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WO2015166461A1 - Procédés de sélection de régime de traitement utilisant des niveaux de médicament de tnf alpha et d'anti-tnf alpha - Google Patents

Procédés de sélection de régime de traitement utilisant des niveaux de médicament de tnf alpha et d'anti-tnf alpha Download PDF

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WO2015166461A1
WO2015166461A1 PCT/IB2015/053176 IB2015053176W WO2015166461A1 WO 2015166461 A1 WO2015166461 A1 WO 2015166461A1 IB 2015053176 W IB2015053176 W IB 2015053176W WO 2015166461 A1 WO2015166461 A1 WO 2015166461A1
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drug
tnfa
patient
individual
weeks
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Xinjun Liu
Anjali Jain
Sharat Singh
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Nestec SA
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Nestec SA
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6863—Cytokines, i.e. immune system proteins modifying a biological response such as cell growth proliferation or differentiation, e.g. TNF, CNF, GM-CSF, lymphotoxin, MIF or their receptors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/435—Assays involving biological materials from specific organisms or of a specific nature from animals; from humans
    • G01N2333/52—Assays involving cytokines
    • G01N2333/525—Tumor necrosis factor [TNF]

Definitions

  • Infliximab is a human- murine chimeric monoclonal antibody (mAb) comprised of a 25% variable murine Fab' region linked to the 75% human IgGl : ⁇ Fc constant region by disulfide bonds (Tracey et al, Pharmacology and Therapeutics, 117:244-279 (2008)).
  • Infliximab binds specifically to soluble and membrane-bound TNF-a, preventing it from binding to one of two possible receptors, TNFRl and TNFR2 (Nesbitt et al. "Certolizumab pegol: a PEGylated anti -tumour necrosis factor alpha biological agent.” In F.M. Veronese (Eds.), PEGylated Protein Drugs: Basic Science and Clinical Applications, Birkhauser Verlag, Switzerland, pp. 229-25). As a bivalent mAb, infliximab can bind 2 soluble TNF trimers simultaneously, which allows multimeric complexes to form.
  • Infliximab is known to reduce the levels of TNFa as well as serum interleukin (IL-6) and acute-phase reactants, such as C-reactive protein (Lee, supra).
  • a typical protocol for treating a T Fa associated disease includes an initial anti- TNFa drug induction phase followed by a drug maintenance treatment phase.
  • the dosing regimen for infliximab is 5 mg/kg body weight given as an IV induction regimen at 0, 2, and 6 weeks followed by a maintenance regimen of 5 mg/kg every 8 weeks thereafter.
  • the maintenance regimen starts at 42 days.
  • the mid-infusion time point is 4 weeks thereafter or about 70 days.
  • the trough time point is 8 weeks after maintenance regimen starts or about 98 days from the beginning of the induction regimen (at the start of drug therapy).
  • the clinical response is strongly correlated with serum concentrations, and it is likely that antibody formation to infliximab decreases serum levels to non-detectable levels.
  • the variable murine region is thought to be the antigenic component that causes the formation of antibodies to infliximab (ATI). Not only does development of ATI lead to increased drug clearance, but it could also result in a range of adverse reactions from mild allergic response to anaphylactic shock.
  • PK Pharmacokinetics
  • Pharmacokinetics provides mathematical basis to understand and assess ADME or absorption, distribution, metabolism and excretion. Understanding these processes allows for a robust understanding of the appropriate drug regimen for the patient.
  • PD Pharmacodynamics
  • the PK and PD of biological therapeutics is especially difficult to understand.
  • the PK of biological therapeutics is highly variable between individuals and can even vary within the same individual. For instance, a patient can lose response over time to a biological therapeutic by developing anti-drug antibodies.
  • the present invention provides methods for determining an anti-TNFa drug regimen for an individual being administered an anti-TNFa drug.
  • the method comprising: i) analyzing a sample obtained from an individual being administered an anti- TNFa drug to determine:
  • the anti-TNFa drug is REMICADE ® (infliximab).
  • the individual is categorized into drug regimen Group I.
  • the individual categorized into drug regimen Group I is monitored to determine differences in concentration time profiles (PK) of the amount of anti-TNFa drug and the amount of TNFa.
  • the individual in Group I is maintained on the anti-TNFa drug treatment regimen or is recommended to be maintained on the anti-TNFa drug treatment regimen.
  • the T/D ratio is between about 0.86 and about 1.5, then the individual is categorized into drug regimen Group II. In some instances, the individual categorized into drug regimen Group II is monitored to determine differences in
  • concentration time profiles of the amount of anti-TNFa drug and the amount of TNFa.
  • the individual in Group II is maintained on the anti-TNFa drug treatment regimen or is recommended to be maintained on the anti-TNFa drug treatment regimen.
  • the individual is categorized into drug regimen Group III. In some instances, the individual categorized into drug regimen Group III is monitored to determine differences in
  • concentration time profiles of the amount of anti-TNFa drug and the amount of TNFa.
  • the dose of anti-TNFa drug is increased. In some instances, the dose is increased from 5 mg/kg to 10 mg/kg. In some embodiments, said individual responds to the increased dose.
  • the individual categorized into drug regimen Group IV is monitored to determine differences in concentration time profiles (PK) of the amount of anti-TNFa drug and the amount of TNFa.
  • PK concentration time profiles
  • the sample from the individual is further analyzed for anti-drug antibody. For example, the presence or absence of anti-drug antibody can be determined using the method described below.
  • the individual is switched to a different anti-TNFa drug if the presence of anti-drug antibody is detected. In some instances, the individual responds to the different anti-TNFa drug.
  • the individual categorized into drug regimen Group IV is switched to a different anti-TNFa drug.
  • the patient is on infliximab and the different anti-TNFa drug is a member selected from the group consisting of ENBREL ® (etanercept), HUMIRA® (adalimumab), CIMZIA® (certolizumab pegol), SIMPONI®
  • individuals categorized in Groups I and II have higher trough levels of anti-TNFa drug than individuals categorized in Groups III and IV.
  • the method further comprises measuring at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 predicator variables from the sample.
  • the at least one predictor variable is a member selected from the group consisting of ADA, EGF, bFGF, PIGF, sFltl, VEGF, GM-CSF, IFN- ⁇ , IL-10, IL-12P70, IL- ⁇ , IL-2, IL-6, IL-8, sT FRII, SAA, ICAM-1, VCAM-1, CRP, ⁇ 4 ⁇ 7 integrin, IL-12 subunit ⁇ ( ⁇ 40), and a combination thereof.
  • the at least one predictor variable is a member selected from the group consisting of EGF, VEGF, IL-2, IL-6, IL-8, ICAM-1, VCAM-1, SAA, and a combination thereof.
  • the method further comprises applying a statistical analysis on at least one predictor variable together with the categorized drug regimen Groups I-IV.
  • a quartile concentration of predictor variables selected from the group consisting of CRP, SAA, ICAM-1, VCAM-1, VEGF, and a combination thereof correlate inversely with the amount of anti-TNFa drug.
  • the sample is selected from the group consisting of serum, plasma, whole blood, and stool.
  • the present invention provides methods for predicting whether a patient will develop anti-drug antibodies (ADA) prior to developing ADA.
  • the method comprises: measuring the level or concentration of anti-TNFa drug (e.g., infliximab) and TNFa at 2 weeks or prior to the second infusion, after the beginning of the induction period in a sample from the patient; and predicting that the patient will develop anti-drug antibodies (ADA) or lose response if the ratio of TNFa to infliximab is greater than or equal to about 0.3.
  • anti-TNFa drug e.g., infliximab
  • TNFa anti-drug antibodies
  • the ratio of TNFa to infliximab is greater than a value selected from the group consisting of about 0.3, 0.4, and 0.5. In other embodiments, the TNFa/IFX ratio is greater than about 0.4. In yet other embodiments, the TNFa/IFX ratio is greater than about 0.5. [0027] In some embodiments, the TNFa/IFX ratio is calculated between 5 days and 20 days from the beginning of the induction period. In other embodiments, the ratio is calculated prior to the second infusion. In some instances, the ratio is calculated between 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19 and 20 days from the beginning of the induction period.
  • the present invention provides methods for predicting whether a patient will develop anti-drug antibodies (ADA) prior to developing ADA.
  • the method comprises: determining the level or concentration of anti-TNFa drug at about 4 weeks after the beginning of the maintenance period (e.g., mid-infusion) in a sample from the patient; and predicting that the patient will develop anti-drug antibodies (ADA) by trough (8 weeks) if the level of anti-TNFa drug at about 4 weeks after the beginning of the maintenance period is 8 ⁇ g/ml or less.
  • the anti-TNFa drug is REMICADE® (infliximab).
  • about 4 weeks after the beginning of the maintenance period is between about 60 to 80 days from the beginning of the drug therapy. In other embodiments, about 4 weeks after the beginning of the maintenance period (mid- infusion) is between about 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76 ,77, 78, 79, or about 80 days from the beginning of the drug therapy.
