Abstract
Drug development, which includes clinical trials, is a lengthy and expensive process that could significantly benefit from predictive modeling and in silico testing. Additionally, current treatments were designed based on the average patient using the “one size fits all” protocol. Therefore, they can be effective on some patients but not for others. There is an urgent need to replace such generalized approaches with personalized and predictive strategies that capture and analyze human diversity and variation at a resolution sufficient to identify and clinically validate personalized treatment paradigms. Utilization of heterogenous datasets, such as Electronic Health Records (EHRs), to build synthetic populations of patients and personalized, predictive models of response to therapy holds enormous promise in precipitating a revolution in precision medicine for IBD. In silico trials can be designed to include multi-modal data sources, including clinical trial data at the individual and aggregated levels, pre-clinical data from animal studies, as well as data from EHR. In silico clinical trials can help inform the design of clinical trials and make prediction at the population and individual level to increase the chances of success. This chapter discusses pioneering work on the use of in silico clinical trials to accelerate the development of new drugs.
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References
Abedi V et al (2016) Phase III placebo-controlled, randomized clinical trial with synthetic crohn’s disease patients to evaluate treatment response. In: Emerging trends in applications and infrastructures for computational biology, bioinformatics, and systems biology. Elsevier, Cambridge, MA, pp 411–427. https://doi.org/10.1016/B978-0-12-804203-8.00028-6
Abedi V et al (2017) Novel screening tool for stroke using artificial neural network. Stroke 48:1678–1681. https://doi.org/10.1161/STROKEAHA.117.017033
Abul-Husn NS et al (2016) Genetic identification of familial hypercholesterolemia within a single U.S. health care system. Science 354:pii: aaf7000. https://doi.org/10.1126/science.aaf7000
Aerts JM, Haddad WM, An G, Vodovotz Y (2014) From data patterns to mechanistic models in acute critical illness. J Crit Care 29:604–610. https://doi.org/10.1016/j.jcrc.2014.03.018
Allen RJ, Rieger TR, Musante CJ (2016) Efficient generation and selection of virtual populations in quantitative systems pharmacology models. CPT Pharmacometrics Syst Pharmacol 5:140–146. https://doi.org/10.1002/psp4.12063
An G (2004) In silico experiments of existing and hypothetical cytokine-directed clinical trials using agent-based modeling. Crit Care Med 32:2050–2060
An G, Bartels J, Vodovotz Y (2011) In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 72:187–200
Brown D et al (2015) Trauma in silico: Individual-specific mathematical models and virtual clinical populations. Sci Transl Med 7:285ra261
Carbo A et al (2013) Predictive computational modeling of the mucosal immune responses during helicobacter pylori infection. PLoS One 8:e73365. https://doi.org/10.1371/journal.pone.0073365
Carbo A, Hontecillas R, Andrew T, Eden K, Mei Y, Hoops S, Bassaganya-Riera J (2014) Computational modeling of heterogeneity and function of CD4+ T cells. Front Cell Dev Biol 2:31. https://doi.org/10.3389/fcell.2014.00031
Clegg LE, Mac Gabhann F (2015) Molecular mechanism matters: benefits of mechanistic computational models for drug development. Pharmacol Res 99:149–154. https://doi.org/10.1016/j.phrs.2015.06.002
Clermont G, Bartels J, Kumar R, Constantine G, Vodovotz Y, Chow C (2004) In silico design of clinical trials: a method coming of age. Crit Care Med 32:2061–2070
Creswell JW (2002) Educational research: planning, conducting, and evaluating quantitative. Prentice Hall, Upper Saddle River, NJ
Dewey FE et al (2016) Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study. Science 354:pii: aaf6814. https://doi.org/10.1126/science.aaf6814
DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012
FDA (2015) FDA’s drug review process: continued. https://www.fda.gov/Drugs/ResourcesForYou/Consumers/ucm289601.htm. Accessed 18 Oct 2017
Forrest S, Beauchemin C (2007) Computer immunology. Immunol Rev 216:176–197. https://doi.org/10.1111/j.1600-065X.2007.00499.x
Friedman LM, Furberg C, DeMets DL (2010) Fundamentals of clinical trials. Springer, New York
Gertrudes JC, Maltarollo VG, Silva RA, Oliveira PR, Honorio KM, da Silva AB (2012) Machine learning techniques and drug design. Curr Med Chem 19:4289–4297
Haidar A, Wilinska ME, Graveston JA, Hovorka R (2013) Stochastic virtual population of subjects with type 1 diabetes for the assessment of closed-loop glucose controllers. IEEE Trans Biomed Eng 60:3524–3533. https://doi.org/10.1109/TBME.2013.2272736
Hoops S, Hontecillas R, Abedi V, Leber A, Philipson C, Carbo A, Bassaganya-Riera J (2015) Ordinary differential equations (ODEs) based modeling. In: Computational immunology: models and tools, p 63
Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24:417
Janes KA, Yaffe MB (2006) Data-driven modelling of signal-transduction networks. Nat Rev Mol Cell Biol 7:820–828. https://doi.org/10.1038/nrm2041
Kim S et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
King TS, Lengerich R, Bai S (2016) What is the role of statistics in clinical research? The Pennsylvania State University. https://onlinecourses.science.psu.edu/stat509/node/2
Kumar R, Chow CC, Bartels JD, Clermont G, Vodovotz Y (2008) A mathematical simulation of the inflammatory response to anthrax infection. Shock 29:104–111
