1. As non-limiting examples, a JW16 variant allele can have a guanine (g) to cytosine (c), guanine (g) to adenine (a), or guanine (g) to thymine (t) substitution at nucleotide 1. In one embodiment, the JW16 variant allele is a change from a guanine (g) to an adenine (a) at nucleotide 118,031.

The term “JW17 variant allele” includes a genetic variation in the 5 ’ untranslated region ofNOD2 at nucleotide position 154,688 of the 28 sequence. The genetic variation at nucleotide 154,688 can be, but is not limited to, a single nucleotide substitution, multiple nucleotide tutions, or a deletion or insertion of one or more nucleotides. The wild-type sequence has a cytosine (c) at on 154,688. As non-limiting examples, a JW17 variant allele can have a cytosine (c) to guanine (g), cytosine (c) to adenine (a), or ne (c) to thymine (t) substitution at nucleotide 154,688. In one embodiment, the JW17 variant allele is a change from a cytosine (c) to a thymine (t) at nucleotide 154,688.

As used herein, the term “JWl 8 variant allele” includes a genetic variation in the 5 ’ untranslated region ofNOD2 at nucleotide position 154,471 of the AC007728 sequence. The genetic variation at nucleotide 154,471 can be, but is not limited to, a single nucleotide substitution, multiple nucleotide substitutions, or a on or ion of one or more tides. The wild-type sequence has a cytosine (c) at position 154,471. As non-limiting examples, a JW18 variant allele can have a cytosine (c) to guanine (g), cytosine (c) to adenine (a), or cytosine (c) to thymine (t) substitution at nucleotide 154,471. In one embodiment, the JW18 variant allele is a change from a cytosine (c) to a thymine (t) at nucleotide 154,471.

[0262] It is understood that the s of the invention can be practiced with these or other NOD2 variant alleles located in a coding region or non-coding region (e.g., intron or promoter region) of the NOD2 locus. It is further understood that the methods of the invention can e determining the ce of one, two, three, four, or more NOD2 variants, including, but not limited to, the SNP 8, SNP 12, and SNP 13 alleles, and other coding as well as non-coding region variants. 11. tical Analysis In some aspects, the present invention provides methods for selecting anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug y, and/or monitoring the efficacy of anti-TNF drug ent by applying a statistical thm to one or more (e.g, a combination of two, three, four, five, six, seven, or more) biochemical markers, serological s, and/or genetic markers to generate a disease activity profile (DAP). In particular embodiments, quantile analysis is applied to the presence, level, and/or genotype of one or more markers to guide treatment decisions for patients receiving anti-TNF drug therapy. In other embodiments, one or a combination of two ofmore learning statistical classifier systems are applied to the presence, level, and/or genotype of one or more s to guide treatment decisions for patients receiving anti-TNF drug therapy. The statistical es of the methods of the present invention ageously provide improved sensitivity, specificity, negative predictive value, positive predictive value, and/or overall accuracy for selecting an initial anti-TNF drug therapy and for ining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti- TNF drug, to combine an NF drug (e.g., at an increased, decreased, or same dose) with one or more immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA), and/or to change the t course of therapy (e.g., switch to a different anti-TNF drug).

The term “statistical analysis” or “statistical algorithm” or stical process” es any of a y of statistical methods and models used to determine relationships between variables. In the present invention, the variables are the presence, level, or genotype of at least one marker of interest. Any number of markers can be analyzed using a statistical analysis described herein. For example, the ce or level of l, 2, 3, 4, 5, 6, 7, 8, 9, 10, ll, 12, l3, 14, 15, l6, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, or more markers can be included in a statistical analysis. In one embodiment, logistic sion is used. In another embodiment, linear regression is used. In yet another embodiment, ordinary least squares regression or unconditional logistic regression is used. In certain preferred embodiments, the statistical analyses of the present invention comprise a quantile measurement of one or more markers, e.g., within a given population, as a variable. Quantiles are a set of “cut points” that divide a sample of data into groups containing (as far as possible) equal numbers of observations. For example, quartiles are values that divide a sample of data into four groups containing (as far as possible) equal numbers of observations. The lower quartile is the data value a quarter way up through the ordered data set; the upper quartile is the data value a quarter way down through the ordered data set. les are values that divide a sample of data into five groups containing (as far as possible) equal numbers of observations. The present invention can also include the use of tile ranges of marker levels (e.g., tertiles, quartile, quintiles, etc), or their cumulative indices (e. g., quartile sums of marker levels to obtain quartile sum scores (QSS), etc.) as variables in the statistical analyses (just as with continuous variables).

In n embodiments, the present invention involves detecting or determining the presence, level (e.g., magnitude), and/or genotype of one or more markers of interest using quartile analysis. In this type of statistical analysis, the level of a marker of interest is defined as being in the first quartile (<25%), second quartile %), third quartile (5 l%-<75%), or fourth le (75-100%) in on to a reference database of samples. These quartiles may be ed a quartile score of l, 2, 3, and 4, respectively. In n instances, a marker that is not detected in a sample is assigned a quartile score of 0 or 1, while a marker that is detected (e. g., present) in a sample (e.g., sample is positive for the marker) is assigned a quartile score of 4. In some embodiments, quartile 1 represents samples with the lowest marker levels, while quartile 4 represent samples with the highest marker levels. In other embodiments, quartile 1 represents samples with a particular marker genotype (e.g., wild- type allele), while quartile 4 ent samples with another particular marker genotype (e.g., allelic variant). The reference database of samples can include a large spectrum of patients with a TNFu-mediated disease or disorder such as, e.g., IBD. From such a database, quartile cut-offs can be established. A non-limiting example of quartile is suitable for use in the present invention is described in, e.g., Mow et al., Gastroenterology, l26:4l4-24 (2004).

In some embodiments, the statistical analyses of the present invention comprise one or more learning statistical classifier s. As used herein, the term “learning statistical classifier system” includes a machine learning algorithmic technique capable of adapting to complex data sets (e.g, panel of markers of st) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a decision/classification tree (e.g., random forest (RF) or classification and regression tree (C&RT)) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more ng statistical classifier systems are used, preferably in tandem. Examples of learning tical classifier systems e, but are not limited to, those using inductive learning (e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), k structures, the Cox Proportional-Hazards Model (CPHM), perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, an learning in belief networks, eta), reinforcement learning (e.g., passive learning in a known environment such as na'ive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown nment, active learning in an unknown environment, learning action-value fianctions, applications ofreinforcement learning, etc), and genetic thms and evolutionary programming. Other learning tical classifier systems include support vector es (e.g., Kernel s), multivariate ve regression splines (MARS), Levenberg-Marquardt algorithms, Gauss- Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ).

Random forests are learning statistical classifier systems that are constructed using an algorithm developed by Leo Breiman and Adele Cutler. Random forests use a large number of dual decision trees and decide the class by choosing the mode (i.e., most frequently ing) of the classes as ined by the individual trees. Random forest analysis can be med, e.g., using the RandomForests software available from Salford Systems (San Diego, CA). See, e.g., Breiman, Machine Learning, 455-32 (2001); and http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm, for a ption ofrandom forests.

Classification and regression trees ent a computer intensive alternative to fitting classical regression models and are typically used to determine the best possible model for a categorical or continuous response of interest based upon one or more predictors.

Classification and regression tree analysis can be performed, e.g., using the C&RT software available from Salford Systems or the Statistica data analysis software available from StatSoft, Inc. (Tulsa, OK). A description of classification and regression trees is found, e.g., in Breiman et al. “Classification and Regression Trees,” Chapman and Hall, New York (1984); and Steinberg et al., “CART: Tree-Structured Non-Parametric Data Analysis,” Salford Systems, San Diego, (1995).

[0269] Neural networks are interconnected groups of artificial neurons that use a mathematical or computational model for information processing based on a connectionist approach to computation. Typically, neural networks are adaptive systems that change their ure based on external or internal information that flows h the network. Specific examples of neural networks include feed-forward neural networks such as perceptrons, -layer perceptrons, multi-layer trons, backpropagation networks, E networks, MADALINE networks, Leammatrix networks, radial basis fianction (RBF) networks, and self-organizing maps or Kohonen self-organizing networks; recurrent neural networks such as simple recurrent networks and Hopfield networks; stochastic neural networks such as Boltzmann machines; modular neural networks such as committee of machines and associative neural networks; and other types of networks such as instantaneously trained neural networks, spiking neural networks, dynamic neural networks, and cascading neural networks. Neural k analysis can be performed, e.g., using the Statistica data analysis software available from StatSoft, Inc. See, e.g., Freeman et al., In “Neural Networks: Algorithms, Applications and Programming Techniques,” Addison- Wesley hing Company ; Zadeh, Information and Control, 8:338-353 (1965); Zadeh, “IEEE Trans. on Systems, Man and etics,” 3:28-44 ; Gersho et al., In “Vector Quantization and Signal Compression,” Kluywer Academic Publishers, Boston, cht, London (1992); and Hassoun, “Fundamentals of Artificial Neural ks,” MIT Press, Cambridge, Massachusetts, London (1995), for a description of neural networks. t vector machines are a set of related supervised learning techniques used for classification and regression and are described, e.g., in Cristianini et al., “An Introduction to Support Vector Machines and Other Kemel-Based Learning Methods,” Cambridge University Press (2000). Support vector machine analysis can be performed, e.g., using the t software developed by Thorsten Joachims (Cornell University) or using the LIBSVM software developed by Chih-Chung Chang and Chih-Jen Lin (National Taiwan University).

The various statistical methods and models described herein can be trained and tested using a cohort of samples (e.g., serological and/or genomic samples) from healthy individuals and patients with a TNFu-mediated disease or disorder such as, e.g., IBD (e.g., CD and/or UC). For example, samples from patients diagnosed by a physician, ably by a gastroenterologist, as having IBD or a clinical e f using a biopsy, colonoscopy, or an immunoassay as described in, e.g., US. Patent No. 6,218,129, are suitable for use in training and testing the statistical methods and models of the t invention. s from patients diagnosed with IBD can also be stratified into Crohn’s disease or ulcerative colitis using an assay as bed in, e.g., US. Patent Nos. 5,750,355 and 5,830,675.

Samples from healthy individuals can include those that were not identified as IBD samples.

One skilled in the art will know of additional techniques and diagnostic criteria for obtaining a cohort of patient samples that can be used in training and testing the statistical methods and models of the present invention.

