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.

Download full-text


Available from: Aiden A Flynn, Jan 22, 2015
34 Reads
  • Source
    • "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]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Treatment strategies blocking tumor necrosis factor (anti-TNF) have proven very successful in patients with rheumatoid arthritis (RA), showing beneficial effects in approximately 50-60% of the patients. However, a significant subset of patients does not respond to anti-TNF agents, for reasons that are still unknown. The aim of this study was to validate five single nucleotide polymorphisms (SNPs) of PTPRC, CD226, AFF3, MyD88 and CHUK gene loci that have previously been reported to predict anti-TNF outcome. In addition, two markers of RA susceptibility, namely TRAF1/C5 and STAT4 were assessed, in a cohort of anti-TNF-treated RA patients, from the homogeneous Greek island of Crete, Greece. The RA patient cohort consisted of 183 patients treated with either of 3 anti-TNF biologic agents (infliximab, adalimumab and etanercept) from the Clinic of Rheumatology of the University Hospital of Crete. The SNPs were genotyped by TaqMan assays or following the Restriction Fragments Length Polymorphisms (RFLPs) approach. Disease activity score in 28 joints (DAS28) at baseline and after 6 months were available for all patients and analysis of good versus poor response at 6 months was performed for each SNP. None of the 7 genetic markers correlated with treatment response. We conclude that the gene polymorphisms under investigation are not strongly predictive of anti-TNF response in RA patients from Greece.
    PLoS ONE 02/2013; 8(9):e74375. DOI:10.1371/journal.pone.0074375 · 3.23 Impact Factor
  • Source
    • "For example, applying the systems approach could help identify the mode of action and potential toxicity of compounds under development, which would allow companies to terminate unfavorable development projects early. The systems approach could also help to identify human subpopulations who may not respond to certain therapeutics, which would bring us closer to the goal of achieving personalized medicine (Trusheim et al. 2011). Merck Research Laboratory (MRL) was among the first research institutes to perform integrated analysis of large scale genomics data sets, and to apply the results toward practical drug development (Dai et al. 2005; Schadt et al. 2009; van't Veer et al. 2002, 2003; van de Vijver et al. 2002). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.
    Current topics in microbiology and immunology 08/2012; 363. DOI:10.1007/82_2012_252 · 4.10 Impact Factor
  • Source
Show more

Similar Publications