title: Prediction of drug disposition in diabetic patients by means of a physiologically based pharmacokinetic model.
abstract: OBJECTIVE
  Accumulating evidence has shown that diabetes mellitus may affect the pharmacokinetics of some drugs, leading to alteration of pharmacodynamics and/or toxic effects. The aim of this study was to develop a novel physiologically based pharmacokinetic (PBPK) model for predicting drug pharmacokinetics in patients with type 2 diabetes mellitus quantitatively.
  
  
  METHODS
  Contributions of diabetes-induced alteration of physiological parameters including gastric emptying rates, intestinal transit time, drug metabolism in liver and kidney functions were incorporated into the model. Plasma concentration-time profiles and pharmacokinetic parameters of seven drugs (antipyrine, nisoldipine, repaglinide, glibenclamide, glimepiride, chlorzoxazone, and metformin) in non-diabetic and diabetic patients were predicted using the developed model. The PBPK model coupled with a Monte-Carlo simulation was also used to predict the means and variability of pharmacokinetic parameters.
  
  
  RESULTS
  The predicted area under the plasma concentration-time curve (AUC) and maximum (peak) concentration (C max) were reasonably consistent (<2-fold errors) with the reported values. Sensitivity analysis showed that gut transit time, hepatic enzyme activity, and renal function affected the pharmacokinetic characteristics of these drugs. Shortened gut transit time only decreased the AUC of controlled-released drugs and drugs with low absorption rates. Impairment of renal function markedly altered pharmacokinetics of drugs mainly eliminated via the kidneys.
  
  
  CONCLUSIONS
  All of these results indicate that the developed PBPK model can quantitatively predict pharmacokinetic alterations induced by diabetes.
authors.affiliation: Center of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Jia
authors.initials: J
authors.lastname: Li
authors.forename: Hai-Fang
authors.initials: HF
authors.lastname: Guo
authors.forename: Can
authors.initials: C
authors.lastname: Liu
authors.forename: Zeyu
authors.initials: Z
authors.lastname: Zhong
authors.forename: Li
authors.initials: L
authors.lastname: Liu
authors.forename: Xiao-Dong
authors.initials: XD
authors.lastname: Liu
mesh_terms: D019540:Area Under Curve; D003198:Computer Simulation; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy / Q000378:metabolism*; D006801:Humans; D007004:Hypoglycemic Agents / Q000097:blood / Q000493:pharmacokinetics*; D007668:Kidney / Q000378:metabolism; D008099:Liver / Q000378:metabolism; D008297:Male; D008954:Models, Biological; D009010:Monte Carlo Method
chemical_list: D007004:Hypoglycemic Agents