Division of Pharmacoengineering and Molecular Pharmaceutics, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, USA.
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, USA.
J Control Release. 2022 Nov;351:883-895. doi: 10.1016/j.jconrel.2022.09.067. Epub 2022 Oct 12.
Effective drug delivery requires ample dosing at the target tissue while minimizing negative side effects. Drug delivery vehicles such as polymeric nanoparticles (NPs) are often employed to accomplish this challenge. In this work, drug release of numerous drugs from surface eroding polymeric NPs was evaluated in vitro in physiologically relevant pH 5 and neutral buffers. NPs were loaded with paclitaxel, rapamycin, resiquimod, or doxorubicin and made from an FDA approved polyanhydride or from acetalated dextran (Ace-DEX), which has tunable degradation rates based on cyclic acetal coverage (CAC). By varying encapsulate, pH condition, and polymer, a range of distinct drug release profiles were achieved. To model the obtained drug release curves, a mechanistic mathematical model was constructed based on drug diffusion and polymer degradation. The resulting diffusion-erosion model accurately described drug release from the variety of surface eroding NPs. For drug release from varied CAC Ace-DEX NPs, the goodness of fit of the developed diffusion-erosion model was compared to several conventional drug release models. The diffusion-erosion model maintained optimal fit compared to conventional models across a range of conditions. Machine learning was then employed to estimate effective diffusion coefficients for the diffusion-erosion model, resulting in accurate prediction of in vitro release of dexamethasone and 3'3'-cyclic guanosine monophosphate-adenosine monophosphate from Ace-DEX NPs. This predictive modeling has potential to aid in the design of future Ace-DEX formulations where optimized drug release kinetics can lead to a desired therapeutic effect.
有效的药物输送需要在靶组织中充分给药，同时最大限度地减少负面副作用。药物输送载体，如聚合物纳米颗粒（NPs），常被用于实现这一挑战。在这项工作中，在生理相关的 pH 值 5 和中性缓冲液中，评估了许多药物从表面侵蚀聚合物 NPs 中的药物释放。NP 装载紫杉醇、雷帕霉素、瑞喹莫德或阿霉素，并由 FDA 批准的聚酸酐或乙酰化葡聚糖（Ace-DEX）制成，后者具有基于环状缩醛覆盖率（CAC）的可调降解率。通过改变封装、pH 值条件和聚合物，可以实现一系列不同的药物释放曲线。为了模拟所获得的药物释放曲线，根据药物扩散和聚合物降解构建了一种机械数学模型。所得的扩散-侵蚀模型准确描述了各种表面侵蚀 NPs 中的药物释放。对于来自不同 CAC Ace-DEX NPs 的药物释放，开发的扩散-侵蚀模型的拟合优度与几种常规药物释放模型进行了比较。在一系列条件下，扩散-侵蚀模型与常规模型相比保持了最佳拟合。然后，采用机器学习来估计扩散-侵蚀模型的有效扩散系数，从而准确预测了从 Ace-DEX NPs 中释放的地塞米松和 3'3'-环鸟苷单磷酸-腺苷单磷酸。这种预测性建模有可能有助于未来的 Ace-DEX 配方设计，其中优化的药物释放动力学可以带来预期的治疗效果。
