Abstract
Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.
Highlights
Clinical drug development has remained relatively unchanged for the last 30 years. This is due, in part, to uncertainties in regulatory requirements, risk aversion, and skepticism about rapidly emerging, yet largely unproven, technologies, and the lack of relevant actionable biomedical data sources and advanced analytics to generate hypotheses that could motivate the development of innovative diagnostics and therapies
An emerging strategy based on co-developing precision diagnostics and therapeutic agents as companion diagnostics for example may produce highly effective drugs with clinical outcomes that greatly exceed standard therapies.[2,3]
Perspectives and commentaries highlighting applications of DNN to imaging data sets, pharmaceutical properties of compounds, clinical diagnoses and genomics, computer vision applications for medical imaging, and applications of Natural Language Processing to EHR have been published.[22,23]. These predominantly focused on data in primary care or hospital ecosystem and early drug discovery applications, and did not describe use cases and regulatory framework derived from a multi-stakeholder perspective for successful embedding of AI and ML and RWE into the process of clinical development outlined in this perspective
Summary
Clinical drug development has remained relatively unchanged for the last 30 years. This is due, in part, to uncertainties in regulatory requirements, risk aversion, and skepticism about rapidly emerging, yet largely unproven, technologies (such as machine learning, and wireless health monitoring devices and sensors), and the lack of relevant actionable biomedical data sources and advanced analytics to generate hypotheses that could motivate the development of innovative diagnostics and therapies. Alternative learning systems that leverage human brain and its neocortex and learn from fewer examples have been proposed as alternatives to deep learning, but have not been widely adopted.[21] Recently, perspectives and commentaries highlighting applications of DNN to imaging data sets, pharmaceutical properties of compounds, clinical diagnoses and genomics, computer vision applications for medical imaging, and applications of Natural Language Processing to EHR have been published.[22,23] These predominantly focused on data in primary care or hospital ecosystem and early drug discovery applications, and did not describe use cases and regulatory framework derived from a multi-stakeholder perspective for successful embedding of AI and ML and RWE into the process of clinical development outlined in this perspective.
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