Pharmaceutical and biotechnology companies are increasingly incorporating patient genetic and genomic information in their clinical development programs. One of the goals of this endeavor is to introduce the ability to identify biomarkers that can be used to target specific subsets of patients with the most effective treatments. However, this strategy can frustrate drug sponsors because many cancer mutations are extraordinarily rare which means that a potential patient pool can erode very quickly making it difficult to enroll the right patient population in a timely fashion. Exacerbating the frustration of drug sponsors is the notion of how to deal with the increasing and constant stream of newly emerging data (often from disparate sources) arising from basic and clinical research that may be relevant to a clinical development program. Given the current poor predictive ability when it comes to the correlation between biomarker status and relevant clinical response rate, incorporating additional relevant data streams will likely generate insights and efficiencies that were not possible even a few years ago.
Clinical development technologies are a powerful partner when it comes to complex precision oncology trials. As these trials continue to evolve and increase in complexity, this evolution will require clinical development technologies with advanced analytics that are driven by massive computing power. These technologies are helping sponsors overcomes challenges related to the dichotomy discussed above — patient scarcity versus abundance of disparate data. This paper examines how clinical development technologies and analysis of a wealth of disparate data can help investigators overcome some of the common pitfalls associated with precision oncology drug development.