Nanomedicine has evolved from a trial-and-error approach to one driven by engineering principles emphasizing accuracy, repeatability, and regulatory trust. The successful implementation of nanoparticle-based vaccines highlights the necessity for scalable production, effective purification, and stringent quality control. This review integrates advanced engineering practices, reliable purification techniques, and data-driven analytics into a cohesive framework. Notable advancements in purification, such as tangential flow filtration and asymmetric field-flow fractionation, facilitate scalable purification processes that maintain nanoparticle integrity and produce stable batches. Additionally, the incorporation of artificial intelligence and real-time process analytical technologies enhances predictive monitoring and adaptive quality control, bridging lab-scale formulation development with industrial manufacturing. However, challenges remain, including batch-to-batch variability, lack of reproducibility across scales, purification-induced functional drift, regulatory standardization gaps, and limited integration of predictive analytics into manufacturing workflows. The synthesis of digital twin frameworks, AI-integrated PAT, adaptive purification systems, and continuous manufacturing processes is poised to transform nanomedicine production into a predictive, robust, and regulatory-compliant paradigm. This comprehensive review is grounded in an extensive literature search through PubMed and Scopus, covering publications up to 2026.
Kushwaha et al. (2026) studied this question.