  • the method further comprises analyzing the sample for anti- drug antibody. In some embodiments, the method further comprises switching the patient to a different anti-TNFa drug.
  • the different anti-TNFa drug is a member selected from the group consisting of ENBREL® (etanercept), HUMIRA® (adalimumab), CIMZIA® (certolizumab pegol), SFMPONI® (golimumab) ENTYVIO® (vedolizumab), STELARA® (ustekinumab), and combinations thereof.
  • ENBREL® etanercept
  • HUMIRA® adalimumab
  • CIMZIA® certolizumab pegol
  • SFMPONI® golimumab
  • ENTYVIO® vedolizumab
  • STELARA® ustekinumab
  • the method comprises: determining the levels or concentrations of TNFa (T) and infliximab (D) at 4 weeks after the beginning of the maintenance period (e.g., mid-infusion); and predicting that the patient will develop ATI by trough (8 weeks) if the ratio the T/D ratio is between 0 and 2 at about 4 weeks after the beginning of the maintenance period [0033] In some embodiments, about 4 weeks after the beginning of the maintenance period (mid-infusion) is between about 60 to 80 days from the beginning of the drug therapy.
  • about 4 weeks after the beginning of the maintenance period (mid- infusion) is between about 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76 ,77, 78, 79, or about 80 days from the beginning of the drug therapy.
  • the method further comprises analyzing the sample for antidrug antibody (e.g., anti-drug antibody to infliximab or ATI). In some embodiments, the method further comprises switching the patient to a different anti-T Fa drug.
  • the different anti-TNFa drug is a member selected from the group consisting of ENBREL® (etanercept), HUMIRA® (adalimumab), CIMZIA® (certolizumab pegol), SF PONI® (golimumab), ENTYVIO® (vedolizumab), STELARA® (ustekinumab) and combinations thereof.
  • FIG. 1A-D show that patients on infliximab (IFX) can be separated or categorized into Groups I-IV according to the patients' TNFa to IFX ratio at day 42.
  • FIG. 1A shows the TNFa/IFX ratios and IFX levels for the groups.
  • FIG. IB illustrates TNFa and IFX in representative patients.
  • FIG. 1C shows the mean TNFa/IFX ratio for Groups I-IV.
  • FIG. ID shows the mean TNFa level for Groups I-IV.
  • FIG. 2A-D show TNFa and IFX levels in patients receiving IFX maintenance therapy.
  • FIG. 2A shows IFX levels at mid-infusion in ATI+ and ATI- patients. There were 4 patients who were ATI- at mid-infusion and ATI+ at trough (triangle).
  • FIG. 2B shows IFX levels at trough in ATI+ and ATI- patients (the same data is presented at two scales).
  • FIG. 2C shows TNFa levels at mid-infusion in ATI+ and ATI- patients.
  • FIG. 2D shows TNFa levels at trough in ATI+ and ATI- patients.
  • FIG. 3A-B show correlations between mid-infusion and trough data in ATI negative samples.
  • FIG. 3A shows that there is a correlation between the level of anti-TNFa drug (e.g., infliximab) at trough versus anti-TNFa drug at mid-infusion for ATI negative samples.
  • FIG. 3B shows there is a correlation of TNFa at trough versus TNFa at mid-infusion.
  • FIG. 4A-C show IFX levels at mid-infusion in ATI positive and negative samples.
  • FIG. 4A represents all the samples tested.
  • FIG. 4B represents paired samples.
  • FIG. 4C shows the data from ATI negative samples.
  • FIG. 5A-D shows TNFa levels and TNFa/infliximab ratios in ATI positive and ATI negative samples.
  • FIG. 5A shows TNFa levels at mid-infusion.
  • FIG. 5B shows TNFa levels at trough.
  • FIG. 5C shows TNFa/infliximab ratios at mid-infusion.
  • FIG. 5D shows TNFa/infliximab ratios at trough.
  • FIG. 6 shows a table of data comparing ATI positive samples versus ATI negative samples.
  • FIG. 7A-C show TNFa levels and TNFa/infliximab ratios in ATI positive and ATI negative samples.
  • FIG. 7A shows infliximab levels in ATI positive samples.
  • FIG. 7B shows ATI negative samples.
  • FIG. 7C shows TNFa levels in ATI positive samples, whereas FIG. 7D shows TNFa levels for ATI negative samples.
  • FIG. 8 shows a table of data comparing TNFa levels during induction therapy, such as as first dosing and at second dosing.
  • FIG. 9A-C illustrates TNFa levels and IFX levels during induction therapy in a representative patient of Group I (Patient K06).
  • FIG. 9A shows TNFa levels and IFX levels across the induction period.
  • FIG. 9B shows IFX levels during induction therapy after the first dosing.
  • FIG. 9C shows IFX levels during induction therapy after the second dosing.
  • FIG. lOA-C illustrates TNFa levels and IFX levels during induction therapy in a representative patient of Group II (Patient K01).
  • FIG. 10A shows TNFa levels and IFX levels across induction.
  • FIG. 10B shows IFX levels during induction therapy after the first dosing.
  • FIG. IOC shows IFX levels during induction therapy after the second dosing.
  • FIG. 11A-C illustrates TNFa levels and IFX levels during induction therapy in a representative patient of Group III (Patient K20).
  • FIG. 11A shows TNFa levels and IFX levels across the induction period.
  • FIG. 11B shows IFX levels during induction therapy after the first dosing.
  • FIG. 11C shows IFX levels after the second dosing (at week 2 of induction).
  • FIG. 12A-C illustrate TNFa levels and IFX levels during induction therapy in a representative patient in Group IV (Patient K05).
  • FIG. 12A shows TNFa levels and IFX levels across the induction period.
  • FIG. 12B shows IFX levels during induction therapy after the first dosing.
  • FIG. 12C shows IFX levels after the second dosing.
  • FIG. 13A-D show TNFa /IFX ratios during induction therapy for the representative patients of FIGS. 9-12.
  • FIG. 13A represents Patient K06 of Group I.
  • FIG. 13B represents Patient KOI of Group II.
  • FIG. 13C represents Patient K20 of Group III.
  • FIG. 13D shows TNFa /IFX ratios during induction therapy for the representative patients of FIGS. 9-12.
  • FIG. 13A represents Patient K06 of Group I.
  • FIG. 13B represents Patient KOI of Group II.
  • FIG. 13C represents Patient K20 of Group III.
  • FIG. 13D shows TNFa /IFX ratios during induction therapy for the representative patients of FIGS. 9-12.
  • FIG. 13A represents Patient K06 of Group I.
  • FIG. 13B represents Patient KOI of Group II.
  • FIG. 13C represents Patient K20 of Group III.
  • FIG. 13D shows TNFa /IFX ratios during induction therapy for the representative patients of FIGS. 9-12.
  • Described herein are methods of predicting a patient's response to an anti-T Fa drug therapy. The methods are based, in part, on monitoring the drug bioavailability due to the patient's rate of drug metabolism and predicting the development of anti-drug antibodies.
  • the present invention provides a method for determining an anti- TNFa drug regimen for an individual being administered an anti-TNFa drug.
  • the method comprises : i) analyzing a sample obtained from the individual being administered an anti- TNFa drug to determine: a) the amount of anti-TNFa drug in ⁇ g/mL after 6 weeks of an induction phase to form a value D; b) the amount of TNFa in pg/mL after 6 weeks of the induction phase to form a value T; and ii) calculating a T/D ratio to categorize the individual into a drug regimen of Groups I-IV.
  • the present invention provides a method for predicting whether a patient will develop anti-drug antibodies (ADA) prior to developing ADA during induction therapy.
  • the method comprises: measuring the level or concentration of anti-TNFa drug (e.g., infliximab) and TNFa at 2 weeks or prior to the second infusion, after the beginning of the induction phase in a sample from the patient; and predicting that the patient will develop anti-drug antibodies (ADA) or lose response if the ratio of TNFa to infliximab is greater than or equal to about 0.3.
  • anti-TNFa drug e.g., infliximab
  • TNFa anti-drug antibodies
  • the present invention provides a method for predicting whether a patient will develop anti-drug antibodies (ADA) prior to developing ADA during
  • the method comprises: determining the level or concentration of anti- TNFa drug at 4 weeks after the beginning of the maintenance period (mid-infusion) in a sample from the patient; and predicting that the patient will develop anti-drug antibodies (ADA) by trough (8 weeks) if the level of anti-TNFa drug at about 4 weeks after the beginning of the maintenance period is 8 ⁇ g/ml or less.
  • the present invention provides a method for predicting whether a patient will develop antibodies to infliximab (ATI) prior to developing ATI during maintenance therapy.
