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Nonlinear mixed-effects pharmacokinetic modeling of the novel COX-2 selective inhibitor vitacoxib in dogs.

Jianzhong WangBenjamin K SchneiderPan SunXiaohui GongJicheng QiuJing LiYeon-Jung SeoJonathan P MochelXingyuan Cao
Published in: Journal of veterinary pharmacology and therapeutics (2019)
The objective of this study was to develop a nonlinear mixed-effects model of vitacoxib disposition kinetics in dogs after intravenous (I.V.), oral (P.O.), and subcutaneous (S.C.) dosing. Data were pooled from four consecutive pharmacokinetic studies in which vitacoxib was administered in various dosing regimens to 14 healthy beagle dogs. Plasma concentration versus time data were fitted simultaneously using the stochastic approximation expectation maximization (SAEM) algorithm for nonlinear mixed-effects as implemented in Monolix version 2018R2. Correlations between random effects and significance of covariates on population parameter estimates were evaluated using multiple samples from the posterior distribution of the random effects. A two-compartment mamillary model with first-order elimination and first-order absorption after P.O. and S.C. administration, best described the available pharmacokinetic data. Final parameter estimates indicate that vitacoxib has a low-to-moderate systemic clearance (0.35 L hr-1  kg-1 ) associated with a low global extraction ratio, but a large volume of distribution (6.43 L/kg). The absolute bioavailability after P.O. and S.C. administration was estimated at 10.5% (fasted) and 54.6%, respectively. Food intake was found to increase vitacoxib oral bioavailability by a fivefold, while bodyweight (BW) had a significant impact on systemic clearance, thereby confirming the need for BW adjustment with vitacoxib dosing in dogs.
Keyphrases
  • clinical trial
  • machine learning
  • electronic health record
  • deep learning
  • high intensity
  • data analysis
  • artificial intelligence
  • open label