SSIPS, Junwani, Bhilai, C.G., India.
Curr Drug Deliv. 2013 Aug;10(4):394-403. doi: 10.2174/1567201811310040004.
This study investigated the utility of a 3(2) factorial design and optimization process for nanoparticle suspension prepared by two different polymers Eudragit(®) RS 100 and Eudragit (®) RL 100 respectively. Total 18 formulations (9 formulations with each polymer) were prepared by solvent displacement technique. In these designs, two factors namely polymer weight (X1) and Aq. phase volume (X2) were evaluated each at three levels and experimental trials were performed at all nine possible combinations. Polymer weight (X1) and aqueous phase volume (X2) were selected as independent variables and particle size (Y1), % entrapment (Y2), drug release at 12(th) hrs. (Y3) are chosen as depended variables. In case of 3(2) factorial design, a full-model polynomial equation was established by subjecting the transformed values of independent variables to multiple regression analysis, and contour plots were drawn using the equation. The derived polynomial equations for particle size and % drug entrapment were verified by check point formulation. The result showed a wide variation in the responses for both of the polymer. For RS 100 polymer, particle size was 112-350 nm, entrapment: 26-72%, drug release was 42- 89% at 12(th) hrs. and for RL 100 polymer, particle size was 114-390 nm, entrapment: 30-72%, drug release was 50-90% at 12(th) hrs. The application of factorial design yielded a statistically systematic approach for the formulation and optimization of nanoparticles with desired particle size and high entrapment efficiency and release profile. The results of the optimized formulations showed particle size 225 nm, 63% drug entrapment and 83% drug release for RS 100 polymer and particle size was 148 nm, drug entrapment was 57%, drug release was 80% for RL 100 polymer.
本研究考察了 3(2)因子设计和优化过程在分别由两种不同聚合物 Eudragit(®) RS 100 和 Eudragit (®) RL 100 制备的纳米颗粒混悬液中的应用。通过溶剂置换技术制备了总共 18 种制剂（每种聚合物 9 种制剂）。在这些设计中，评估了两个因素，即聚合物重量（X1）和水相体积（X2），每个因素各有三个水平，在所有九种可能的组合中进行了实验。聚合物重量（X1）和水相体积（X2）被选为自变量，粒径（Y1）、包封率（Y2）、12 小时药物释放率（Y3）被选为因变量。在 3(2)因子设计中，通过将自变量的转换值进行多元回归分析，建立了全模型多项式方程，并使用该方程绘制了等高线图。通过检查点制剂验证了粒径和药物包封率的导出多项式方程。结果表明，两种聚合物的响应都有很大的变化。对于 RS 100 聚合物，粒径为 112-350nm，包封率为 26-72%，12 小时药物释放率为 42-89%；对于 RL 100 聚合物，粒径为 114-390nm，包封率为 30-72%，12 小时药物释放率为 50-90%。因子设计的应用为具有所需粒径、高包封效率和释放特性的纳米粒子的制剂和优化提供了一种统计学上系统的方法。优化制剂的结果显示，对于 RS 100 聚合物，粒径为 225nm，药物包封率为 63%，药物释放率为 83%；对于 RL 100 聚合物，粒径为 148nm，药物包封率为 57%，药物释放率为 80%。
