Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis

BMC Bioinformatics

Brian J Schmidt¹  Fergal P Casey¹  Thomas Paterson¹  Jason R Chan¹ 

[1] Entelos Holding Corporation, 2121 South El Camino Real, Suite 600, San Mateo, CA 94403, USA

关键词: Personalized medicine;    development;    Pharmaceutical research &;    Biomarker;    Patient population;    Biosimulation;    Mechanistic modeling;    Drug development;   

Others  :  1087815
DOI  :  10.1186/1471-2105-14-221

 received in 2012-12-27, accepted in 2013-07-05,  发布年份 2013

【 摘 要 】

Background

Mechanistic biosimulation can be used in drug development to form testable hypotheses, develop predictions of efficacy before clinical trial results are available, and elucidate clinical response to therapy. However, there is a lack of tools to simultaneously (1) calibrate the prevalence of mechanistically distinct, large sets of virtual patients so their simulated responses statistically match phenotypic variability reported in published clinical trial outcomes, and (2) explore alternate hypotheses of those prevalence weightings to reflect underlying uncertainty in population biology. Here, we report the development of an algorithm, MAPEL (Mechanistic Axes Population Ensemble Linkage), which utilizes a mechanistically-based weighting method to match clinical trial statistics. MAPEL is the first algorithm for developing weighted virtual populations based on biosimulation results that enables the rapid development of an ensemble of alternate virtual population hypotheses, each validated by a composite goodness-of-fit criterion.

Results

Virtual patient cohort mechanistic biosimulation results were successfully calibrated with an acceptable composite goodness-of-fit to clinical populations across multiple therapeutic interventions. The resulting virtual populations were employed to investigate the mechanistic underpinnings of variations in the response to rituximab. A comparison between virtual populations with a strong or weak American College of Rheumatology (ACR) score in response to rituximab suggested that interferon β (IFNβ) was an important mechanistic contributor to the disease state, a signature that has previously been identified though the underlying mechanisms remain unclear. Sensitivity analysis elucidated key anti-inflammatory properties of IFNβ that modulated the pathophysiologic state, consistent with the observed prognostic correlation of baseline type I interferon measurements with clinical response. Specifically, the effects of IFNβ on proliferation of fibroblast-like synoviocytes and interleukin-10 synthesis in macrophages each partially counteract reductions in synovial inflammation imparted by rituximab. A multianalyte biomarker panel predictive for virtual population therapeutic responses suggested population dependencies on B cell-dependent mediators as well as additional markers implicating fibroblast-like synoviocytes.

Conclusions

The results illustrate how the MAPEL algorithm can leverage knowledge of cellular and molecular function through biosimulation to propose clear mechanistic hypotheses for differences in clinical populations. Furthermore, MAPEL facilitates the development of multianalyte biomarkers prognostic of patient responses in silico.

