ROLE OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL ANALYSIS METHOD DEVELOPMENT AND VALIDATION

Dhinesh Kumar R.*, Murugan S.

ABSTRACT

Artificial Intelligence (AI) is rapidly transforming pharmaceutical analytical science by improving analytical method development, validation, data interpretation, and quality control. This review highlights the applications of Machine Learning (ML), Deep Learning (DL), and other AI approaches in pharmaceutical analysis, with particular emphasis on HPLC, spectroscopy, and mass spectrometry. AI enables prediction and optimization of analytical conditions, reduction of experimental trials, automated peak identification, anomaly detection, spectral interpretation, and impurity profiling. Its integration with Quality by Design (QbD), Design of Experiments (DoE), and risk-based validation approaches can enhance method robustness, efficiency, and reliability. The review also discusses current challenges, including data quality, model interpretability, regulatory acceptance, and validation requirements. Future integration of AI with Process Analytical Technology (PAT), Internet of Things (IoT), digital twins, and autonomous laboratories may further transform pharmaceutical analytical workflows. Overall, AI represents a promising approach for developing faster, more reliable, and data-driven pharmaceutical analytical methods.

Keywords: Artificial Intelligence; Machine Learning; Pharmaceutical Analysis; Method Development; Method Validation; HPLC; Spectroscopy.