Abstract

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN PHARMACEUTICAL ANALYSIS: RECENT ADVANCES IN METHOD DEVELOPMENT AND DATA INTERPRETATION

Ankit Anchliya*, Nirmal Dongre 

ABSTRACT

The pharmaceutical analysis process now uses Artificial Intelligence (AI) and Machine Learning (ML) as revolutionary technologies which produce better results and faster operations and improved data management capabilities. The existing analytical methods encounter difficulties because they require excessive time to analyze complicated data at extensive scales. The implementation of AI and ML techniques has transformed pharmaceutical sciences by improving their method development and optimization and validation processes. The AI-driven models enable scientists to predict chromatographic conditions and spectral analysis results and impurity profiles with greater speed while decreasing the need for experiments and increasing the consistency of results. The application of supervised and unsupervised learning machine learning algorithms allows researchers to identify patterns and detect anomalies and create predictive models for complex analytical data. The AI system enables continuous monitoring of analytical equipment while automating its operation, which results in better quality control and regulatory compliance. The study demonstrates how artificial intelligence technology applies to high-performance liquid chromatography (HPLC) and spectroscopy and dissolution testing and stability studies. The system provides benefits yet must address three main challenges which include data quality issues and problems with model explainability and difficulties with regulatory approval. The review examines current research studies about AI and ML technologies used in pharmaceutical analysis which help develop and improve methods and analyze data. The study analyzes existing restrictions and regulatory viewpoints while exploring future possibilities for using intelligent systems in pharmaceutical analysis processes.

Keywords: Artificial Intelligence; Machine Learning; Pharmaceutical Analysis; Method Development; Data Interpretation; HPLC; Spectroscopy; Predictive Modeling; Analytical Chemistry; Quality Control.