Real-World Data to Generate Real-World Evidence in Regulatory Decision-Making

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PROJECT SUMMARY The rationale for a more detailed understanding of the safety and efficacy of new therapies for cancer is compelling. While cancer therapies have always had an element of risk to both well-being and to quality of life, much has changed in therapeutics in the past ten years. Most important, a greater understanding of the biological characteristics of cancer has led to more nuanced definitions of populations that may benefit than were previously possible. With greater use of genomics and resulting specification of precise molecular characteristics of target cancers, and through (though early) markers of sensitivity and resistance to immunotherapeutics, both the indications and the use of novel therapeutics is resulting in higher response rates and more favorable outcomes, at least as judged by progress in clinical trials. Parallel advances in medical care and the universal adoption of an electronic health record (EHR), coupled with advances in quality of life (QOL) and other patient-reported outcomes (PROs), have made possible a deeper analysis of the effects and the risks of new therapies as their use is diffused throughout the medical system. Following upon several guidances to the effect of encouraging and defining the use of real-world data (RWD) to provide real- world evidence (RWE), this funding mechanism is designed to foster approaches to capture, organize, and analyze RWD to produce RWE. We propose herein a many-pronged approach that brings together two strategies: 1) a reimagining of how acquisition of robust, representative, and accurate RWD could be obtained from existing clinical trials' processes; 2) how an effective RWD approach could generate robust RWE in rare and less common tumors alike. To assist in this goal, we have developed a collaboration with Optum Life Sciences, both to help recruit diverse populations with specific molecular profiles, and to characterize populations with extended databases in various domains. Our Aims are first, to harmonize Phase III and Phase IV evaluations of new therapies to obtain real-world evidence of the standard therapy in Phase III, and subsequently of the successful therapy in Phase IV; second, using this cooperative group model, to establish feasibility of a real-world data Phase IV approach to the evaluation of new therapies for rare tumors. We expect that by developing these approaches to rigorous data collection in a short timeframe, we will be complementary to informatics approaches to data collection developed in parallel and provide for them a robust database for comparative analyses.