An analysis of regulatory submissions of drug and biological products to the US Food and Drug Administration from 2016 to 2021 demonstrated an increasing number of submissions that included artificial intelligence/machine learning (AI/ML). AI/ML was used to perform a variety of tasks, such as informing drug discovery/repurposing, enhancing clinical trial design elements, dose optimization, enhancing adherence to drug regimen, end-point/biomarker assessment, and postmarketing surveillance. AI/ML is being increasingly explored to facilitate drug development. Over the past decade, there has been a rapid expansion of artificial intelligence/machine learning (AI/ML) applications in biomedical research and therapeutic development. In 2019, Liu et al. provided an overview of how AI/ML was used to support drug development and regulatory submissions to the US Food and Drug Administration (FDA). The authors envisioned that AI/ML would play an increasingly important role in drug development.1 That prediction has now been confirmed by this landscape analysis based on drug and biologic regulatory submissions to the FDA from 2016 to 2021. This analysis was performed by searching for submissions with key terms "machine learning" or "artificial intelligence" in Center for Drug Evaluation and Research (CDER) internal databases for Investigational New Drug applications, New Drug Applications, Abbreviated New Drug Applications, and Biologic License Applications, as well as submissions for Critical Path Innovation Meeting and the Drug Development Tools Program. We evaluated all data from 2016 to 2021. Figure 1a demonstrates that submissions with AI/ML components have increased rapidly in the past few years. In 2016 and 2017, we identified only one such submission each year. From 2017 to 2020, the numbers of submissions increased by approximately twofold to threefold yearly. Then in 2021, the number of submissions increased sharply to 132 (approximately 10-fold as compared with that in 2020). This trend of increasing submissions with AI/ML components is consistent with our expectation based on the observed increasing collaborations between the pharmaceutical and technology industries. Figure 1b illustrates the distributions of these submissions by therapeutic area. Oncology, psychiatry, gastroenterology, and neurology were the disciplines with the most AI/ML–related submissions from 2016 to 2021. Figure 1c summarizes the distributions of these submissions by the stage of therapeutic development life cycle. In these submissions, most of the AI/ML applications happen at the clinical drug development stage, but they also happen at the drug discovery, preclinical drug development, and postmarketing stages. It is important to note that the frequency by which AI/ML is mentioned in regulatory submissions to the FDA likely only represents a fraction of its increasingly widespread use in drug discovery. As demonstrated from this analysis, AI/ML is being utilized across many aspects of the drug development life cycle. AI/ML holds great promise to help improve both the efficiency of drug development and to further inform the understanding of the efficacy and safety of the treatment. There is an increasing trend of AI/ML applications for drug development in recent years, and the authors anticipate that this trend will likely only increase over time. Both opportunities and challenges lie ahead for the potential uses of AI/ML, and pharmaceutical and technology companies are actively investing in this area. Moreover, academic researchers are continuing to investigate current and future applications. The FDA has also been preparing to manage and evaluate AI/ML uses by engaging with a broad set of stakeholders on these issues and building its capacity in these scientific fields, in order to promote responsible innovation in this area. In 2021, the FDA and other regulatory agencies jointly identified 10 guiding principles that can inform the development of Good Machine Learning Practice to help promote safe, effective, and high-quality medical devices that use AI/ML.10 Although these Good Machine Learning Practice guiding principles were developed for medical device development, many of them (e.g., multi-disciplinary collaboration; data quality assurance, data management, and robust cybersecurity practices; representativeness of study participants and data sets; independence of the training and testing data sets) are also applicable to drug development. Liu et al. discussed some expectations for the application of AI/ML in drug development (e.g., fit-for-purpose and risk-based expectations, proper validation, generalizability, explainability, etc.).1 It is important to note that the regulatory considerations for the application of AI/ML in drug development are evolving and will require input from all stakeholders in various disciplines. Effective communication and active collaboration will serve an increasingly important role in fostering innovation, helping to advance regulatory science, and aiding in the promotion and protection of public health in the United States and the world. The authors thank Rajanikanth Madabushi for critical review of the manuscript, and Giang Ho and Kimberly Bergman for their assistance in the production of Figure 2. This work was supported in part by an appointment to the Research Participation Program at the Office of Clinical Pharmacology/Center for Drug Evaluation and Research, US Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the US Department of Energy and the US Food and Drug Administration. Julie Hsieh and Mo Tiwari were ORISE fellows contributing to this work. No funding was received for this work. The authors declared no competing interests for this work. The contents of this article reflect the views of the authors and should not be construed to represent the FDA's views or policies. No official support or endorsement by the FDA is intended or should be inferred.
Authors: Qi Liu, Ruihao Huang, Julie Hsieh, Hao Zhu, Mohit Tiwari, Guan‐Sheng Liu, Daphney Jean, M. Khair ElZarrad, Tala Fakhouri, Steven Berman, Billy Dunn, Matthew C. Diamond, Shiew‐Mei Huang
DOI: https://doi.org/10.1002/cpt.2668
Publish Year: 2022
