基于低成本替代纳米颗粒的知情预测模型加速连续流系统中的纳米药物开发
Accelerating nanodrug development in continuous flow systems using informed prediction models based on low-cost surrogate nanoparticles
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中文总结 AI 辅助
本研究提出并验证基于形状约束的预测建模方法，结合少量实验数据可准确预测纳米药物开发中的颗粒特性，减少实验流程，加速连续流系统中纳米药物的开发。
中文摘要 AI 辅助
纳米药物的开发通常需要大量的经验优化，因为纳米颗粒的性质（如尺寸和多分散性指数（PDI））对工艺参数的微小变化十分敏感。制剂浓度、流速、混合比例等因素会显著影响临床疗效和治疗结果。由于缺乏预测性数学框架，必须进行迭代实验筛选，这既增加了成本又延长了开发时间。本研究引入并验证了一种基于形状约束的预测建模方法，旨在提高对不同工艺条件下纳米颗粒特性的估算精度。利用可控微流控方法，在不同脂质浓度、流速及水油混合比例下系统制备脂质体和脂质纳米颗粒。随后，基于实验数据和专家知识构建的形状约束模型，在仅使用少量经验数据的情况下，针对一项药物应用进行了验证。结果表明，形状约束建模可实现对纳米颗粒尺寸和分散性的准确预测，减少了对大量实验流程的需求。该框架为纳米药物系统的制造提供了合理且高效的工艺开发支持。
英文摘要
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.
