Machine-based learning to predict pharmaceutical properties of molecular compounds and targets for drug discovery 12, 13 11 Broadly academic research labs, biotechnology corporations, and technology companies have been exploring the use of AI and ML in three key areas: Machine learning and computer vision have enhanced many aspects of human visual perception to identify clinically meaningful patterns in, e.g., imaging data, 10 and neural networks are been used for variety of tasks ranging from medical image segmentation, generation, classification, and prediction of clinical data sets. 5, 6, 7, 8 Securing, standardizing, and enhancing routinely collected EHR data as a source of credible medical evidence based on RWD can facilitate the organization of clinical trials at the point-of-care and should serve to improve the clinical development process. 4 Promising approaches to overcoming this challenge are more streamlined processes, exploiting new digital clinical endpoints and treatment response biomarkers amenable to close and efficient monitoring (such as circulating tumor DNA), improve safety and efficacy while reducing toxicity and adverse events and greater insights into the patient journey via sensors, and low cost imaging. 2, 3Īnother key challenge in the clinical development process is linked to reporting the results of most conventional clinical trials of average treatment effects that may not easily translate into making individualized treatment decisions at the routine point-of-care. 1 In this light, 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. FDA in recent years were designed to target specific aberrations implicated in disease initiation and maintenance-a hallmark of precision medicine-which aims to tailor interventions based on individual characteristics of patients. For example, the majority of the new molecular entities approved by the U.S. The application of emerging digital technologies, such as next-generation sequencing, though, have increased both our understanding of disease mechanisms in larger pool of patients and the potential for developing personalized therapies. Testing new biomedical treatments for safety and efficacy will also require new strategies, since it has been shown that existing therapies often only work for a small number of indicated individuals. 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. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.Ĭlinical drug development has remained relatively unchanged for the last 30 years. 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. 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. 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. 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.
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