Advancing GTI Detection in Pharmaceuticals Through AI Technologies

Genotoxic impurities (GTIs) in pharmaceutical formulations raise critical concerns due to their potential to cause genetic mutations and possibly lead to cancer. While conventional detection techniques like gas and liquid chromatography are reliable, they are often complex and resource-heavy. Recently, artificial intelligence (AI) has emerged as a powerful tool, offering new approaches for identifying, predicting, and measuring GTIs. By leveraging methods such as machine learning (ML), deep learning (DL), and quantitative structure–activity relationship (QSAR) models, AI enables efficient analysis and precise impurity profiling. This article presents an overview of AI-based techniques used for GTI detection, including their integration with spectroscopic and chromatographic technologies. Additionally, the paper highlights regulatory perspectives and quality control strategies supported by AI, pointing to a shift toward smarter, quicker, and safer pharmaceutical manufacturing.