Systems pharmacology-based identification of pharmacogenomic determinants of adverse drug reactions using human iPSC-derived cell lines

Few pharmacogenomic predictors of adverse drug reactions (ADRs) are currently available. Pharmacogenomic ADR studies are challenged by the multifactorial nature of ADRs and by insufficient sample sizes. Identification of pharmacogenomic predictors for personalized prediction of ADR risk may be enabled by development of large-scale libraries of patient-derived induced pluripotent stem cells. Using such libraries, ADR-related transcriptomic signatures can be mapped to the pharmacokinetics and pharmacodynamics of drugs, and correlated with clinical datasets and genomic profiles of individuals. Integration of these different data using computational quantitative systems pharmacology models based on machine learning-based algorithms can enable systematic mechanism-based characterization of ADRs. Establishing large scale cell line libraries, and databases and development of algorithms will lead to a knowledge-base that can be used to predict ADR risk in individual patients and for new drug candidates.