Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, United Kingdom.
Department of Pharmaceutics, UCL School of Pharmacy, University College London, 29-39 Brunswick Square, London WC1N 1AX, United Kingdom.
Int J Pharm. 2024 Dec 25;667(Pt A):124905. doi: 10.1016/j.ijpharm.2024.124905. Epub 2024 Nov 2.
Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanoprecipitation is distinguished by its simplicity and scalability. However, it requires careful optimisation to achieve the desired NP characteristics, making the process potentially lengthy and costly. This study aimed to assess and compare the predictive performance of Design of Experiments (DOE) and Machine Learning (ML) models for the optimisation of PLGA nanoparticle size and zeta potential produced by nanoprecipitation. Various ML methods were employed to predict particle size, with Extreme Gradient Boosting (XGBoost) identified as the best performing. The key finding is that integrating ML with DOE provides deeper insights into the dataset than either method alone. While ML outperformed DOE in predictive performance, as evidenced by lower root mean squared error values and higher coefficients of determination, both methods struggled to accurately predict zeta potential, generating models with high errors. However, ML proved more effective in identifying the parameters that most significantly influence NP size, even with a smaller DOE dataset. Combining DOE datasets with ML for parameter importance was particularly advantageous in situations where data is limited, offering superior predictive power and the potential to streamline experimental design and optimisation. These results suggest that the synergistic use of ML and DOE can lead to more robust feature analysis and improved optimisation outcomes, particularly for NP size. This integrated approach can enhance the accuracy of predictions and supports more efficient experimental design, streamlining nanoparticle production processes, especially under resource-constrained conditions.
聚（乳酸-共-乙醇酸）（PLGA）是一种在药物传递和纳米颗粒（NP）制剂中广泛使用的可生物降解聚合物，因为它具有可控的药物释放特性和安全特性。在可用于 NP 生产的方法中，纳米沉淀因其简单性和可扩展性而脱颖而出。然而，它需要仔细优化以实现所需的 NP 特性，从而使该过程潜在地冗长且昂贵。本研究旨在评估和比较实验设计（DOE）和机器学习（ML）模型在优化由纳米沉淀产生的 PLGA 纳米颗粒大小和 Zeta 电位方面的预测性能。采用各种 ML 方法预测粒径，其中极端梯度增强（XGBoost）被确定为表现最佳的方法。关键发现是，将 ML 与 DOE 集成提供了比单独使用任何一种方法更深入的数据集见解。虽然 ML 在预测性能方面优于 DOE，这表现在较低的均方根误差值和较高的确定系数上，但这两种方法都难以准确预测 Zeta 电位，生成的模型误差较高。然而，ML 在确定对 NP 大小影响最大的参数方面更为有效，即使 DOE 数据集较小。将 DOE 数据集与 ML 结合进行参数重要性分析在数据有限的情况下特别有利，提供了更高的预测能力，并有可能简化实验设计和优化。这些结果表明，ML 和 DOE 的协同使用可以导致更稳健的特征分析和改进的优化结果，特别是对于 NP 大小。这种集成方法可以提高预测的准确性，并支持更有效的实验设计，简化纳米颗粒生产过程，特别是在资源有限的情况下。