【 授权许可】

   
2013 Schmidt et al.; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]Firestein GS: Evolving concepts of rheumatoid arthritis. Nature 2003, 423:356-361.
• [2]Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, Birnbaum NS, Burmester GR, Bykerk VP, Cohen MD, Combe B, Costenbader KH, Dougados M, Emery P, Ferraccioli G, Hazes JMW, Hobbs K, Huizinga TWJ, Kavanaugh A, Kay J, Kvien TK, Laing T, Mease P, Ménard HA, Moreland LW, Naden RL, Pincus T, Smolen JS, Stanislawska-Biernat E, Symmons D, et al.: Rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Arthritis & rheum 2010, 62:2569-81.
• [3]McInnes IB, Schett G: Cytokines in the pathogenesis of rheumatoid arthritis. Nat Rev Immunol 2007, 7:429-442.
• [4]Felson D, Anderson J, Boers M: The American College of Rheumatology preliminary core set of disease activity measures for rheumatoid arthritis clinical trials. Arthritis & rheum 1993, 36:729-740.
• [5]Anderson JJ, Wells G, Verhoeven AC, Felson DT: Factors predicting response to treatment in rheumatoid arthritis: the importance of disease duration. Arthritis & rheum 2000, 43:22-29.
• [6]Lipsky PE, van der Heijde D, St Clair E, Furst D, Breedveld F, Kalden J, Smolen J, Weisman M, Emery P, Feldmann M, Harriman G, Maini R: Infliximab and methotrexate in the treatment of rheumatoid arthritis. NEJM 2000, 343:1594-1602.
• [7]Brennan FM, Mcinnes IB: Evidence that cytokines play a role in rheumatoid arthritis. Cytokine 2008, 118:3537-3545.
• [8]Pammolli F, Magazzini L, Riccaboni M: The productivity crisis in pharmaceutical R&D. Nat Rev Drug Discovery 2011, 10:428-438.
• [9]Demin I, Hamrén B, Luttringer O, Pillai G, Jung T: Longitudinal model-based meta-analysis in rheumatoid arthritis: an application toward model-based drug development. J Clin Pharm Ther 2012, 92:352-359.
• [10]Schmidt BJ, Papin JA, Musante CJ: Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today 2013, 18:116-127.
• [11]Sheiner L, Wakefield J: Population modelling in drug development. Stat Methods Med Res 1999, 8:183-193.
• [12]Powell LM, Lo A, Cole MS, Trimmer J: Application of Predictive Biosimulation to the Study of Atherosclerosis: Development of the Cardiovascular PhysioLab Platform and Evaluation of CETP Inhibitor Therapy. Stuttgart, Germany: Proceedings of the FOSBE; 2007:295-302.
• [13]Kansal AR, Trimmer J: Application of predictive biosimulation within pharmaceutical clinical development : examples of significance for translational medicine and clinical trial design. IEE Proc-Syst Biol 2005, 152:214-220.
• [14]Klinke DJ: Integrating epidemiological data into a mechanistic model of type 2 diabetes: validating the prevalence of virtual patients. Ann Biomed Eng 2008, 36:321-334.
• [15]Woodhead JL, Howell BA, Yang Y, Harrill AH, Clewell HJ, Andersen ME, Siler SQ, Watkins PB: An analysis of N-acetylcysteine treatment for acetaminophen overdose using a systems model of drug-induced liver injury. JPET 2012, 342:529-540.
• [16]Howell BA, Yang Y, Kumar R, Woodhead JL, Harrill AH, Clewell HJ, Andersen ME, Siler SQ, Watkins PB: In vitro to in vivo extrapolation and species response comparisons for drug-induced liver injury (DILI) using DILIsymTM: a mechanistic, mathematical model of DILI. J Pharmacokinet Phar 2012, 39:527-541.
• [17]Meeuwisse CM, van der Linden MP, Rullmann TA, Allaart CF, Nelissen R, Huizinga TW, Garritsen A, Toes RE, van Schaik R, van der Helm-van Mil AH: Identification of CXCL13 as a marker for rheumatoid arthritis outcome using an in silico model of the rheumatic joint. Arthritis & rheum 2011, 63:1265-1273.
• [18]Rullman J, Struemper H, Defranoux N, Ramanujan S, Meeuwisse C, van Elsan A: Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform. IEE Proc-Syst Biol 2005, 152:256-262.
