Quantifying factors for the success of stratified medicine

Massachusetts Institute of Technology, Sloan School of Management and Center for Biomedical Innovation, 100 Main Street, Cambridge, Massachusetts 02139, USA.
Nature Reviews Drug Discovery (Impact Factor: 41.91). 11/2011; 10(11):817-33. DOI: 10.1038/nrd3557
Source: PubMed


Co-developing a drug with a diagnostic to create a stratified medicine - a therapy that is targeted to a specific patient population on the basis of a clinical characteristic such as a biomarker that predicts treatment response - presents challenges for product developers, regulators, payers and physicians. With the aim of developing a shared framework and tools for addressing these challenges, here we present an analysis using data from case studies in oncology and Alzheimer's disease, coupled with integrated computational modelling of clinical outcomes and developer economic value, to quantify the effects of decisions related to key issues such as the design of clinical trials. This illustrates how such analyses can aid the coordination of diagnostic and drug development, and the selection of optimal development and commercialization strategies. It also illustrates the impact of the interplay of these factors on the economic feasibility of stratified medicine, which has important implications for public policy makers.

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Available from: Aiden A Flynn, Jan 22, 2015
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    • "Ideally, clinicians would like to have predictors of response, in order to select the appropriate agent in individual basis. Reliable predictors (biomarkers, clinical predictors) are clinically important to avoid potential side effects from agents that will not have clinical benefit and will be an important first step as we move towards an era of stratified medicine [5]. "
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