An Economic Framework for Evaluating Personalized Medicine

Abstract

Individual variation accounts for a wide range of medical and economic consequences, from inefficiencies in drug discovery and development to ineffectiveness of drug treatment to drug-induced morbidity and mortality. Addressing these consequences could benefit patients, health care providers and payers, and the pharmaceutical industry. When appropriate markers are known, diagnostic tests allow precise diagnosis and dosing, prediction of disease progression, prediction of treatment response and prediction of adverse drug reactions for individual patients. There may also be substantial savings realized by eliminating costs associated with failed treatment. We developed an analytical framework for analyzing the potential value of using a diagnostic test in clinical practice. Our framework determines the economic consequences of implementing pharmacogenomics in the clinic using a diagnostic test to predict drug response. We offer an empirical test of these ideas: we calculated the cost offset realized by predicting the likelihood of response to an alternative existing treatment using a hypothetical pharmacogenomic test in an asthma population. Because the diagnostic test is hypothetical, our framework is general and could be applied to other indications where diagnostic tests have not been developed. Our results could potentially guide future economic evaluation of new diagnostic tests. Importantly, they may also influence biomarker discovery strategies to ensure consistency between market priorities and the future stream of product introductions.