AI increases efficiency of drug development

The AI/ML models are now extensively used to expedite drug discovery; the FDA has taken proactive actions to ensure that machine-made decisions are at least as robust and reliable as human analysis; in certain instances, AI/ML is now used to enhance human decision-making ability. Dozens of regulations, guidelines, and White Papers have been introduced to ensure that machine-driven decisions are continuously challenged. Of the tremendous significance of AI/ML, the first is its ability to process extensive data that is impossible to achieve otherwise. The entry of quantum computers is waiting to make these analyses more efficient. Second, data analysis in the research and manufacturing to optimize the process and validate findings. An excellent example of how this aspect has been the mainstream is the 21CFR Part 11 compliance that ensures digital data storage remains accurate and unalterable. Third, AI/ML allows experimental design creation that can be the most creative application to avoid following traditional paths, despite their efficiency or inaccuracies. The design space includes statistical modeling that can help reduce the study size by calculation from large databases that would generally not be possible using human inquiry alone. The AI/ML applications will expand exponentially; while, in 2021, more than hundreds of FDA filings deployed AI/ML tools, I anticipate most filings to engage these tools somehow; thus, scientists must receive proper training in using these tools. The FDA and related agencies have published many guidelines, policies, and data handling suggestions that require personnel trained to identify redundancies in implementing AI/ML at every stage of development and manufacturing. The role of AI/ML in manufacturing brings the traditional models to a different stage—advanced manufacturing that has benefitted significantly from automation, machine learning, and decision-making in continuous process control. None of these improvements were possible by human interaction alone. While AI/ML enhance productivity, this will not impact the job market since humans will still be needed to create the algorithms, supervise their operation, and finally judge their conclusions. To introduce the many applications of AI/ML, the FDA is open to earlier meetings to discuss the nature and extent of reliance on AI/ML in drug development or manufacturing. Developers should capitalize on these opportunities. It should be noted that the FDA guidelines admit that they are learning too when these interactions arise; the goal is to make the discovery more efficient, and thus more affordable; and manufacturing more reliable and efficient. All these plans will help reduce the cost of drugs to patients, and that alone should be a sufficient reason to remove mindsets about AI/ML, perhaps using another AI/ML model. However, it all starts with assuring that the myths around AI/ML are curbed at the onset. The industry should trust the FDA’s efforts to make drug discovery more productive, manufacturing more compliant, and clinical testing more ethical.