As used herein, the term “sensitivity” includes the probability that a method of the t invention for selecting anti-TNF drug therapy, optimizing anti-TNF drug y, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of anti-TNF drug treatment gives a positive result when the sample is positive, e.g., having the predicted therapeutic response to NF drug y or toxicity associated with anti-TNF drug therapy. ivity is calculated as the number of true positive results divided by the sum of the true positives and false negatives. Sensitivity essentially is a measure ofhow well the present invention correctly identifies those who have the predicted eutic response to anti-TNF drug therapy or toxicity associated with anti-TNF drug y from those who do not have the ted therapeutic response or toxicity. The statistical methods and models can be selected such that the ivity is at least about 60%, and can be, e.g., at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0273] The term “specificity” includes the probability that a method of the present invention for selecting anti-TNF drug therapy, optimizing anti-TNF drug therapy, reducing toxicity associated with anti-TNF drug therapy, and/or monitoring the efficacy of anti-TNF drug treatment gives a negative result when the sample is not positive, e.g., not having the predicted therapeutic response to anti-TNF drug therapy or toxicity associated with anti-TNF drug therapy. Specificity is calculated as the number of true ve s divided by the sum of the true negatives and false positives. Specificity essentially is a measure of how well the present invention excludes those who do not have the predicted therapeutic response to anti-TNF drug therapy or toxicity associated with anti-TNF drug therapy from those who do have the predicted therapeutic response or toxicity. The statistical methods and models can be selected such that the specificity is at least about 60%, and can be, e.g., at least about 65%, 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

The term “negative predictive value” or “NPV” es the probability that an individual identified as not having the predicted eutic response to anti-TNF drug therapy or toxicity ated with anti-TNF drug therapy actually does not have the predicted therapeutic response or toxicity. Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. ve predictive value is determined by the characteristics of the methods of the present invention as well as the prevalence of the disease in the population analyzed. The statistical methods and models can be selected such that the negative predictive value in a population having a disease ence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. 2012/037375 The term “positive predictive value” or “PPV” includes the probability that an individual fied as having the predicted therapeutic response to anti-TNF drug therapy or toxicity ated with anti-TNF drug therapy actually has the predicted therapeutic response or toxicity. Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. ve predictive value is determined by the characteristics of the methods of the present ion as well as the prevalence of the disease in the population analyzed. The tical methods and models can be selected such that the ve predictive value in a population having a disease prevalence is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

Predictive values, including ve and positive predictive , are influenced by the prevalence of the disease in the population analyzed. In the present ion, the statistical methods and models can be selected to produce a desired clinical parameter for a clinical population with a particular prevalence for a TNFu-mediated disease or disorder such as, e.g, IBD. As a non-limiting example, statistical methods and models can be selected for an IBD prevalence ofup to about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 15%, 20%, %, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, or 70%, which can be seen, e.g., in a clinician’s office such as a gastroenterologist’s office or a general practitioner’s office.

[0277] As used herein, the term “overall agreement” or “overall accuracy” includes the accuracy with which a method of the present invention selects anti-TNF drug therapy, optimizes anti-TNF drug therapy, reduces toxicity associated with anti-TNF drug y, and/or monitors the y of anti-TNF drug treatment. Overall accuracy is calculated as the sum of the true positives and true negatives divided by the total number of sample results and is affected by the prevalence of the disease in the population ed. For example, the statistical methods and models can be selected such that the overall accuracy in a patient population having a disease prevalence is at least about 40%, and can be, e.g., at least about 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

III. Examples The present invention will be described in greater detail by way of specific examples. The following examples are offered for illustrative purposes, and are not intended to limit the invention in any manner. Those of skill in the art will readily recognize a variety of noncritical parameters which can be changed or modified to yield essentially the same results.

The Examples set forth in US. Provisional Application No. 61/444,097, filed February 17, 2011, and PCT Application No. l25, flled October 26, 2010, are hereby incorporated by reference in their entirety for all es.

Example 1. Disease Activity ng for Identifying Responders and Non-Responders to NFa Biologics.

This example describes methods for personalized therapeutic management of a TNFd-mediated e in order to optimize therapy or monitor therapeutic efficacy in a subject using the disease activity ng of the present invention to identify ts as responders or non-responders to anti-TNF drug therapy.

Figure 1 rates an ary IBD wound response profile in which wound progression is divided into inflammatory, proliferative, and remodeling phases. As non- limiting examples, inflammatory response phase markers tested include: anti-TNF drugs such as Remicade (infliximab); anti-drug antibodies (ADA) such as HACA; inflammatory markers such as GM-CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, and sTNF RH; and anti- inflammatory markers such as IL-l2p70 and IL-10. miting examples of eration response phase markers tested include tissue repair/remodeling factors (also referred to as mucosal healing markers) such as AREG, EREG, HB-EGF, HGF, NRGl , NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-oc, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGFl, FGF2, FGF7, FGF9, and TWEAK.

A COMMIT (Combination Of Maintenance Methotrexate-Infliximab Trial) study was performed to evaluate the safety and efficacy of Remicade (infliximab) in combination with methotrexate for the long-term treatment of Crohn’s disease (CD). Treatment success was defined by the proportion of subjects in clinical remission (i.e., complete discontinuation of prednisone therapy and a s Disease Activity Index (CDAI) score of <150) at week 14, and maintenance of clinical ion between study weeks 14 and 50. In particular, clinical ment with CDAI was performed at week 0, 46, 50, and 66. Subjects with CDAI > 150 were identified as sponders. Additional information on the COMMIT study is provided at htt ://www.clinicaltrials. Iov/th/show/NCTQOl32899, the disclosure of which is incorporated by reference in its entirety for all purposes. e activity profiling was performed on a number of subjects in the COMMIT study. In particular, the following array of markers were measured at various time points during treatment with Remicade (infliximab) only or a ation of Remicade (infliximab) with methotrexate: (l) Remicade (infliximab) and HACA; (2) inflammatory markers GM- CSF, IFN—y, IL-lB, IL-2, IL-6, IL-8, TNF-(x, and sTNF R11; (3) anti-inflammatory markers IL-12p70 and IL-lO; and (4) tissue repair markers EGF, bFGF, PIGF, sFltl, and VEGF. The disease ty profile (DAP) for 7 of these subjects, which provides a comparison between responder and non-responder profiles, is illustrated . These patient examples show that markers for ation and tissue repair correlated with infliximab and HACA levels in select active CD patients, certain markers may predict the disease activity profile, and disease ty profiling will filrther guide patient therapy and identify mucosal healing markers. In addition, these t examples show that whenever anti-inflammatory cytokines such as IL- 12p70 and IL-10 are elevated, the patient responds, indicating that they may be markers of mucosal healing, and that tissue repair markers (TRM) go up in non-responders.

Table of Personalized Disease Activity Profiling: Levels of IFX, HACA, Inflammatory Markers, Anti-Inflammatory s, and Mucosal Healing Markers Patient Treatment Clinical Inflammatory Anti- Mucosal ID No. Regimen Definition Markers inflammatory g Markers Markers 12209 IFX+ MTX t=O, CDAI was Non- HIGH LOW MEDIUM 202. responder t=wk 26, CDAI was 183 t=wk 66, CDAI=152. 11010 t=O, CDAI was Responder HIGH HIGH 262. t=wk 46, CDAI was 85. 10118 t=O, CDAI was Responder MEDIUM HIGH HIGH 251. t=wk 46, CDAI was 109. 11602 IFX+ MTX t=O, CDAI was der HIGH HIGH 217. t=wk 46, CDAI was 68. 11505 t=O, CDAI was Non- Very Low . MEDIUM LOW HIGH 272. responder at trough t=wk 46, CDAI (wk 14) was 145. t=wk 66, CDA|=195. 11601 IFX + MTX t=O, CDAI was Responder High at HACA+. HIGH HIGH MEDIUM 207. LOW t=wk 46, CDAI was 0.

IFX=infliximab. thotrexate. ND = no detectable level of HACA.

Patient 12209: Infliximab + Methotrexate MTX Treated.

CDAI at time 0 was 202. At week 46, CDAI was 183 (“Delta 19” or 202-19=183).

At week 66, CDAI was 152 (“Delta 50” or 202-50=152). Clinically defined as non-responder.

Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed that this patient had low infliximab (IFX) levels at trough (“T”; Week 14), the presence of a able concentration level ofHACA (“HACA +”), high inflammatory marker levels, low anti-inflammatory marker levels, and medium tissue repair marker (TRM) levels. Suggested alternative treatment options may include, for example, increasing the dose of IFX, switching to therapy with adalimumab (HUMIRAT'V'), treating with a different immunosuppressive drug such as azathioprine (AZA), and/or switching to therapy with a drug that targets a ent mechanism (e.g., an anti-INFy antibody such as fontolizumab).

Patient 1 1010: Infliximab Treated.

[0285] CDAI at week 0 was 262. At week 46, CDAI was 85 (“Delta 177” or 262-177=85).

Clinical responder. Disease activity profile (DAP) accurately identified this patient. In ular, DAP showed that this patient had high infliximab (IFX) levels at trough (“T”; Week 14), no detectable level ofHACA (“HACA --”), low inflammatory marker levels, high anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. For example, anti-inflammatory cytokines IL-12p70 and IL-10 were high. As shown with the patients in this example, whenever anti-inflammatory cytokines were high, the patient responded most probably with mucosal g. In on, bFGF concentration levels were low at all time points, although other TRM levels were high, indicating that tissue growth was muted, such that tissue repair had already occurred.

Patient 101 18: Infliximab Treated.

CDAI at week 0 was 251. At week 46, CDAI was 109 (“Delta 142” or 251- 142=109). al responder. Disease ty profile (DAP) accurately identified this patient. In ular, DAP showed that this patient had high mab (IFX) levels at trough (“T”; Week 14), no detectable level ofHACA (“HACA --”), medium inflammatory marker levels, high anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. For example, nflammatory cytokines IL-12p70 and IL-10 were high. Again, as shown with the patients in this example, whenever anti-inflammatory cytokines were high, the patient ded most probably with mucosal healing. In addition, bFGF concentration levels were low at all time points and remained fiat over the course of therapy, although other TRM levels were higher, indicating that tissue growth was muted, such that tissue repair had already occurred.

Patient 11602: Infiiximab + Methotrexate MTX Treated.

CDAI at week 0 was 217. At week 46, CDAI was 68 (“Delta 149” or 217-149=68).

