Translational Modeling & Simulation in Support of Rational Drug Discovery: Principles, Challenges and Examples

One of the most serious threats to sustained pharmaceutical innovation is the rapidly rising cost of research and development experienced over the past several decades. This exponential rise in cost has been attributed to a variety of factors including increasing technology investments, rising regulatory and commercial hurdles, and higher attrition rates. Among these factors, attrition due to insufficient efficacy and / or safety stands out as a major contributor. Historically high rates of efficacy and safety related attrition suggest that that there is often insufficient knowledge upon which critical decisions in drug discovery are made, namely: which biochemical targets to pursue and which chemical matter to bring forward into the clinic. Preclinical modeling and simulation (e.g. systems biology, PK/PD) is a useful approach to integrating biological information toward the selection of the most promising targets and the design of the most promising chemical matter. As such, over the past several years, quantitative modeling and simulation has become increasingly popular among pharmaceutical companies seeking to reduce attrition. This presentation will provide the rationale for quantitative modeling and simulation, guiding principles for successful implementation of fit for purpose models, and examples illustrative of value added in drug discovery.