'Impedance matching' of data sets in biomarker research.

The goal of biomarker research in drug development is to identify better ways of monitoring the effects of drugs on biological systems. Biomarker data are used to support decision making at various stages in the drug development process, and are also used to provide information on how drug use might be optimized in different patient populations. The evaluation and qualification of new safety biomarkers includes a rigorous analysis of the ability of a given biomarker to report specific pathological events at the cellular, tissue or systemic level. This evaluation often relies on the mapping of a continuous data set (eg, biomarker levels) onto discrete phenotypic descriptors (eg, pathology scores). The approach has been applied successfully to discover new and improved biomarkers of tissue injury, but may involve uncertainty when used to evaluate the ability of a biomarker to detect early events or events occurring near the threshold of drug action. Alternative approaches based on study endpoints that provide continuous descriptions of a disease state or drug action, coupled with measurements of changes in biological function, may provide a better 'impedance match' between input and output data in biomarker research, and improve the early assessment and prediction of drug safety issues.