Clinical responder. Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed that this patient had high infiiximab (IFX) levels at trough (“T”; Week 14), no detectable level ofHACA (“HACA --”), low inflammatory marker levels, high anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. For example, nflammatory cytokines IL-12p70 and IL-10 were high. Again, as shown with the patients in this example, whenever anti-inflammatory nes were high, the t ded most probably with mucosal healing. In addition, bFGF concentration levels were lower at all time points compared to the other TRM levels, indicating that tissue growth was muted, such that tissue repair had already occurred.

Patient 11505: Infiiximab Treated.

[0288] CDAI at time 0 was 272. At week 46, CDAI was 145 (“Delta 127” or 7 = 145). At week 66, CDAI was 195. ally defined as non-responder. Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed that this patient had very low infiiximab (IFX) levels at trough (“T”; Week 14), a high concentration level of HACA (“HACA ++”), medium inflammatory marker levels, low anti-inflammatory marker levels, and high tissue repair marker (TRM) levels. In non-responders, the levels of TRM such as bFGF go up, while in responders they either go down or do not change. ted alternative treatment options may include, for e, increasing the dose of IFX, switching to therapy with adalimumab (HUMIRAT'V'), treating with an immunosuppressive drug such as MTX or oprine (AZA), and/or switching to y with a drug that targets a different mechanism (e.g., an anti-INFy antibody such as fontolizumab).

Patient 11601: Infiiximab + Methotrexate MTX Treated.

CDAI at week 0 was 207. At week 46, CDAI was 0 (“Delta 207” or 207-207=0).

The patient was clinically defined as responder. . Disease activity profile (DAP) accurately identified this t. In particular, DAP showed that this patient had high infliximab (IFX) levels at trough (“T”; Week 14), low HACA levels (“HACA +”), high inflammatory marker levels, high anti-inflammatory marker levels, and medium tissue repair marker (TRM) levels.

For example, nflammatory cytokines IL-12p70 and IL-10 were high. Again, as shown with the patients in this e, whenever anti-inflammatory cytokines were high, the t responded most probably with l healing, clearly indicating that anti- inflammatory markers are very important. The presence of high inflammation may be due to complication.

Patient 101 13: Infliximab d.

[0290] CDAI at time 0 was 150. At week 46, CDAI was 96 (“Delta 54” or 150-54=96). At visit 10 (“V10”), CDAI was 154, and at visit 11 (“V11”), CDAI was 169. As such, CDAI started at 150 and stayed around 150. The patient was clinically defined as non-responder.

Disease activity profile (DAP) accurately identified this patient. In particular, DAP showed that this patient had low infliximab (IFX) levels at trough (“T”; Week 14), a detectable concentration level of HACA (“HACA +”), medium inflammatory marker levels, low anti- inflammatory marker levels, and medium tissue repair marker (TRM) . Again, TRM levels go up in non-responders, while in responders they either go down or do not change.

Suggested alternative treatment options may include, for example, increasing the dose of IFX, switching to therapy with adalimumab (HUMIRAT'V'), treating with an immunosuppressive drug such as MTX or oprine, and/or switching to y with a drug that targets a different mechanism (6.g. , an anti-INFy antibody such as fontolizumab).

Example 2. Disease Activity Profiling ng.

An exemplary 3-dimensional graph rendering of the disease activity profile (DAP) of the present invention includes each of the different markers t in the array of markers on the x-axis, ized marker levels on the y-axis, and time on the z-axis (e.g., time points wherein samples are taken and marker levels measured). An exemplary topographic map of the DAP of the present invention (also referred to herein as a personalized disease profile) includes each of the different markers present in the array of markers the y-axis, time on the x-axis (e.g., time points wherein samples are taken and marker levels measured), and relative marker levels in grayscale.

The 3D models described herein represent a novel paradigm for treatment because they are individualized and able such that dose adjustments are made in a personalized manner. For example, marker panels including markers such as inflammatory, proliferative, 2012/037375 and remodeling markers enable a determination in real-time of the best course of treatment for a patient on therapy such as anti-TNF drug y, e.g., for treating CD or RA. As a result, both the time course and the concentration levels of markers in the panel or array of markers are ant for therapy ment and monitoring to personalize and individualize therapy and determine optimal doses or dose adjustments. In n instances, the change in one or more marker levels over time is an important consideration for therapy adjustment and monitoring. In particular embodiments, the desired therapeutic zone for the set or a subset of the markers in the array or panel is within a defined range in the 3D graph or topographic map.

Example 3. Infliximab Non-Detection This example represents a model for “time-to-event.” In other words, this example uses the Cox Proportional-Hazards Model (CPHM) to model the time it takes for “an event” to occur and the risk of such an event happening. The model is a sion analysis with to-event” on the Y axis, which is a response variable, and “predictor variables” on the X axis. In this example, the non-detection of infliximab (i.e., the concentration of infliximab falling below a detection threshold) is the event, with the potential predictors of such an event being biomarkers: e.g., CRP, IL-2, VEGF, and the like and or clinical ation such as age, MTX treatment, gender, and the like.

In this e, the “Hazard” is the risk of infliximab not being detected (e.g., non- ion) by an analytical assay such as a mobility shift assay. For example, Figure 11 shows infliximab concentration levels for various patients during their course of treatment.

An event occurs in this example when the concentration of infliximab falls below a predetermined detection threshold. In certain instances, the CPHM is being used to predict the risk of the event occurring (infliximab non-detection). The e also identifies biomarkers indicative of such a risk occurring.

Using the CPHM, time is modeled until infliximab is not detectable by a mobility shift assay. In the model, the predetermined threshold is 0.67 ug/mL, which is the lower bound of the reference range. If the infliximab concentration level is less than the threshold at time “t,” then the event has occurred at time “t.” In Figure 12, patients were ranked by their time to the event. The event occurred for various patients at different points during treatment and is denoted with a bullet point.

In the initial model, there were various markers and clinical information used to predict the hazard or the risk of infliximab tection by the mobility shift assay. These markers included the following markers in the Table: PIGF Months since dianosis VEGF TNF-(x Disease @ small intestine G—CMM ——

[0297] From the initial marker list, the following list was derived as being the red s indicative of the event: sRNFRII Disease @ small intestine MM-M —— The following Table lists the significant predictors of mab non-detection risk or the hazard: Predictor coef m_ GM-CSF -1.92E-01 IL-2 1.42E-01 TNF-a 2.33E-02 sTNFRH 3.57E-01 SAA 6.13E-06 Months since -3.20E-03 0.997 1.45E-03 2.68E-02 Disease @ l.lOE+00 2.995 4.46E-01 1.39E-02 small intestine s 8.84E-Ol 2.421 3.13E-01 4.72E-03 The s in the above Table indicate the following are predictors of the hazard i.e., risk of the non-detection of infliximab: GM-CSF: holding all other variables constant, an extra ng/ul of GM-CSF reduces the weekly hazard of infliximab tection by a factor of 0.826, or 17.4 %.

IL-2: An additional 1 ng/ul of IL-2 increases the hazard by a factor of 1.153, or .3 %.

TNF-(x: A 1 ng/ul of TNF-(x ses the hazard by a factor of 1.024 / 2.4 %. sTNFRII: A 1 ng/ul of sTNFRH increases the hazard by a factor of 1.429 / 42.9 %.

SAA: A 1 ng/ul of SAA increases the hazard by a factor of 1.000006/ 0.0006 %, which is very small, but still a detectable effect (small SE).

Months since diagnosis: Each additional month since diagnosis decreases the hazard by a factor of 0.997, or 0.3 %. e site at the small intestine (categorical le): If the disease is located at the small intestine, the hazard is increased by a factor of 2.995, or nearly 200 %.

[0307] Success (categorical variable): Also a predictive of hazard; in non-successful patients the hazard is increased by a factor of 2.421 or 142 %.

In summary, the following markers appear to be good predictors of infliximab “clearance” /or non-detection: 1) GM-CSF; 2) IL-2; 3) TNF-u; 4) sTNFRII; and 5) the disease being situated in the small intestine.

[0309] As such, in one embodiment, the present invention es: A method for predicting the likelihood the concentration of an anti-TNF therapeutic or antibody during the course of treatment will fall below a threshold value, the method comprising: measuring a panel of markers selected from the group ting of 1) GM-CSF; 2) IL-2; 3) TNF-(x; 4) sTNFRH; and 5) the disease being situated in the small intestine; and ting the likelihood the concentration of an anti-TNF therapeutic or antibody will fall below the threshold based upon the concentration of the markers.

Example 4. Detection of Antidrug Antibody to Infliximab (“ATP’ or “HACA”) This example uses the Cox Proportional-Hazards Model (CPHM) to model the time that it takes for an event to occur. This is a similar analysis to e 3 above, but with the appearance of the anti-drug antibody also known as ATI or HACA as the event and risk of ATI formation (detection) as the hazard. Figure 13 shows the concentration ofATI (HACA) in various patients during the course of treatment. In Figure 14, patients were ranked by their time to the event. The event occurred for various patients at different points during treatment and is denoted with a bullet point. The risk of ATI detection is the hazard. Significant predictors of the hazard include: tor —-_—_ VEGF GM-CSF IL-2 IL-8 TNF-a VCAM 1.28E-03 1.001 2.01E-04 1.87E-10 The data in the above table indicates that EGF, VEGF, IL-8, CRP and VCAM-l all have very small, but significant s on the .

GM-CSF: Holding all other variables constant, an extra ng/ul of GM-CSF reduces the weekly hazard ofATI detection by a factor of 0.762, or 27.4 %.

[0313] IL-2: A 1 ng/ul increase of IL-2 increases the hazard by a factor of 1.85, or 85 %.

TNF-u: A 1 ng/ul increase of TNF-(x increases hazard by a factor of 1.024, or 2.4 %.

In summary, the tors of ATI detection hazard are GM-CSF, IL-2 and TNF-u.

As such, in one embodiment, the present invention provides a method for predicting the likelihood that anti-drug antibodies will occur in an individual on anti-TNF therapy or antibodies, said method comprising: ing a panel of markers selected from the group consisting of EGF, VEGF, IL-8, CRP and VCAM-l; and predicting the hood that anti-drug dies will occur in an individual on anti-TNF therapy based on the concentration of marker levels. e 5. Disease Activity Profiling for Crohn’s Disease Prognosis Using COMMIT Study Samples.

