The Food and Drug Administration (FDA) published a pair of discussion papers this week in an effort to outline areas artificial intelligence and machine learning may help in manufacturing pharmaceutical drugs along with regulatory issues it presents.
The FDA’s two discussion papers aim to spur a conversation with stakeholders in the medical community such as pharmaceutical companies, physicians, ethicists, patients and patient advocacy groups and regulatory authorities about the use of artificial intelligence (AI) and machine learning (ML) in developing and producing medications.
"AI/ML’s growth in data volume and complexity, combined with cutting-edge computing power and methodological advancements, have the potential to transform how stakeholders develop, manufacture, use and evaluate therapies," Patrizia Cavazzoni, director of the Center for Drug Evaluation and Research, wrote.
Ultimately, AI/ML can help bring safe, effective and high-quality treatments to patients faster."
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Cavazzoni noted that AI and machine learning may help health care providers scan medical literature for findings and offer predictions about how individual patients may respond to certain treatment options and whether they may be more at risk of side effects.
She also wrote that AI chatbots may help answer questions about participation in clinical trials or reporting adverse events, or even create digital "twins" of patients to model a medical intervention before a real-life intervention is done with a human patient.
The FDA is already seeing an influx of drug and biological product applications referencing the use of AI and machine learning as those technologies have advanced in recent years. More than 100 submissions in 2021 alone mentioned the use of those technologies.
The most common use of AI and ML was in the clinical development and research phase, but they were also deployed in drug discovery, clinical trial enrichment and safety surveillance.
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The discussion paper by several divisions of the FDA about AI and machine learning in developing drug and biological products touched on several areas where those technologies are being applied or may be applied in the future:
It also noted there are some risks involved in the use of AI and machine learning in the development of drugs and biological products that may arise due to biases in the data used to train ML algorithms, inaccuracies or incomplete data sets.
Further, the paper emphasized the importance of human-led governance of AI and machine learning to "help ensure adherence to legal and ethical values, where accountability and transparency are essential for the development of trustworthy AI."
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The drug manufacturing discussion paper by the FDA’s Center for Drug Evaluation and Research noted areas where AI and machine learning could be applied in pharmaceutical manufacturing processes, such as:
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Both FDA papers posed questions to stakeholders regarding the various use cases for AI and machine learning, best practices, barriers to their use, views on transparency, processes for auditing, current or future regulatory frameworks and more.
