A Bayesian approach for estimating detection times in horses: exploring the pharmacokinetics of a urinary acepromazine metabolite

Abstract

M. A Bayesian approach for estimating detection times in horses: exploring the pharmacokinetics of a urinary acepromazine metabolite. J. vet. Pharmacol. Therap. 36, 31-42.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/F PM ) and volume of distribution (V/F PM ), 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.(Paper

Simulation-based fully Bayesian experimental design for mixed effects models

“…We are also interested in determining the optimal number of horses to include in the study, and the optimal number of samples to take per horse (subject to cost constraints). Our case study will be a retrospective study design which makes use of the data collected and analysed by McGree et al (2012). We are interested in re-designing the study to precisely estimate the mean PK parameters of the population of horses.…”

“…The horses were trained to urinate to the sound of a whistle. Here, we will re-design the urine sampling times from those used in McGree et al (2012) so that accurate measures of PK parameters of interest can be obtained.…”

“…Whilst previous studies have found optimal Bayesian designs for NLMEMs by searching over a finite set of designs (e.g., Han andChaloner, 2004, Palmer andMüller, 1998), to our knowledge, no studies have searched over a continuous design space to find optimal static Bayesian designs for NLMEMs. This work is motivated by a PK study conducted by McGree et al (2012) which models the PK of an acepromazine metabolite in racing horses. Our design problem consists of finding the optimal sampling times for the PK study, as well as the optimal number of subjects to incorporate into the study, and the optimal number of samples to take per subject.…”

“…Cl is the clearance rate, F E is the fraction of HEPS that is excreted renally, and V is the volume of distribution (a theoretical measure). In this model it is assumed that k a = 35.87 is a constant (as per McGree et al, 2012) and D = 30 000 µg is the drug dose. This model assumes a firstorder absorption and elimination of the drug which is administered orally.…”

“…In the study conducted by McGree et al (2012), 30 mg of ACP was administered to 12 horses (geldings) and urine samples were taken at the following times: 2, 4, 6, 8, 12, 24, 36 and 48 h after administration. The horses were trained to urinate to the sound of a whistle.…”