Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed‐effects to characterize and quantify variability in drug pharmacokinetics. Issue 2 (11th December 2017)

• Record Type: Journal Article
• Authors: Bon, C.; Toutain, P. L.; Concordet, D.; Gehring, R.; Martin‐Jimenez, T.; Smith, J.; Pelligand, L.; Martinez, M.; Whittem, T.; Riviere, J. E.; Mochel, J. P.
• Is Part Of: Journal of veterinary pharmacology and therapeutics. Volume 41:Issue 2(2018)
• Page Start: 171
• Page End: 183
• Publication Date: 2017-12-11
• Subjects: covariates -- nonlinear mixed effects -- population pharmacokinetics -- sparse sampling -- veterinary medicine; Veterinary pharmacology -- Periodicals; Therapeutics -- Periodicals; 636.0895105
• DOI: 10.1111/jvp.12473
• Languages: English
• ISSNs: 0140-7783

Abstract

Abstract : A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between‐patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food‐producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population‐associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.