  • the method comprises: determining the levels or concentrations of TNFa (T) and infliximab (D) at 4 weeks after the beginning of the maintenance period (mid- infusion); and predicting that the patient will develop anti-drug antibodies (ADA) by trough (8 weeks) if the ratio the T/D ratio is between 0 and 2 at about 4 weeks after the beginning of the maintenance period.
  • course of therapy includes any therapeutic approach taken to relieve or prevent one or more symptoms associated with a TNaF-mediated disease or disorder.
  • the term encompasses administering any compound, drug, procedure, and/or regimen useful for improving the health of an individual with a TNFa-mediated disease or disorder and includes any of the therapeutic agents described herein.
  • the course of therapy or the dose of the current course of therapy can be changed (e.g., increased or decreased) based upon the presence or concentration level of TNFa, anti-TNF drug, and/or anti-drug antibody using the methods of the present invention.
  • determining the course of therapy 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 existing drug or dose.
  • a derived or measured index can be used to determine the course of therapy.
  • drug induction therapy or “induction therapy” refers the first phase of a treatment regimen designed to induce a therapeutic response, such as disease remission, in an individual.
  • infliximab induction therapy includes an intravenous injection of 5 mg/kg at week 0, week 2 and week 6 after the initial dosing.
  • drug maintenance therapy or “maintenance therapy” refers the second phase of a treatment regimen designed to maintain a positive therapeutic response, such as disease remission, in an individual. Maintenance therapy is used to prevent or minimize the chance of disease relapse.
  • infliximab maintenance therapy includes an intravenous injection of 5 mg/kg every 8 weeks after the completion of the drug induction therapy. Mid-induction of maintenance therapy can be at 4 weeks after the initiation of the therapy. Trough can be at 8 weeks after the initiation and before the maintenance dose is administered.
  • TNFa is intended to include a human cytokine 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 TNFa is described further in, for example, Jones et al., Nature, 338:225-228 (1989).
  • the term TNFa is intended to include human TNFa, a recombinant human TNFa (rhTNF-a), or TNFa that is at least about 80% identity to the human TNFa protein.
  • Human TNFa consists of a 35 amino acid (aa) cytoplasmic domain, a 21 aa transmembrane segment, and a 177 aa extracellular domain (ECD) (Pennica, D. et al. (1984) Nature 312:724). Within the ECD, human TNFa shares 97% aa sequence identity with rhesus TNFa, and 71% to 92% aa sequence identity with bovine, canine, cotton rat, equine, feline, mouse, porcine, and rat TNFa. TNFa can be prepared by standard recombinant expression methods or purchased commercially (R & D Systems, Catalog No. 210-TA, Minneapolis, Minn.).
  • TNFa is an "antigen," which includes a molecule or a portion of the molecule capable of being bound by an anti-TNF-a drug.
  • TNFa can have one or more than one epitope.
  • TNFa will react, in a highly selective manner, with an anti-TNFa antibody.
  • Preferred antigens that bind antibodies, fragments, and regions of anti-TNFa antibodies include at least 5 amino acids of human TNFa.
  • TNFa is a sufficient length having an epitope of TNFa that is capable of binding anti-TNFa antibodies, fragments, and regions thereof.
  • TNF inhibitor e.g., TNF-a inhibitor
  • TNFa antagonist e.g., anti-TNFa drug
  • agents including proteins, antibodies, antibody fragments, fusion proteins (e.g., Ig fusion proteins or Fc fusion proteins), multivalent binding proteins (e.g., DVD Ig), small molecule TNF-a antagonists and similar naturally- or nonnaturally-occurring molecules, and/or recombinant and/or engineered forms thereof, that, directly or indirectly, inhibits TNF a activity, such as by inhibiting interaction of TNF-a with a cell surface receptor for TNF-a, inhibiting TNF-a protein production, inhibiting TNF-a gene expression, inhibiting TNFa secretion from cells, inhibiting TNF-a receptor signaling or any other means resulting in decreased TNF-a activity in a subject.
  • fusion proteins e.g., Ig fusion proteins or Fc fusion proteins
  • multivalent binding proteins e.g., DVD Ig
  • TNFa inhibitor preferably includes agents which interfere with TNF-a activity.
  • TNF-a inhibitors include etanercept (ENBRELTM, Amgen), infliximab (REMICADETM, Johnson and Johnson), human anti-TNF monoclonal antibody adalimumab (D2E7/HUMIRATM, Abbott Laboratories), human anti-TNF monoclonal antibody golimumab (SIMPONI®, CNTO 148), ENTYVIO® (vedolizumab, Millennium Pharmaceuticals), STELARA® (ustekinumab, Janssen Biotech), CDP 571 (Celltech), and pegylated Fab' fragment of a humanized TNF inhibitor monoclonal antibody (certolizumab pegol (CIMZIA®, UCB, INC.), as well as other compounds which inhibit TNF-a activity, such that when administered to a subject suffering from or at risk of suffering from a disorder in which TNF-a activity is detrimental
  • predicting responsiveness to a TNFa inhibitor is intended to refer to an ability to assess the likelihood that treatment of a subject with a TNF inhibitor will or will not be effective in (e.g., provide a measurable benefit to) the subject.
  • an ability 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 measurable benefit) has been observed in the subject.
  • TNFa inhibitors or anti-TNFa drugs are biologic agents that have been approved by the FDA for use in humans in the treatment of rheumatoid arthritis, which agents include adalimumab (HUMIRA ® ), infliximab (REMICADE ® ), CIMZIA ® (certolizumab pegol), SIMPONI ® (golimumab), ENTYVIO ® (vedolizumab), STELARA ® (ustekinumab) and etanercept (E BREL ® ), most preferably nfliximab (REMICADE ® ).
  • HUMIRA ® adalimumab
  • REMICADE ® infliximab
  • CIMZIA ® certolizumab pegol
  • SIMPONI ® g., ENTYVIO ®
  • STELARA ® ustekinumab
  • E BREL ® eta
  • immunosuppressive agent includes any substance capable of producing an immunosuppressive effect, 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.
  • suitable immunomodulating agents include, without limitation, thiopurine drugs such as azathioprine (AZA) and metabolites thereof; anti-metabolites such as methotrexate (MTX); sirolimus (rapamycin); temsirolimus; everolimus; tacrolimus (FK- 506); FK-778; anti -lymphocyte globulin antibodies, anti-thymocyte globulin antibodies, anti- CD3 antibodies, anti-CD4 antibodies, and antibody-toxin conjugates; cyclosporine;
  • thiopurine drugs such as azathioprine (AZA) and metabolites thereof
  • anti-metabolites such as methotrexate (MTX); sirolimus (rapamycin); temsirolimus; everolimus; tacrolimus (FK- 506); FK-778; anti -lymphocyte globulin antibodies, anti-thymocyte globulin antibodies, anti- CD3 antibodies, anti-CD4 antibodies, and antibody
  • mycophenolate mizoribine monophosphate; scoparone; glatiramer acetate; metabolites thereof; pharmaceutically acceptable salts thereof; derivatives thereof; prodrugs thereof; and combinations thereof.
  • thiopurine drug includes azathioprine (AZA), 6-mercaptopurine (6-MP), or any metabolite thereof that has therapeutic efficacy and includes, without 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 triphosphate), 6-thioxanthosine nucleotides ⁇ e.g., 6-thioxanthosine monophosphate, 6-thioxanthosine diphosphate, 6-thioxanthosine triphosphate), derivatives thereof, analogues thereof, and combinations thereof.
  • AZA azathioprine
  • 6-MP 6-mercap
  • sample includes any biological specimen obtained from a patient.
  • 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 ⁇ i.e., feces), sputum, bronchial lavage fluid, tears, fine needle aspirate ⁇ e.g., harvested 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.
  • PBMC peripheral blood mononuclear cells
  • PMN polymorphonuclear
  • the sample is whole blood or a fractional component thereof such as plasma, serum, or a cell pellet.
  • the sample is obtained by isolating PBMCs and/or PMN cells using any technique known in the art.
  • the sample is a tissue biopsy, e.g., from a site of inflammation such as a portion of the gastrointestinal tract or synovial tissue.
  • histology includes microscopic study of cells and tissues from an individual. In some instances, histology of tissue can be used for the diagnosis or prognosis of a disease state including TNFa-associated diseases or disorders. An assessment of a patient can be performed to quantitative measure the microscopic architecture of a patient's tissue biopsy and to compare the measurement to a standardized grading system.
  • CDAI Crohn's Disease Activity Index
  • 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 weighting factors are the following:
  • 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 Crohn's disease patient is defined as a fall of the CDAI of greater than 70 points.
  • CDEIS Crohn's Disease Endoscopic Index of Severity
  • an anti-TNFa therapy should maintained or modified, for example, by escalating the drug dose or switching to another anti-TNFa drug, to improve therapeutic response.
  • method for predicting whether a patient will develop anti-drug antibodies (ADA) during the course of anti-TNFa drug therapy such as during induction therapy or maintenance therapy.
