Takayama K, Fujikawa M, Nagai T
Department of Pharmaceutics, Hoshi University, Tokyo, Japan.
Pharm Res. 1999 Jan;16(1):1-6. doi: 10.1023/a:1011986823850.
One of the difficulties in the quantitative approach to designing pharmaceutical formulations is the difficulty in understanding the relationship between causal factors and individual pharmaceutical responses. Another difficulty is desirable formulation for one property is not always desirable for the other characteristics. This is called a multi-objective simultaneous optimization problem. A response surface method (RSM) has proven to be a useful approach for selecting 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. The aim of this review is to describe the basic concept of the multi-objective simultaneous optimization technique in which an artificial neural network (ANN) is incorporated. ANNs are being increasingly used in pharmaceutical research to predict the non-linear relationship between causal factors and response variables. The usefulness and reliability of this ANN approach is demonstrated by the optimization for ketoprofen hydrogel ointment as a typical numerical example, in comparison with the results obtained with a classical RSM approach.
定量设计药物制剂的困难之一在于难以理解因果因素与个体药物反应之间的关系。另一个困难是，对于一种特性而言理想的制剂，对于其他特性不一定理想。这被称为多目标同时优化问题。响应面法（RSM）已被证明是选择药物制剂的一种有用方法。然而，基于RSM中常用的二阶多项式方程对药物反应进行预测，通常局限于低水平，导致对最佳制剂的估计不佳。本综述的目的是描述一种结合了人工神经网络（ANN）的多目标同时优化技术的基本概念。人工神经网络在药物研究中越来越多地用于预测因果因素与反应变量之间的非线性关系。通过将酮洛芬水凝胶软膏作为典型数值示例进行优化，并与经典RSM方法得到的结果进行比较，证明了这种人工神经网络方法的实用性和可靠性。
