Optimization of transdermal and transmucosal drug delivery systems utilizing artificial neural networks

药物制剂由几个制剂因素和工艺变量组成。必须同时优化与有效性、有用性、稳定性和安全性相关的几个响应。因此，需要专门知识和经验来设计可接受的药物制剂。响应面法（RSM）已被广泛用于选择可接受的药物制剂。然而，基于RSM中常用的二阶多项式方程的药物响应预测通常限于低水平，导致最佳制剂的估计较差。提出了一种结合人工神经网络的多目标同时优化方法。将该方法应用于以1-O-乙基-3-正丁基环己醇为吸收促进剂的酮洛芬水凝胶处方的优化。人工神经网络越来越多地用于药物研究，以预测因果因素和响应变量之间的非线性关系。在优化配方中的几个特性的观察结果与预测吻合良好，表明上级功能的人工神经网络方法。

A pharmaceutical formulation is composed of several formulation factors and process variables. Several responses relating to the effectiveness, usefulness, stability, as well as safety must be optimized simultaneously. Consequently, expertise and experience are required to design acceptable pharmaceutical formulations. A response surface method (RSM) has widely been used for selecting acceptable pharmaceutical formulations. However, prediction of pharmaceutical responses based on the second-order polynomial equation commonly used in RSM, is often limited to low levels, resulting in poor estimations of optimal formulations. A multi-objective simultaneous optimization method incorporating an artificial neural network (ANN) was developed. The method was applied to the optimization of ketoprofen hydrogel formulations including 1-O-ethyl-3-n-butylcyclohexanol as absorption enhancer. ANNs are being increasingly used in pharmaceutical research to predict the nonlinear relationship between causal factors and response variables. The observed results of several characteristics in the optimum formulations coincided well with the predictions, suggesting superior function of the ANN approach.