This example rates methods for personalized therapeutic management of a TNFd-mediated disease in order to optimize therapy or monitor therapeutic efficacy in a subject using the disease activity ng of the present invention. This examples illustrates disease activity profiling which comprises detecting, measuring, or determining the presence, level and or activation of one or more specific biomarkers (e.g., drug levels, anti-drug antibody levels, inflammatory markers, anti-inflammatory markers, and tissue repair markers).

[0318] This example describes disease activity profiling on a number of samples from the COMMIT study. As bed in Example 1, the COMMIT nation of Maintenance rexate-Inflixamab Trial) study was performed to evaluate the safety and efficacy of de (inflixamab) in combination with methotrexate (MTX) for the long-term treatment of Crohn’s Disease (CD). In particular, the following array of markers was measured at various time points during treatment with Remicade (infliximab; IFX) only or a treatment of Remicade with MTX: (1) Remicade (inflixamab) and antidrug antibodies to infliximab (ATI); (2) inflammatory s CRP, SAA, ICAM, VCAM; and (3) tissue repair marker VEGF. This example shows that the markers of inflammation and tissue repair correlated with IFX and ATI levels in select patients of TNF-oc mediated disease (e.g, Crohn’s Disease and Ulcerative Colitis). In some instances, arrays of s may predict a disease activity index (e.g., Crohn’s Disease Activity Index). Analysis of the COMMIT study is rated herein.

The relationship between the presence ofATI and serum levels of IFX concentration was investigated. For the evaluation, total ATI levels below the level of quantitation (BLOQ) were 3.13 U/ml, and were set to 0. IFX concentrations below the level of detection (BLOD) were set to 0. Per the sample comparison, only trough samples were used and a total of 219 were used in the evaluation. 24 samples were determined to be ATI positive (ATI+). It was determined that the median level of IFX was 0 [Lg/ml in ATI+ samples, while the median level of IFX was 8.373 [Lg/ml in ATI negative (ATI-) samples (p=3.7l x 10'9 by Mann Whitney U test). Figure 4A rates an association between the presence of ATI and the level of IFX in t s. Patient s with no detectable level of ATI had a significantly higher IFX median concentration, compared to ATI+ samples.

The relationship between CDAI and the presence ofATI was evaluated. In the is ATI of 3.13 U/ml was set as the f; only trough s were evaluated and ATI BLOQ was set as 0. 195 samples were ATI-, while 24 samples from a total of 4 patients were ATI+. The results showed that the median CDAI for ATI+ samples was 121.5 while the median CDAI for ATI- samples was 82 (p=0.0132 by Mann Whitney U test). Figure 4B illustrates that the presence of ATI correlates with higher CDAI. The results show that ATI+ samples have significantly higher CDAI than ATI- s.

The relationship between the presence of ATI and combination therapy of IFX and immunosuppressant agent (e.g., MTX) was investigated. ATI+ samples at any trough time point were analyzed. The s showed that there was no significant difference in odds of having ATI between IFX therapy alone and IFX+MTX combination therapy. The high odds ratio (e.g., 2.851) indicates that MTX can prevent a patient from developing an immune se to therapeutic biologics. Figure 4C shows that concurrent suppressant therapy (e.g, MTX) is more likely to suppress the presence of ATI.

[0322] The relationship between ATI and clinical outcome at follow-up was also investigated. ATI+ samples at any trough time point were analyzed. Clinical outcome as described from the al data received from the study was parsed as either “success” or “non-success”. No significant ence in odds of being ATI+ was seen regardless of ent n. The low odds ratio (6.g. indicates that ATI+ patients , 0.1855, p=0. 1459) tend to have poor clinical outcomes. Figure 5A shows that patients with ATI are more likely to p a poor response to treatment.

This example also illustrates an association of an exemplary PRO Inflammatory Index and serum levels of infliximab (IFX) or the presence of antibodies to IFX (ATI) in a patient sample. Figure 5B illustrates that the inflammatory marker CRP is associated with increased levels of ATI. The data shows that the median CRP level was 8.11 ug/ml in ATI+ samples and 1.73 [Lg/ml in ATI- samples (p = 2.67 x 10'6 by Mann Whitney U Test). Other inflammatory and tissue repair markers were evaluated. Figure 6 illustrates that the protein levels of an array of one or more inflammatory and tissue repair markers correlate to the formation of antibodies to IFX. The data shows that of a combination of five markers (e.g., CRP, SAA, ICAM, VCAM, VEGF and including at least one inflammatory marker) was expressed in 23 out of 24 ATI positive samples (Figure 7A, grey box). The inflammatory marker SAA was found to be positive in 19 of the 24 ATI positive samples that were also clinically described as having “high inflammation”. The results also show that VEGF and CRP are the most non-overlapping markers in the analysis.

This example further shows an exemplary PRO Inflammatory Index (PII). The inflammatory index score is created by logarithmic transformation of a combination of values representing determined sion levels of a plurality of markers (e.g., PII = log(CRP + SAA + ICAM + VCAM + . Figure 7B illustrates that an exemplary PRO Inflammatory Index (PII) correlates with levels of IFX (p<0.0001 and R2 = -0.129) in t samples of the COMMIT study. The results show that ATI positive samples have a significantly higher inflammatory index score compared to ATI negative samples (P = 6.4x10'8; see Figure 7C).

As such, in one embodiment, the present invention provides a method for ring an infliximab ent regimen, said method comprising: a) measuring infliximab and ug antibodies to infliximab (ATI); b) ing inflammatory markers CRP, SAA, ICAM, VCAM; c) measuring tissue repair marker VEGF; and d) correlating the ements to therapeutic efficacy.

Example 6. Disease Activity Profiling For TNF-oc Mediated Disease Prognosis Using Clinical Study #1 Samples.

This example describes methods for monitoring therapeutic efficacy in a subject using the disease activity ng of the present invention to identify subjects as responders or non-responders to anti-TNF drug therapy. This e illustrates the use of disease actiVity profiling with a number of patient samples from a Crohn’s Disease clinical trial #1.

In particular, an array of s was measured at various time points during ent with Remicade (infliximab; IFX) only or a treatment of Remicade with MTX: Remicade (inflixamab), antibodies to infliximab (ATI), and neutralizing antibodies to IFX.

This example shows that a disease actiVity profile can show the relationship among ATI, IFX and neutralizing antibodies. Analysis of clinical study #1 is illustrated .

Figure 8A-B illustrates the correlation between Crohn’s Disease Activity Index (CDAI) score and the concentration of mab in serum in a number of patients in clinical study #1. In brief, 894 samples were analyzed. An IFX concentration 3 0.1 [Lg/ml at the limit of detection (LOD) was defined to be “present”. The results showed that IFX negative (IFX-) samples also have significantly higher CDAI (p= 0.0254, calculated by Mann- Whitney U test), compared to IFX positive samples (IFX+). r analysis revealed that the presence of ATI correlates with lower IFX concentrations. It was assumed that total ATI below the level of quantitation (BLOQ) of 3.13 U/ml was set as 0 and IFX concentration below the level of detection (BLOD) was set at 0. It was determined that 24% of the patients (62/258) in the study were ATI+, as defined as ve total ATI levels at one of three time . The is of 894 samples showed a correlation between IFX concentration and ATI levels. In particular, the median IFX was 0 [Lg/ml for ATI+ samples and 7.95 [Lg/ml for ATI- samples (p <2.2 x 10'16 by Mann-Whitney U test). Figure 9A illustrates the association between IFX concentration and the presence of antidrug antibodies to mab in samples ed.

Analysis shows that a high concentration ofATI in samples correlates with the ce of neutralizing antibodies that target TNF-oc biologics. In some embodiments, assays can be used to detect neutralizing antibodies. Neutralizing antibodies were detected in t samples with the highest concentrations of ATI. Figure 9B illustrates that a high concentration ofATI can lead to the presence of neutralizing antibodies and undetectable levels of IFX.

Longitudinal analysis of the relationship of CDAI and the presence ofATI was ted in samples collected at clinic visit #1 and #3 from 283 patients. A correlation n the presence ofATI at visit #1 (V1) was established with CDAI at visit #3 (V3).

The median CDAI was 109 at V1 in ATI+ samples, while the median CDAI was 78 in ATI- samples (p=0.027 by Mann Whitney U test). The results indicate a causal relationship between ATI positivity and CDAI. Figure 9C illustrates that ATI+ samples determined at an early time point were more likely to have a higher CDAI at a later time. The results indicate that disease activity profiling at an early time point can predict CDAI at a later time point.

Figure 9D illustrates that in Clinical Study #1, patients had lower odds of developing ATI if receiving a combination therapy of infiiximab (IFX) and an immunosuppressant agent (e.g., MTX and AZA). The odds ratio was 0.320 (p = 0.0009 by Fisher’s Exact test). In this analysis, ATI positivity (ATI+) was defined as total ATI 3 3.13U/ml. 2012/037375 Example 7. Disease Activity Profiling For TNF-oc ed Disease Prognosis Using Clinical Study #2 Samples.

A. Clinical Study #2A This example illustrates the use of a method for monitoring therapeutic y in patients ing Remicade (inflixamab) alone or in combination with an immunosuppressant agent (e.g., methotrexate, azathioprine and/0r corticosteroids). This example describes using methods of the prevent invention to determine the disease activity profiles of samples from a series of clinical .

In the analysis, we investigated the relationship between antidrug antibodies to inflixamab (ATI) and IFX concentrations in the cohort. It was determined that 90.6% of the patients were ATI+ (5 8/64), when ATI+ samples were defined to be those with total ATI > 3.13 U/ml at at least one time point. The median concentration of IFX in ATI positive samples was 0 [Lg/ml and 3.74 [Lg/ml in ATI negative samples (P<2.2 10-16 by Mann Whitney U Test). The concentration of neutralizing antibodies was 0 in ATI+ samples. The results suggest that the presence of ATI reduces IFX concentration in a patient on IFX therapy. The range of IFX concentration for ATI- samples was 0.0-67.28 [Lg/ml. In ATI+ s the IFX concentration was 00-26. 15 [Lg/ml. In ATI+ samples with neutralizing antibodies (Nab) the IFX concentration ranged from 0-1.07 [Lg/ml. Figure 10A shows that correlation between IFX concentration and the presence ofATI in samples of clinical study #2A. The results also demonstrated that the odds ofbeing ATI positive versus ATI negative are significantly less for samples treated with an immunosuppressant agent (ISA, e.g., methotrexate, azathioprine, corticosteroids, and combinations thereof). In this analysis 814 samples were evaluated. The odds of being ATI+ was significantly less for ISA-treated samples than of being ATI- (odd ratio = 0.564; p < 0.00001 by Fisher’s Exact Test). In addition, fewer ISA treated samples expressed lizing ATIs. Of the 34 ATI+ samples with neutralizing antibodies analyzed, 9 0f the 34 s were ISA-treated and 25 samples were non-ISA d samples. This tes that ISA therapy can reduce the progression to ATI, and even neutralizing antibodies to IFX. Figure 10B illustrates the relationship between ISA therapy and the presence of ATI in the study.

