Population physiologically based modeling of pirlimycin milk concentrations in dairy cows

A. P. Woodward, Dawn Morin, Ted Whittem

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Predictions of drug residues in milk are critical in food protection and are a major consideration in the economics of treatment of mastitis in dairy cows. Nonlinear mixed-effects modeling (NLME) has been advocated as a suitable pharmaco-statistical method for the study of drug residues in milk. Recent developments in physiologically based pharmacokinetic (PBPK) modeling of intramammary drugs allow the combination of a mechanistic description of milk pharmacokinetics with NLME methods. The PBPK model was applied to NLME analysis of a data set consisting of milk drug concentrations from 78 healthy cows and 117 with clinical mastitis. Pirlimycin milk pharmacokinetics were adequately described by the model across the range of observed concentrations. Mastitis was characterized by increased variance in milk production volume. Udder residual volume was larger in cows with 1, or 2 or greater diseased mammary glands than in the healthy cows. Low-producing cows had a greater risk of prolonged milk residues. With the exclusion of the low-production cows, the model predicted that healthy cows required a milk discard time 12 h longer than that indicated by the label, and the diseased cows 36 h longer than indicated by the label. More pirlimycin was systemically absorbed in the gram-positive infected compared with the gram-negative infected or healthy cows, suggesting a greater risk of violative meat residues in gram-positive infected cows. Using NLME and PBPK models, we identified factors associated with changes in pirlimycin milk residues that may affect food safety. This model extends the verification of a simple physiologically based framework for the study of intramammary drugs.

Original languageEnglish
Pages (from-to)10639-10650
Number of pages12
JournalJournal of Dairy Science
Volume103
Issue number11
DOIs
Publication statusPublished - Nov 2020
Externally publishedYes

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