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      Pharmacokinetic and Pharmacodynamic Integration and Resistance Analysis of Tilmicosin Against Mycoplasma gallisepticum in an In Vitro Dynamic Model


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          Mycoplasma gallisepticum is the major pathogen causing chronic respiratory disease in chickens. In the present study, we successfully established a one-compartment open model with first-order absorption to determine the relationship between tilmicosin pharmacokinetic and pharmacodynamic (PK/PD) indices and M. gallisepticum in in vitro. The aim was to simulate the PK/PD of tilmicosin against M. gallisepticum in lung tissues. The results of static time-killing curves at constant drug concentrations [0–64 minimum inhibitory concentration (MIC)] showed that the amount of M. gallisepticum was reduced to the limit of detection after 36 h when the drug concentration exceeded 1 MIC, with a maximum kill rate of 0.53 h -1. In dynamic time-killing studies, tilmicosin produced a maximum antimycoplasmal effect of 6.38 Log 10 CFU/ml reduction over 120 h. The area under the concentration–time curve over 24 h divided by the MIC (AUC 24h/MIC) was the best PK/PD parameter to predict the antimicrobial activity of tilmicosin against M. gallisepticum [R 2 = 0.87, compared with 0.49 for the cumulative time that the concentration exceeds the MIC (%T > MIC)]. Therefore, tilmicosin showed concentration-dependent activity. Seven M. gallisepticum strains (M1–M7) with decreased susceptibility to tilmicosin were isolated from seven dose groups. These strains of M. gallisepticum had acquired resistance to erythromycin as well as to tylosin. However, no change in susceptibility to amikacin and doxycycline was observed in these strains. Gene mutation analysis was performed on the basis of annotated single nucleotide polymorphisms using the genome of strain S6 as the reference. For strain M5, a G495T mutation occurred in domain II of the 23S rrnA gene. In strain M3, resistance was associated with a T854A mutation in domain II of the 23S rrnB gene and a G2799A mutation in domain V of 23S rrnB. To the best of our knowledge, these tilmicosin resistance-associated mutations in M. gallisepticum have not been reported. In conclusion, tilmicosin shows excellent effectiveness and concentration-dependent characteristics against M. gallisepticum strain S6 in vitro. Additionally, these results will be used to provide a reference to design the optimal dosage regimen for tilmicosin in M. gallisepticum infection and to minimize the emergence of resistant bacteria.

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          Most cited references29

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          Erythromycin resistance by ribosome modification.

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            Mutant selection window hypothesis updated.

            The mutant selection window hypothesis postulates that, for each antimicrobial-pathogen combination, an antimicrobial concentration range exists in which selective amplification of single-step, drug-resistant mutants occurs. This hypothesis suggests an antimutant dosing strategy that is keyed to the upper boundary of the selection window: the mutant prevention concentration. Correlations are described between the mutant prevention concentration--a static parameter that is measured with agar plates--and fluctuating drug concentrations that restrict mutant amplification in vitro and in animals. When drug resistance is acquired stepwise, the mutant selection window increases, making the suppression of each successive mutant increasingly more difficult. For agents that kill drug-resistant mutants in a drug concentration-dependent manner, the use of the area under the 24-h time-drug concentration curve value divided by the value of the mutant prevention concentration is suggested as an index for designing antimutant dosing regimens. The need for such regimens is emphasized by a clinical example in which acquisition of drug resistance occurs concurrently with eradication of susceptible bacterial cells. These data support using the mutant selection window to optimize antimicrobial dosing regimens.
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              Semimechanistic pharmacokinetic/pharmacodynamic model for assessment of activity of antibacterial agents from time-kill curve experiments.

              Dosing of antibacterial agents is generally based on point estimates of the effect, even though bacteria exposed to antibiotics show complex kinetic behaviors. The use of the whole time course of the observed effects would be more advantageous. The aim of the present study was to develop a semimechanistic pharmacokinetic (PK)/pharmacodynamic (PD) model characterizing the events seen in a bacterial system when it is exposed to antibacterial agents with different mechanisms of action. Time-kill curve experiments were performed with a strain of Streptococcus pyogenes exposed to a wide range of concentrations of the following antibiotics: benzylpenicillin, cefuroxime, erythromycin, moxifloxacin, and vancomycin. Bacterial counts were monitored with frequent sampling during the experiment. A simultaneous fit of all data was accomplished. The degradation of the drugs was monitored and corrected for in the model, and a link model was used to account for an effect delay. In the final PK/PD model, the total bacterial population was divided into two subpopulations: one growing drug-susceptible population and one resting insusceptible population. The drug effect was included as an increase of the killing rate of bacteria in the susceptible state, according to a maximum-effect (E(max)) model. An internal model validation showed that the model was robust and had good predictability. In conclusion, for all drugs, the final PK/PD model successfully described bacterial growth and killing kinetics when the bacteria were exposed to different antibiotic concentrations. The semimechanistic model that was developed might, after further refinement, serve as a tool for the development of optimal dosing strategies for antibacterial agents.

                Author and article information

                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                12 June 2019
                : 10
                [1] 1Guangdong Key Laboratory for Veterinary Drug Development and Safety Evaluation, South China Agricultural University , Guangzhou, China
                [2] 2Technical Center for Inspection and Quarantine, Zhuhai Entry–Exit Inspection and Quarantine Bureau , Zhuhai, China
                [3] 3School of Life Science and Engineering, Foshan University , Foshan, China
                Author notes

                Edited by: Salvatore Salomone, University of Catania, Italy

                Reviewed by: Zhangrui Cheng, Royal Veterinary College (RVC), United Kingdom; Nadezhda A. German, Texas Tech University Health Sciences Center, United States

                *Correspondence: Huanzhong Ding, hzding@ 123456scau.edu.cn

                This article was submitted to Experimental Pharmacology and Drug Discovery, a section of the journal Frontiers in Pharmacology

                Copyright © 2019 Huang, Wu, Zhou, Xia, Gu, Cai, Shen, Yang and Ding

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 6, Tables: 5, Equations: 0, References: 36, Pages: 10, Words: 5036
                Original Research

                Pharmacology & Pharmaceutical medicine
                tilmicosin,mycoplasma gallisepticum,dynamic model,pharmacokinetics,pharmacodynamics,resistance,chronic respiratory disease


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