Heuristic modeling of macromolecule release from PLGA microspheres.
Heuristic modeling of macromolecule release from PLGA microspheres.
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DOI：
10.2147/ijn.s53364
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发表时间：
2013
影响因子：
8
通讯作者：
Mendyk A
中科院分区：
文献类型：
--
作者：
Szlęk J;Pacławski A;Lau R;Jachowicz R;Mendyk A
蛋白质大分子从聚(乳酸-乙醇酸)(PLGA)微粒中的溶解是一个复杂的过程，目前还没有完全了解。因此，在设计、开发和优化基于PLGA的多颗粒剂型的医疗应用和毒性评估中，获得可能具有基本意义的预测模型是困难的。在本研究中，提出了两个具有相当拟合度的模型来预测PLGA微米和纳米粒中的大分子溶出度。在这两种情况下，都使用了启发式技术，如人工神经网络(ANN)、特征选择和遗传编程。Fscaret软件包提供的特征选择和ANN执行的灵敏度分析将原始输入向量从总共300个输入变量减少到21、17、16和11个；为了更好地了解泛化误差，每种方法都提出了两个截止点。用单调多层感知器神经网络(MON-MLP)建立神经网络模型，其均方根误差(RMSE)为15.4，输入向量由11个输入组成。从一个包含17个输入的数据库中得出的复杂经典方程能够产生14.3的较好的泛化误差(RMSE)。该方程由四个参数描述，因此适用于标准的非线性回归技术。启发式建模得到的ANN模型描述了PLGA微球中的大分子释放曲线，具有良好的预测效率。此外，遗传编程技术使经典方程具有与人工神经网络模型相当的可预测性。
Dissolution of protein macromolecules from poly(lactic-co-glycolic acid) (PLGA) particles is a complex process and still not fully understood. As such, there are difficulties in obtaining a predictive model that could be of fundamental significance in design, development, and optimization for medical applications and toxicity evaluation of PLGA-based multiparticulate dosage form. In the present study, two models with comparable goodness of fit were proposed for the prediction of the macromolecule dissolution profile from PLGA micro- and nanoparticles. In both cases, heuristic techniques, such as artificial neural networks (ANNs), feature selection, and genetic programming were employed. Feature selection provided by fscaret package and sensitivity analysis performed by ANNs reduced the original input vector from a total of 300 input variables to 21, 17, 16, and eleven; to achieve a better insight into generalization error, two cut-off points for every method was proposed. The best ANNs model results were obtained by monotone multi-layer perceptron neural network (MON-MLP) networks with a root-mean-square error (RMSE) of 15.4, and the input vector consisted of eleven inputs. The complicated classical equation derived from a database consisting of 17 inputs was able to yield a better generalization error (RMSE) of 14.3. The equation was characterized by four parameters, thus feasible (applicable) to standard nonlinear regression techniques. Heuristic modeling led to the ANN model describing macromolecules release profiles from PLGA microspheres with good predictive efficiency. Moreover genetic programming technique resulted in classical equation with comparable predictability to the ANN model.