Department of Pharmaceutical Sciences, University of the Sciences in Philadelphia, United States.
Department of Pharmaceutical Sciences, University of the Sciences in Philadelphia, United States.
Int J Pharm. 2022 Aug 25;624:121994. doi: 10.1016/j.ijpharm.2022.121994. Epub 2022 Jul 6.
The integration of mechanistic modeling and machine learning facilitates the understanding and engineering of drug release from controlled release systems. Here, we present hybrid models to predict the effect of drug loading on levonorgestrel release from spray-dried poly(L-lactic acid) microparticles. We developed three Monte Carlo methods that differ in the consideration of polymer's degradability and crystallinity, to simulate drug release from the matrices using the Python programming language. To build each method, we utilized data from the characterization of the particles, such as the actual drug content (ranges from 6% to 52%), size (Dv(50) ∼ 5 μm), and polymer crystallinity (ranges from 0% to 15%). We trained each method using drug release data from particles of 4 batches and derived appropriate machine learning models through regression analysis. Results indicate the contribution of drug diffusion and polymer degradation to drug release for particles of lower drug content (<20 %w/w). At higher drug loadings, particles encountered a combination of burst and diffusional release. We validated the predictive powers of the machine learning models by testing them against experimental data. This paper specifically highlights the power of hybrid modeling to engineer drug release for long-term contraception.
机制建模与机器学习的融合有助于理解和设计控释系统中的药物释放。在这里，我们提出了混合模型来预测载药量对喷雾干燥聚（L-乳酸）微球中左炔诺孕酮释放的影响。我们开发了三种蒙特卡罗方法，它们在考虑聚合物的降解性和结晶性方面有所不同，以便使用 Python 编程语言模拟药物从基质中的释放。为了构建每种方法，我们利用了对颗粒的特性进行的表征数据，例如实际药物含量（范围为 6%至 52%）、粒径（Dv(50)~5μm）和聚合物结晶度（范围为 0%至 15%）。我们使用 4 批颗粒的药物释放数据对每种方法进行训练，并通过回归分析得出适当的机器学习模型。结果表明，对于药物含量较低（<20%w/w）的颗粒，药物扩散和聚合物降解对药物释放的贡献。在更高的药物载量下，颗粒遇到了突释和扩散释放的组合。我们通过将机器学习模型的预测能力与实验数据进行比较来验证它们的预测能力。本文特别强调了混合建模在为长期避孕设计药物释放方面的强大功能。
