AI-Powered Analysis of FDA/EP Correspondence: Unveiling Trends and Critical Insights for Pharmaceutical Advancements

Yousif Ayoub,, Teva Pharmaceuticals LTD, Kfar Sava, Israel (yousif.ayoub@teva.co.il)
Michael Reich, Teva Pharmaceuticals Ltd, Kfar Sava, Israel

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Short Biography of Presenting Author

As the world’s largest generic drug development company, we, in Teva, have developed ToMoTo, an advanced AI-driven tool for analyzing regulatory review letters from global authorities such as the FDA and EMA. Utilizing Natural Language Processing (NLP) one of the Artificial Intelligence (AI) tools, ToMoTo processes the industry's most extensive and diverse database of regulatory correspondence. ToMoTo enables rapid topic identification, trend analysis over time, and extraction of actionable insights. These capabilities are critical under the GDUFA framework, where efficient responses to regulatory queries are essential for expediting approvals. By systematically identifying emerging regulatory trends and leveraging internal knowledge, ToMoTo revolutionizes the preparation and submission of responses to review letters. This is to speed up the response to RL, ensure its accuracy, and shape it to best meet the requirements of the authorities. You might add that it helps us design the best phrasing of our responses/justifications. This presentation will discuss the design and implementation of ToMoTo, its applications in regulatory RL analysis, and its potential impact on improving the speed and efficiency of regulatory interactions in drug development focusing on analytical development case studies.