FDA supports AI for drug development

FDA supports AI for drug development

AI technologies are more widely used in the pharmaceutical industry today to enable for its extensive review of scientific and medical literature. Extensive data obtained through AI can support the discovery of potential biomarkers, accelerate drug development and identify repurposing opportunities.1 The FDA sees tremendous potential in technologies such as machine and deep learning in healthcare.


Keen interest in using automated technologies is rapidly expanding across the pharmaceutical and biotechnology industries. The FDA, has been developing a new regulatory framework to promote innovation and to “support the use of AI-based technologies”, according to Dr. Scott Gottlieb, the FDA Commissioner, speaking in 2018 at the Health Datapalooza in Washington DC. “AI holds enormous promise for the future of medicine,” said Gottlieb.


The industry has established the use of AI models and algorithms in order to speed up the analysis of large datasets to identify potential drug candidates. The process is much faster than manual analysis where a researcher is generally able to annotate upto 300 articles in a year. When compared, the efficiency of machine learning tools is not only unquestionable, but also the insights generated through search queries are accurate, relevant and based on the latest published research.


Using AI technologies and AI-based tools, scientific literature can be utilized to generate disease insights and develop more robust hypotheses. Owing to these advantages, the world’s first AI-generated drug, DSP-1181, a compound expected to treat obsessive-compulsive disorder has already entered Phase I of clinical trials earlier in 2020.2 The key milestone in healthcare was achieved by Sumitomo Dainippon Pharma. By using AI technologies, drug discovery time was reduced from 4-5 years to 1 year.


In 2017, the FDA issued a ‘Digital Health Innovation Action Plan’3 to outline its approach to implement effective technologies in healthcare and support the use of AI-based applications in research and drug development. Moreover, the FDA is supporting initiatives to develop and apply advanced predictive models in streamlining the drug review process.4 With an efficient regulatory framework, safe and effective therapies can be discovered, developed, and marketed much faster than ever.




Werner Seiz

Werner Seiz is the Vice President of Translational Science at Innoplexus. He brings more than 30 years of experience in Clinical Pharmacology and transition of cardiovascular, metabolic, respiratory, and immunology projects from pre-clinics into clinical development up to and including PoC. Prior to joining Innoplexus, Werner served as Director Clinical Research at Sanofi, where he provided scientific and medical leadership to design, optimize, implement and conduct global development strategy for assigned clinical programs.

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