[0334] Next, we igated the relationship between ATI and atory markers. As described herein, total ATI BLOQ was set at 0. CRP concentration was determined by methods such as a CEER assay. The s show that the median tration of CRP was lowest (5.0 [Lg/ml) in ATI- samples and higher (10.0 [Lg/ml) in ATI+ samples. Sample 2012/037375 expressing neutralizing ATI had a yet higher median concentration of CRP (10.0 [Lg/ml). All pair-wise comparisons between CRP concentrations and ATI status should that the values were significantly different (p < 0.0001 by Mann Whitney U tests). Figure 10C illustrates the relationship between CRP concentrations and the presence ofATI (ATI and/or neutralizing ATI).

We also igated the relationship between ATI and loss of response to y.

In the cohort, samples were marked as having a nse”, “loss of se” and “no information” regarding IFX therapy. The s were fiarther categorized as being “True” if having a loss of response or “False” if not having a loss of response. In total 777 samples were analyzed. The results showed that in samples marked as “True”, there was a significantly higher odds ratio of also being ATI positive (odds ratio = 2.254, p<0.0001 by Fisher’s Exact Test). Surprisingly, more samples that were positive for neutralizing antibodies to IFX were determined to be responsive to IFX, as compared to being no longer responsive. Of 34 neutralizing ATI+ samples, 21 were marked as “response” and 8 were marked as “loss of response”. Figure 10D illustrates the relationship between loss of responsiveness to IFX therapy and the presence ofATI in the study. Figure 11 illustrates that levels of ATI and lizing dies can be determined over time in a series of samples from various patients We compared the concentration of IFX to the presence of the inflammatory marker CRP. We defined “IFX presence” per sample as “True” if IFX was >= 0.1 ug/ml which is the LCD of the assay. The results suggest that the median CRP concentration was not different between samples with IFX present or without IFX present. The median CRP level was 7.40 [Lg/ml in samples with IFX, while median CRP = 7.55 [Lg/ml in samples with IFX absent (p = 0.591 by Mann Whitney U Test). Figure 12A illustrates the comparison of CRP levels to the presence of IFX.

We also compared the relationship between infusion reaction to the ce of ATI. The analysis included a total of 797 samples; 30 samples were categorized as having infusion reaction (“Yes”) and 767 samples were categorized as having no infusion reaction (“No”). 29 s that had an infusion reaction were also ATI+ (odds ratio = 35.54, p<0.0001 by Fisher’s Exact Test). Figure 12B illustrates the relationship n the presence of ATI and the on reaction. Patients expressing ATI were more likely to have had an infusion reaction. Yet, for the 27 samples with neutralizing ATI, no infilsion reaction was observed in 22 samples. The remaining 5 samples with neutralizing ATI had infusion reaction.

B. Clinical Study #2B In this analysis of clinical study #2B, we investigated the relationships n the presence of ATI, IFX concentration, administration of ISA, the expression of inflammatory markers (e.g., CRP), and loss of se to IFX treatment. We determined that the median IFX concentration was higher in samples expressing ATI compared to those not sing the antidrug antibodies. 15.2% of the patients (16 out of 105) were ATI+ with a total ATI >3.13 U/ml at at least one time point. Of the 489 samples analyzed, the median IFX concentrations were 0.59 ug/ml in ATI+ samples and 7.78 ug/ml in ATI- samples (p <2.2 x '16 by Mann Whitney U Test). Figure 12C illustrates the relationship between IFX concentration and the presence ofATI in the . The analysis showed that there are high odds of developing antibodies to IFX when immunosuppressants have been awn (odds ratio = 0.412, p = 0.0367 by Fisher’s Exact Test). Figure 12D illustrates the correlation between the presence ofATI and the withdrawal of ISA therapy at a specific, given date. We determined that ATI positive samples have a higher median concentration of CRP (9.6 ug/ml, p = 1.25 x 10'12 by Mann Whitney U Test), compared to ATI negative samples (median CRP = 1.5 ug/ml). Figure 13A illustrates the relationship between ATI and the inflammatory marker CRP. Our is showed that the odds of experiencing a loss of response to IFX was higher in patients determined to be ATI positive at any time point. (odds ratio = 3.967, p = 0.0374 for Fisher’s Exact Test). Figure 13B illustrates the correlation between the presence ofATI at any time point and responsiveness to IFX ent. Loss of response to IFX was also correlated to a higher median concentration of the inflammatory marker CRP. In the analysis there were 14 samples with loss of response at follow-up and 91 samples from responders. The median CRP levels were 11.767 ug/ml for those with loss of response and 2.585 ug/ml for those with se. Patients who had lost response to IFX had a cantly higher mean CRP (p = 7.45 x 10'5 by Mann Whitney U Test). Figure 13C shows that loss of response can be related to an se in CRP. CRP was also significantly higher in samples lacking detectable IFX 2. Samples were determined to have IFX (“IFX present”) if the level of IFX was >= to 0.1 ug/ml per sample (e.g., LOD of the assay). The median CRP was 1.6 ug/ml in IFX t s and 13 ug/ml in IFX absent samples (p = 3.69x10'5 by Mann Whitney U Test). Figure 13D illustrates the association between the presence of IFX and CRP levels. In this study “ATI+” was defined as a sample with total ATI >3.13 U/ml at at least one time point.

C. Clinical Study #2C In this is of al study #2C, we investigated the relationship between IFX levels and the presence of ATI. It was determined that ATI+ have a significantly lower median IFX of 0.43 [Lg/ml as compared to ATI- samples which have a median IFX of 3.28 [Lg/ml (p = 1.95x10'4 by Mann Whitney U test). Figure 14A shows that lower IFX levels are associated with the presence of ATI.

As such, in one embodiment, the present ion provides a method for determining r an individual is a candidate for combination therapy wherein said individual is administered infliximab, the method comprising:measuring for the presence or e ofATI in said individual; and administering an suppressant (e. g., MTX) is the individual has significant levels of ATI. In certain aspects, the concentration level of CRP is indicative of the presence of ATI.

Example 8. e Activity ng For TNF-oc ed Disease Prognosis Using Patient Samples from Clinical Study #3.

[0341] This example illustrates using methods of the present invention to monitor the therapeutic efficacy of anti-TNF drug therapy. In particular, pooled data including study data, pharmacokinetics data, follow-up study data of clinical study #3 were analyzed. The results showed that the median IFX tration of 0.0 [Lg/ml was lower in ATI positive samples compared to an IFX concentration of 12.21 [Lg/ml ATI negative samples (P < 2.2 x 10-16 by Mann Whitney U test). Figure 14B shows that lower IFX levels are associated with the presence ofATI in these al samples. Figure 14C illustrates that the same correlation between IFX levels and ATI was also present in the study data, follow-up study and in the pharmacokinetics study (p< 0.05 by Mann Whitney U tests). We also used methods of the present invention to determine that a high concentration of ATI in a sample have a neutralizing effect on IFX. In particular, high concentrations of ATI act as neutralizing antibodies to infiixamab. Samples with a high concentration ofATI had an IFX level of 0 [Lg/ml. Figure 15A illustrates the relationship between ATI levels including neutralizing ATI and IFX.

Example 9. Methods of Disease Activity Profiling Including the PRO Inflammatory Index in Patients Receiving Humira.

This example illustrates methods of the present invention including determining the level of TNF-Oc biologic (e.g., adalimumab a); ADL) and the presence of anti-drug antibodies to the TNF-oc biologic (e.g., ATA) in a patient sample. In this analysis, one sample represents one patient and a total of 98 CD s were evaluated. 2.04% (2 out of 98 CD patients) of the samples were positive for ATA., when ATA vity was set as total ATA> 0. singly, the two ATA positive samples also had the highest concentrations of ADL. Figure 15B illustrates an association between ADL concentration and the presence of ATA in patient samples.

This e describes an exemplary PRO Inflammatory Index (PII). The example also illustrates the use of the P11 in patient samples receiving Humira (adalimumab) and different drug combinations. Figure 16A describes the details of an exemplary PRO Inflammatory Index. The PII can represent a single per-sample score describing inflammation levels based on five biomarkers. The score is ed from the logarithmic ormation of the sum of the five biomarkers. In some embodiments, the biomarkers include VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml.

Figure 16B illustrates that there is no obvious relationship between the P11 and the concentration ofADL in an array of samples with ADL alone or in combination with other drugs. This could be due to the appearance of high ADL trough serum concentration in the sample cohort. These is a significant negative correlation between PII and ADL concentration (p=l .66x10'5 and Spearman’s Rho =-0.459). A similar negative correlation relationship was found between IFX and P11.

[0344] We also compared the relationship between the P11 and the presence of eutic agents used to treat TNF-oc mediated diseases. ADL positive samples were defined as samples with an ADL concentration of greater than 0 [Lg/ml. The results showed that a higher PII was detected in ts on Humira compared to ts on Remicade and Humira. Figure 17 shows a plot of the P11 scores for patients receiving Humira and Humira in combination with other drug such as Remicade, Cimzia, Asathioprine and Methotrexate.

As such, in one embodiment, the present invention provides a method for monitoring Crohn’s disease activity, the method sing: determining an inflammatory index sing the measurement of a panel of markers comprising VEGF in pg/ml, CRP in ng/ml, SAA in ng/ml, ICAM in ng/ml and VCAM in ng/ml; comparing the index to an efflcacy scale or index to monitor and manage the disease. 2012/037375 Example 10. Methods for Improved Patient Management.

This example describes methods for ed patient management to assist in developing personalized patient treatment.

In some embodiments, patients with active CD and UC can be analyzed using a ty shift assay (see, e.g, PCT Publication No. , the disclosure of which is hereby incorporated by reference in its ty for all purposes) in conjunction with disease activity profiling. Figure 18 shows details of the methods of the present invention for improving the management of patients with CD and/or UC. In some embodiments, the methods of disease ty profiling comprise pharmacokinetics, and determining the presence and/or levels of disease activity profile s and/or mucosal healing markers.