  • ADA anti-drug antibodies
  • the onset of an immunogenic response to anti-TNFa therapy e.g., the presence of ADA, can decrease serum drug levels, and in turn, reduce the patient's response to the drug.
  • the methods provided herein are used to determine if an anti- TNFa therapy should be maintained in an individual being administered an anti-TNFa drug or is recommended to be maintained on the anti-TNFa drug treatment regimen.
  • the method comprises: analyzing a sample obtained from the individual administered an anti-TNFa drug to determine the levels or concentrations of TNFa and anti-TNFa drug; and determining an anti-TNFa drug regimen based upon the levels or concentrations of TNFa and anti-TNFa drug. For example, in a first step the amount of anti-T Fa drug in ⁇ g/mL at 6 weeks (day 42) of an induction phase to form a value D is determined.
  • the method includes calculating a T/D ratio to categorize the individual into a drug regimen of Groups I-IV (Group I, II, III or IV). Methods for measuring the level or concentration (e.g., amount) of TNFa and anti-TNFa drug are described below.
  • the amount of anti-TNFa drug in ⁇ g/mL is determined during the induction phase of the treatment, e.g., at week 1, at week 2, at week 3, at week 4, at week 5, or at week 6 of the induction phase, to form a value D.
  • the amount of TNFa in pg/mL is determined during the induction phase of the treatment, e.g., at week 1, at week 2, at week 3, at week 4, at week 5, or at week 6 of the induction phase, to form a value T.
  • the amount of anti-TNFa drug in ⁇ g/ mL and/or the amount of TNFa in pg/mL is determined at week 6 (day 42) of the induction phase. In a preferred embodiment, the amount of anti-TNFa drug in ⁇ g/ml and/or the amount of TNFa in pg/mL is determined at week 2 (day 14) of the induction phase.
  • FIG. 1A shows that patients on anti-TNFa drug (e.g., infliximab) can be separated or categorized into Groups I-IV according to the patients' T/D ratio at day 42. As shown therein, if the T/D ratio is between about 0.25 and about 0.85, e.g.
  • the individual categorized into drug regimen Group I is monitored to determine differences in concentration time profiles (PK) of the amount of anti-TNFa drug and the amount of TNFa. Further, if the individual is categorized in Group I, then the individual is benefiting from anti-TNFa drug treatment. In some embodiments, the individual is maintained on the anti-TNFa drug treatment regimen such as infliximab or is recommended to be maintained on the anti-TNFa drug treatment regimen. In some embodiments, it is predicted that the patient is not ATI positive.
  • PK concentration time profiles
  • a patient on anti-TNFa drug can be separated or categorized into Group II.
  • the T/D ratio is between about 0.86 and about 1.5, e.g., 0.86, 0.87, 0.88, 0.89, 0.90, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1.0, 1.1, 1.2, 1.3, 1.4 or 1.5 at day 42, then the individual is categorized into drug regimen Group II.
  • the individual categorized into drug regimen Group II is monitored to determine differences in concentration time profiles (PK) of the amount of anti-T Fa drug and the amount of T Fa.
  • PK concentration time profiles
  • the individual is categorized in drug regimen Group II, then the individual is likely benefiting from the anti-TNFa drug treatment.
  • the individual is maintained on the anti-TNFa drug treatment regimen such as infliximab or is recommended to be maintained on the anti-TNFa drug treatment regimen.
  • Group II individuals are monitored such that their T/D ratio stays with the Group II limits.
  • FIG. 1 A shows that a patient on anti-TNFa drug can be separated or categorized into Group III. If the T/D ratio is between about 1.6 and about 6.0, e.g., 1.6, 1.7, 1.8, 1.9, 2.0,
  • the individual is categorized into drug regimen Group III.
  • the individual categorized into drug regimen Group III is monitored to determine differences in
  • concentration time profiles of the amount of anti-TNFa drug and the amount of TNFa.
  • PK concentration time profiles
  • the individual is administered an increased dose of anti-TNFa drug, such as 10 mg/kg if the initial dose was 5 mg/kg.
  • the individual has a high probability of having a complete or partial response upon receiving the increase dose.
  • Suitable drugs include anti-TNFa drugs include, but are not limited to ENBREL ® (etanercept), HUMIRA ® (adalimumab), CIMZIA ® (certolizumab pegol), SIMPONI ®
  • the individual is categorized into drug regimen Group IV.
  • the individual categorized into drug regimen Group IV is monitored to determine differences in concentration time profiles (PK) of the amount of anti-TNFa drug and the amount of TNFa.
  • PK concentration time profiles
  • the sample is analyzed for anti-drug antibody. The presence of anti-drug antibody indicates that the individual categorized into drug regimen Group IV should be switched to a different anti-T Fa drug.
  • the individual has a high probability (e.g., is likely) of having a complete or partial response upon switching to a different anti-TNFa drug.
  • the individual categorized into drug regimen Group IV is switched to a different anti-TNFa drug.
  • Suitable drugs include anti-TNFa drugs include, but are not limited to, ENBREL ® (etanercept), HUMIRA ® (adalimumab), CIMZIA ® (certolizumab pegol), SIMPONI ®
  • the methods provided herein are used to determine from a sample taken from a patient during the drug induction phase if the patient will develop antidrug antibodies (ADA) prior to developing ADA.
  • the sample can be taken 2 weeks after the beginning of the induction phase or prior to the second infusion during this phase.
  • the method comprises: determining the level or concentration of anti-TNFa drug (e.g.,
  • infliximab REMICADE ® (infliximab) and TNFa in the sample, and predicting if the patient will develop ADA or lose response to the anti-TNFa drug, if the ratio of TNFa to infliximab is greater than or equal to 0.3, e.g., 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, or more.
  • the TNFa/IFX ratio is greater than 0.4, e.g., 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, or more, then the patient will develop ADA or lose drug response. In other embodiments, if the TNFa/IFX ratio is greater than 0.5, e.g., 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, or more, then the patient will develop ADA or lose drug response.
  • the ratio of TNFa to infliximab is calculated as the amount of TNFa in pg/ml divided by the amount of infliximab in ⁇ g/ml.
  • the sample is taken 2 weeks (e.g., 14 days) after the first dosing of the induction phase. In other instances, the sample is taken between 5 days and 20 days, e.g., 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 11 days, 12 days, 13 days, 14 days, 15 days, 16 days, 17 days, 18 days, 19 days, or 20 days, from the beginning of the induction phase.
  • the TNFa to infliximab (e.g., T/D) ratio is determined between 5 days and 20 days, e.g., 5 days, 6 days, 7 days, 8 days, 9 days, 10 days, 11 days, 12 days, 13 days, 14 days, 15 days, 16 days, 17 days, 18 days, 19 days, or 20 days, from the beginning of the induction.
  • the ratio is determined prior to the second infusion, such as at day 14 from the beginning of the induction phase.
  • the first drug dosing is given at day 0 and the second dosing is given 14 days after.
  • a patient receiving infliximab is likely to be ATI positive, if the patient has a TNFa to infliximab ratio > 0.3. The patient can also exhibit a loss of drug response. In contrast, a patient with a TNFa to infliximab ratio ⁇ 0.3 is likely to be ATI negative. The patient may be responding to the drug.
  • the methods described herein that are directed to predicting a patient's therapeutic response to infliximab during the induction phase can be used as a model for other anti-TNFa drugs. For instance, the methods can be modified or adjusted, such that, the TNFa to drug ratios for Group I-IV are different or the days to obtain sample are changed.
  • the methods can predict that the patient will be ADA positive at a later time, e.g., at the end of the maintenance phase or at trough. [0090] In some embodiments, the methods predict whether a patient will develop ADA prior to developing ADA. In some embodiments, the method includes determining the level or concentration of anti-TNFa drug (e.g., infliximab) at 4 weeks after the beginning of the maintenance phase (at mid-infusion) in a sample from the patient. If the level or
  • concentration of anti-TNFa drug is 8 ⁇ g/ml or less, e.g., 7.9 ⁇ g/mL, 7.5 ⁇ g/mL, 7.0 ⁇ g/mL, 6.5 ⁇ g/mL, 6.0 ⁇ g/mL, 5.5 ⁇ g/mL, 5.0 ⁇ g/mL, 4.5 ⁇ g/mL, 4.0 ⁇ g/mL, 3.5 ⁇ g/mL, 3.0 ⁇ g/mL, 2.5 ⁇ g/mL, 2.0 ⁇ g/mL, 1.5 ⁇ g/mL, 1.0 ⁇ g/mL, 0.5 ⁇ g/mL, or less, it is predicted that the patient will develop anti-drug antibodies (ADA) by trough (8 weeks).
  • ADA anti-drug antibodies
  • the level or concentration of anti-TNFa drug is measured between about 60 days to about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase.
  • the level or concentration of anti-TNFa drug is measured between about 60 days to about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase.
