Mechanistic systems modeling to guide drug discovery and development

A crucial question that must be addressed in the drug development process is whether the proposed therapeutic target will yield the desired effect in the clinical population. Pharmaceutical and biotechnology companies place a large investment on research and development, long before confirmatory data are available from human trials. Basic science has greatly expanded the computable knowledge of disease processes, both through the generation of large omics data sets and a compendium of studies assessing cellular and systemic responses to physiologic and pathophysiologic stimuli. Given inherent uncertainties in drug development, mechanistic systems models can better inform target selection and the decision process for advancing compounds through preclinical and clinical research.

► Large-scale disease models for drug discovery and clinical advancement. ► Clinical phenotype-driven models of disease pathophysiology. ► Biological network simulations. ► Modeling and simulation predictions for the safety and efficacy of torcetrapib. ► Computational approaches for biomarker identification.