Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers

Abstract

The potential to use biomarkers for identifying patients that are more likely to benefit or experience an adverse reaction in response to a given therapy, and thereby better match patients with therapies, is anticipated to have a major effect on both clinical practice and the development of new drugs and diagnostics. In this article, we consider current and emerging examples in which therapies are matched with specific patient population characteristics using clinical biomarkers - which we call stratified medicine - and discuss the implications of this approach to future product development strategies and market structures.

The Influence of Genotype Information on Psychiatrists’ Treatment Recommendations: More Experienced Clinicians Know Better What to Ignore

“…Understanding the role that genetics plays in treatment response could help clinicians maximize treatment response and minimize treatment side effects [42].…”

“…Tailoring treatments according to this anticipated response is known as stratified, or personalized, medicine [2]. In psychiatry, some genetic profiles in the population are associated with an increased risk of schizophrenia.…”

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“…Integrating the large omic data sets with drug and disease databases could guide the prediction of drug efficacy and side effects. Moving from the empirical to individualized therapy, three key factors are needed in order to define specific treatments: refined molecular fingerprints, clinical biomarkers and multiple therapeutic options [24].…”

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“…Precision medicine is inclusive of, but not limited to, the targeting of health care interventions to patients that share a specific and identifiable set of characteristics [2]. Most applications to date have used a single test, as a companion diagnostic, to target a defined medicine to a known subgroup of patients.…”