  • the level or concentration of anti-TNFa drug is measured between about 60 days to about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase.
  • the level or concentration of anti-TNFa drug e.g., infliximab
  • concentration of anti-TNFa drug is measured between about 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, or about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase.
  • the patient will develop ADA during the maintenance phase at or before trough, e.g., week 8, week 7, week 6, week 5, week 4 after the beginning of maintenance.
  • the patient can become ADA positive at or after mid-infusion, e.g., at week 4, week 5, week 6, week 7 and week 8 after the beginning of maintenance.
  • a patient is predicted to develop ADA (e.g., ATI)
  • the patient can be switched to a different anti-T Fa drug.
  • suitable anti-TNFa drugs include ENBREL ® (etanercept), HUMIRA ® (adalimumab), CIMZIA ® (certolizumab pegol), SIMPONI ® (golimumab), ENTYVIO ® (vedolizumab), STELARA ® (ustekinumab), and combinations thereof.
  • the presence of ADA in the patient can be detected using standard methods described below.
  • the step of detecting ADA in a patient's sample is used to confirm the prediction.
  • the presence of ADA indicates disease relapse.
  • the methods predict whether a patient will develop antibodies to infliximab (ATI) prior to developing ATI.
  • the method comprises: measuring the levels or concentrations of TNFa (T) and infliximab (D) at about 4 weeks after the beginning of the maintenance phase (mid-infusion). If the T/D ratio is between 0 and 2, e.g. 0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0, it is predicted that the patient will develop ATI by trough (8 weeks) of the maintenance phase.
  • the level or concentration of infliximab is measured between about 60 days to about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase. In other instances, the level or concentration of infliximab is measured between about 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, or about 80 days from the beginning of the drug therapy, e.g., at the first dosing of the drug induction phase.
  • the presence of ATI in the patient can be detected using standard methods described below.
  • the prediction is confirmed by detecting ATI in the patient's sample.
  • the presence of ATI indicates disease relapse.
  • anti-TNFa drugs include ENBREL ® (etanercept), HUMIRA ® (adalimumab), CIMZIA ® (certolizumab pegol), SIMPONI ®
  • the patient may receive a new course of drug induction therapy.
  • the methods described herein for predicting the presence of ATI during the maintenance phase can be used as a template for other anti-TNFa drugs.
  • the methods can be modified or adjusted to determine whether the patient receiving an anti-TNFa drug other than infliximab has anti-drug antibodies to that drug.
  • the method comprises determining the presence and/or level of anti-TNFa drug (e.g., level of free anti-TNFa therapeutic antibody such as infliximab) and/or anti-drug antibody (ADA) (e.g., level of autoantibody to the anti-TNFa drug such as HACA) in a patient sample (e.g., a serum sample from a patient on anti-TNFa drug therapy) at multiple time points, e.g., before, during, and/or after the course of therapy.
  • anti-TNFa drug e.g., level of free anti-TNFa therapeutic antibody such as infliximab
  • ADA anti-drug antibody
  • HACA anti-drug antibody
  • the presence and/or level of anti-TNFa drug and/or ADA is determined with a homogeneous mobility shift assay (HMSA) using size exclusion chromatography.
  • HMSA homogeneous mobility shift assay
  • the methods are particularly useful for measuring the presence or level of TNFa inhibitors as well as autoantibodies (e.g., HACA, HAHA, etc.) that are generated against them.
  • autoantibodies e.g., HACA, HAHA, etc.
  • the presence and/or level of TNFa and/or biomarkers is determined with a CEERTM assay.
  • CEERTM Cold Enzyme Enhanced Reactive- immunoassay
  • an antibody-microarray based platform is utilized to form a unique "triple-antibody-enzyme-channeling" immuno-complex capable of measuring analytes of limited availability in a sample.
  • a CEERTM assay using an anti-TNFa drug e.g., infliximab (IFX), etanercept, adalimumab (ADL), certolizumab pegol, or golimumab
  • an anti-TNFa drug e.g., infliximab (IFX), etanercept, adalimumab (ADL), certolizumab pegol, or golimumab
  • the assays decribed can determine an analyte to less than 50 pg/mL, less than 25 pg/mL, less than 20 pg/mL, less than 10 pg/mL, less than 5 pg/mL, less 1 pg/mL or even less.
  • a detailed description of CEERTM is found in, e.g., U.S. Patent No. 8,163,499, which is hereby incorporated by reference in its entity for all purposes.
  • an immunoassay such as a sandwich assay or ELISA can be used to measure TNFa.
  • Non-limiting examples include Human TNF-a High Sensitivity ELISA (Cat. No. BMS223HS, eBioscience, San Diego, CA), Erenna Human TNFa immunoassay (Cat. No. 03-0022-xx, Singulex, Alameda, CA), Human TNFa cytokine assay (Cat. No. K151BHA-5, Meso Scale Diagnostics (MSD), Rockville, MD)) and a muli-marker immunoassay (e.g., as described in U.S. Patent No. 8,450,069; Singulex).
  • a muli-marker immunoassay e.g., as described in U.S. Patent No. 8,450,069; Singulex.
  • the assays decribed can determine an analyte to less than 50 pg/mL, less than 25 pg/mL, less than 20 pg/mL, less than 10 pg/mL, less than 5 pg/mL, less 1 pg/mL or even less.
  • the present invention provides pharmacokinetic models to predict the likelihood of developing anti-drug antibodies.
  • Pharmacokinetic models are ways to mathematically understand the fate of drugs in vivo.
  • the drug-concentration time profile shows a monophasic response, and is described by a single exponential.
  • the body is assumed to be a homogeneous unit with instantaneous distribution of the drug.
  • a one-compartment model shows a linear relationship between log concentrations in plasma (C p ) versus time.
  • a two-compartment model resolves the body into two units, a central unit and a peripheral unit.
  • the log concentration in plasma (C p ) versus time profile is biphasic.
  • C p log concentration in plasma
  • the biphasic model there is a rapid decline in drug concentration followed by a slower decline.
  • the present invention provides an algorithmic model to predict patient response to anti-TNFa drugs.
  • the model uses one or more markers such as an inflammatory marker which include cytokines and chemokines and the like, a signaling molecule, an acute phase protein, a cellular adhesion molecule and a combination thereof.
  • the markers also include the presence or absence of ADA, the levels of TNFa, the concentration or levels of anti-TNFa drugs and the like.
  • An algorithmic model includes any of a variety of statistical methods and models used to determine relationships between variables.
  • the variables are the presence or level of at least one marker of interest. Any number of markers can be analyzed using a statistical analysis described herein (see, "Statistical Analysis” section). For example, the presence or level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, or more markers can be included in a statistical analysis.
  • quantile analysis is applied to the presence and/or level of one or more markers to guide treatment decisions for patients receiving anti-TNFa drug therapy.
  • one or a combination of two of more learning statistical classifier systems are applied to the presence and/or level of one or more markers to guide treatment decisions for patients receiving anti-TNFa drug therapy.
  • the statistical analyses of the methods of the present invention advantageously assist in determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-TNFa drug, to combine an anti-TNFa 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 current course of therapy (e.g., switch to a different anti-TNF drug).
  • immunosuppressive agents such as methotrexate (MTX) or azathioprine (AZA)
  • the algorithmic model includes the level or concentration of the one or more markers along with a statistic algorithm such as a learning statistical algorithm.
  • a statistic algorithm such as a learning statistical algorithm.
  • the model has been trained with known outcomes using a training set cohort of samples.
  • the algorithm is then validated using a validation cohort. Patient unknown samples can then be predicted based on the trained algorithms.
  • 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 therapy, reducing toxicity, and/or monitoring the efficacy of therapeutic treatment with one or more therapeutic agents such as biologies (e.g., anti-TNFa drugs).
  • biologies e.g., anti-TNFa drugs
  • the methods described herein utilize one or more predictor variables to aid or assist in predicting disease course, selecting an appropriate anti-TNFa drug therapy, optimizing anti-TNFa drug therapy, reducing toxicity associated with anti-TNFa drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-TNFa drug.
  • Non-limiting examples of inflammatory markers include cytokines, chemokines, signal transduction molecule, growth factors, acute phase proteins, cellular adhesion molecules, SI 00 proteins, and/or other inflammatory markers.
  • the inflammatory markers comprise at least one signal transduction molecule, at least one cytokine and at least one acute phase protein.
  • the inflammatory markers comprise at least one signal transduction molecule, at least one cytokine and at least one acute phase protein.
  • inflammatory markers comprise EGF, bFGF, sFltl, VEGF, CM-CSF, IL-2, IL-6, IL-8 and CRP.
  • the treat to target protocol includes other markers and statistical algorithms, such as TNFa response models.
  • cytokine includes any of a variety of polypeptides or proteins secreted by immune cells that regulate a range of immune system functions and encompasses small cytokines such as chemokines.
