title: Population pharmacokinetic and pharmacodynamic models of remifentanil in healthy volunteers using artificial neural network analysis.
abstract: OBJECTIVE
  An ordinary sigmoid E(max) model could not predict overshoot of electroencephalographic approximate entropy (ApEn) during recovery from remifentanil effect in our previous study. The aim of this study was to evaluate the ability of an artificial neural network (ANN) to predict ApEn overshoot and to evaluate the predictive performance of the pharmacokinetic model, and pharmacodynamic models of ANN with respect to data used.
  
  
  METHODS
  Using a reduced number of ApEn instances (n = 1581) to make NONMEM modelling feasible and complete ApEn data (n = 24 509), the presence of overshoot was assessed. A total of 1077 measured remifentanil concentrations and ApEn data, and a total of 24 509 predicted concentrations and ApEn data were used in the pharmacodynamic model A and B of ANN, respectively. The testing subset of model B (n = 7352) was used to evaluate the ability of ANN to predict overshoot of ApEn. Mean squared error (MSE) was calculated to evaluate the predictive performance of the ANN models.
  
  
  RESULTS
  With complete ApEn data, ApEn overshoot was observed in 66.7% of subjects, but only in 37% with a reduced number of ApEn instances. The ANN model B predicted 77.8% of ApEn overshoot. MSE (95% confidence interval) was 57.1 (3.22, 71.03) for the pharmacokinetic model, 0.148 (0.004, 0.007) for model A and 0.0018 (0.0017, 0.0019) for model B.
  
  
  CONCLUSIONS
  The reduced ApEn instances interfered with the approximation of true electroencephalographic response. ANN predicted 77.8% of ApEn overshoot. The predictive performance of model B was significantly better than that of model A.
authors.affiliation: School of Health Adminstration Program, Inje University, Kimhae, Korea.
authors.forename: S H
authors.initials: SH
authors.lastname: Kang
authors.forename: M R
authors.initials: MR
authors.lastname: Poynton
authors.forename: K M
authors.initials: KM
authors.lastname: Kim
authors.forename: H
authors.initials: H
authors.lastname: Lee
authors.forename: D H
authors.initials: DH
authors.lastname: Kim
authors.forename: S H
authors.initials: SH
authors.lastname: Lee
authors.forename: K S
authors.initials: KS
authors.lastname: Bae
authors.forename: O
authors.initials: O
authors.lastname: Linares
authors.forename: S E
authors.initials: SE
authors.lastname: Kern
authors.forename: G J
authors.initials: GJ
authors.lastname: Noh
mesh_terms: D000465:Algorithms; D000701:Analgesics, Opioid / Q000493:pharmacokinetics*; D004569:Electroencephalography / Q000187:drug effects*; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D010880:Piperidines / Q000493:pharmacokinetics*; D000077208:Remifentanil
chemical_list: D000701:Analgesics, Opioid; D010880:Piperidines; D000077208:Remifentanil