A Bayesian approach for estimating detection times in horses: exploring the pharmacokinetics of a urinary acepromazine metabolite |
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| Authors: | J. M. Mcgree G. Noble F. Schneiders A. J. Dunstan A. R. Mckinney R. Boston M. Sillence |
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| Affiliation: | 1. Mathematical Sciences, Queensland University of Technology, , Brisbane, Qld, Australia;2. School of Animal and Veterinary Sciences, Charles Sturt University, , Wagga Wagga, NSW, Australia;3. Australian Racing Forensic Laboratory, , Kensington, NSW, Australia;4. School of Veterinary Medicine, New Bolton Center, University of Pennsylvania, , Kennett Square, PA, USA |
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| Abstract: | We describe the population pharmacokinetics of an acepromazine (ACP) metabolite (2‐(1‐hydroxyethyl)promazine) (HEPS) in horses for the estimation of likely detection times in plasma and urine. ACP (30 mg) was administered to 12 horses, and blood and urine samples were taken at frequent intervals for chemical analysis. A Bayesian hierarchical model was fitted to describe concentration–time data and cumulative urine amounts for HEPS. The metabolite HEPS was modelled separately from the parent ACP as the half‐life of the parent was considerably less than that of the metabolite. The clearance (Cl/FPM) and volume of distribution (V/FPM), scaled by the fraction of parent converted to metabolite, were estimated as 769 L/h and 6874 L, respectively. For a typical horse in the study, after receiving 30 mg of ACP, the upper limit of the detection time was 35 h in plasma and 100 h in urine, assuming an arbitrary limit of detection of 1 lg/L and a small (≈0.01) probability of detection. The model derived allowed the probability of detection to be estimated at the population level. This analysis was conducted on data collected from only 12 horses, but we assume that this is representative of the wider population. |
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