  • cytokine also includes adipocytokines, which comprise a group of cytokines secreted by adipocytes that function, for example, in the regulation of body weight, hematopoiesis, angiogenesis, wound healing, insulin resistance, the immune response, and the inflammatory response.
  • the presence or level of at least one cytokine including, but not limited to, granulocyte-macrophage colony-stimulating factor (GM-CSF), IFN- ⁇ , IL- ⁇ , IL-2, IL-6, IL-8, TNF-a, soluble tumor necrosis factor-a receptor II (sTNF RII), TNF-related weak inducer of apoptosis (TWEAK), osteoprotegerin (OPG), IFN-a, IFN- ⁇ , IL-la, IL-1 receptor antagonist (IL-lra), IL-4, IL-5, soluble IL-6 receptor (sIL-6R), IL-7, IL-9, IL-12, IL- 13, IL-15, IL-17, IL-23, and IL-27 is determined in a sample.
  • GM-CSF granulocyte-macrophage colony-stimulating factor
  • IFN- ⁇ soluble tumor necrosis factor-a receptor II
  • the presence or level of at least one chemokine such as, for example, CXCLl/GROl/GROa, CXCL2/GR02, CXCL3/GR03, CXCL4/PF-4, CXCL5/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG, CXCLlO/IP-10,
  • CXCLl l/I-TAC CXCL12/SDF-1, CXCL13/BCA-1, CXCL 14/BRAK, CXCL15, CXCL16, CXCL17/DMC, CCL1, CCL2/MCP-1, CCL3/MIP-la, CCL4/MIP-lp, CCL5/RANTES, CCL6/C10, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10, CCL11/Eotaxin, CCL12/MCP-5, CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5, CCL16/LEC, CCL17/TARC, CCL18/MIP- 4, CCL19/MIP-3p, CCL20/MIP-3a, CCL21/SLC, CCL22/MDC, CCL23/MPIF1,
  • CCL24/Eotaxin-2, CCL25/TECK, CCL26/Eotaxin-3, CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX3CLI is determined in a sample.
  • the presence or level of at least one adipocytokine including, but not limited to, leptin, adiponectin, resistin, active or total plasminogen activator inhibitor- 1 (PAI-1), visfatin, and retinol binding protein 4 (RBP4) is determined in a sample.
  • the presence or level of GM-CSF, IFN- ⁇ , IL- ⁇ , IL-2, IL-6, IL-8, TNF-a, sTNF RII, and/or other cytokines or chemokines is determined.
  • the presence or level of a particular cytokine or chemokine is detected at the level of mRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay.
  • an assay such as, for example, a hybridization assay or an amplification-based assay.
  • 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.
  • 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.
  • the human TNFa polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_000585.
  • the human TNFa mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_013693.
  • TNFa is also known as tumor necrosis factor, cachectin, TNF-alpha, tumor necrosis factor ligand superfamily member 2 and TNF-a.
  • the human GM-CSF polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_000749.
  • the human GM-CSF mRNA (coding) sequence is set forth in, e.g.,
  • GM-CSF is also known as granulocyte-macrophage colony-stimulating factor, colony-stimulating factor, CSF, molgramostin and sargramostin.
  • the human IL- ⁇ polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000567.
  • the human IL- ⁇ mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000576.
  • IL- ⁇ is also known as interleukin- 1 beta, IL-1 beta and catabolin.
  • the human IL-2 polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000577.
  • the human IL-12 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000586.
  • IL-2 is also known as IL2, interleukin-2, T-cell growth factor, TCGF, and aldesleukin.
  • IL-2 polypeptide can be detected in a precursor form or mature form.
  • the human IL-6 polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000591.
  • the human IL-6 mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM 000600.
  • IL-6 is also known as interferon beta 2, IFNB2, HGF, HSF, and BSF2.
  • the human IL-8 polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000575.
  • the human IL-8 mRNA (coding) sequence is set forth in, e.g., Genbank
  • IL-8 is also known as CXCL8, K60, NAF, GCP1, LECT, LUCT, NAP1, 3-10C, GCP-1, LYNAP, MDNCF, MONAP, NAP-1, SCYB8, TSG-1, AMCF-I, and b-ENAP.
  • the human IL-10 polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000563.
  • the human IL-10 mRNA (coding) sequence is set forth in, e.g., Genbank
  • IL-10 is also known as interleukin 10, IL10, cytokine synthesis inhibitory factor, CSIF, IL10A, TGIF, T-cell growth inhibitory factor, and GVHDS.
  • the human IL12p70 polypeptide is a heterodimeric cytokine comprising an IL12 subunit a (IL 12a or IL 12 A) and an IL- 12 subunit ⁇ (IL 12 ⁇ or IL 12B).
  • the subunits are encoded by two genes, IL12 subunit a (p35) and IL-12 subunit ⁇ ( (p40).
  • the human IL12a subunit polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 000873.
  • the human IL12a mRNA (coding) sequence is set forth in, e.g., Genbank Accession No.
  • IL12a is also known as interleukin 12 A, IL-12A, IL-12a, NKSF1, CLMF, NFSK, cytotoxic lymphocyte maturation factor 35 kDa subunit, CLMF p35, p35, IL35 subunit, natural killer cell stimulatory factor 1 (35kD subunit) and NF cell stimulatory factor chain 1.
  • the human IL12P subunit polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_002178.
  • the human IL12P mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_002187.
  • IL12 ⁇ is also known as interleukin 12B, IL-12B, IL-12p, NKSF2, CLMF2, NFSK, cytotoxic lymphocyte maturation factor 40 kDa subunit, CLMF p40, p40, IL40 subunit, natural killer cell stimulatory factor 2 (40kD subunit) and NF cell stimulatory factor chain 2.
  • the human IFN- ⁇ polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_000610.
  • the human IFN- ⁇ mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000619.
  • IFN- ⁇ is also known as interferon gamma and IFN-gamma.
  • the human sTNRII polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_.
  • the human sTNRII mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_.
  • sTNRII is also known as tumor necrosis factor receptor superfamily member IB, tumor necrosis factor receptor 2
  • Acute-phase proteins are a class of proteins whose plasma concentrations increase (positive acute-phase proteins) or decrease (negative acute-phase proteins) in response to inflammation. This response is called the acute-phase reaction (also called acute-phase response).
  • positive acute-phase proteins include, but are not limited to, C-reactive protein (CRP), D-dimer protein, mannose-binding protein, alpha 1 -antitrypsin, alpha 1-antichymotiypsin, alpha 2-macroglobulin, fibrinogen, 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.
  • the presence or level of CRP and/or SAA is determined.
  • the presence or level of a particular acute-phase protein is detected at the level of mRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay.
  • an assay such as, for example, a hybridization assay or an amplification-based assay.
  • the presence 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.
  • an immunoassay e.g., ELISA
  • an immunohistochemical assay e.g., a sandwich colorimetric ELISA assay available from Alpco Diagnostics (Salem, NH) can be used to determine the level of CRP in a serum, plasma, urine, or stool sample.
  • an ELISA kit available 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., U.S. Patent Nos. 6,838,250 and 6,406,862; and U.S. Patent Publication Nos.
  • Additional methods for determining CRP levels include, e.g., immunoturbidimetry assays, rapid immunodiffusion assays, and visual agglutination assays.
  • Suitable ELISA kits for determining the presence or level of SAA in a sample such as serum, plasma, saliva, urine, or stool are available from, e.g., Antigenix America Inc. (Huntington Station, NY), Abazyme (Needham, MA), USCN Life (Missouri City, TX), and/or U.S. Biological (Swampscott, MA).
  • CRP C-reactive protein
  • 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 proteins are predominantly produced by the liver. The conservation of these proteins throughout 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 sequence is set forth in, e.g., Genbank Accession No. NP_000322.
  • the human SAA mRNA (coding) sequence is set forth in, e.g. , Genbank Accession No. NM_000331.
  • SAA is also known as PIG4, TP53I4, MGC111216, and SAA1.
  • immunoglobulin superfamily cellular adhesion molecule 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 function in intercellular adhesion and/or signal transduction. In many cases, IgSF CAMs are transmembrane proteins.
  • IgSF CAMs include Mucosal addressin cell adhesion molecule l(MADCAMl), Neural Cell Adhesion Molecules (NCAMs; e.g., NCAM-120, NCAM-125, NCAM-140, NCAM-145, NCAM-180, NCAM-185, etc.), Intercellular Adhesion Molecules (ICAMs, e.g., ICAM-1, ICAM-2, ICAM-3, ICAM-4, and ICAM-5), Vascular Cell Adhesion Molecule-1 (VCAM-1), Platelet-Endothelial Cell Adhesion Molecule-1 (PECAM-1), LI Cell Adhesion Molecule (LI CAM), cell adhesion molecule with homology to LI CAM (close homolog of LI) (CHL1), sialic acid binding Ig-like lectins (SIGLECs; e.g., SIGLEC-1, SIGLEC-2, SIGLEC-3, SIGLEC-4, etc.
