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

Abstract

Pharmaceutical bioanalysis has undergone remarkable technological advancement with the widespread adoption of liquid chromatography-mass spectrometry (LC-MS) platforms, enabling highly sensitive and selective quantification of pharmaceuticals, metabolites, biomarkers, peptides, proteins, and oligonucleotides. Despite these developments, persistent challenges, including matrix effects, analytical instability, biological matrix complexity, data overload, and sustainability concerns, continue to compromise analytical reliability and translational applicability. 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 discovery, and autonomous decision-making. Particular emphasis is placed on White Analytical Chemistry (WAC) as a multidimensional framework that simultaneously considers analytical performance, environmental sustainability, and practical feasibility. The review critically assesses the limitations of current AI-assisted and sustainability-driven approaches, highlighting issues with algorithmic transparency, regulatory acceptance, standardization, and insufficient real-world validation. Furthermore, it explores intelligent analytical ecosystems that integrate advanced LC-MS platforms, explainable AI, digital twins, automation, and cloud-based infrastructure. Finally, realistic future priorities are proposed to facilitate the transition to autonomous, reliable, and environmentally sustainable pharmaceutical bioanalysis that supports precision medicine and next-generation drug development.