PK/PD APPROACH AND ANTIMICROBIAL RESISTANCE Pierre-Louis Toutain, Ecole Nationale Vétérinaire INRA & National veterinary School of Toulouse, France 7th International Conference on antimicrobial agents in veterinary medicine Berlin September 16-19,2014

Introduction PK/PD concepts are now well established to determine the dosing regimen for antimicrobials (AM) in veterinary medicine. the previous presentation by Stefan The main goal is to optimize clinical efficacy, but the risk of resistance development has been generally ignored in these dose optimizations

But of what resistance are we speaking?

Prevent emergence of resistance: but of what resistance? Target pathogens Zoonotics Commensal Efficacy in animals Efficacy in man Global ecological problem Human Individual issue Public health issue Food chain Environment Animal health issue

Bacterial load exposed to antibiotics during a treatment Infected Lungs Digestive tract Test tube Manure waste 1µg 1 mg Several Kg Several tons Food chain Soil, plant….

Duration of exposure of bacteria exposed to antibiotics Infected Lungs Digestive tract Manure Sludge waste Test tube Few days 24h Several weeks/months Food chain Soil, plant….

The priorities of a sustainable veterinary antimicrobial therapy is related to public health issues, not to animal health issues A trade-off between these two objectives is difficult or even impossible to achieve due to the non selectivity of most veterinary antibiotics.

Outline of the presentation The classical integrated PK/P indices and the question of resistance: the MSW The limits of the MSW The mechanism-based models Options to mitigate emergence and selection of resistance Early/low dose treatment Duration of treatment Dug combinations

The mutant Selective Window (MSW) Currently the MSW is the only PK/PD index that is use to mitigate the emergence of resistance

Traditional hypothesis on emergence of AMR Concentration MIC Selective pressure for antibiotic concentration lower than the MIC Time

Current view for the emergence and selection of resistance : situation II No antibiotics & low inoculum size Mutation rate10-8 105 CFU Wild pop No Mutant pop With antibiotics eradication résistant susceptible

Current view for the emergence and selection of resistance : situation II No antibiotics & high inoculum Mutation rate10-8 108 CFU Mutant pop 5-10xMIC=MPC Wild pop With antibiotics Mutation rate10-8 eradication susceptible Mutants population

The selection window hypothesis Mutant prevention concentration (MPC) (to inhibit growth of the least susceptible, single step mutant) MIC Selective concentration (SC) to block wild-type bacteria Mutant Selection window Plasma concentrations All bacteria inhibited Growth of only the most resistant subpopulation Growth of all bacteria

MIC & MPC for the main veterinary quinolones for E. coli & S. aureus

The MSW The concept PK/PD of MSW was shown to be useful for quinolones for which resistance develops by mutational alterations of the drug target, but the concept is less clear for others mechanisms of resistance (e.g. plasmid mediated resistance) and for other classes of antibiotics even if the MIC/MPC ratio has been proposed for a variety of veterinary antibiotics (macrolides, cephalosporines, florfenicol)

Comparative MIC and MPC values for 285 M Comparative MIC and MPC values for 285 M. haemolytica strains collected from cattle MIC50 MIC90 MPC50 MPC90 MPC/MIC Ceftiofur 0.016 1 2 125 Enrofloxacine 0.125 0.25 8 Florfenicol 4 Tilmicosine 16 >32 ≈8 Tulathromycine Vet Microbiol 2012 Blondeau JM

MSW: target pathogen vs. commensal flora

In the present study, we used fecal samples collected from these volunteers during and after ciprofloxacin treatment to analyze the dynamics of the emergence of resistance in E. coli over time in each volunteer.

Effect of ciprofloxacine on E coli During antibiotic exposure (on days 8 and 14), no E. coli could be detected in most volunteers. This was explained by the high fecal concentrations of ciprofloxacin, which was several thousand times greater than both the MIC and the mutant-prevention concentration against the dominant flora . Selection of resistance was unlikely during treatment. The appearance of QREC strains 4 weeks after the end of ciprofloxacin therapy was observed explained by the pharmacokinetics of ciprofloxacin in stool, because ciprofloxacin concentrations slowly decreased from day 14 to day 42, when they were undetectable, with ciprofloxacin concentrations passing through the mutant selection window between days 14 and 42, when emergence of resistance was eventually detected in the fecal microbiota.