• [19]Thurlings RM, Boumans M, Tekstra J, van Roon JA, Vos K, van Westing DM, van Baarsen LG, Bos C, Kirou KA, Gerlag DM, Crow MK, Bijlsma JW, Verweij CL, Tak PP: Relationship between the type I interferon signature and the response to rituximab in rheumatoid arthritis patients. Arthritis & rheum 2010, 62:3607-3614.
• [20]Shoda L, Kreuwel H, Gadkar K, Zheng Y, Whiting C, Atkinson M, Bluestone J, Mathias D, Young D, Ramanujan S: The Type 1 Diabetes PhysioLab® Platform: a validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse. Clin & Exp Imm 2010, 161:250-267.
• [21]Ihaka R: R : A language for data analysis and graphics. J Comput Graph Stat 1996, 5:299-314.
• [22]Edwards JCW, Szczepanski L, Szechinski J, Filipowicz-Sosnowska A, Emery P, Close DR, Stevens RM, Shaw T: Efficacy of B-cell-targeted therapy with rituximab in patients with rheumatoid arthritis. NEJM 2004, 350:2572-2581.
• [23]Kremer JM, Blanco R, Brzosko M, Burgos-Vargas R, Halland A-M, Vernon E, Ambs P, Fleischmann R: Tocilizumab inhibits structural joint damage in rheumatoid arthritis patients with inadequate responses to methotrexate: results from the double-blind treatment phase of a randomized placebo-controlled trial of tocilizumab safety and prevention of structural joint damage at one year. Arthritis & rheum 2011, 63:609-621.
• [24]Smolen JS, Beaulieu A, Rubbert-Roth A, Ramos-Remus C, Rovensky J, Alecock E, Woodworth T, Alten R: Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (OPTION study): a double-blind, placebo-controlled, randomised trial. Lancet 2008, 371:987-997.
• [25]Keystone EC, Kavanaugh AF, Sharp JT, Tannenbaum H, Hua Y, Teoh LS, Fischkoff SA, Chartash EK: Radiographic, clinical, and functional outcomes of treatment with adalimumab (a human anti-tumor necrosis factor monoclonal antibody) in patients with active rheumatoid arthritis receiving concomitant methotrexate therapy: a randomized, placebo-controlled, 52-week trial. Arthritis & rheum 2004, 50:1400-11.
• [26]Schiff M, Keiserman M, Codding C, Songcharoen S, Berman A, Nayiager S, Saldate C, Li T, Aranda R, Becker J-C, Lin C, Cornet PLN, Dougados M: Efficacy and safety of abatacept or infliximab vs placebo in ATTEST: a phase III, multi-centre, randomised, double-blind, placebo-controlled study in patients with rheumatoid arthritis and an inadequate response to methotrexate. Ann Rheum Dis 2008, 67:1096-1103.
• [27]Klaassen CAJ, Wellner JA, Mar N: Efficient estimation in the bivariate normal copula model: normal margins are least favourable. Bernoulli 1997, 3:55-77.
• [28]Package “LEAPS”: Regression subset selection. http://cran.r-project.org/web/packages/leaps webcite
• [29]Emery P, Deodhar A, Rigby W, Isaacs J, Combe B, Racewicz A, Latinis K, Abud-Mendoza C, Szczepanski L, Roschmann R, Chen A, Armstrong G, Douglass W, Tyrrell H: Efficacy and safety of different doses and retreatment of rituximab: a randomised, placebo-controlled trial in patients who are biological naive with active rheumatoid arthritis and an inadequate response to methotrexate. Ann Rheum Dis 2010, 69:1629-1635.
• [30]Alvaro-Gracia J, Zvaifler N, Firestein G: Cytokines in chronic inflammatory arthritis. V. Mutual antagonism between interferon-gamma and tumor necrosis factor-alpha on HLA-DR expression, proliferation, collagenase production, and granulocyte macrophage colony-stimulating factor production by rheumatoid arthritis synoviocytes. J Clin Invest 1990, 86:1790-1798.
• [31]Nakajima H, Hiyama Y, Tsukada W, Warabi H, Uchida S, Hirose S: Effects of interferon gamma on cultured synovial cells from patients with rheumatoid arthritis: inhibition of cell growth, prostaglandin E2, and collagenase release. Ann Rheum Dis 1990, 49:512-516.
• [32]Brinckerhoff C, Guyre P: Increased proliferation of human synovial fibroblasts treated with recombinant immune interferon. J Immunol 1985, 134:42-46.
