1. Quantifying factors for the success of stratified medicine
- Author
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Steven D. Averbuch, Breon Burgess, Federico Goodsaid, Alfons Lieftucht, Sean Xinghua Hu, Ernst R. Berndt, Jian Wang, Judy Milloy, Mark R. Trusheim, Theresa Long, Aiden A. Flynn, Abhijit Mazumder, Michael C. Palmer, David Swank, and Peter M. Shaw
- Subjects
Pharmacology ,medicine.medical_specialty ,Management science ,business.industry ,Alternative medicine ,Public policy ,Computational Biology ,General Medicine ,Disease ,Commercialization ,Biomarker (cell) ,Clinical trial ,Risk analysis (engineering) ,Drug development ,Clinical Trials, Phase III as Topic ,Alzheimer Disease ,Research Design ,Neoplasms ,Drug Discovery ,medicine ,Humans ,Medicine ,Product (category theory) ,business - Abstract
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.
- Published
- 2011