Time for Digital Selection Biomarkers

By Arthur Combs, Senior Clinical Advisor

Did you know that the use of selection biomarkers in clinical development improves the success rate in every phase (I-III)? And that use of selection biomarkers improves the overall success from Phase I through regulatory approval more than 3-fold? Examples include using tumour markers to select patients for cancer therapy trials or knowing the ejection fraction (EF) for heart failure patients since those with preserved EF behave and respond differently from those with reduced EF.

Unfortunately, many patient selection processes are dependent on an overarching inclusive diagnosis, a subjective rating scale or both. These are opportunities for the use of actigraphy to objectively and precisely characterize the functional status of study candidates. Oncology, neurodegenerative diseases, and many others depend on subjective rating scales for patient selection. For example, oncology subjects are often selected based on an the Eastern Cooperative Oncology Score (ECOG) score.

This score characterizes the ability of a subject to perform daily activities and any restrictions in place. Clinical development studies often select ECOG subjects judged to be ECOG 0, 1 or 2 to ensure that a subject is able to tolerate the study therapy. But performance and functional data from wearable technologies show that what were thought to be homogeneous [ECOG] groups with a given score, in actuality are groups that demonstrate significantly heterogeneity in their total activity, step counts, stairs climbed and other actigraphy metrics.

It is time to consider true functional status as a digital biomarker of patients’ suitability for a given trial. Given the benefit of using selection biomarkers in clinical development, the development and application of digital selection biomarkers through use of wearable sensors and functional assessment in real-world settings could significantly improve both the efficiency and success of clinical development trials. This is especially true in conditions where the selection of study subjects is based on broad diagnosis, subjective classification, surveys and patient-reported outcomes.