A predictive mechanistic model of drug release from surface eroding polymeric nanoparticles.
A predictive mechanistic model of drug release from surface eroding polymeric nanoparticles.
复制标题
DOI：
10.1016/j.jconrel.2022.09.067
复制
发表时间：
2022-11
影响因子：
10.8
通讯作者：
Ainslie, Kristy M.
中科院分区：
文献类型：
--
作者：
Stiepel, Rebeca T.;Pena, Erik S.;Ehrenzeller, Stephen A.;Gallovic, Matthew D.;Lifshits, Liubov M.;Genito, Christopher J.;Bachelder, Eric M.;Ainslie, Kristy M.
有效的药物递送需要在靶组织中提供充足的剂量，同时尽量减少负面副作用。通常采用聚合物纳米颗粒（NPs）等药物递送载体来完成这一挑战。在这项工作中，在生理相关的pH 5和中性缓冲液中，体外评估了许多药物从表面腐蚀的聚合物NPs中释放的药物。NPs装载紫杉醇、雷帕霉素、瑞喹莫特或阿霉素，由FDA批准的多酸酐或醋酸化葡聚糖（Ace-DEX）制成，其降解率可根据环缩醛覆盖（CAC）进行调节。通过改变包封物、pH条件和聚合物，获得了一系列不同的药物释放曲线。为了模拟得到的药物释放曲线，建立了基于药物扩散和聚合物降解的机理数学模型。生成的diffusion-erosion模型准确地描述各种表面侵蚀NPs药物释放。对于不同CAC Ace-DEX NPs的药物释放，比较了所建立的扩散-侵蚀模型与几种常规药物释放模型的拟合优度。与传统模型相比，扩散-侵蚀模型在一系列条件下保持最佳拟合。然后利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Ace-DEX NPs中地塞米松和3 ' 3 ' -环鸟苷单磷酸-单磷酸腺苷的体外释放。这个预测建模有潜在的帮助在未来Ace-DEX配方的设计优化的药物释放动力学可以导致预期的治疗效果。
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.
登录
查看更多内容
影响因子：
--
作者：
Amreddy N;Babu A;Muralidharan R;Panneerselvam J;Srivastava A;Ahmed R;Mehta M;Munshi A;Ramesh R
通讯作者：
Ramesh R
影响因子：
--
作者：
Gao Y;Zuo J;Bou-Chacra N;Pinto Tde J;Clas SD;Walker RB;Löbenberg R
通讯作者：
Löbenberg R
影响因子：
4.9
作者：
Collier MA;Junkins RD;Gallovic MD;Johnson BM;Johnson MM;Macintyre AN;Sempowski GD;Bachelder EM;Ting JP;Ainslie KM
通讯作者：
Ainslie KM
影响因子：
4.2
作者：
Bhattacharjee, Sourav
通讯作者：
Bhattacharjee, Sourav
DOI：
10.1073/pnas.0901592106
发表时间：
2009-04-07
影响因子：
11.1
作者：
Broaders, Kyle E.;Cohen, Joel A.;Frechet, Jean M. J.
通讯作者：
Frechet, Jean M. J.