  • cell adhesion molecules that are useful in the present invention include, but are not limited to integrins (e.g., CD49a, CD49b, CD49c, CD49d, CD49e, CD49f, CD 103, CDl la, CDl lb, CDl lc, CDl ld, CD51, CD41, integrin a4, integrin a7, integrin a8, integrin a9, integrin alO, integrin al l, CD29, CD18, CD61, CD104, integrin ⁇ 5, integrin ⁇ 6, integrin ⁇ 7, and integrin ⁇ 8), cadherins (e.g., E-cadherins, P-cadherins, N-cadherins, R-cadherins, B- cadherins, T-cadherins, and M-cadherins), selectins (e.g., E-selectin, L-s
  • the presence or level of a cell adhesion molecule is detected at the level of mRNA expression with an assay such as, for example, a hybridization assay or an amplification-based assay.
  • an assay such as, for example, a hybridization assay or an amplification-based assay.
  • the presence or level of a cell adhesion molecule 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 MADCAMl, ICAMl, VCAMl, integrin a4, integrin ⁇ 7 and/or ⁇ 4 ⁇ 7 integrin (LP AM) in a sample such as a tissue sample, biopsy, serum, plasma, saliva, urine, or stool are available from, e.g., Invitrogen (Camarillo, CA), Santa Cruz Biotechnology, Inc. (Santa Cruz, CA), and/or Abeam Inc. (Cambridge, MA).
  • the human MADCAMl polypeptide sequence is set forth in, e.g., Genbank
  • MADCAMl is also known as addressin, mucosal vascular addressin cell adhesion molecule 1, MAdCAM-1, and hMAdCAM-1.
  • the human ICAMl polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_000192.
  • the human ICAMl mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_000201.
  • ICAMl is also known as intercellular adhesion molecule 1, BB2, CD54, major group rhinovirus receptor, ICAM-1, P3.58, cell surface glycoprotein P3.58, intercellular adhesion molecule 1 (CD54), CD54 antigen, and cluster of differentiation 54.
  • the human VCAMl polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_001069.
  • the human VCAMl mRNA (coding) sequence is set forth in, e.g.,
  • VCAMl is also known as VCAM-1, V-CAM1, INCAM-100, CD antigen 106, cluster of differentiation 106, and CD 106.
  • the human integrin ⁇ 7 polypeptide sequence is set forth in, e.g., Genbank
  • integrin ⁇ 7 is also known as integrin b7, integrin beta-7, ITGB7 and gut homing receptor beta subunit.
  • the human integrin a4 polypeptide sequence is set forth in, e.g., Genbank
  • integrin a4 is also known as ITGA4, CD49D, alpha 4 subunit of VLA-4 receptor, integrin alpha-IV, CD49 antigen-like family member D, and integrin alpha 4. 5. Signaling Molecules
  • the methods described herein utilize the detection of 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 drug response, selecting an appropriate anti-TNFa drug therapy, optimizing anti-TNFa drug therapy, reducing toxicity associated with anti-TNFa drug therapy, and/or monitoring the efficacy of therapeutic treatment with an anti-TNFa drug.
  • the total (e.g., expression) level of one or more signaling molecules in one or more signaling pathways is measured.
  • signaling molecule includes proteins and other molecules that acts as an extracellular signal or stimulus to a cell, typically by forming a complex with its cognate receptor on the surface of the cell.
  • signaling molecules include, but are not limited to, growth factors (e.g., EGF, bFGF, VEGF, sFlt, PIGF-1), hormones,
  • the human EGF polypeptide sequence is set forth in, e.g., Genbank Accession No. NP_00171601 , NP_001171602, and NP_001954.
  • the human EGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001178130, NM_001178131, and NM 001963.
  • EGF is also known as epidermal growth factor, HOMG4, URG, beta-urogastrone, and pro-epidermal growth factor.
  • the human bFGF polypeptide sequence is set forth in, e.g., Genbank Accession No. NP 001997.
  • the human bFGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_002006.
  • bFGF is also known as basic FGF, basic fibroblast growth factor, FGF2, FGFB, heparin-binding growth factor 2, HBGF-2, and prostatropin.
  • the human PIGF polypeptide sequence is set forth in, e.g., Genbank Accession No. NP OOl 193941.
  • the human PIGF mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001207012.
  • PIGF is also known as placental growth factor, PGF, PLGF, PGFL, D12S1900, PIGF-2, and placental growth factor-like.
  • the human sFltl polypeptide sequence is set forth in, e.g., Genbank Accession No. NP OOl 153392.
  • the human sFltl mRNA (coding) sequence is set forth in, e.g., Genbank Accession No. NM_001 159920.
  • sFltl is also known as soluble isoform of fms-related tyrosine kinase 1, fms-related tyrosine kinase 1 transcript variant 2, soluble FLT, sVEGFR-1, sVEGFRl, sFRT, sFlt-1, VEGFR1 isoform 2, and vascular endothelial growth factor receptor 1 isoform 2.
  • sFltl is a splice variant of Fit 1.
  • the human VEGF polypeptide sequence is set forth in, e.g., Genbank Accession No.
  • VEGF is also known as vascular endothelial growth factor, VEGF 1 , VEGF- A, VEGF A, VPF, vascular permeability factor, and MVCD 1.
  • the present invention provides methods for selecting anti-TNFa drug therapy, optimizing anti-TNFa drug therapy, reducing toxicity associated with anti- TNFa drug therapy, and/or monitoring the efficacy of anti-TNFa drug treatment by applying one or more statistical algorithm to one or more (e.g., a combination of two, three, four, five, six, seven, or more) pharmacodynamic and/or inflammatory markers.
  • quantile analysis is applied to the presence and/or level of one or more markers to guide treatment decisions for patients receiving anti-TNFa drug therapy.
  • one or a combination of two of more learning statistical classifier systems are applied to the presence and/or level of one or more markers to guide treatment decisions for patients receiving anti-TNFa drug therapy.
  • the statistical analyses of the methods of the present invention advantageously assist in determining when or how to adjust or modify (e.g., increase or decrease) the subsequent dose of an anti-TNFa drug, to combine an anti-TNFa 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 current course of therapy (e.g., switch to a different anti-TNF drug).
  • MTX methotrexate
  • AZA azathioprine
  • the term "statistical analysis” or “statistical algorithm” or “statistical process” includes any of a variety of statistical methods and models used to determine relationships between variables.
  • the variables are the presence or level of at least one marker of interest. Any number of markers can be analyzed using a statistical analysis described herein. For example, the presence or level of 1, 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, 55, 60, or more markers can be included in a statistical analysis.
  • logistic regression is used.
  • linear regression is used.
  • ordinary least squares regression or unconditional logistic regression is used.
  • 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.
  • Quintiles 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 percentile 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).
  • percentile ranges of marker levels e.g., tertiles, quartile, quintiles, etc.
  • cumulative indices e.g., quartile sums of marker levels to obtain quartile sum scores (QSS), etc.
  • 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.
  • level e.g., magnitude
  • genotype of one or more markers of interest
  • the level of a marker of interest is defined as being in the first quartile ( ⁇ 25%), second quartile (25-50%), third quartile (51%- ⁇ 75%), or fourth quartile (75-100%) in relation to a reference database of samples.
  • quartiles may be assigned a quartile score of 1, 2, 3, and 4, respectively.
  • 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.
  • quartile 1 represents samples with the lowest marker levels
  • quartile 4 represent samples with the highest marker levels.
  • quartile 1 represents samples with a particular marker genotype (e.g., wild- type allele), while quartile 4 represent samples with another particular marker genotype (e.g., allelic variant).
  • the reference database of samples can include a large spectrum of patients with a TNFa-mediated disease or disorder such as, e.g., IBD.
  • quartile cut-offs can be established.
  • a non-limiting example of quartile analysis suitable for use in the present invention is described in, e.g., Mow et al, Gastroenterology, 126:414-24 (2004).
  • the statistical analyses of the present invention comprise one or more learning statistical classifier systems.
  • learning statistical classifier system includes a machine learning algorithmic technique capable of adapting to complex data sets ⁇ e.g., panel of markers of interest) and making decisions based upon such data sets.
  • 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.
  • RF random forest
  • C&RT classification and regression tree
  • a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem.
  • Examples of learning statistical classifier systems include, 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), network structures, the Cox Proportional-Hazards Model (CPHM), perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning ⁇ e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action-value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming.
  • inductive learning e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.),
  • learning statistical classifier systems include support vector machines ⁇ e.g., Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss- Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ).
