Department of Materials Design and Innovation , University at Buffalo , Buffalo , New York 14260 , United States.
Mol Pharm. 2019 May 6;16(5):1917-1928. doi: 10.1021/acs.molpharmaceut.8b01272. Epub 2019 Apr 26.
Drug delivery vehicles can improve the functional efficacy of existing antimicrobial therapies by improving biodistribution and targeting. A critical property of such nanomedicine formulations is their ability to control the release kinetics of their payloads. The combination of (and interactions among) polymer, drug, and nanoparticle properties gives rise to nonlinear behavioral relationships and large data space. These factors complicate both first-principles modeling and screening of nanomedicine formulations. Predictive analytics may offer a more efficient approach toward the rational design of nanomedicines by identifying key descriptors and correlating them to nanoparticle release behavior. In this work, antibiotic release kinetics data were generated from polyanhydride nanoparticle formulations with varying copolymer compositions, encapsulated drug type, and drug loading. Four antibiotics, doxycycline, rifampicin, chloramphenicol, and pyrazinamide, were used. Linear manifold learning methods were used to relate drug release properties with polymer, drug, and nanoparticle properties, and key descriptors were identified that are highly correlated with release properties. However, these linear methods could not predict release behavior. Nonlinear multivariate modeling based on graph theory was then used to deconvolute the governing relationships between these properties, and predictive models were generated to rapidly screen lead nanomedicine formulations with desirable release properties with minimal nanoparticle characterization. Release kinetics predictions of two drugs containing atoms not included in the model showed good agreement with experimental results, validating the model and indicating its potential to virtually explore new polymer and drug pairs not included in the training data set. The models were shown to be robust after the inclusion of these new formulations, in that the new inclusions did not significantly change model regression. This approach provides the first step toward the development of a framework that can be used to rationally design nanomedicine formulations by selecting the appropriate carrier for a drug payload to program desirable release kinetics.
药物输送载体可以通过改善生物分布和靶向来提高现有抗菌疗法的功能疗效。此类纳米医学制剂的关键特性是其控制有效载荷释放动力学的能力。聚合物、药物和纳米颗粒特性的结合（和相互作用）产生了非线性行为关系和大数据空间。这些因素使纳米医学制剂的第一性原理建模和筛选变得复杂。通过识别关键描述符并将其与纳米颗粒释放行为相关联，预测分析可能为合理设计纳米药物提供更有效的方法。在这项工作中，我们使用不同共聚物组成、包裹药物类型和药物载量的聚酸酐纳米颗粒制剂生成了抗生素释放动力学数据。使用了四种抗生素，即强力霉素、利福平、氯霉素和吡嗪酰胺。我们使用线性流形学习方法将药物释放特性与聚合物、药物和纳米颗粒特性相关联，并确定了与释放特性高度相关的关键描述符。然而，这些线性方法无法预测释放行为。然后，基于图论的非线性多变量建模被用于剖析这些特性之间的控制关系，并生成预测模型，以快速筛选具有理想释放特性的先导纳米医学制剂，而无需对纳米颗粒进行最小特征化。对于包含模型中未包含原子的两种药物的释放动力学预测，与实验结果吻合良好，验证了模型并表明其具有虚拟探索新的聚合物和药物对的潜力，这些新的聚合物和药物对不在训练数据集内。在包含这些新制剂后，模型表现出稳健性，因为新制剂的加入并没有显著改变模型回归。这种方法为开发一种框架提供了第一步，该框架可以通过选择适合药物有效载荷的载体来合理设计纳米医学制剂，以编程理想的释放动力学。
