Pharmacokinetic/pharmacodynamic integration and modelling of florfenicol for the pig pneumonia pathogens Actinobacillus pleuropneumoniae and Pasteurella multocida

Abstract

Pharmacokinetic-pharmacodynamic (PK/PD) integration and modelling were used to predict dosage schedules for florfenicol for two pig pneumonia pathogens, Actinobacillus pleuropneumoniae and Pasteurella multocida. Pharmacokinetic data were pooled for two bioequivalent products, pioneer and generic formulations, administered intramuscularly to pigs at a dose rate of 15 mg/kg. Antibacterial potency was determined in vitro as minimum inhibitory concentration (MIC) and Mutant Prevention Concentration in broth and pig serum, for six isolates of each organism. For both organisms and for both serum and broth MICs, average concentration:MIC ratios over 48 h were similar and exceeded 2.5:1 and times greater than MIC exceeded 35 h. From in vitro time-kill curves, PK/PD modelling established serum breakpoint values for the index AUC24h/MIC for three levels of inhibition of growth, bacteriostasis and 3 and 4log10 reductions in bacterial count; means were 25.7, 40.2 and 47.0 h, respectively, for P. multocida and 24.6, 43.8 and 58.6 h for A. pleuropneumoniae. Using these PK and PD data, together with literature MIC distributions, doses for each pathogen were predicted for: (1) bacteriostatic and bactericidal levels of kill; (2) for 50 and 90% target attainment rates (TAR); and (3) for single dosing and daily dosing at steady state. Monte Carlo simulations for 90% TAR predicted single doses to achieve bacteriostatic and bactericidal actions over 48 h of 14.4 and 22.2 mg/kg (P. multocida) and 44.7 and 86.6 mg/kg (A. pleuropneumoniae). For daily doses at steady state, and 90% TAR bacteriostatic and bactericidal actions, dosages of 6.2 and 9.6 mg/kg (P. multocida) and 18.2 and 35.2 mg/kg (A. pleuropneumoniae) were required. PK/PD integration and modelling approaches to dose determination indicate the possibility of tailoring dose to a range of end-points.

Integration of pharmacokinetic–pharmacodynamic for dose optimization of doxycycline againstHaemophilus parasuisin pigs

“…Pharmacokinetic–pharmacodynamic (PK‐PD) modeling reflects the relationship between the drug, bacteria, animal affected, and quantifying the activity, as well as the likely efficacy of antimicrobials against target pathogens (Aljayyoussi et al., ; Nielsen & Friberg, ; Yan et al., ). There have been a number of studies demonstrating that the PK‐PD integration model can prevent resistance development by providing optimal dosage strategies (Dorey, Pelligand, Cheng, & Lees, ; Nielsen & Friberg, ; Yan et al., ). In other words, the utilization of PK‐PD modeling offers an opportunity to reduce the rate of incidence of antibiotic resistance, by providing insight into dose regimen optimization.…”

Optimal regimens regimens based on PK/PD cutoff evaluation of ceftiofur against Actinobacillus pleuropneumoniae in swine

“…For example, the AUC 24 h /MIC values for three levels of preventive, therapeutic, and bacterial eradication were 28.40, 29.51 and 29.13 h, respectively, in previous study on PK/PD modeling of Ceftiofur Sodium against Haemophilus parasuis infection in pigs [26]. In another study, the AUC 24 h /MIC values for three levels of preventive, therapeutic, and bacterial eradication were 24.6, 43.8 and 58.4 h, respectively [32]. Prior to our study, no PK-PD integration modeling analyses were performed for ceftiofur against Actinobacillus pleuropneumoniae.…”

“…According to the MIC distribution in China, the predicted daily doses for the 50% and 90% targets of ceftiofur with bactericidal activity against Actinobacillus pleuropneumoniae were 2.97 and 3.21 mg/kg body weight, respectively. The Monte Carlo simulation combined with the dosage equation and taking into consideration the PK/PD integration could set the target percentage to simulate models for all data in relation to incidence in swine [36,37].…”

Population variability in animal health: Influence on dose‐exposure‐response relationships: Part II: Modelling and simulation

“…For example, using the distribution of PK parameter values derived from population PK studies, a specified dosage regimen, and a PK‐PD target, we can define the minimum inhibitory concentration (MIC) at which 90% of the treated patients will achieve that PK–PD target (Maaland, Papich, Turnidge, & Guardabassi, ). Alternatively, it can be used to estimate the doses needed to achieve some targeted therapeutic effect (Dorey, Pelligand, Cheng, & Lees, ; Rey et al., ).…”

“…& Guardabassi, 2013). Alternatively, it can be used to estimate the doses needed to achieve some targeted therapeutic effect (Dorey, Pelligand, Cheng, & Lees, 2017;Rey et al, 2014).…”