Adverse drug reactions (ADRs), more commonly known as drug side effects, are estimated to cause over 100,000 deaths in the US annually, are responsible for 6.5% of all hospital admissions, and 28% of clinical trial failures. Current pharmacological modeling efforts that are commonly used in the pharma industry (such as PK/PD) are generally empirical at the biomolecular level, meaning simplified mathematical terms are used to represent complex physiology. These modeling approaches are used to quantitatively understand therapeutic exposure-response relationships for clinical dosing, but have been less commonly applied to describe toxicological exposure-response relationships, with a few exceptions, as they lack the power to predict systemic cellular effects of pharmaceuticals that underlie ADRs. Elucidating the downstream and systemic effects of pharmaceuticals is critical to understanding ADR pathogenesis. Drugs can affect multiple proteins and each protein that they modulate may play roles in multiple cellular processes. Understanding this multi- factorial physiological response using systems biology methods will ultimately aid in better predicting ADRs before clinical trials using in vitro data. With the increasing emphasis on amassing large datasets, there is more and more publically available knowledge on pharmaceuticals, and their effects on cells, organs, and patients. However throughout all biomedical fields, analyzing complex datasets in a biologically coherent fashion has been a difficult challenge. The goal of this program is to develop a predictive computational platform analyzing gene-expression data sets from drug perturbed in vitro cell lines with metabolic and protein interaction networks for better understanding the systemic effects of over 700 approved pharmaceuticals with known ADRs. The platform, named ADR Predict, will use statistical machine learning approaches to identify network perturbation signatures that are highly predictive of specific ADRs. Developing ADR Predict has significant implications for the pharmaceutical industry. The initial commercialization opportunity of ADR Predict will is through service partnerships with pharmaceutical companies for accelerating and improving the drug development pipeline by mitigating risk of clinical trial safety failures. Further, this proposal will elucidate mechanisms of ADR pathogenesis that will be subsequently experimentally validated in Phase 2.
Adverse drug reactions, more commonly known as drug side effects, cause over 100,000 deaths in the US annually, are responsible for 6.5% of all hospital admissions, and 28% of clinical trial failures. This proposal will develop a computational platform that can both predict drug side effects from preclinical data and elucidate associated biochemical mechanisms. The predictive platform will accelerate pharmaceutical partners' drug development pipelines by mitigating risk of safety failures during clinical trials of novel compounds.
