Precision medicine (PM), defined as the ability to deliver the right treatment to the right patient at the right time, is currently hailed as the Holy Grail of medicine. However, it relies on predictive biomarker diagnostics and, as two experts write in the July 29 issue of Science, numerous obstacles prevent the efficient development of reliable biomarker assays for routine clinical use.
Spencer Phillips Hey, PhD, and Aaron S. Kesselheim, MD, of the Center for Bioethics at Brigham and Women’s Hospital and Harvard Medical School in Boston, highlight several concerns with current biomarker diagnostic development in their Policy Forum piece.
These include inadequately validated biomarker diagnostics; low-quality and unreliable reports in the scientific literature; and uncertainty about how to use and interpret the diagnostic test results.
“Although various experimental modalities can provide some evidence of biomarker validity (e.g., retrospective designs, enrichment designs),” they write, “a decisive test of a biomarker hypothesis requires that participants are prospectively stratified according to both biomarker status and treatment assignment. Unfortunately, this rarely happens in PM development.”
Unlike traditional drug development that relies on a randomized, controlled trial to assess the safety and efficacy of a treatment, PM is designed to identify an underlying biologic hypothesis as to why a specific drug will work for an individual. This theory informs clinical testing and use. If the underlying theory is incorrect or not adequately tested, Hey and Kesselheim warn, it can lead to systematic misclassification and poor patient outcomes.
They also note that PM trials are extremely complex, involving not only the therapeutic compound, but also the biomarker thought to play a crucial role in the disease pathway, and the diagnostic assay needed to determine a patient’s biomarker “status.” With so many variables, they write, it can be difficult to determine why the results are negative or even why they are positive.
In addition, the “open science” model under which biomarker research exists adds another level of complexity because it enables individual stakeholders to guide the development and use outside of any single stakeholder, such as a drug company or government entity. This, they conclude, “can lead to inefficiencies at the research system level.”
The lack of rigorous clinical studies on biomarkers, in turn, puts patients at risk and threatens to undermine public support for such research.
To overcome these obstacles, Hey and Kesselheim propose “radical change” in scientific oversight. President Obama’s Precision Medicine Initiative, while a step in the right direction, is not enough, they write.
Instead, they recommend that major research institutions such as the National Human Genome Research Institute and the National Cancer Institute join with regulatory agencies to manage the coordination and evaluation of PM research.
Specifically, these agencies should prospectively map out the parameter space of a promising new biomarker ensemble and then track the accumulating state of evidence through a centrally hosted, publicly accessible and dynamic portfolio database. This, they predict, would help the scientific community communicate and coordinate in real time. It will also, they concluded, “help achieve the promise that the field (of PM) offers.”