Abstract

In livestock, the regulation of drugs used to treat livestock has received increased attention and it is currently unknown how much of the phenotypic variation in drug metabolism is due to the genetics of an animal. Therefore, the objective of the study was to determine the amount of phenotypic variation in fenbendazole and flunixin meglumine drug metabolism due to genetics. The population consisted of crossbred female and castrated male nursery pigs (n = 198) that were sired by boars represented by four breeds. The animals were spread across nine batches. Drugs were administered intravenously and blood collected a minimum of 10 times over a 48 h period. Genetic parameters for the parent drug and metabolite concentration within each drug were estimated based on pharmacokinetics (PK) parameters or concentrations across time utilizing a random regression model. The PK parameters were estimated using a non-compartmental analysis. The PK model included fixed effects of sex and breed of sire along with random sire and batch effects. The random regression model utilized Legendre polynomials and included a fixed population concentration curve, sex, and breed of sire effects along with a random sire deviation from the population curve and batch effect. The sire effect included the intercept for all models except for the fenbendazole metabolite (i.e., intercept and slope). The mean heritability across PK parameters for the fenbendazole and flunixin meglumine parent drug (metabolite) was 0.15 (0.18) and 0.31 (0.40), respectively. For the parent drug (metabolite), the mean heritability across time was 0.27 (0.60) and 0.14 (0.44) for fenbendazole and flunixin meglumine, respectively. The errors surrounding the heritability estimates for the random regression model were smaller compared to estimates obtained from PK parameters. Across both the PK and plasma drug concentration across model, a moderate heritability was estimated. The model that utilized the plasma drug concentration across time resulted in estimates with a smaller standard error compared to models that utilized PK parameters. The current study found a low to moderate proportion of the phenotypic variation in metabolizing fenbendazole and flunixin meglumine that was explained by genetics in the current study.

Highlights

  • The regulation of drugs used to treat livestock has received increased attention due to animal welfare concerns, food safety, and implications of antibiotic resistance on human health (Landers et al, 2012)

  • To gain a better understanding of the variation and impact of genetic variability on swine drug metabolism, a resource population was created and initially described in Howard et al (2014). It was shown by Howard et al (2014) that differences in pharmacokinetic (PK) parameters exist across four major swine breeds (i.e., Duroc, Hamphsire, Yorkshire, and Landrace) and sex. This was further verified at the genomic level based on gene expression differences for genes previously shown to play a role in drug metabolism, which include SULT1A1 and CYP2E1 for flunixin meglumine and SULT1A1 for fenbendazole (Howard et al, 2015)

  • The heritability estimate for time at which maximum concentration occurs (Tmax) for the metabolite for flunixin meglumine was outside the bounds and was not shown

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Summary

Introduction

The regulation of drugs used to treat livestock has received increased attention due to animal welfare concerns, food safety, and implications of antibiotic resistance on human health (Landers et al, 2012). To gain a better understanding of the variation and impact of genetic variability on swine drug metabolism, a resource population was created and initially described in Howard et al (2014). Within this resource population, it was shown by Howard et al (2014) that differences in pharmacokinetic (PK) parameters exist across four major swine breeds (i.e., Duroc, Hamphsire, Yorkshire, and Landrace) and sex. The amount of variation in the parameters that describe the rate at which a drug is metabolized that is attributable to genetics is currently not wellunderstood

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