Department of Pharmaceutical Science and Technology, School of Chemical and Pharmaceutical Sciences, University of Chile, Santos Dumont 964, 4to piso, Of. 09, Independencia, 8380494, Santiago, Chile.
Department of Pharmacology, Pharmacy and Pharmaceutical Technology, University of Santiago, 15782, Santiago de Compostela, Spain.
Drug Deliv Transl Res. 2018 Dec;8(6):1797-1806. doi: 10.1007/s13346-017-0446-8.
Nanoprecipitation is a simple and fast method to produce polymeric nanoparticles (Np); however, most applications require filtration or another separation technique to isolate the nanosuspension from aggregates or polydisperse particle production. In order to avoid variability introduced by these additional steps, we report here a systematic study of the process to yield monomodal and uniform Np production with the nanoprecipitation method. To further identify key variables and their interactions, we used artificial neural networks (ANN) to investigate the multiple variables which influence the process. In this work, a polymethacrylate derivative was used for Np (NpERS) and a database with several formulations and conditions was developed for the ANN model. The resulting ANN model had a high predictability (> 70%) for NpERS characteristics measured (mean size, PDI, zeta potential, and number of particle populations). Moreover, the model identified production variables leading to polymer supersaturation, such as mixing time and turbulence, as key in achieving monomodal and uniform NpERS in one production step. Polymer concentration and type of solvent, modifiers of polymer diffusion and supersaturation, were also shown to control NpERS characteristics. The ANN study allowed the identification of key variables and their interactions and resulted in a predictive model to study the NpERS production by nanoprecipitation. In turn, we have achieved an optimized method to yield uniform NpERS which could pave way for polymeric nanoparticle production methods with potential in biological and drug delivery applications.
纳米沉淀法是一种生产聚合物纳米粒子（Np）的简单快速方法；然而，大多数应用需要过滤或其他分离技术来将纳米混悬液与聚集体或多分散颗粒生产分离。为了避免这些额外步骤带来的可变性，我们在此报告了一项系统研究，以采用纳米沉淀法生产单分散和均匀的 Np。为了进一步确定关键变量及其相互作用，我们使用人工神经网络（ANN）研究了影响该过程的多个变量。在这项工作中，使用了一种聚丙烯酸酯衍生物作为 Np（NpERS），并为 ANN 模型开发了具有多种配方和条件的数据库。所得的 ANN 模型对所测量的 NpERS 特性（平均粒径、PDI、Zeta 电位和颗粒群体数量）具有很高的可预测性（>70%）。此外，该模型确定了导致聚合物过饱和度的生产变量，例如混合时间和湍流，是在一步生产中实现单分散和均匀 NpERS 的关键。聚合物浓度和溶剂类型、聚合物扩散和过饱和度调节剂也被证明可以控制 NpERS 特性。ANN 研究能够识别关键变量及其相互作用，并产生了一个预测模型来研究纳米沉淀法生产 NpERS。反过来，我们已经实现了一种优化的方法来生产均匀的 NpERS，这可能为具有生物和药物输送应用潜力的聚合物纳米颗粒生产方法铺平道路。
