Speaker
Description
Abstract:
The ICH M7 guideline prioritizes controlling DNA-reactive impurities in pharmaceuticals to reduce carcinogenic risk, yet experimental data for many generic drug impurities remain limited. This talk highlights how machine learning–based (Q)SAR methodologies, combined with expert review, can bridge this gap. Statistical models trained on large curated datasets deliver broad coverage and predictive accuracy, while expert rule–based systems provide mechanistic insight through structural alerts and mitigating features. Used together, these complementary approaches enable systematic ICH M7 classification and informed decision-making. Case studies will demonstrate how this framework offers timely, cost-effective, and scientifically robust evaluations of DNA-reactive properties in generic drug impurities, ultimately supporting safer medicines.
