Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, Torino, 10129, Italy.
Department of Biomedical Engineering, Yonsei University, Wonju, 26493, Gangwon State, Republic of Korea.
J Mol Model. 2024 Jun 19;30(7):219. doi: 10.1007/s00894-024-06023-x.
The rapid growth and diversification of drug delivery systems have been significantly supported by advancements in micro- and nano-technologies, alongside the adoption of biodegradable polymeric materials like poly(lactic-co-glycolic acid) (PLGA) as microcarriers. These developments aim to reduce toxicity and enhance target specificity in drug delivery. The use of in silico methods, particularly molecular dynamics (MD) simulations, has emerged as a pivotal tool for predicting the dynamics of species within these systems. This approach aids in investigating drug delivery mechanisms, thereby reducing the costs associated with design and prototyping. In this study, we focus on elucidating the diffusion mechanisms in curcumin-loaded PLGA particles, which are critical for optimizing drug release and efficacy in therapeutic applications.
We utilized MD to explore the diffusion behavior of curcumin in PLGA drug delivery systems. The simulations, executed with GROMACS, modeled curcumin molecules in a representative volume element of PLGA chains and water, referencing molecular structures from the Protein Data Bank and employing the CHARMM force field. We generated PLGA chains of varying lengths using the Polymer Modeler tool and arranged them in a bulk-like environment with Packmol. The simulation protocol included steps for energy minimization, T and p equilibration, and calculation of the isotropic diffusion coefficient from the mean square displacement. The Taguchi method was applied to assess the effects of hydration level, PLGA chain length, and density on diffusion.
Our results provide insight into the influence of PLGA chain length, hydration level, and polymer density on the diffusion coefficient of curcumin, offering a mechanistic understanding for the design of efficient drug delivery systems. The sensitivity analysis obtained through the Taguchi method identified hydration level and PLGA density as the most significant input parameters affecting curcumin diffusion, while the effect of PLGA chain length was negligible within the simulated range. We provided a regression equation capable to accurately fit MD results. The regression equation suggests that increases in hydration level and PLGA density result in a decrease in the diffusion coefficient.
微纳技术的进步以及可生物降解聚合物材料（如聚乳酸-共-羟基乙酸）（PLGA）作为微载体的采用，极大地促进了药物传递系统的快速发展和多样化。这些发展旨在降低药物传递中的毒性并提高靶向特异性。使用计算方法，特别是分子动力学（MD）模拟，已成为预测这些系统中物种动力学的关键工具。这种方法有助于研究药物传递机制，从而降低设计和原型制作的成本。在这项研究中，我们专注于阐明负载姜黄素的 PLGA 颗粒中的扩散机制，这对于优化药物释放和治疗应用中的疗效至关重要。
我们利用 MD 来探索姜黄素在 PLGA 药物传递系统中的扩散行为。使用 GROMACS 执行的模拟，在 PLGA 链和水中的代表性体积元中模拟了姜黄素分子，参考了蛋白质数据库中的分子结构，并使用 CHARMM 力场。我们使用 Polymer Modeler 工具生成了不同长度的 PLGA 链，并使用 Packmol 将它们排列在类似块状的环境中。模拟协议包括能量最小化、T 和 p 平衡以及从均方位移计算各向同性扩散系数的步骤。Taguchi 方法用于评估水合水平、PLGA 链长和密度对扩散的影响。
我们的结果深入了解了 PLGA 链长、水合水平和聚合物密度对姜黄素扩散系数的影响，为设计高效药物传递系统提供了机制理解。通过 Taguchi 方法获得的敏感性分析确定了水合水平和 PLGA 密度是影响姜黄素扩散的最重要输入参数，而在模拟范围内 PLGA 链长的影响可以忽略不计。我们提供了一个能够准确拟合 MD 结果的回归方程。该回归方程表明，水合水平和 PLGA 密度的增加会导致扩散系数降低。