In some embodiments, disease activity profiling comprises methods of detecting, measuring, and determining the presence and/or levels of biomarkers, nes, and/or growth factors. Non-limiting examples of cytokines that can be used in disease activity profiling include bFGF, TNF-oc, IL-lO, IL-l2p70, IL- 1 [3, IL-2, IL-6, GM-CSF, IL- 1 3, IFN—y, TGF-Bl, TGF-BZ, TGF-[33, and combinations thereof. Non-limiting examples of inflammatory markers include SAA, CRP, ICAM, VCAM, and combinations thereof. Non- limiting examples of anti-inflammatory markers include TGF-B, IL-10, and combinations thereof. Non-limiting es of growth factors include amphiregulin (AREG), epiregulin (EREG), heparin binding epidermal growth factor (HB-EGF), hepatocye growth factor (HGF), lin-Bl (HRG) and isoforms, neuregulins (NRGl, NRG2, NRG3, NRG4), betacellulin (BTC), epidermal growth factor (EGF), insulin growth factor -1 (IGF-l), transforming growth factor (TGF), platelet-derived growth factor (PDGF), vascular endothelial growth factor (VEGF), stem cell factor (SCF), et d growth factor (PDGF), soluble fms-like tyrosine kinase 1 (sFltl), placenta growth factor (PIGF), fibroblast growth s , and combinations thereof.

In other embodiments, disease activity ng comprises detecting, ing and determining pharmacokinetics and mucosal healing. In some aspects, mucosal healing can be ed by the presence and/or level of selected biomarkers and/or endoscopy. In some instances, mucosal healing can be defined as the absence of friability, blood, erosions and ulcers in all visualized segments of gut . In some embodiments, biomarkers of mucosal healing, include, but are not limited to, AREG, EREG, HG-EGF, HGF, NRGl, NRG2, NRG3, NRG4, BTC, EGF, IGF-l, HRG, FGFl, FGF2 (bFGF), FGF7, FGF9, SCF, PDGF, TWEAK, GM-CSF, TNF-oc, IL-12p70, IL-1[3, 11—2, IL-6, IL-10, IL-13, IFN-y, TGF-oc, 1, TGF-[32, TGF-[33, SAA, CRP, ICAM, VCAM, and combinations thereof. In some embodiments, a growth factor index can be established using tical analyses of the detected levels of biomarkers of mucosal healing. In some instances, the growth factor index can be associated with other markers of disease activity, and utilized in methods of the present invention to personalize patient treatment.

Figure 19 shows the effect of the TNF-oc pathway and related pathways on different cell types, cellular mechanisms and disease (e.g., Crohn’s Disease (CD), rheumatoid arthritis (RA) and Psoriasis (Ps)). Figure 20 illustrates a schematic of an exemplary CEER lex growth factor array. In particular embodiments, the methods of the present invention can employ this array. As non-limiting examples, Figure 21A-F illustrate lexed growth factor profiling of patient samples using this array. In particular, longitudinal analysis of growth factors, such as AREG, EREG, HB-EGF, HGF, HRG. BTC, EGF, IGF, TGFoc, and VEGF, was performed on a collection of patient samples. Figures 21B and E illustrate the determination of the level of serological and immune s, such as ASCA-a, ASCA-g, Cbirl and OmpC, in samples from Patient 10109, Patient 10118 and Patient 10308. Figure 21G shows the exemplary growth factor arrays performed on samples from healthy controls, patients with IBS-C, and patients with IBS-D.

A series of multiplexed CEER growth factor and CRP arrays was performed on patient samples. Tables A-D (below) highlight longitudinal analysis of mucosal healing in patient samples. The ing Table (A) shows that CRP and growth factors can be predictive of mucosal healing: Subject Collection TGF TGF ID Date CRP VEGF Tweak beta1 beta2 10101 tion 1 IIIIIIIIIIIII 10101 tion 2 IIIIIIIIIIIIII! 10103 colloollon l MIIIIIIIIIIII 10103 oolloollon 2 IIIIIIIIIIIIIIflIl 10109 colloollon l IIIIIII 10109 oolloollon 2 IIIIlllIIIIIIII 10118 oolloollon l IIIIIIIIIIIII 10118 collocllon 2 IIIIIIIIIIIIIIIIIII 10308 oolloollon l IIIIIIIIIIIII 10308 oolloollon 2 IIIIIIIIIIII! 10503 oollocllon l IIIIIIIIIIIII 10503 llon 2 IIIIIIIIIIIIIIIII 11003 oolloollon l IIIIIIIIIIIII 11003 colloollon 2 IIIIIIIIIIIIII 11601 collocllon l IIIIIIIIIIIII 11601 oolloollon 2 IIIIIflIIIIIEIIIIIlIII WO 54987 11602 Collection 1 1.. 71 327. 92 15.31 562.30 12.82 12.13 58.15 1120.06 11602 Collection 2 IIIIIIIII! 12121 Collection 1 IIIIIIIIIIII 12121 Collection 2 IIIflIIIflIIIII 12121 Collection 3 IIIIIIIIIIIIIII 190 Collection 1 IIIIIIIIIIIII 190 Collection 2 IIIIIIIIIIIIIIII 492 Collection 1 MIIIIIIIIIIII 492 Collection 2 IIIIIIIIIIIIIII 2546 Collection 1 IIIIIIIIIIIII 2546 Collection 2 IIIIIIIIIIIIII “N" and “P" denote a negative or positive relationship between pairs of ations for each marker, respectively per subject. Underlined data are number pairs above upper limit of quantitation and are d to have a positive relationship.

[0352] The following Table B lists CRP and growth factors predictive of mucosal healing: Subject Collection TGF ID Date CRP BTC alpha IIIIIIIIIIEIIIEIII IEIIIIIIIIIIIIIII IEIIIIIIIIIIIIEIEI IIIIIIIIIIIIIIIIIEII@II IIIIIIIIIIIIIEIII IIIIIIIIIIIIIEIEII IIIIIIIIIIIIMIII IIIIIIIIIIIIIIIIIIIII IIIIIIEIIIIEIIIIEIIIEI IIIIIEIIIIIIIEIIIIIEIIIEI IIIIIIIIIEIIIIEIIIEII IIIIIIIIIIIIIIIIIEIIIII IIIIIIIEIII IEIIIIIIIIIIIIIII IIIIIIIEIIIIEIIII IflmIIIIflflflflflfl IIIIIIIIEIIIIEIIIII IIIIIEIIIIIIIIIEIIIII MEI-IIIIIIIIIEIIII IIIIIEIIIIIIIIEIIIIII IIIIIIIEIIIIEIIIIEIII IIIIIEIIIIIM IEIIIIIIIIIIIIEIII IIIIIIIIIIIIIIIWI IIIIIIIIIIIIIEIII IIIIIIIIIIIIIIIIIEIIIII “N" and “P" denote a negative or positive relationship between pairs of observations for each marker, respectively per subject. Underlined data are number pairs above upper limit of quantitation and are assumed to have a positive relationship.

[0353] The following Table C shows that CRP and growth s can be predictive of mucosal healing: Subject Collection TGF ID Date CRP VEGF Tweak beta 1 2834 Collection 1 6.88 604.22 624.03 2.00 68.05 2834 Collection 2 24.33 P 631.31 P 509.73 3.72 P 44.79 N 3570 Collection 1 105.46 1046.04 191.49 5.51 33.61 3570 tion 2 1.31 Z 487.25 N 237.91 P 6.33 P 41.29 3713 Collection 1 7.76 1117.85 1267.74 3.94 45.08 3713 Collection 2 107.22 P 633.56 N 957.18 Z 5.44 P 39.59 2 5301 Collection 1 7.62 32.19 5301 Collection 2 36.61 P 217.02 389.33 2.88 30.89 2 7757 Collection 1 838.39 11.24 7.90 43.35 7757 Collection 2 138.56 P 705.18 N 5.33 N 7966 Collection 1 3.03 120.82 326.72 5.59 38.67 7966 Collection 2 31.04 P 1089.52 P 691.29 P 6.81 P 48.68 8075 Collection 1 6.81 968.26 840.06 8.10 58.65 8075 Collection 2 34.62 P 620.97 N 876.55 P 6.27 N 51.36 8127 tion 1 34.41 323.51 310.67 5.54 41.13 8127 Collection 2 2.78 2 318.02 N 284.46 Z 6.87 P 51.87 8431 tion 1 4.53 1829.91 214.78 2.18 52.82 8431 Collection 2 30.51 P 816.10 N 301.14 P 3.47 P 58.41 3831 Collection 1 32.95 804.87 491.46 6.83 36.16 3831 Collection 2 0.29 Z 491.17 N 912.29 P 7.31 P 23.62 N 3852 Collection 1 68.59 494.06 252.18 6.10 32.76 3852 Collection 2 1.00 Z 291.49 N 122.66 Z 6.56 P 39.22 3852 Collection 3 0.60 Z 375.97 N 100.53 1.34 N 22.83 5477 Collection 1 23.17 550.58 485.76 7.51 36.73 5477 Collection 2 2.12 N 1101.83 P 575.69 P 7.55 P 34.98 N 7456 Collection 1 35.21 51.23 452.45 6.13 22.05 7456 Collection 2 0.89 N 496.87 P 366.73 N 14.19 “N" and “P" denote a negative or positive relationship between pairs of ations for each marker, tively per subject. Underlined data are number pairs above upper limit of quantitation and are assumed to have a positive relationship.

[0354] The following Table D shows that CRP and growth factors can be predictive of mucosal healing: Collection Date CRP __----_-— __-Il--_-— _—----_-— _—----_-— mum—“Im- _-_-_—IIIIIIN _—--_—---- Collection 1 _-_------- ImI—Im-m-m-mm —I-II-m-lm-m- ——m---—-—------- _—--__-----Im-Immumm-n ———-—---m---lm-- _—-_--__MIIIm-- “N" and “P" denote a negative or positive relationship between pairs of observations for each marker, respectively per subject. Underlined data are number pairs above upper limit of quantitation and are assumed to have a positive relationship.

