Recent Advances in Bioanalytical Method Development: Innovations in Analytical Techniques, Experimental Design, and Artificial Intelligence Applications

Significance of Optimization Tools in Analytical Method Development

The increasing complexity of pharmaceutical, biotechnological, and environmental matrices is shifting analytical method development from empirical trial-and-error approaches toward structured, predictive, and data-driven strategies. These strategies emphasize robustness, reproducibility, and regulatory compliance a...

Comprehensive Review on AQbD-Enabled Latest Advancements of Bioanalysis Using Hyphenated Techniques in Drug Product Development

Bioanalysis is essential in current drug discovery and development research for the healthcare industry. Bioanalysis involves the assessment of analytes, such as medicines, metabolites, and biomarkers, in biological specimens. This method includes several stages, from sample collection to analysis and subseq...

AI-assisted sustainable pharmaceutical bioanalysis: critical review of LC-MS, matrix effects, and white analytical chemistry.

This critical review comprehensively evaluates the current state of pharmaceutical bioanalysis by examining emerging LC-MS technologies, sustainable analytical strategies, and the growing role of artificial intelligence (AI) and machine learning (ML) in analytical optimization, spectral interpretation, biomarker discov...

Advances in Analytical Techniques for Anticancer Drugs: Method Development, Validation and Insights into Newly Approved Therapies

Analytical strategies for complex injectables: the expanding role of LC-MS technologies.

Complex injectable therapeutics, including lipid nanoparticles, antibody-drug conjugates, biotherapeutics, and long-acting delivery systems, present analytical challenges that frequently exceed the capabilities of conventional pharmaceutical testing approaches. Liquid chromatography-mass spectrometry (LC-MS) has emerge...

Analytical determination of meloxicam and tenoxicam: matrix-dependent challenges, emerging technologies, and sustainable future perspectives.

Future progress should shift from isolated improvements in detection sensitivity toward matrix-adaptive, digitally assisted, sustainability-aware analytical systems that integrate automated sample preparation, intelligent data interpretation, robust validation, and lifecycle-based performance assessment.