Abstract
The emergence of nitrosamine impurities in widely used medications including sartans, ranitidine, metformin, and rifampicin has triggered global safety concerns, leading to extensive product recalls and rigorous regulatory intervention. This review systematically evaluates the chemical mechanisms and formation pathways of nitrosamines and Nitrosamine Drug Substance-Related Impurities (NDSRIs) across the pharmaceutical lifecycle. It further critically examines the role of Process Analytical Technology (PAT), chemometrics, and machine learning in predictive nitrosamine risk assessment and proactive control strategies. Key drivers of formation are analyzed, including the interplay between nitrosating agents, amine-bearing active pharmaceutical ingredients (APIs), excipients, manufacturing conditions, and storage environments. Through a critical analysis of the evolving regulatory frameworks established by the FDA, EMA, and ICH M7, the article outlines risk-based permissible intake limits and lifecycle quality risk management (QRM) mandates for both legacy and novel drug products. We evaluate the current analytical frontier, discussing the capabilities and technical constraints of LC–MS/MS, GC–MS/MS, and high-resolution mass spectrometry (HRMS) for ultra-trace detection and non-targeted structural elucidation. A central focus is placed on the transition toward Industry 4.0 methodologies. We highlight the integration of chemometrics, machine learning (ML), digital twins, and Process Analytical Technology (PAT) for the predictive risk assessment and real-time, closed-loop management of nitrosamine generation. By synthesizing industrial case studies and identifying critical scientific gaps, this review advocates for a sustainable, data-centric mitigation strategy. This integrated approach combines mechanistic rigor with advanced computational modeling to ensure pharmaceutical quality and patient safety in contemporary manufacturing environments.