Reverse Translation: The Art of Cyclical Learning

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Excerpt

“The goal is to turn data into information, and information into insights.”
—Carly Fiorina
The theme for this issue of Clinical Pharmacology & Therapeutics was catalyzed at the inception of planning for a symposium on quantitative (model‐informed) reverse translation at the 2017 American Society for Clinical Pharmacology and Therapeutics Annual Meeting. The objective was to increase appreciation of the value of a reverse translation mindset across the Discovery–Development–Utilization continuum and identify opportunities for quantitative pharmacology as a core enabler for tactical implementation. The session illustrated application of quantitative methods (e.g., pharmacokinetic/pharmacodynamic (PK/PD), systems modeling, model‐based meta‐analysis, network analysis) to mine data from failed and successful clinical trials, safety and efficacy of drugs with related mechanisms, population pharmacology datasets, and electronic medical records, to inform biology, enhance trial design and proof‐of‐concept strategies, influence principled drug development decision‐making, optimize therapeutics, and rationalize prescribing and risk management. Attendees were engaged in a discussion of results of a questionnaire survey that was broadly administered prior to the meeting. Survey objectives were to assess the drivers, contexts, tools, and consistency in the application of quantitative reverse translation within the respondents' respective organizations and thereby distill cross‐sector perspectives on current trends and critical success factors for implementing a culture of reverse translation. Details of the survey results are discussed by Heatherington et al.1 Although based on a majority representation from the industry sector, the results of this crowd‐sourced survey support thematic inference both with respect to the support for implementation of quantitative reverse translation in pharmaceutical research and development, and opportunities for greater utilization of real‐world data (RWD).
An early figure in Reverse Translation who has been highlighted recently is William Heberden (1710–1801), an 18th‐century physician‐scientist (Figure1a). Heberden is recognized for providing the first description of angina pectoris and for defining the osteoarthritis nodes that are named after him. Importantly, and in relation to the theme of Reverse Translation, Heberden's greatest impact on medicine was achieved through education and information sharing.2 Parallels can be described in medical history from the East, in the historical contributions of Sushruta (ca. 600 BC), the ancient Indian surgeon (Figure1b). While Sushruta is best known for pioneering surgical methods as chronicled in the ancient Sanskrit text Sushruta Samhita, Sushruta also described diabetes, angina, and obesity through the power of observation and analytical reasoning. He described diabetes (madhumeha) as a disease characterized by the passage of large amounts of urine, sweet in taste, reflected in the Sanskrit name “madhumeha” that translates to “honey‐like urine”—a fine example of reverse translational discovery.3 Today, we are faced with information far greater in scope and volume than the days of Sushruta and Heberden, making initiatives such as the National Cancer Moonshot Program crucial to enabling reverse translation.4 For example, the National Cancer Institute's Genomic Data Commons (GDC) is a unified data repository that promotes sharing of genomic and clinical data between researchers, enabling access to comprehensive cancer genomics datasets in the world, comprising more than two petabytes of data, as noted in their June 2016 newsletter (one petabyte is equivalent to 223,000 DVDs filled to capacity with data).
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