• [33]Thurlings R, Vos K, Wijbrandts C, Zwinderman A, Gerlag D, Tak P: Synovial tissue response to rituximab: mechanism of action and identification of biomarkers of response. Ann Rheum Dis 2008, 67:917-925.
• [34]Dass S, Rawstron AC, Vital EM, Henshaw K, McGonagle D, Emery P: Highly sensitive B cell analysis predicts response to rituximab therapy in rheumatoid arthritis. Arthritis & rheum 2008, 58:2993-2999.
• [35]Treschow AP, Teige I, Nandakumar KS, Holmdahl R, Issazadeh-Navikas S: Stromal cells and osteoclasts are responsible for exacerbated collagen-induced arthritis in interferon-beta-deficient mice. Arthritis & rheum 2005, 52:3739-3748.
• [36]Porrini A, Gambi D, Reder A: Interferon effects on interleukin-10 secretion. Mononuclear cell response to interleukin-10 is normal in multiple sclerosis patients. J Neuroimmunol 1995, 61:27-34.
• [37]Wang X, Chen M, Wandinger K, Williams G, Dhib-Jalbut S: IFN-beta-1b inhibits IL-12 production in peripheral blood mononuclear cells in an IL-10-dependent mechanism: relevance to IFN-beta-1b therapeutic effects in multiple sclerosis. J Immunol 2000, 165:548-557.
• [38]Rudick R, Ransohoff R, Peppler R, VanderBrug M, Lehmann P, Alam J: Interferon beta induces interleukin-10 expression: relevance to multiple sclerosis. Ann Neurol 1996, 40:618-627.
• [39]Duncan MR, Berman B: Gamma interferon is the lymphokine and beta interferon the monokine responsible for inhibition of fibroblast collagen production and late but not early fibroblast proliferation. J Exp Med 1985, 162:516-527.
• [40]Vilcek J, Kohase M, Henrikson-DeStefano D: Mitogenic effect of double-stranded RNA in human fibroblasts: role of autogenous interferon. J Cell Physiol 1987, 130:37-43.
• [41]Kohase M, Henrikson-DeStefano D, May LT, Vilcek J, Sehgal PB: Induction of β2-interferon by tumor necrosis factor: a homeostatic mechanism in the control of cell proliferation. Cell 1986, 6:659-666.
• [42]Vosslamber S, Raterman HG, van der Pouw Kraan TCTM, Schreurs MWJ, von Blomberg BME, Nurmohamed MT, Lems WF, Dijkmans BAC, Voskuyl AE, Verweij CL: Pharmacological induction of interferon type I activity following treatment with rituximab determines clinical response in rheumatoid arthritis. Ann Rheum Dis 2011, 70:1153-1159.
• [43]Strassmann G, Patil-Koota V, Finkelman F, Fong M, Kambayashi T: Evidence for the involvement of interleukin 10 in the differential deactivation of murine peritoneal macrophages by prostaglandin E2. J Exp Med 1994, 180:2365-2370.
• [44]Niho Y, Niiro H, Tanaka Y, Nakashima H, Otsuka T: Role of IL-10 in the crossregulation of prostaglandins and cytokines in monocytes. Acta Haematol 1998, 99:165-170.
• [45]Fadok VA, McDonald P, Bratton DL, Henson PM: Regulation of macrophage cytokine production by phagocytosis of apoptotic and post-apoptotic cells. Biochem Soc Trans 1998, 26:653-656.
• [46]Sheibanie A, Khayrullina T, Safadi F, Ganea D: Prostaglandin E2 exacerbates collagen-induced arthritis in mice through the inflammatory interleukin-23/interleukin-17 axis. Arthritis & rheum 2007, 56:2608-2619.
• [47]Sheibanie A, Tadmori I, Jing H, Vassiliou E, Ganea D: Prostaglandin E2 induces IL-23 production in bone marrow-derived dendritic cells. FASEB J 2004, 18:1318-1320.
• [48]Jonsson MV, Szodoray P, Jellestad S, Jonsson R, Skarstein K: Association between circulating levels of the novel TNF family members APRIL and BAFF and lymphoid organization in primary Sjögren’s syndrome. J Clin Imm 2005, 25:189-201.
• [49]Pers J-O, Daridon C, Devauchelle V, Jousse S, Saraux A, Jamin C, Youinou P: BAFF overexpression is associated with autoantibody production in autoimmune diseases. Ann NY Acad Sci 2005, 1050:34-39.
• [50]Zhang M, Ko K-H, Lam QLK, Lo CKC, Srivastava G, Zheng B, Lau Y-L, Lu L: Expression and function of TNF family member B cell-activating factor in the development of autoimmune arthritis. Int Immunol 2005, 17:1081-1092.