Koneksa Health Chief Medical Officer John A. Wagner, MD, Ph.D., recently gave a video presentation titled “Multi-Component Biomarkers: Promise, Practice and Perspective on Terminology,” which was uploaded to the U.S. Food and Drug Administration’s YouTube channel.
Dr. Wagner began with a general introduction to biomarkers. A biomarker is a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions. Some biomarkers that will be familiar even to people without a strong background in the field are hemoglobin A1C, blood pressure, radiographic evidence of tumor shrinkage, and HIV-RNA reduction.
Biomarkers are crucial to drug development, but there is a lot of confusion around the language of biomarkers and endpoints. Dr. Wagner recommended the FDA’s BEST (Biomarkers, EndpointS, & other Tools) Resource as a remedy for biomarker language confusion.
Another important thing to understand is the concept of an endpoint. In clinical trials, endpoints are measurements of what happens to people in the trial. Researchers typically use clinical or surrogate endpoints. Clinical outcomes are the most reliable clinical trial endpoints, but surrogate endpoints may be used when the clinical outcomes might take a very long time to study, or in cases where the clinical benefit of improving the surrogate endpoint, such as controlling blood pressure, is well understood. From a U.S. regulatory standpoint, there are three levels of endpoints characterized by the level of clinical validation: validated surrogate endpoint, reasonably likely surrogate endpoint, and candidate surrogate endpoint, said the Koneksa CMO.
Dr. Wagner then moved on to multi-component biomarkers. The idea of multi-component biomarkers isn’t a new concept. Multi-component biomarker subtypes begin with integrative—that is, they are an overall summary measure, a single number derived from multiple measurements, and composite endpoints form a conceptual basis. The second subtype is multiplex—a set of individual biomarkers, or panels and patterns of biomarkers with a focus on phenotyping. Examples include vital signs and liver enzyme levels. Finally, classification biomarkers use multiple biomarkers and a defined algorithm to create an interpretation on categorization. He stressed that he put forward this framework with the intent of starting a dialogue.
Dr. Wagner provided a proposed definition of “multi-component biomarker”: Multiple biomarkers used individually, for pattern recognition, or a single calculated value derived from aa defined set of biomarkers with a known algorithm. Multi-component biomarkers area required when the characteristic being researched is not adequately captured by single measurement.
A number of issues, questions, and special cases will need to be resolved as part of advancing multi-component biomarker science, use, and regulation. First, analytical validation may be challenged by large numbers of measures. The interpretation of one biomarker in the category or context of another is also a concern, as well as the fact that some measures share features of biomarkers and clinical endpoints. Finally, we must ask the question, when have enough measures been added to a multi-component biomarker?
Finally, Dr. Wagner concluded by summarizing the discussion and thanking his Koneksa colleagues, Matt Cantor, Robert Ellis, and Elena Izmailova, as well as people from the business, education, government, and nonprofit sectors who provided inspiration and feedback.
Watch the video here.