Artificial Intelligence and Machine Learning in Drug Development

by | May 12, 2023 | AI, FDA, ML, Product Development, Regulatory

With the growth of computational capabilities, artificial intelligence (AI) and machine learning (ML) have become a beacon of interest from both the product development side and the regulatory side. Implementation of AI and ML methodologies into the day-to-day life of our society is a real and ever-growing adaptive process. The FDA has just released a document that facilitates discussion and brings about important ideas for its place in society and how it will be regulated [1]. In 2021, over one hundred submissions to the FDA included some usage of AI/ML [2].

AI and ML implemented into the drug development process shows great promise for detection of ‘cocktail’ drug effects. Having a process that can help predict harmful drug-to-drug interactions would open a whole new side to prescribing as well as drug development. During the drug development phase, AI and ML could also be implemented to enhance process control by detecting persistent problems and monitoring the process. This would lead to less batch losses, ultimately making the development and manufacturing processes safer, easier, and more reliable.

Ultimately, AI and ML will be commonalities across nearly all technology-heavy sectors, private or public, regardless of its initial skepticisms. Its prospective capabilities will help drive a majority of research and development of products from drugs/medical devices all the way to keeping produce fresh while in transit. We could even see implementations of AI and ML in our governing bodies as checks for ethical values such as biases and discriminatory behaviors.

If you need help determining the proper route for your product per FDA compliance, the team of experts at EMMA International can help! Contact us today at info@emmainternational.com or by calling 248-987-4497.

[1] FDA. (2023, May 10). Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products. U.S. Food and Drug Administration. https://www.fda.gov/media/167973/download

[2] FDA. (2023, May 10). FDA Releases Two Discussion Papers to Spur Conversation about Artificial Intelligence and Machine Learning in Drug Development and Manufacturing. U.S. Food and Drug Administration. https://www.fda.gov/news-events/fda-voices/fda-releases-two-discussion-papers-spur-conversation-about-artificial-intelligence-and-machine

Chris Powell

Chris Powell

Chris is a highly skilled research scientist with a focus on bioinformatics, ecological modeling, big data analysis, genomics/proteomics, phylogenomics, and molecular clock analyses. He holds a PhD in Biology from Oakland University, specializing in Bioinformatics, Genomics, and Phylogenetics. Additionally, he has a Master of Science degree in Biology, where he worked in ecology and specialized in ecological data analyses using various techniques, such as timeseries analyses, statistical inference, survival modeling, and stochastic processes modeling. Chris has a strong background in Linux, computer hardware, mathematical modeling, and programming languages, including HTML, CSS, JavaScript, Perl, and R Programming Language. He brings 5+ years of teaching experience and over 12 years of research experience to EMMA International, showcasing a keen interest in data sciences that utilize big data to answer pressing questions and further our understanding of the world.

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