Bayesian Calibration of a Physiologically Based Pharmacokinetic/Pharmacodynamic Model of Carbaryl Cholinesterase Inhibition

Abstract

Carbaryl, an N-methyl carbamate (NMC), is a common insecticide that reversibly inhibits neuronal cholinesterase activity. The objective of this work was to use a hierarchical Bayesian approach to estimate the parameters in a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model from experimental measurements of carbaryl in rats. A PBPK/PD model was developed to describe the tissue dosimetry of carbaryl and its metabolites (1-naphthol and “other hydroxylated metabolites”) and subsequently to predict the carbaryl-induced inhibition of cholinesterase activity, in particular in the brain and blood. In support of the model parameterization, kinetic tracer studies were undertaken to determine total radioactive tissue levels of carbaryl and metabolites in rats exposed by oral or intravenous routes at doses ranging from 0.8 to 9.2 mg/kg body weight. Inhibition of cholinesterase activity in blood and brain was also measured from the exposed rats. Markov Chain Monte Carlo (MCMC) calibration of the rat model parameters was implemented using prior information from literature for physiological parameter distributions together with kinetic and inhibition data on carbaryl. The posterior estimates of the parameters displayed at most a twofold deviation from the mean. Monte Carlo simulations of the PBPK/PD model with the posterior distribution estimates predicted a 95% credible interval of tissue doses for carbaryl and 1-naphthol within the range of observed data. Similar prediction results were achieved for cholinesterase inhibition by carbaryl. This initial model will be used to determine the experimental studies that may provide the highest added value for model refinement. The Bayesian PBPK/PD modeling approach developed here will serve as a prototype for developing mechanism-based risk models for the other NMCs.

The authors acknowledge Drs. Melvin E. Andersen, R. Woodrow Setzer, Miyoung Yoon, and Jerry Campbell for their valuable reviews and comments. This work was supported by the Bayer CropScience and the American Chemistry Council Long-Range Research Initiative.