Haidar Sam H, Johnson Steven B, Fossler Michael J, Hussain Ajaz S
Office of Clinical Pharmacology and Biopharmaceutics, FDA, Rockville, Maryland 20857, USA.
Pharm Res. 2002 Jan;19(1):87-91. doi: 10.1023/a:1013611617787.
To develop a predictive population pharmacokinetic/ pharmacodynamic (PK/PD) model for repaglinide (REP), an oral hypoglycemic agent, using artificial neural networks (ANNs).
REP, glucose concentrations, and demographic data from a dose ranging Phase 2 trial were divided into a training set (70%) and a test set (30%). NeuroShell Predictor was used to create predictive PK and PK/PD models using population covariates: evaluate the relative significance of different covariates; and simulate the effect of covariates on the PK/PD of REP. Predictive performance was evaluated by calculating root mean square error and mean error for the training and test sets. These values were compared to naive averaging (NA) and randomly generated numbers (RN).
Covariates found to have an influence on PK of REP include dose, gender. race, age, and weight. Covariates affecting the glucose response included dose, gender, and weight. These differences are not expected to be clinically significant.
We came to the following three conclusions: 1) ANNs are more precise than NA and RN for both PK and PD; 2) the bias was acceptable for ANNs as compared with NA and RN; and 3) neural networks offer a quick and simple method for predicting, for identifying significant covariates, and for generating hypotheses.
使用人工神经网络（ANNs）开发一种用于口服降糖药瑞格列奈（REP）的预测性群体药代动力学/药效学（PK/PD）模型。
将来自一项剂量范围探索性2期试验的瑞格列奈、血糖浓度和人口统计学数据分为训练集（70%）和测试集（30%）。使用NeuroShell Predictor软件，利用群体协变量创建预测性PK和PK/PD模型：评估不同协变量的相对重要性；并模拟协变量对瑞格列奈PK/PD的影响。通过计算训练集和测试集的均方根误差和平均误差来评估预测性能。将这些值与简单平均法（NA）和随机生成数（RN）进行比较。
发现对瑞格列奈PK有影响的协变量包括剂量、性别、种族、年龄和体重。影响血糖反应的协变量包括剂量、性别和体重。预计这些差异在临床上不具有显著意义。
我们得出以下三个结论：1）对于PK和PD，人工神经网络比简单平均法和随机生成数更精确；2）与简单平均法和随机生成数相比，人工神经网络的偏差是可接受的；3）神经网络为预测、识别显著协变量和生成假设提供了一种快速简单的方法。
