Di Natale Concetta, Onesto Valentina, Lagreca Elena, Vecchione Raffaele, Netti Paolo Antonio
Center for Advanced Biomaterials for Health Care (CABHC), IstitutoItaliano di Tecnologia, Largo Barsanti Matteucci 53, 80125 Napoli, Italy.
Interdisciplinary Research Centre on Biomaterials (CRIB), University of Naples Federico II, P.leTecchio 80, 80125 Naples, Italy.
Materials (Basel). 2020 Apr 11;13(8):1807. doi: 10.3390/ma13081807.
In recent years, drug delivery systems have become some of the main topics within the biomedical field. In this scenario, polymeric microparticles (MPs) are often used as carriers to improve drug stability and drug pharmacokinetics in agreement with this kind of treatment. To avoid a mere and time-consuming empirical approach for the optimization of the pharmacokinetics of an MP-based formulation, here, we propose a simple predictive in silico-supported approach. As an example, in this study, we report the ability to predict and tune the release of curcumin (CUR), used as a model drug, from a designed combination of different poly(d,l-lactide-co-glycolide) (PLGA) MPs kinds. In detail, all CUR-PLGA MPs were synthesized by double emulsion technique and their chemical-physical properties were characterized by Mastersizer and scanning electron microscopy (SEM). Moreover, for all the MPs, CUR encapsulation efficiency and kinetic release were investigated through the UV-vis spectroscopy. This approach, based on the combination of in silico and experimental methods, could be a promising platform in several biomedical applications such as vaccinations, cancer-treatment, diabetes therapy and so on.
近年来，药物递送系统已成为生物医学领域的一些主要话题。在这种情况下，聚合物微粒（MPs）常被用作载体，以根据这种治疗方式提高药物稳定性和药物药代动力学。为避免对基于MP的制剂的药代动力学进行单纯且耗时的经验性优化方法，在此，我们提出一种简单的计算机辅助预测方法。例如，在本研究中，我们报告了从不同聚（d，l-丙交酯-共-乙交酯）（PLGA）MPs种类的设计组合中预测和调节姜黄素（CUR）（用作模型药物）释放的能力。详细地说，所有CUR-PLGA MPs均通过双乳液技术合成，其化学物理性质通过马尔文粒度分析仪和扫描电子显微镜（SEM）进行表征。此外，对于所有MPs，通过紫外可见光谱研究了CUR的包封效率和动力学释放。这种基于计算机模拟和实验方法相结合的方法，在疫苗接种、癌症治疗、糖尿病治疗等多种生物医学应用中可能是一个有前景的平台。
