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Chapter 5 : Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance

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Abstract:

This chapter reviews current concepts of dosage optimization to achieve optimal therapeutic effect with minimal resistance. As Schentag and Schentag have emphasized, the most important contribution of the pharmacologist to the resistance debate will be to design dosage schedules that minimize opportunities for its development. A given population of mixed microorganisms may comprise many subpopulations with different levels of susceptibility to antibiotics. In addition, for a given bacterial species genetic variation can confer resistance to one or more antimicrobial drugs of a given class, e.g., aminoglycosides, penicillins, or fluoroquinolones. The chapter briefly reviews categories of antimicrobial drugs and their pharmacokinetic (PK) and pharmacodynamic (PD) properties. For the majority of infections, the free (non-protein-bound) concentration in plasma is the best predictor of concentration in the biophase, as most infections are extracellular. However, when an anatomical or pathological barrier exists, lipid solubility is an important determinant of penetration of drug to the infection site. In addition, there are for some drugs (e.g., macrolides and ketolides) and some situations (e.g., biofilms) additional complications. However, ex vivo studies in the laboratory suggested two potentially important differences between marbofloxacin and danofloxacin investigated against a calf pathogen. The ultimate goal of population PK-PD analysis is to design dosage regimens that take account of animal or group characteristics.

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 1

Illustration of PK-PD integration for danofloxacin in a goat receiving danofloxacin intramuscularly, showing values of C max /MIC, AUC/MIC (AUIC), and T >MIC against a pathogen for which the drug's MIC (in serum) is 0.03 μg/ml. The serum sample was collected 9 h after intramuscular dosing with 1.25 mg of danofloxacin/kg. Reproduced with permission from AliAbadi and Lees (1).

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 2

Sigmoidal Emax relationship for bacterial count versus ex vivo AUC0–24/MIC (AUIC24h) in a representative goat for a pathogenic strain of M. haemolytica, illustrating values required for bacteriostatic and bactericidal effects and eradication of bacteria. Reproduced with permission from AliAbadi and Lees (1).

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 3

(A) Plasma concentration of doxycycline in 215 pigs under field conditions. Doxycycline was administered by the oral route as a metaphylactic treatment: the first dose (5 mg/kg, nominal dose) was given between 1800 and 1900 h (evening dose) and the second dose (5 mg/kg, nominal dose) was administered between 0800 and 0900 h (morning dose) on the following day. Blood samples were obtained approximately 30 min before, and approximately 1.8, 4.5, 6.7, and 11.5 h after, the second administration. For 25% of the pigs, a final blood sample was obtained 24 h after the second administration. Visual inspection of the raw data indicates the large variability of plasma doxycycline concentration. (B) Histogram of the area under the concentration-time curve (AUC from 0 to 24 h) for the 215 pigs. The range of exposure is approximately 4 to 5.

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 4

Dose-titration vs. PK-PD approach for the establishment of an optimal dosage regimen for antimicrobial drugs. Based on current regulatory policies (EMEA, U.S. Food and Drug Administration), dosage regimens for antimicrobial drugs are usually established by dose titration, generally in an experimental infection model. Dosage is determined taking account of clinical outcomes as pivotal criteria of efficacy (clinical cure). The dose-titration approach determines an efficacious dose, i.e., a dose significantly different from placebo (with respect to clinical outcomes), but it is unable to determine an “optimal dosage regimen” because clinical outcomes are not unequivocally related to bacteriological cure (an essential objective for a rational dosage therapy). The PK-PD approach offers a promising alternative to classical dose titration because it directly and mechanistically addresses antimicrobial efficacy. This approach also has the advantage of accounting for the two main sources of variability (pharmacokinetics and pharmacodynamics) influencing the response of the pathogen to the drug, and hence it is more able than a dose-titration study to determine an optimal dosage regimen. When using the PK-PD approach, two situations may be considered to determine a dosage regimen: the empirical vs. the targeted antibiotherapy approaches. For a company and for regulatory authorities, the dosage regimen selected and authorized is that which guarantees in a given percentage of the target population (e.g., 90% of animals) attainment of an “ideal” breakpoint value for the PK-PD index. This is the optimal dosage regimen for an empirical antibiotherapy, i.e., when the clinician undertakes an antibiotherapy with no knowledge of the pharmacokinetic characteristics in the treated animals and MIC for the involved pathogen. If the MIC is known (from epidemiological or laboratory data), variability is reduced solely to pharmacokinetic variability and a dosage regimen can be established using Monte Carlo simulation with the MIC in question, thus establishing a dosage regimen for a targeted antibiotherapy.

