AI Driven Analytical Method Development

Abstract

The integration of artificial intelligence (AI) into pharmaceutical analytical method development is revolutionizing the field, enabling faster, more efficient, and precise outcomes. Traditional analytical method development often involves labor-intensive experimentation and relies heavily on expert intuition, resulting in prolonged timelines and variability in results. AI-driven approaches, including machine learning (ML) and deep learning (DL), can predict optimal analytical conditions, enhance method robustness, and minimize trial-and-error experiments. This review provides a comprehensive overview of AI applications in analytical method development, highlighting current strategies, benefits, challenges, and future perspectives. Key areas include chromatographic method optimization, spectroscopic analysis, dissolution testing, and quality control. The paper also discusses AI-enabled platforms for automated method development, offering insights into regulatory acceptance and practical implementation in pharmaceutical industries.

KEYWORDS: Artificial intelligence, Analytical method development, Machine learning, Chromatography, Pharmaceutical analysis, Automation