Costache Aurora D, Sheihet Larisa, Zaveri Krishna, Knight Doyle D, Kohn Joachim
New Jersey Center for Biomaterials and Department of Chemistry, Rutgers-The State University of New Jersey, 145 Bevier Road, Piscataway, New Jersey 08854, USA.
Mol Pharm. 2009 Sep-Oct;6(5):1620-7. doi: 10.1021/mp900114w.
A combination of molecular dynamics (MD) simulations and docking calculations was employed to model and predict polymer-drug interactions in self-assembled nanoparticles consisting of ABA-type triblock copolymers, where A-blocks are poly(ethylene glycol) units and B-blocks are low molecular weight tyrosine-derived polyarylates. This new computational approach was tested on three representative model compounds: nutraceutical curcumin, anticancer drug paclitaxel and prehormone vitamin D3. Based on this methodology, the calculated binding energies of polymer-drug complexes can be correlated with maximum drug loading determined experimentally. Furthermore, the modeling results provide an enhanced understanding of polymer-drug interactions, revealing subtle structural features that can significantly affect the effectiveness of drug loading (as demonstrated for a fourth tested compound, anticancer drug camptothecin). The present study suggests that computational calculations of polymer-drug pairs hold the potential of becoming a powerful prescreening tool in the process of discovery, development and optimization of new drug delivery systems, reducing both the time and the cost of the process.
采用分子动力学（MD）模拟和对接计算相结合的方法，对由ABA型三嵌段共聚物组成的自组装纳米颗粒中的聚合物-药物相互作用进行建模和预测，其中A嵌段为聚乙二醇单元，B嵌段为低分子量酪氨酸衍生的聚芳酯。这种新的计算方法在三种代表性模型化合物上进行了测试：营养保健品姜黄素、抗癌药物紫杉醇和前体激素维生素D3。基于该方法，聚合物-药物复合物的计算结合能可与实验测定的最大药物载量相关联。此外，建模结果有助于更深入地理解聚合物-药物相互作用，揭示了可能显著影响药物载量有效性的细微结构特征（如对第四种测试化合物抗癌药物喜树碱的研究所示）。本研究表明，聚合物-药物对的计算有望成为新药递送系统发现、开发和优化过程中的强大预筛选工具，从而减少该过程的时间和成本。
