Development of synthetic patient populations and in silico clinical trials

Ramin Zand, Vida Abedi, Raquel Hontecillas, Pinyi Lu, Nariman Noorbakhsh-Sabet, Meghna Verma, Andrew Leber, Nuria Tubau-Juni, Josep Bassaganya-Riera

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAccelerated Path to Cures
PublisherSpringer International Publishing
Pages57-77
Number of pages21
ISBN (Electronic)9783319732381
ISBN (Print)9783319732374
DOIs
StatePublished - Apr 25 2018

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Immunology and Microbiology(all)
  • Computer Science(all)
  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)

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