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 5

Monte Carlo simulation and the PK-PD index population distribution. The application of Monte Carlo simulation accounts in a balanced manner for variability in drug exposure (doxycycline AUC; upper left) as well as pathogen susceptibility data (MIC distribution for P. multocida; lower left) to establish the population distribution of the PK-PD index (AUC/MIC; right). The exposure distribution (AUC0–24) is that obtained with a second doxycycline dose of 5 mg/kg administered approximately 14 h after an initial 5-mg/kg dose and is assumed to be representative of steady-state exposure. The curves at right give the percentage of pigs in a population attaining a given value of the PK-PD predictor (AUC/MIC). Two curves were generated, one for an empirical initial antibiotherapy (MIC distribution known, but the MIC for the involved pathogen unknown) and one for a targeted antibiotherapy (the MIC for the involved pathogen known to be 0.25 μg/ml). The administered dose of doxycycline was 10 mg/kg, i.e., the recommended daily dose regimen. Visual inspection of the curve indicates that 72% of pigs were able to attain a PK-PD index of 24 h (i.e., a daily mean plasma concentration equal to the corresponding MIC) for an empirical antibiotherapy. For a targeted antibiotherapy having a MIC of 0.25 μg/ml, a 10-mg/kg daily dosage regimen is able to cover 90% of the pig population. These simulations were performed using total plasma concentrations of doxycycline. If it is assumed that only free drug is active, the percentage of pigs above the breakpoint should be divided by approximately 10, as the extent of plasma protein binding for doxycycline is about 90% (D. Concordet et al., unpublished results).

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 6

Dose-effect relationship for doxycycline in the pig using Monte Carlo simulation. The percentage of a pig population able to attain a given value of the PK-PD index (AUC/MIC) is given for three daily dose levels of doxycycline (5, 10, and 20 mg/kg) used against P. multocida (empirical antibiotherapy). For a PK-PD breakpoint value of 24 h, only the 20-mg/kg dose achieves the breakpoint in 90% of pigs; i.e., to guarantee a mean doxycycline plasma total concentration equal to the unknown MIC for the pathogen over 24 h, a dose of 20 mg/kg is required (Concordet et al., unpublished).

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Figure 7

MIC breakpoint determination for P. multocida in the pig using Monte Carlo simulation. The distribution curves of the PK-PD indices (AUC/MIC) for doxycycline were generated by Monte Carlo simulations using a nominal daily dose of doxycycline of 10 mg/kg and a PK-PD breakpoint value to be achieved of 24 h. The critical MIC was 0.25 μg/ml (total doxycycline concentration) or 0.025 μg/ml (free plasma concentration). Pathogens for which the MIC is higher than 0.25 μg/ml (or 0.025 μg/ml if it is assumed that only the free doxycycline concentration is active) should be declared “clinically” resistant if the critical criterion is to maintain in 90% of a pig population a daily mean plasma concentration equal to the MIC with a daily 10-mg/kg dose of doxycycline.

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Tables

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Table 1

Summary of mechanisms and types of action of antibacterial drugs

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
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Table 2

Killing actions of antimicrobial drugs (tentative classification)

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
Generic image for table
Table 3

Lipid solubility of antibacterial drugs and effects on tissue distribution

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
Generic image for table
Table 4

Comparison of PK/PD approach with dose titration or clinical trials a

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
Generic image for table
Table 5

Critical ex vivo AUC0-24/MIC values for danofloxacin and marbofloxacin in serum to achieve bacteriostasis, bactericidal action, or bacterial eradication in four ruminant species a

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5
Generic image for table
Table 6

Average 24-h concentrations of danofloxacin and marbofloxacin in serum a

Citation: Lees P, Concordet D, Toutain P, Aliabadi F. 2006. Drug Selection and Optimization of Dosage Schedules To Minimize Antimicrobial Resistance, p 49-72. In Aarestrup F (ed), Antimicrobial Resistance in Bacteria of Animal Origin. ASM Press, Washington, DC. doi: 10.1128/9781555817534.ch5

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