Process and purification synergies with data analytics driving clinical translation in nanomedicine
Nishabh Kushwaha, Asha Patel, Kshitija Akarte, Shruti Patel, Drishti Panjwani, Mange Ram Yadav, Rajesh Kesarla
Abstract
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.