Artificial Intelligence in LC-MS/MS Data Analysis for Pharmacokinetic Studies Dayananda Sagar University, India

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) plays a critical role in pharmacokinetic research by enabling precise quantification of drugs and their metabolites in biological matrices such as blood and tissue samples. Owing to its high sensitivity and accuracy, the technique is extensively employed during drug development and evaluation processes. Beyond simple quantification, LC-MS/MS provides comprehensive insights into drug absorption, distribution, metabolism, and excretion. Recent advancements in computational technologies have significantly transformed the interpretation of LC-MS/MS data. Advanced software systems and machine learning algorithms facilitate the identification of complex patterns that may not be readily detectable through conventional analytical approaches. In addition to improving signal discrimination and reducing analytical noise, these technologies enhance the detection of low-abundance compounds and improve performance when analyzing complex biological samples. Consequently, analytical workflows have become more efficient while maintaining high levels of reliability and reproducibility. Artificial intelligence (AI) and machine learning applications further contribute to pharmacokinetic investigations by predicting drug disposition patterns, identifying metabolic pathways, and assessing potential drug-drug interactions. Compared with traditional analytical methods, AI-driven systems enable rapid data processing, minimize manual intervention, ensure greater consistency, and support compliance with regulatory standards. Recent developments demonstrate that these systems are increasingly capable of recognizing intricate chemical signatures, detecting anomalous findings, and generating more accurate predictive outcomes. Despite these advancements, several challenges remain. Interpretation of complex datasets, validation of algorithmic reliability, and regulatory acceptance continue to limit widespread implementation. Furthermore, emerging innovations such as adaptive analytical workflows, cloud-based laboratory platforms, and integration of pharmacogenomic data with pharmacokinetic studies are beginning to reshape the field. Overall, the integration of AI with LCMS/ MS is advancing pharmacokinetic research by improving analytical precision, streamlining workflows, and enhancing consistency. These developments are expected to support personalized medicine approaches and facilitate more efficient drug development processes. Rather than replacing scientific expertise, artificial intelligence serves as a complementary tool that enhances the accuracy, efficiency, and reliability of pharmaceutical research and drug evaluation.