Application of back-propagation artificial neural network and curve estimation in pharmacokinetics of losartan in rabbit

作者：Lin, Bin; Lin, Gaotong; Liu, Xianyun; Ma, Jianshe; Wang, Xianchuan; Lin, Feiyan^*; Hu, Lufeng

来源：International Journal of Clinical and Experimental Medicine, 2015, 8(12): 22352-22358.

• Artificial neural network
• back-propagation
• pharmacokinetics
• losartan

In order to develop pharmacokinetic model, a well-known multilayer feed-forward algorithm back-propagation artificial neural networks (BP-ANN) was applied to the pharmacokinetics of losartan in rabbit. The plasma concentrations of losartan in twelve rabbits, which were divided into two groups and given losartan 2 mg/kg by intravenous (Iv) and intragastrical (Ig) administration, were determined by LC-MS. The BP-ANN model included one input layer, hidden layers, and one output layer was constructed and compared with curve estimation based on the time-concentration data of losartan. The results showed the BP-ANN model had high goodness of fit index and good coherence (R > 0.99) between forecasted concentration and measured concentration both in Iv and Ig administration. The residuals of each concentrations generated by BP-ANN model were all smaller than Curve estimation. The pharmacokinetic result showed there was no significant difference between measured and simulated pharmacokinetic parameters including AUC((0-t)), AUC((0-8)), MRT(0-t), MRT(0-8), T-1/2 V and C-max (P > 0.05). In conclusion, the BP-ANN model has remarkably accurate predictions ability, which better than Curve estimation, and can be used as a utility tool in pharmacokinetic experiment.

• 出版日期2015
• 单位温州市中心医院; 温州医科大学