Prediction of mycophenolic acid exposure in renal transplantation recipients by artificial neural network.
- Author:
Bin REN
1
;
Qiu-Yi HE
;
Qiong XU
;
Chang-Xi WANG
;
Jie CHEN
;
Zhi-Hua ZHENG
;
Shu-Xia LI
;
Xiao CHEN
Author Information
1. The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou 510080, China. renbinsums@163.com
- Publication Type:Journal Article
- MeSH:
Administration, Oral;
Adolescent;
Adult;
Aged;
Area Under Curve;
Drug Monitoring;
methods;
Female;
Humans;
Immunosuppressive Agents;
administration & dosage;
pharmacokinetics;
Kidney Transplantation;
Linear Models;
Male;
Middle Aged;
Mycophenolic Acid;
administration & dosage;
analogs & derivatives;
blood;
pharmacokinetics;
Neural Networks (Computer);
Young Adult
- From:
Acta Pharmaceutica Sinica
2009;44(12):1397-1401
- CountryChina
- Language:Chinese
-
Abstract:
The paper is aimed to establish an artificial neural network (ANN) for predicting mycophenolic acid (MPA) area under the plasma concentration-time curve (AUC) in renal transplantation recipients. 64 Chinese renal transplantation recipients receiving mycophenolate mofetil (MMF) were investigated. 10 timed samples were drawn at different days after transplantation. Plasma MPA concentration was determined by HPLC method and area under curve over the period of 0 to 12 h (AUC(0-12 h)) was calculated using the linear trapezoidal rule. ANN was established after network parameters were optimized using momentum method in combination with genetic algorithm. Furthermore, the predictive performance of ANN was compared with that of multiple linear regression (MLR). When using plasma MPA concentration of 0, 0.5, 2 h after MMF administration to predict MPA AUC(0-12 h), mean prediction error and mean absolute prediction error were -1.53% and 9.12%, respectively. Accuracy and precision of prediction by ANN were superior to that of MLR prediction, and similar results could be found when using plasma MPA concentration of 0, 0.5 h to predict MPA AUC(0-12h). The accuracy and precision of ANN prediction were superior to that of MLR prediction, and ANN can be used to predict MPA AUC(0-12 h).