  • Kernel methods multivariate adaptive regression splines (MARS)
  • Levenberg-Marquardt algorithms Gauss- Newton algorithms
  • Gauss- Newton algorithms mixtures of Gaussians
  • gradient descent algorithms gradient descent algorithms
  • LVQ learning vector quantization
  • 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 individual decision trees and decide the class by choosing the mode ⁇ i.e., most frequently occurring) of the classes as determined by the individual trees. Random forest analysis can be performed, e.g., using the RandomForests software available from Salford Systems (San Diego, CA). See, e.g., Breiman, Machine Learning, 45:5-32 (2001); and http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm, for a description of random forests.
  • Classification and regression trees represent 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
  • 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.
  • neural networks are adaptive systems that change their structure based on external or internal information that flows through the network.
  • Specific examples of neural networks include feed-forward neural networks such as perceptrons, single-layer perceptrons, multi-layer perceptrons, backpropagation networks, AD ALINE networks, MAD ALINE networks, Learnmatrix networks, radial basis function (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
  • feed-forward neural networks such as perceptrons, single-layer perceptrons, multi-layer perceptrons, backpropagation networks, AD ALINE networks, MAD ALINE networks, Learnmatrix networks, radial basis function (RBF) networks, and
  • Neural network 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 Publishing Company (1991); Zadeh, Information and Control, 8:338-353 (1965); Zadeh, “IEEE Trans, on Systems, Man and Cybernetics," 3 :28-44 (1973); Gersho et al., In “Vector Quantization and Signal Compression,” Kluywer Academic Publishers, Boston, Dordrecht, London (1992); and Hassoun, “Fundamentals of Artificial Neural Networks,” MIT Press, Cambridge, Massachusetts, London (1995), for a description of neural networks.
  • Support vector machines are a set of related supervised learning techniques used for classification and regression and are described, e.g.,
  • Support vector machine analysis can be performed, e.g., using the SVM l ' 8ht software developed by Thorsten Joachims (Cornell University) or using the
  • 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 TNFa-mediated disease or disorder such as, e.g., IBD (e.g., CD and/or UC) or rheumatoid arthritis.
  • samples e.g., serological and/or genomic samples
  • IBD e.g., CD and/or UC
  • rheumatoid arthritis e.g., rheumatoid arthritis.
  • 6,218, 129 are suitable for use in training and testing the statistical methods and models of the present invention.
  • Samples from patients diagnosed with IBD can also be stratified into
  • 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.
  • Example 1 Method for predicting ATI status during the IFX induction therapy.
  • This example illustrates a method for determining whether a patient receiving anti- TNFa drug therapy (e.g., infliximab or IFX) will develop anti-drug antibodies (e.g., antibodies to infliximab or ATI).
  • anti-drug antibodies e.g., antibodies to infliximab or ATI.
  • pharmacokinetic models were generated from data of 20 patients receiving IFX.
  • the analysis established a relationship between the presence of ATI and the levels of TNFa and IFX and determined that the ratio of TNFa to IFX is indicative of a therapeutic response to induction therapy.
  • these patients received a standard dosing regimen for infliximab.
  • a 5 mg IFX per kg body weight dose was administered at weeks 0, 2 and 6 during the induction regimen, followed by the maintenance treatment of 5 mg/kg doses every 8 weeks. Samples were taken from the patients throughout the induction treatment, such as at day 0, day 1, day 2, day 4, day 7, day 1 1, day 14 before dosing, day 14 after dosing, day 18, day 21, day 28, day 34, and day 42 before dosing.
  • the patients were categorized into 4 groups (e.g., Groups I- IV) according to their TNFa/ IFX ratios at week 6 (day 42) of the induction regimen.
  • Group I Patients with a ratio between about 0.25 and about 0.85 were defined as Group I; patients with a ratio between about 0.86 and about 1.5 were defined as Group II; patients with a ratio between about 1.6 and about 6.0 were defined as Group III; and patient with substantially no detectable IFX are placed in Group IV.
  • Group I and 2 patients had low TNFa/ IFX ratios and higher IFX levels compared to those in Group III (FIGS. 1 A and IB). TNFa levels were higher and IFX levels were lower in Group III patients versus Groups I and II (FIG. 1C). The data is presented in FIGS. 1C and 8.
  • IFX The median level of IFX was 13.21 ⁇ 1.82 ⁇ g/ml for Groups I and II, 6.28 ⁇ 2.76 ⁇ g/ml for Group III, and not detectable for Group IV (FIG. ID).
  • Patient K06 of Group I had 5.34 pg/mL TNFa, 1 1.92 ⁇ g/mL IFX and a TNFa/ IFX ratio of 0.45 (FIG. 1C).
  • Pharmacokinetic analysis shows that IFX and TNFa levels negatively correlated during the course of therapy (FIG. 9A). Analysis of drug half- life after the first dosing (from day 0 to week 2 of the induction regimen; FIG.
  • FIG. 9B shows that the patient has benefited from (e.g., positively responded to) IFX therapy.
  • the patient' s TNFa/ IFX ratio was ⁇ 0.45 at the end of the induction period (Table 1 and FIG. 13 A).
  • Table 1 and FIG. 13 A Table 1.
  • Patient KOI was categorized as Group II because at day 42, the patient's TNFa/ IFX ratio was 0.97. Comparison of TNFa and IFX levels during induction treatment shows a negative correlation between the levels (FIG. 10A). Drug half-life analysis during the first dosing period (day 1 to day 15; FIG. 10B) and the second dosing period (day 15 to day 42; FIG. IOC) shows that this patient responded to IFX therapy (Table 2 and FIG. 13B).
  • Patient K20 is representative of patients in Group III.
  • the level of TNFa increased after the second IFX dosing to a high at day 42 (FIG. 11 A).
  • Analysis of IFX levels shows that fast clearance of the drug after each dosing (FIG. 1 IB and 11C). It was predicted that patient K20 may relapse and requires further monitoring (Table 3 and FIG. 13C).
  • the data shows that TNFa/ IFX ratios at day 42 after the start of induction therapy can be used to predict whether a patient is responding to IFX.
  • the ratios can be used to categorize patient into 4 groups: Groups I and II are predicted to be responders, Group III to be partial responders, and Group IV to be non-responders.
  • the results also demonstrate that patients with a ratio of ⁇ 2 are likely to be responding to the drug therapy and those with a ratio > 2 are predicted to be non-responsive.
  • the data shows that T Fa/ IFX ratios at day 14 or 15 after the start of induction (before the second infusion) can be predictive of IFX response (Table 5). For example, if the cut-off is set at 0.5, then subjects with TNFa/IFX ⁇ 0.5 are predicted to be responders to IFX. The data shows that at this cut-off, there were 6 true positives, 1 false positive and 4 false negatives. If the cut-off is set at 0.4, the data set contained 8 true positives, 3 false positives and 2 false negatives.
  • TNF/IFX ratio is set to 10.
  • R response.
  • NR non-response.
  • This example illustrates a method for determining whether a patient receiving anti- TNFa drug maintenance therapy (e.g., infliximab or IFX) will develop anti-drug antibodies (e.g., antibodies to infliximab or ATI).
  • the prediction is based, in part, on analyzing a sample from the patient at week 4 of the treatment regimen.
  • Patients in this study received infliximab maintenance therapy (5 mg/kg body weight; intravenous infusion; every 8 weeks). Samples were taken at mid-infusion, e.g., at week 4 of the therapy cycle) and at trough, e.g., at week 8 of the therapy cycle. Levels of IFX and TNFa, as well as the presence or absence of ATI were detected in the sample.
  • FIG. 6 provides a table of the data presented in the figures herein, e.g., FIGS. 2A-D.
  • FIG. 4A represents a table of the subjects who have an IFX level ⁇ 8 ⁇ g/ml and who are predicted to relapse at a later time point during IFX maintenance treatment.
  • TNFa levels at mid-infusion shows that the levels are not significantly different between ATI+ and ATI- subjects (FIG. 5 A), yet they are different at trough (FIG. 5B).
  • the TNFa/IFX ratio at mid-infusion (FIG. 5C) and at trough (FIG. 5D) were different between ATI- and ATI- subjects.
  • ATI- subjects with an IFX > 8 ⁇ g/ml had a lower TNFa/IFX ratio at mid-infusion and at trough, compared to those with IFX ⁇ 8 ⁇ .
  • the TNFa/ IFX ratio at mid-infusion can predict whether a subject will be ATI+ at trough (e.g., at week 8 of maintenance therapy). For instance, a patient with a ratio ⁇ 2 is likely to be ATI negative at trough and/or respond to the maintenance therapy. In contrast, a patient with a ratio of > 2 is predicted to be ATI positive at trough. Furthermore, the level of IFX in a subject at mid-infusion can be used to determine if the subject will be ATI positive at trough.

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Abstract

L'invention concerne des procédés permettant de déterminer si un régime de traitement de médicament anti-TNFα est efficace sur le plan thérapeutique pour des individus recevant un médicament anti-TNFα. L'invention concerne également des procédés permettant de prédire si l'individu va développer des anticorps anti-médicaments pendant une thérapie d'induction ou une thérapie de maintien.
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