Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs
Empirical search for factors affecting mean particle size of PLGA microspheres containing macromolecular drugs
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DOI：
10.1016/j.cmpb.2016.07.006
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发表时间：
2016-10-01
影响因子：
6.1
通讯作者：
Mendyk, Aleksander
中科院分区：
文献类型：
--
作者：
Szlek, Jakub;Paclawski, Adam;Mendyk, Aleksander
背景和目的：聚乳酸-羟基乙酸（PLGA）已成为在设计、开发和优化医疗应用领域最有前途的聚合物之一。基于plga的多颗粒剂型通常制备为微球，其大小从5到100 μ m，取决于给药途径。该研究的主要目的是开发平均体积粒度的预测模型，并在其基础上提取含有蛋白质形成行为的PLGA知识。方法：在本研究中，提出了一个预测平均体积粒径的模型，该模型是由R环境的rgp软件包开发的。在数据挖掘过程中还应用了fscaret、monmlp、fugeR、MARS、SVM、kNNreg、Cubist、randomForest和分段线性回归等工具。结果：fscaret包提供的特征选择将原始输入向量从总共295个输入变量减少到10、16和19个。建立的模型具有良好的预测能力，在10倍交叉验证训练过程中，归一化均方根误差（NRMSE）为6.8 ~ 11.1%。并利用外部实验数据对最佳模型进行了验证。rgp模型具有较好的预测能力，模型为经典方程形式，标准化均方根误差（NRMSE）为6.1%。结论：提出了一种新的PLGA微球平均粒径计算模型和基于树的规则提取方法。特征选择导致揭示化学描述变量，这对预测PLGA微球的大小很重要。为了更好地理解粒径与配方特性之间的关系，采用了表面分析法和规则提取程序。2016爱思唯尔爱尔兰有限公司版权所有。
Background and objectives: Poly(lactic-co-glycolic acid) (PLGA) has become one of the most promising in design, development, and optimization formedical applications polymers. PLGA-based multiparticulate dosage forms are usually prepared as microspheres where the size is from 5 to 100 mu m, depending on the route of administration. The main objectives of the study were to develop a predictive model of mean volumetric particle size and on its basis extract knowledge of PLGA containing proteins forming behaviour.Methods: In the present study, a model for the prediction of mean volumetric particle size developed by an rgp package of R environment is presented. Other tools like fscaret, monmlp, fugeR, MARS, SVM, kNNreg, Cubist, randomForest and piecewise linear regression are also applied during the data mining procedure.Results: The feature selection provided by the fscaret package reduced the original input vector from a total of 295 input variables to 10, 16 and 19. The developed models had good predictive ability, which was confirmed by a normalized root-mean-square error (NRMSE) of 6.8 to 11.1% in 10-fold cross validation training procedure. Moreover, the best models were validated using external experimental data. The superior predictiveness had a model obtained by rgp in the form of a classical equation with a normalized root-mean-squared error (NRMSE) of 6.1%.Conclusions: A new approach is proposed for computational modelling of the mean particle size of PLGA microspheres and rules extraction from tree-based models. The feature selection leads to revealing chemical descriptor variables which are important in predicting the size of PLGA microspheres. In order to achieve better understanding in the relationships between particle size and formulation characteristics, the surface analysis method and rules extraction procedures were applied. (C) 2016 Elsevier Ireland Ltd. All rights reserved.