title: Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent using neural networks.
abstract: OBJECTIVE
  To develop a predictive population pharmacokinetic/ pharmacodynamic (PK/PD) model for repaglinide (REP), an oral hypoglycemic agent, using artificial neural networks (ANNs).
  
  
  METHODS
  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).
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Office of Clinical Pharmacology and Biopharmaceutics, FDA, Rockville, Maryland 20857, USA. haidars@cder.fda.gov
authors.forename: Sam H
authors.initials: SH
authors.lastname: Haidar
authors.forename: Steven B
authors.initials: SB
authors.lastname: Johnson
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fossler
authors.forename: Ajaz S
authors.initials: AS
authors.lastname: Hussain
mesh_terms: D001786:Blood Glucose / Q000032:analysis; D002219:Carbamates / Q000493:pharmacokinetics* / Q000494:pharmacology*; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000188:drug therapy; D005260:Female; D006801:Humans; D007004:Hypoglycemic Agents / Q000493:pharmacokinetics* / Q000494:pharmacology*; D008297:Male; D016571:Neural Networks, Computer; D010880:Piperidines / Q000493:pharmacokinetics* / Q000494:pharmacology*; D011237:Predictive Value of Tests
chemical_list: D001786:Blood Glucose; D002219:Carbamates; D007004:Hypoglycemic Agents; D010880:Piperidines; C072379:repaglinide