Use Of AI In Drug Manufacturing, Testing 'More Accurate And Human Pertinent'
Artificial Intelligence (AI) is creating a big difference by replacing the old methods of performing tasks, and the change is visible in all sectors. The use of AI in drug development and testing relevant to human biology has been gaining increased popularity among drug manufacturers and scientists.
Traditionally, regulators and pharmaceutical companies have used animal models’ responses to determine and assess the drug’s quality, efficiency, and safety. Then only the drug will be proceeded with human trials. However, in recent years, there have been increased submissions from drugmakers using AI or a machine-learning component in the drug development and testing process.
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The US Food and Drug Administration (FDA) on January 6 proposed draft guidelines on the use of AI to evaluate the safety of drugs, adding that it has seen significant growth in submissions regarding the AI inclusion in the last ten years.
In 2021, the FDA reported a 10-fold increase with at least 132 submissions, including an AI and/or machine-learning component. The FDA says that the AI is being used to analyze a vast amount of data from both clinical trials and observational studies, helping to make inferences regarding the safety and effectiveness of the drug being evaluated.
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According to the influential drug body, the AI has the potential to inform the design and efficiency of clinical trials, including in decentralised clinical trials. The AI algorithms can analyze and optimize drug candidates by considering various factors, including efficacy, safety, and pharmacokinetics.
At present, the drug development has many drawbacks, including the extended period with the conventional animal-based models. The traditional model also has the shortcoming of decreased success rate. Additionally, the use of AI can also make the ‘human-relevant drug development more accurate,’ unlike experimenting on animals, which might vary the result.
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As in every sector, researchers are now using AI in the drug development cycle, as there are now AI tools that can comprehend data from humans about how their bodies absorb, eliminate, and distribute drugs.
Issues With the Use of AI in Drug Development
Though there are countless possibilities offered by the AI in the drug and pharmaceutical sector, outperforming the traditional processes, there are also reasons to be alert. One of the most challenging aspects of the use of AI in the drug manufacturing process is the reliability of the data analysis performed by an AI tool. The accuracy of the data analysis would be determined by the quality of the data the model is trained with.
The use of biased data of targeted samples will also compromise the result of the AI analysis. In addition, the lack of transparency in the inner workings of the AI restricts independent scrutiny.
However, the use of AI could not be postponed or withheld merely because of the challenges that come along with it, as the FDA has been open to discussing the possibilities of the AI. The body has sought to address the issues, seek potential, and accept submissions in the matter. The guidelines released by the FDA on the use of AI models have also stressed the importance of addressing the challenges associated with them, as the incorrect predictions from the machines can be probably life-threatening.
Given the fact that the output from AI will be as per the new inputs and the result of self-learning, continuous monitoring and maintenance are recommended by the drug body. The FDA has asked the AI industry to engage with the body to advance in the area.
Other pharmaceutical bodies have also issued similar documents on the use of AI in drug manufacturing and testing. As per the New Drugs and Clinical Trials (Amendments) Rules, India passed in 2023; it has been allowed to use the AI-generated data to be used to evaluate the safety and efficacy of the new drugs.
