Shalaby Karim S, Soliman Mahmoud E, Casettari Luca, Bonacucina Giulia, Cespi Marco, Palmieri Giovanni F, Sammour Omaima A, El Shamy Abdelhameed A
Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt.
Department of Biomolecular Sciences, School of Pharmacy, University of Urbino, Urbino, Italy.
Int J Nanomedicine. 2014 Oct 23;9:4953-64. doi: 10.2147/IJN.S68737. eCollection 2014.
In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations.
在本研究中，基于聚乙二醇和聚丙交酯的二嵌段和三嵌段共聚物通过开环聚合反应合成，并通过质子核磁共振和凝胶渗透色谱进行表征。使用纳米沉淀法从这些可生物降解且具有生物相容性的共聚物制备了含有那可丁的纳米颗粒。对制备的纳米颗粒进行了粒径和药物包封率表征，并通过透射电子显微镜成像检查了它们的形态和尺寸。构建了人工神经网络，并测试了其使用制备步骤中所采用的不同因素（即聚合物分子量、聚合物与药物的比例以及构成聚合物的嵌段数）来预测所形成纳米颗粒中那可丁粒径和包封率的能力。利用这些网络发现，聚合物分子量对粒径影响最大。另一方面，发现聚合物与药物的比例是对药物包封率最具影响的因素。本研究证明了人工神经网络不仅能够预测所形成纳米颗粒的粒径，还能预测药物包封率。这可能对基于聚乙二醇和聚丙交酯的共聚物设计产生重大影响，并可用于定制所需的目标制剂。
