Revisiting nanomedicine design strategies for follow-on products: A model-informed approach to optimize performance
Abstract
The field of nanomedicine is undergoing a seismic transformations with the rise of nanosimilars, reshaping the pharmaceutical landscape and expanding beyond traditional innovators and generic manufacturers. Nanodrugs are increasingly replacing conventional therapies, offering improved efficacy and safety, while the demand for follow-on products drives market diversification. However, the transition from preclinical to clinical stages presents challenges due to the complex biopharmaceutical behavior of nanodrugs. This review highlights the integration of Quality-by-Design (QbD), in vitro-in vivo correlations (IVIVCs), machine learning, and Model-Informed Drug Development (MIDD) as key strategies to address these complexities. Additionally, it discusses the role of high-throughput processes in the optimization of the nanodrug development pipelines. Covering generations of delivery systems from liposomes to RNA-loaded nanoparticles, this review underscores the evolving market dynamics driven by recent advances in nanomedicine.
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Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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