Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles

Abstract:The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and polydispersity index (PDI), to minor changes in process parameters. Factors like formulation concentration, flow rates, and mixing ratios can significantly influence clinical efficacy and therapeutic outcomes. The absence of predictive mathematical frameworks has made iterative experimental screening necessary, increasing both costs and development time. This study introduces and validates a predictive modeling approach based on shape constraints, aiming to enhance the estimation of nanoparticle characteristics across various process conditions. Using controlled microfluidic methods, liposomes and lipid nanoparticles were systematically prepared under varying lipid concentrations, flow rates, and aqueous-to-organic mixing ratios. The shape-constrained model, informed by both experimental data and expert knowledge, was subsequently validated for a pharmaceutical application using minimal empirical data. Results reveal that shape-constrained modeling facilitates accurate prediction of nanoparticle size and dispersity, reducing the need for extensive experimental workflows. This framework supports rational and efficient process development for manufacturing nanomedicine systems.

Machine Learning (cs.LG); Computational Engineering, Finance, and Science (cs.CE)

arXiv:2608.05761 [cs.LG]

(or arXiv:2608.05761v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.05761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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