Tables A, B, C and D Show marker values and relationships between pairs of observations in CRP and growth factor data. Using a ion of or = 0.1, we identified an ation between three growth factors and CRP. The ing Table (E) shows a - two gency table that highlights that an increase or decrease in AREG, HRG and TGF was found to be significantly associated with an increase or decrease of CRP: AREG* TGF-alha*** * denotes ** denotes *** denotes p= 0.034. p= 0.026. p= 0.07.

Figure 22 illustrates the association between CRP levels and the growth factor index score in determining disease remission.

Further studies for identifying predictive markers of mucosal healing may e samples from several clinical s. As one non-limiting example, Clinical Study A may include 413 samples (paired samples with 1-5 samples per patient). Clinical data may detail patient age, sex, weight, date of diagnosis, disease location, sample collection dates, dose, colonoscopy, improvement of mucosa, presence of mucosal healing, and/or concomitant medication useage. In al Study A, colonoscopy may be performed prior to first drug infusion. As another non-limiting example, in Clinical Study B, 212 UC samples may be analyzed (110 samples were diagnosed for CD at follow-up and 102 samples were diagnosed for UC based on mucosal g). Clinical data may detail patient age, sex, weight, date of diagnosis, disease location, sample collection dates, IFX dose, colonoscopy s copic activity score), albumin level, CRP level, and/or Mayo score. In Clinical Studies A and B, three infusions may occur at week 0, 2 and 6 during ion. 6 additional drug infusions may be performed during the maintenance phase at week 14, 22, 30, 38, 46 and 52.

A second colonoscopy may be performed during the maintenance phase. A third colonscopy may be performed during follow-up and patients may continue treatment if responsive to drug.

The methods of the present invention can be used to create personalized therapeutic ment of a TNFoc-mediated disease. A personalized therapeutic regimen for a patient diagnosed with IBD can be selected based on predictors of e status and/or long-term outcome as described herein, including, but not limited to, a Crohn’s prognostic test (see, e. g., PCT Publication No. 14, the disclosure of which is hereby incorporated by reference in its entirety for all purposes), a disease activity profile (e.g., disease burden), a mucosal status index, and/or a PRO Inflammatory Index as described in Example 5. Using the methods of the present invention, it can be determined that a patient has mild disease activity and the clinician can recommend, prescribe, and/or administer a nutrition-based therapy (Figure 23A). Yet, if it is determined that a patient has mild disease activity with an aggressive ype, a ion-based therapy in addition to thiopurines can be ended, prescribed, and/or administered. A r therapy can be ended, prescribed, and/or administered if it is determined that the patient has moderate disease activity (Figure 23B). If it is determined that a patient has moderate e activity with an aggressive phenotype, either a combination of rines and nutrition therapy (Nx) or an appropriate anti-TNF drug can be recommended, prescribed, and/or stered. In some instances, an anti-TNF monitoring test (see, e.g., PCT Publication No. WO 56590, the disclosure of which is hereby incorporated by reference in its entirety for all purposes) can be used to determine if the patient is likely to d to the therapy. In the case when severe disease activity is determined, an appropriate anti-TNF drug administered at an optimized dose can be recommended and/or prescribed (Figure 23C). In such instances, an NF monitoring test (see, e. g., PCT Publication No. , the disclosure of which is hereby incorporated by reference in its entirety for all purposes) can be used to predict if the patient is likely to be responsive to drug. In other instances, it can be recommended and/or prescribed that a patient having severe e ty also receive nutrition-based therapy.

In some embodiments, the methods of the present invention can be used in a ent gm to personalize patient treatment (Figure 24). First, treatment can be selected based on the expression of mucosal status markers. Next, drug dose can be selected based on disease burden (e.g., disease activity index). After the therapeutic drug is administered, the initial response can be ined from the expression of markers of mucosal healing. ATM monitioring can be used to identify patient who are responsive or non-responsive to therapy. Non-responsive patients can then be prescribed an appropriate anti-TNF drug.

Example 11. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are Predictive of Disease ty in Patients with Crohn’s disease (CD).

Previous studies te that patients with CD who have a higher trough concentration of IFX during maintenance dosing are more likely to benefit from treatment.

However, development ofATIs can result in increased drug clearance and loss of response.

Therapeutic drug monitoring may allow clinicians to maintain effective drug concentrations. gh previous ATI assays have been limited by the inability to measure ATIs in the presence of drug, fluid-phase IFX and ATI assays have overcome this problem (see, e. g., PCT Publication No. WO 56590, the disclosure of which is hereby incorporated by reference in its entirety for all purposes). We used these assays to evaluate the relationship between serum IFX concentration, ATIs and disease activity.

Methods: 2021 serum samples from 532 participants in 4 prospective CD RCTs or cohort studies (COMMIT, Leuven dose optimization study, Canadian Multicenter and IMEDEXl) that evaluated the maintenance phase of IFX treatment were used, and data were combined for analysis. IFX and ATI serum levels were ed using a HPLC-based fluid phase assay. CRP, measured by ELISA, was used to assess disease activity. ROC analysis determined the IFX threshold that best minated disease activity, as measured by CRP.

We examined pairs of samples taken over sequential time points and evaluated the onship between IFX and ATI presence in the pair’s first data point and CRP in the subsequent measurement. There were 1205 such observations. We identified four ct t groups, namely IFX 2 threshold and ATI-, IFX < threshold and ATI-, IFX 2 threshold and ATI+, and IFX < threshold and ATI+. Regression es assessed the ial interaction between IFX and ATI as predictors of CRP.

Results: CRP can best differentiate IFX status with an IFX concentration threshold of 3 ug/ml (ROC AUC = 74 %). Using paired sequential samples both ATI and IFX were associated with median CRP (Table 2). Although ATI+ patients had higher CRP levels overall, within this group there was no association n IFX higher than threshold and subsequent CRP. In ATI- ts, CRP was significantly higher in patients with IFX levels <3 ug/ml. In the regression analysis ATI positivity, IFX Z 3 ug/ml and the interaction term were all significant predictors of CRP. CRP was 31 % higher in ATI positive patients than 2012/037375 those who were ATI negative and 62 % lower in patients with IFX levels 2 3 ug/ml compared to those with IFX < 3 ug/ml. sions: We have shown that ATI positivity is predictive of increased disease activity, while an IFX tration above the threshold value of 3 ug/ml is predictive of significantly lower disease activity. In ATI+ patients, IFX trations above 3ug/ml had no effect on CRP, indicating that the benefits of IFX are diminished in the presence of ATI despite the presence of optimal drug concentration. These findings t the concept that therapeutic drug monitoring is an important tool in optimizing IFX therapy. Using paired sequential samples and regression analysis, both ATI and IFX were associated with median CRP as shown in the following table: Median CRP Concentration (ng/ml; interquartile range) Significance In ATI— Patients In ATI+ ts IFX < 3 ug/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) m IFX 2 3 ug/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) Median CRP concentrations and interquartile ranges (in parentheses) in ng/ml. Asterisks denote significance levels of two-sample Mann-Whitney U tests (***, p < 0.001; **, p < 0.01; *, p < 0.05; NS, not significant).

Example 12. Novel Infliximab (IFX) and Antibody-to-Infliximab (ATI) Assays are Predictive of Disease Activity in Patients with Crohn’s disease (CD).

This example illustrates the use of infiiximab (IFX) and antibody-to-infiiximab (ATI) assay in predicting disease ty in ts with Crohn’s disease (CD). This e also illustrates a method of determining the threshold of IFX that can best discriminate disease activity as measured by C-reactive protein (CRP) levels. This example also illustrates the association of both ATI and IFX to CD and CRP levels, which can serve as a measure of disease activity.

Previous studies have indicated that patients with CD who have a higher trough concentration of IFX during maintenance dosing are more likely to benefit from treatment.

However, pment ofATIs can result in increased drug clearance and loss of response.

Therapeutic drug monitoring may allow clinicians to in effective drug concentrations.

Although previous ATI assays have been limited by the inability to measure ATIs in the presence of drug, the fluid-phase IFX and ATI assays described in PCT Publication No. WO 201 1/056590 (the disclosure of which is hereby incorporated by nce in its entirety for all purposes) have overcome this problem. 2012/037375 In this study we used fluid-phase IFX and ATI assays to evaluate the relationship between serum IFX concentration, ATIs and disease activity, as measured by CRP. We analyzed 2,021 serum samples from 532 participants in 4 ctive CD randomized controlled trials (RCTs) or cohort studies, including COMMIT, Leuven dose optimization study, Canadian enter and IMEDEXl. The combined analysis was restricted to samples during maintenance of IFX treatment. There was evidence of non-heterogeneity among pooled CRP.

IFX and ATI serum levels were measured using a HPLC-based fluid phase assay.

CRP was measured by ELISA and used to assess disease activity. Receiver-operator curve (ROC) is was performed to determine the IFX trough threshold (e.g., amount or concentration) that can best discriminate disease activity (e.g., between high and low CRP values). Figure 25 shows the ROC analysis. CRP and nine IFX trough thresholds were analyzed and the ROC area under receiver-operator characteristic curve (AUC) are as follows: "III-IN.“ ROC AUC 0.682 0.727 0.733 0.743 0.727 0.717 0.699 0.689 0.678 The ROC is showed that CRP can best differentiate IFX status with an IFX concentration threshold of 3 ug/ml (ROC AUC = 74 %). For example, at an IFX through concentration threshold of 3.0 ug/ml, a randomly chosen sample with a “low” IFX serum concentration will have a higher CRP level than a randomly chosen sample with a “high” IFX serum concentration 74.3% of the time. In the IFX, ATI and CRP association analysis, a serum IFX trough threshold of 3.0 ug/ml was used.

To determine the association of serum IFX concentration, ATI, and CRP levels over time, we examined pairs of samples taken over sequential time points. A 100-day time gap limit was d for the time . We evaluated the relationship between the presence of IFX and ATI in the pair’s first data point and CRP in the subsequent measurements (Figure 26A). Figure 26B shows CRP levels, IFX serum concentration and ATI status at tial time points for a sample. In total, 1,205 observations were examined.