Law V et al (2014) DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42:D1091–D1097. https://doi.org/10.1093/nar/gkt1068
Leber A et al (2016) Modeling the role of lanthionine synthetase C-like 2 (LANCL2) in the modulation of immune responses to helicobacter pylori infection. PLoS One 11:e0167440. https://doi.org/10.1371/journal.pone.0167440
Leber A, Hontecillas R, Abedi V, Tubau-Juni N, Zoccoli-Rodriguez V, Stewart C, Bassaganya-Riera J (2017) Modeling new immunoregulatory therapeutics as antimicrobial alternatives for treating Clostridium difficile infection. Artif Intell Med 78:1–13. https://doi.org/10.1016/j.artmed.2017.05.003
Li N, Verdolini K, Clermont G, Mi Q, Rubinstein EN, Hebda PA, Vodovotz Y (2008) A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PLoS One 3:e2789
Lu P, Abedi V, Mei Y, Hontecillas R, Hoops S, Carbo A, Bassaganya-Riera J (2015) Supervised learning methods in modeling of CD4+ T cell heterogeneity. BioData Min 8:27. https://doi.org/10.1186/s13040-015-0060-6
Lund K, Vase L, Petersen GL, Jensen TS, Finnerup NB (2014) Randomised controlled trials may underestimate drug effects: balanced placebo trial design. PLoS One 9:e84104
Machado D, Costa RS, Rocha M, Ferreira EC, Tidor B, Rocha I (2011) Modeling formalisms in systems biology. AMB Express 1:45. https://doi.org/10.1186/2191-0855-1-45
Meinert CL (2012) ClinicalTrials: design, conduct and analysis. Oxford University Press, Oxford
Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM (2012) Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36:142–152. https://doi.org/10.1016/j.immuni.2012.01.002
Nguyen DV, Rocke DM (2002) Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18:39–50
Phippard D (2015) Big data analytics: the next evolution in drug development. http://www.bioprocessonline.com/doc/big-data-analytics-the-next-evolution-in-drug-development-0001
PhRMA (2015) Biopharmaceutical R&D: the process behind new medicines. http://www.phrma.org/report/biopharmaceutical-research-and-development-the-process-behind-new-medicines. Accessed 18 Oct 2017
Piantadosi S (2013) Clinical trials: a methodologic perspective. Wiley, New York
Porta M (2014) A dictionary of epidemiology. Oxford University Press, Oxford
Prilutsky D, Shneider E, Shefer A, Rogachev B, Lobel L, Last M, Marks RS (2011) Differentiation between viral and bacterial acute infections using chemiluminescent signatures of circulating phagocytes. Anal Chem 83:4258–4265. https://doi.org/10.1021/ac200596f
Principal component analysis (PCA) procedure. (2016) The Pennsylvania State University. https://onlinecourses.science.psu.edu/stat505/node/52
Ramsundar B, Kearnes S, Riley P, Webster D, Konerding D, Pande V (2015) Massively multitask networks for drug discovery arXiv preprint arXiv:150202072
Rigden DJ, Fernandez-Suarez XM, Galperin MY (2016) The 2016 database issue of Nucleic Acids Research and an updated molecular biology database collection. Nucleic Acids Res 44:D1–D6. https://doi.org/10.1093/nar/gkv1356
Romero K et al (2015) The future is now: model-based clinical trial design for Alzheimer’s disease. Clin Pharmacol Ther 97:210–214
Schmidt BJ, Casey FP, Paterson T, Chan JR (2013a) Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC Bioinformatics 14:221. https://doi.org/10.1186/1471-2105-14-221
Schmidt BJ, Papin JA, Musante CJ (2013b) Mechanistic systems modeling to guide drug discovery and development. Drug Discov Today 18:116–127. https://doi.org/10.1016/j.drudis.2012.09.003
Segovia-Juarez JL, Ganguli S, Kirschner D (2004) Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. J Theor Biol 231:357–376. https://doi.org/10.1016/j.jtbi.2004.06.031
Siettos CI, Russo L (2013) Mathematical modeling of infectious disease dynamics. Virulence 4:295–306. https://doi.org/10.4161/viru.24041
Sullivan L (2016) Hypothesis testing—analysis of variance (ANOVA). Boston University. http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTesting-ANOVA/index.html
van Die MD, Bone KM, Burger HG, Teede HJ (2009) Are we drawing the right conclusions from randomised placebo-controlled trials? A post-hoc analysis of data from a randomised controlled trial. BMC Med Res Methodol 9:41
Viceconti M, Henney A, Morley-Fletcher E (2016) In silico clinical trials: how computer simulation will transform the biomedical industry. Int J Clin Trials 3:37–46
Wells BJ, Chagin KM, Nowacki AS, Kattan MW (2013) Strategies for handling missing data in electronic health record derived data. EGEMS 1:1035. https://doi.org/10.13063/2327-9214.1035
Wendelsdorf K, Bassaganya-Riera J, Hontecillas R, Eubank S (2010) Model of colonic inflammation: immune modulatory mechanisms in inflammatory bowel disease. J Theor Biol 264:1225–1239. https://doi.org/10.1016/j.jtbi.2010.03.027
White IR, Royston P, Wood AM (2011) Multiple imputation using chained equations: issues and guidance for practice. Stat Med 30:377–399. https://doi.org/10.1002/sim.4067
Wise A, Bar-Joseph Z (2014) SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data. Bioinformatics 31(8):1250–1257
Zhang L, Athale CA, Deisboeck TS (2007) Development of a three-dimensional multiscale agent-based tumor model: simulating gene-protein interaction profiles, cell phenotypes and multicellular patterns in brain cancer. J Theor Biol 244:96–107. https://doi.org/10.1016/j.jtbi.2006.06.034
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Zand, R. et al. (2018). Development of Synthetic Patient Populations and In Silico Clinical Trials. In: Bassaganya-Riera, J. (eds) Accelerated Path to Cures. Springer, Cham. https://doi.org/10.1007/978-3-319-73238-1_5
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