用形状约束模型预测纳米药物制备参数，减少实验试错。
Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles
- 基于形状约束与实验数据构建预测模型
- 准确预测脂质体尺寸与分散性，减少实验次数
- 适合药物研发中快速优化纳米制剂流程
纳米药物开发常因颗粒尺寸、多分散指数（PDI）对工艺参数微小变化敏感，需大量经验优化。配方浓度、流速和水-有机相混合比等参数显著影响临床疗效。由于缺乏预测性数学框架，传统依赖反复实验筛选，导致成本高、周期长。本研究提出并验证了一种基于形状约束的预测建模方法，利用微流控技术系统制备脂质体与脂质纳米颗粒，考察不同脂质浓度、流速及水/有机相比例下的性能表现。该模型融合实验数据与专家知识，在仅需少量实测数据情况下即完成验证。结果表明，该方法能准确预测纳米颗粒的粒径与分散性，大幅减少实验工作量。该框架可实现纳米药物制造过程的理性化与高效开发。
原文摘要 · Abstract (English)
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.
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