Regression analysis (e.g, ordinary least squares regression) was performed to assess the potential ction n prior IFX and prior ATI as predictors of disease (i.e., CRP levels). In particular, CRP was log transformed at the second time point observation. Prior WO 54987 2012/037375 IFX is the first time point with IFX concentration above or below the calculated trough threshold of 3 ug/ml. Prior ATI is the first time point ATI is above or below 3.13 U/ml which is the limit of detection (LOD). Using paired sequential samples and regression analysis, both ATI and IFX were associated with median CRP as shown in the following table: Median CRP Concentration ; interquartile range) Significance In ATI— Patients In ATI+ Patients IFX < 3 [Lg/ml 5.65 (1.68, 16.1) 8.40 (3.10, 20.1) 14* IFX 2 3 pg/ml 1.50 (1.00, 4.70) 9.90 (5.82, 20.2) Median CRP concentrations and interquartile ranges (in parentheses) in ng/ml. Asterisks denote significance levels of two-sample hitney U tests (***, p < 0.001; **, p < 0.01; *, p < 0.05; NS, not significant).

The results shows that the factors and interactions between the factors are significant. The regression coefficients were ated to be 0.272 for ATI+ samples and -0.979 for IFX 3 3 ug/ml.

We identified four distinct patient groups: (1) IFX 2 threshold and ATI-, (2) IFX < threshold and ATI-, (3) IFX 2 threshold and ATI+, and (4) IFX < threshold and ATI+. Of the 1,205 observations used in the analysis, 605 were IFX 2 threshold and ATI-; 196 were IFX < threshold and ATI-; 41 were IFX 2 threshold and ATI+; and 363 were IFX < threshold and ATI+.

Although ATI+ patients had higher CRP levels overall, within this group there was no association between IFX levels higher than threshold and CRP (Figure 27). In ATI- patients, CRP levels were significantly higher in patients with IFX levels less than threshold (Figure 27).

[0374] In the regression analysis, ATI positivity, IFX Z 3 ug/ml and their interaction were all significant predictors of CRP . CRP was 31 % higher in ATI + patients than those who were ATI-, and 62 % lower in patients with IFX levels 2 3 ug/ml compared to those with IFX < 3 ug/ml. The onship n IFX concentration and CRP levels differs n ATI+ and ATI- patient groups.

[0375] In this study we showed that ATI positivity is predictive of increased disease actiVity, as measured by CRP. We also showed that IFX concentration above the threshold value of 3 ug/ml is predictive of significantly lower disease activity. In ATI+ patients, IFX concentrations above 3 ug/ml had no effect on CRP levels, suggesting that the benefits of IFX are diminished in the presence ofATI even despite the presence of optimal drug concentration.

We showed that disease activity, as measured by CRP, is strongly linked to both IFX and ATI in a large combined dataset. Thus, ts with active Crohn’s disease can benefit from knowledge of both IFX and ATI levels at . Based on the experimental derivation of these onships, the following ent paradigms were created. For instance, a symptomatic t with Crohn’s disease with IFX< threshold at trough and ATI- can benefit from an increased dose of IFX therapy. A patient with IFX 3 threshold and ATI- can benefit from receiving endoscopy or switching therapy. A symptomatic patient with IFX< threshold at trough and ATI+ can benefit from switching therapy ifATI is high or optimizing therapy dose if ATI is low. A patient with IFX 3 threshold and ATI+ can benefit from switching y if disease activity (e.g., CRP level) is high. Alternatively, if disease activity (e.g., CRP level) is low in that patient, further monitoring is recommended. The treatment paradigms are described in the ing table: Switch therapy (high ATI) IFX < threshold se dose Optimize dose (low ATI) Check egldoscopy Sw1tch therapyr(h1gh act1v1ty) IFX 2 threshold Switch therapy Monitor (low ty) These s demonstrate that therapeutic drug monitoring using methods of the present invention are important tools in optimizing IFX therapy.

Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, one of skill in the art will appreciate that certain changes and modifications may be practiced within the scope of the appended claims. In addition, each reference ed herein is incorporated by reference in its entirety to the same extent as if each reference was individually incorporated by reference.

Claims (33)

1. A non-invasive method for ing mucosal g in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen, the method comprising: 5 (a) measuring the levels of an array of mucosal healing markers in a sample obtained from the individual; (b) comparing the levels of an array of l healing markers in the individual to that of a control to compute the mucosal healing index of the individual, n the mucosal healing index ses a representation of the extent of mucosal healing; and 10 (c) determining whether the individual undergoing mucosal healing should maintain the therapy regimen.

2. A method for monitoring therapeutic efficiency in an individual with inflammatory bowel disease (IBD) receiving therapy, the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample 15 obtained from the dual at a ity of time points over the course of therapy with a therapeutic antibody; (b) applying a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; and 20 (d) determining whether the therapy is appropriate for the individual to promote mucosal healing.

3. A method for selecting a y regimen for an individual with inflammatory bowel e (IBD), the method comprising: (a) measuring the levels of an array of mucosal healing markers in a sample 25 obtained from the individual at a plurality of time points over the course of therapy, the individual ing a therapeutic antibody; (b) ng a statistical algorithm to the level of the one or more markers determined in step (a) to generate a mucosal healing index; (c) comparing the individual’s mucosal healing index to that of a control; and 30 (d) selecting an appropriate therapy regimen for the individual, wherein the therapy regimen promotes mucosal healing.

4. The method of any one of claims 1 to 3, wherein the IBD comprises Crohn’s disease or ulcerative colitis.

5. The method of any one of claims 1 to 3, wherein the IBD comprises Crohn’s disease. 5

6. The method of any one claims 1 to 5, wherein the mucosal healing marker is a member selected from the group consisting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGFD , FGF1, FGF2, FGF7, FGF9, TWEAK and combinations thereof.

7. The method of any one of claims 1 to 6, wherein the markers are ed in a 10 sample ed from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a tissue biopsy.

8. The method of any one of claims 1 to 7, wherein the therapy is selected from the group consisting of TNFα inhibitor therapy, an immunosuppressive agent, a 15 corticosteroid, a drug that targets a different mechanism, nutrition therapy, and combinations thereof.

9. The method of claim 8, wherein the TNFα inhibitor y comprises an anti- TNFα antibody.

10. The method of claim 9, wherein the anti-TNFα antibody is a member selected 20 from the group ting of infliximab, etanercept, adalimumab, izumab pegol, and combinations thereof.

11. The method of claim 8, n the suppressive agent is a member ed from the group consisting of azathioprine, 6-mercaptopurine, methotrexate, and combinations thereof. 25

12. The method of claim 8, wherein the drug that targets a different mechanism is a member selected from the group consisting of an IL-6 receptor inhibiting antibody, an anti-integrin molecule, a JAK-2 inhibitor, a tyrosine kinase inhibitor, and combinations thereof.

13. The method of claim 8, wherein the nutrition therapy comprises a special ydrate diet.

14. The method of any one of claims 1 to 13, wherein the array of mucosal healing markers further ses at least one member ed from the group consisting of an 5 anti-TNFα antibody, an anti-drug antibody (ADA), an inflammatory marker, an antiinflammatory marker, a mucosal healing marker, and combinations thereof.

15. The method of claim 14, wherein the anti-TNFα antibody is a member selected from the group ting of infliximab, etanercept, umab, certolizumab pegol, and combinations thereof. 10

16. The method of claim 14, wherein the anti-drug antibody (ADA) is a member selected from the group consisting of a human anti-chimeric antibody (HACA), a human anti-humanized antibody (HAHA), a human anti-mouse dy (HAMA), and combinations thereof.

17. The method of claim 14, wherein the mucosal healing marker is a member 15 selected from the group consisting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, FGF2, FGF7, FGF9, TWEAK and combinations thereof.

18. The method of claim 14, n the inflammatory marker is a member selected from the group consisting of GM-CSF, IFN-γ, IL-1β, IL-2, IL-6, IL-8, TNF-α, sTNF 20 RII, and combinations thereof.

19. The method of claim 14, wherein the anti-inflammatory marker is a member selected from the group consisting of IL-12p70, IL-10, and ations thereof.

20. A method for reducing or minimizing the risk of surgery in an dual diagnosed with inflammatory bowel disease (IBD) being administered a therapy 25 regimen, said method comprising: (a) measuring an array of l healing markers at a plurality of time points over the course of therapy with a therapeutic antibody in samples obtained from an individual; (b) generating the individual’s mucosal healing index comprising a representation of the presence and/or concentration levels of each of the markers over time; (c) comparing the individual’s l healing index to that of a l; and 5 (d) selecting an appropriate therapy regimen to reduce or ze the risk of surgery.

21. The method of claim 20, wherein the mucosal healing marker is a member selected from the group ting of AREG, EREG, HB-EGF, HGF, NRG1, NRG2, NRG3, NRG4, BTC, EGF, IGF, TGF-, VEGF-A, VEGF-B, VEGF-C, VEGF-D, FGF1, 10 FGF2, FGF7, FGF9, TWEAK and ations thereof.

22. The method of claim 20 or claim 21, wherein the control is a healthy control.

23. The method of any one of claims 20 to 22, wherein the therapeutic antibody comprises an anti-TNF antibody.

24. The method of claim 23, wherein the anti-TNF antibody is a member selected 15 from the group consisting of infliximab, etanercept, adalimumab, certolizumab pegol, and combinations thereof.

25. The method of any one of claims 20 to 24, wherein the markers are measured in a sample selected from the group consisting of serum, plasma, whole blood, stool, peripheral blood mononuclear cells (PBMC), polymorphonuclear (PMN) cells, and a 20 tissue biopsy.

26. The method of any one of claims 20 to 25, wherein the plurality of time points comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, or more time points.

27. The method of any one of claims 20 to 26, wherein the IBD comprises Crohn’s 25 e or ulcerative colitis.

28. The method of any one of claims 20 to 27, wherein the first time point in the plurality of time points is prior to the course of therapy with the therapeutic antibody.

29. The method of any one of claims 20 to 27, n the first time point in the plurality of time points is during the course of therapy with the therapeutic antibody.

30. A non-invasive method for measuring mucosal g in an individual diagnosed with inflammatory bowel disease (IBD) receiving a therapy regimen 5 according to claim 1, ntially as herein described with reference to any one or more of the examples but excluding comparative examples.

31. A method for monitoring therapeutic efficiency in an individual with inflammatory bowel disease (IBD) receiving therapy according to claim 2, substantially as herein described with reference to any one or more of the es but excluding 10 comparative examples.

32. A method for selecting a therapy regimen for an individual with inflammatory bowel disease (IBD) according to claim 3, substantially as herein described with reference to any one or more of the examples but excluding comparative examples.

33. A method for reducing or minimizing the risk of surgery in an individual 15 diagnosed with matory bowel disease (IBD) being administered a therapy regimen ing to claim 20, substantially as herein described with reference to any one or more of the examples but excluding comparative examples.