All Times EDT
Keywords: Good Machine Learning Practice, Regulatory Environment, Data Integrity, Real World Evidence
Artificial Intelligence and Machine Learning (AI/ML) methods assist in analyzing a huge volume of patient data and can potentially transform biopharmaceutical development. But how and to which extent are these methods used in a regulatory environment? This talk highlights cases where AI/ML has been used in FDA. Reference will be made to FDA-Health Canada and EMA ‘Good Machine Learning Practice for Medical Device Development: Guiding Principles’ (https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles) and how these principles can be suitably adaptable to drugs and biologics. Additionally, the talk will cover cases where AI/ML methods have been successfully employed to address topics such as data integrity and suspicious clinical sites identification, post-marketing drug evaluation, pharmacokinetic/pharmacodynamic (PK/PD) studies, precision medicine, patient enrichment and Real World Evidence (RWE), among others.