Meunier M, Goupil A, Lienard P
Dassault Systèmes Biovia, 334 Milton Road Science Park, Cambridge CB4 0WN, UK.
Dassault Systèmes Biovia, 10, rue Marcel Dassault, Velizy-Villacoublay, 78140, France.
Int J Pharm. 2017 Jun 30;526(1-2):157-166. doi: 10.1016/j.ijpharm.2017.04.043. Epub 2017 Apr 21.
Polymer nanoparticles present advantageous physical and biopharmaceutical properties as drug delivery systems compared to conventional liquid formulations. Active pharmaceutical ingredients (APIs) are often hydrophobic, thus not soluble in conventional liquid delivery. Encapsulating the drugs in polymer nanoparticles can improve their pharmacological and bio-distribution properties, preventing rapid clearance from the bloodstream. Such nanoparticles are commonly made of non-toxic amphiphilic self-assembling block copolymers where the core (poly-[d,l-lactic acid] or PLA) serves as a reservoir for the API and the external part (Poly-(Ethylene-Glycol) or PEG) serves as a stealth corona to avoid capture by macrophage. The present study aims to predict the drug affinity for PLA-PEG nanoparticles and their effective drug loading using in silico tools in order to virtually screen potential drugs for non-covalent encapsulation applications. To that end, different simulation methods such as molecular dynamics and Monte-Carlo have been used to estimate the binding of actives on model polymer surfaces. Initially, the methods and models are validated against a series of pigments molecules for which experimental data exist. The drug affinity for the core of the nanoparticles is estimated using a Monte-Carlo "docking" method. Drug miscibility in the polymer matrix, using the Hildebrand solubility parameter (δ), and the solvation free energy of the drug in the PLA polymer model is then estimated. Finally, existing published ALogP quantitative structure-property relationships (QSPR) are compared to this method. Our results demonstrate that adsorption energies modelled by docking atomistic simulations on PLA surfaces correlate well with experimental drug loadings, whereas simpler approaches based on Hildebrand solubility parameters and Flory-Huggins interaction parameters do not. More complex molecular dynamics techniques which use estimation of the solvation free energies both in PLA and in water led to satisfactory predictive models. In addition, experimental drug loadings and Log P are found to correlate well. This work can be used to improve the understanding of drug-polymer interactions, a key component to designing better delivery systems.
与传统液体制剂相比，聚合物纳米颗粒作为药物递送系统具有有利的物理和生物药剂学性质。活性药物成分（API）通常具有疏水性，因此不溶于传统液体递送体系。将药物封装在聚合物纳米颗粒中可以改善其药理和生物分布性质，防止药物从血液中快速清除。此类纳米颗粒通常由无毒的两亲性自组装嵌段共聚物制成，其中核心部分（聚[d，l-乳酸]或PLA）作为API的储存库，外部部分（聚乙二醇或PEG）作为隐形冠层以避免被巨噬细胞捕获。本研究旨在使用计算机模拟工具预测药物对PLA-PEG纳米颗粒的亲和力及其有效载药量，以便虚拟筛选用于非共价封装应用的潜在药物。为此，已使用不同的模拟方法，如分子动力学和蒙特卡罗方法，来估计活性成分在模型聚合物表面的结合情况。最初，针对一系列有实验数据的颜料分子对这些方法和模型进行了验证。使用蒙特卡罗“对接”方法估计药物对纳米颗粒核心的亲和力。然后使用希尔德布兰德溶解度参数（δ）估计药物在聚合物基质中的混溶性，以及药物在PLA聚合物模型中的溶剂化自由能。最后，将现有的已发表的ALOGP定量结构-性质关系（QSPR）与该方法进行比较。我们的结果表明，通过对PLA表面进行对接原子模拟建模得到的吸附能与实验载药量具有良好的相关性，而基于希尔德布兰德溶解度参数和弗洛里-哈金斯相互作用参数的更简单方法则不然。使用PLA和水中溶剂化自由能估计的更复杂分子动力学技术得出了令人满意的预测模型。此外，发现实验载药量与Log P具有良好的相关性。这项工作有助于增进对药物-聚合物相互作用的理解，而这是设计更好的递送系统的关键要素。
