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首页> 外文期刊>International Journal of Pharmaceutics >Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets
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Comparison of multi-linear regression, particle swarm optimization artificial neural networks and genetic programming in the development of mini-tablets

机译：多线性回归，粒子群优化人工神经网络与迷你平板电脑开发中的遗传规划比较

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摘要

In the present study, the preparation of pharmaceutical mini-tablets was attempted in the framework of Quality by Design (QbD) context, by comparing traditionally used multi-linear regression (MLR), with artificially-intelligence based regression techniques (such as standard artificial neural networks (ANNs), particle swarm optimization (PSO) ANNs and genetic programming (GP)) during Design of Experiment (DoE) implementation. Specifically, the effect of diluent type and particle size fraction for three commonly used direct compression diluents (lactose, pregelatinized starch and dibasic calcium phosphate dihydrate, DCPD) blended with either hydrophilic or hydrophobic flowing aids was evaluated in terms of: a) powder blend properties (such as bulk (Y1) and tapped (Y-2) density, Carr's compressibility index (Y-3, CCI), Kawakita's compaction fitting parameters a (Y-4) and 1/b (Y-5)), and b) mini-tablet's properties (such as relative density (Y-6), average weight (Y-7) and weight variation (Y-8)). Results showed better flowing properties for pregelatinized starch and improved packing properties for lactose and DPCD. MLR analysis showed high goodness of fit for the Y-1, Y-2, Y-4, Y-6 and Y-8 with RMSE values of Y-1 = 0.028, Y-2 = 0.032, Y-4 = 0.019, Y-6 = 0.015 and Y-8 = 0.130; while for rest responses, high correlation was observed from both standard ANNs and GP. PSO-ANNs fitting was the only regression technique that was able to adequately fit all responses simultaneously (RMSE values of Y-1 = 0.026, Y-2 = 0.022, Y-3 = 0.025, Y-4 = 0.010, Y-5 = 0.063, Y-6 = 0.013, Y-7 = 0.064 and Y-8 = 0.104).
机译：在本研究中，通过比较传统使用的多线性回归（MLR），在设计（QBD）上下文中，在质量框架中尝试制备药物迷你片，通过比较传统使用的多线性回归（MLR），具有基于人工智能的回归技术（例如标准人工实验（DOE）实施期间，神经网络（ANNS），粒子群优化（PSO）ANN和遗传编程（GP））。具体地，对三种常用的直接压缩稀释剂（乳糖，预胶化淀粉和二元磷酸二元钙二水合物，DCPD）的稀释剂型和粒度级分的作用依次评价与亲水或疏水流动助剂共混的：a）粉末混合物性能（如散装（Y1）和螺纹（Y-2）密度，Carr的可压缩性指数（Y-3，CCI），Kawakita的压实拟合参数A（Y-4）和1 / B（Y-5））和B. ）迷你平板电脑的性质（如相对密度（y-6），平均重量（y-7）和重量变化（y-8））。结果表明，用于乳糖和DPCD的预鉴定淀粉和改进的填充性能更好的流动性能。 MLR分析表现为Y-1，Y-2，Y-4，Y-6和Y-8的高性度，RMSE值Y-1 = 0.028，Y-2 = 0.032，Y-4 = 0.019， Y-6 = 0.015和Y-8 = 0.130;虽然对于休息响应，从标准ANN和GP观察到高相关。 PSO-Anns拟合是能够同时充分适应所有响应的唯一回归技术（Y-1 = 0.026，Y-2 = 0.022，Y-3 = 0.025，Y-4 = 0.010，Y-5 = 0.010，Y-5 = 0.063，Y-6 = 0.013，Y-7 = 0.064和Y-8 = 0.104）。

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