Brier M E, Aronoff G R
Department of Veterans Affairs Medical Center, Louisville, Kentucky, USA.
Int J Clin Pharmacol Ther. 1996 Nov;34(11):510-4.
Drug dosages and drug choices are determined by a knowledge of the drug's pharmacokinetics and pharmacodynamics. Often, insufficient information is available to determine the pharmacokinetics of a drug or which drug will have a desired effect for an individual patient. We propose that a form of nonlinear regression, an artificial neural network, can be used. We have demonstrated this use with 2 examples. In the first example we use a neural network to predict gentamicin peak and trough concentrations from routine therapeutic drug monitoring. In the second example we predict delayed renal allograft function as a guide for induction of immunosuppression therapy. Predictions were made using a multilayer feedforward perceptron and compared to nonlinear mixed effect modeling (NONMEM) and logistic regression. Neural network peak and trough gentamicin predictions were more precise and less biased than control predictions made using NONMEM. Prediction error for peak serum concentrations averaged 16.5% for the neural networks and 18.6% for NONMEM. Prediction error for trough concentrations were 48.3% for neural networks and 59.0% for NONMEM. When used for the prediction of delayed graft function, the neural network correctly predicted immediate graft function 73% of the time and delayed graft function 65% of the time. For those patients predicted to develop delayed graft function, alternate induction using anti-lymphocyte globulin may be indicated. These 2 examples demonstrate how an artificial neural network may be applied to predictions in clinical pharmacology.
药物剂量和药物选择取决于对药物药代动力学和药效学的了解。通常，可用于确定药物药代动力学或哪种药物对个体患者会产生预期效果的信息不足。我们提出可以使用一种非线性回归形式——人工神经网络。我们用两个例子证明了这种应用。在第一个例子中，我们使用神经网络根据常规治疗药物监测来预测庆大霉素的峰浓度和谷浓度。在第二个例子中，我们预测肾移植延迟功能，作为免疫抑制治疗诱导的指导。使用多层前馈感知器进行预测，并与非线性混合效应建模（NONMEM）和逻辑回归进行比较。神经网络对庆大霉素峰浓度和谷浓度的预测比使用NONMEM进行的对照预测更精确且偏差更小。神经网络对血清峰浓度的预测误差平均为16.5%，NONMEM为18.6%。神经网络对谷浓度的预测误差为百分之48.3，NONMEM为59.0%。当用于预测移植延迟功能时，神经网络正确预测即时移植功能的时间为73%，预测移植延迟功能的时间为65%。对于那些预计会发生移植延迟功能的患者，可能需要使用抗淋巴细胞球蛋白进行替代诱导。这两个例子展示了人工神经网络如何应用于临床药理学预测。