MSW: biophase vs Feces Stools QREC From environment MSW 100 10 Target pathogen MPC 1 MIC Time MSW MSW

What is the better option : Collective vs. selective treatment Treated I have now my MSW

The limit MSW and of PK/PD indices and of their breakpoint values regarding the resistance issues

The limit of MSW simulations with more advanced semi-mechanistic PK/PD models showed that the classical PK/PD indices, including the MSW, have several major limitations and there is a need to go beyond these summary PK/PD variables to efficiently combat resistance by designing appropriate dosage regimens.

The three mains limits of classical PK/PD and MSW indices They ignore information on the time-course of the PK and PD. The U shaped curve of the MSW They rely on the MIC that is not a PD parameter but a hybrid variable. They are established on 24 hours, a too short period to study the adaptation of the bacteria to antibiotic drug exposure and selection of resistant bacterial subpopulations

T>MIC for 40-50% of the dosing interval: Daily dosing vs T>MIC for 40-50% of the dosing interval: Daily dosing vs. long-acting drug Daily formulation Long-acting drug/formulation MIC Both treatments ensure plasma concentrations above MIC for 50% of the dosing interval (1 or 14 days) but they are not equivalent

MICs estimated with different inoculmum densities, relative to that MIC at 2x105 Ciprofloxacin Gentamicin Linezolid Daptomycin Oxacillin Vancomycin

What is a MIC? An hybrid variable Its reflects: The drug potency The drug efficacy the bacterial growth rate, the bacterial death rate and many other factors associated to its in vitro measurement (inoculum size, selected milieu, etc.).

What is a MIC? An hybrid variable From a mechanistic model point of view AM Potency   AM efficacy Rate of death Defense mechanisms Rate of growth (supply shortage)

Effect of resistance on Kkill The observed killing rate is a function of the natural death rate(0) times a scalar given by the Emax function Target site alteration Reduced Emax=Kkillmax/Kdeath   Drug efflux pumps and enzymatic drug deactivation increase EC50 Can be surmounted by a higher dose

What is a MIC The MIC value is only a snaphot measure of the net effect of the antibiotic under well standardized conditions (18-24h, constant AM concentration).

Clinical Pharmacokinetics 2005 44 201-210

Investigation of resistance require more than 24h

the residual population to be eradicated by the immune system Impact on the total population of Staphylococcus aureus over time by two regimens of garenoxacin (in vitro model) The less intense regimen ceases to be effective after a delay of 5 days. the residual population to be eradicated by the immune system

Impact on the less-susceptible population of S Impact on the less-susceptible population of S. aureus over time by two regimens of garenoxacin. If therapy had been ended at day 4 or 5, little or no resistant mutant amplification would have occurred

Limits of the classical PK/PD indices to limit resistance Therefore, the classical PK/PD indices are not well suited to understand and predict the emergence of resistance. They are also unable to characterize the effect of drug combinations that are one of the best options to combat resistance

Mechanism-based model of antimicrobials

The value of mechanism-based models These models aim to give a better understanding of the PK/PD relationship when modeling the full time-course of bacterial growth and killing.

A major review

The mechanism-based models: 4 submodels Models including equations to describe: The microorganisms growth: microorganisms sub-model, the changing drug concentration: PK model The effect of AM drug: PD sub-model to describe the interaction between the two preceding sub-models. They can also include a sub-model for the host defenses.

Mechanism-based model of antimicrobials Equation with: no replication inhibition Time-invariant susceptibility to drug Constant replication rate  

The microorganism sub-models The microorganism sub-models can consider simultaneously different bacterial subpopulations with different levels of susceptibility and they can differentiate different mechanisms of resistance (alteration of the mutation rate, adaptative resistance, persisters)

PK/PD model for resistance and predicted bacterial time-kill curves B1, compartment with drug sensitive bacteria; B2, compartment with less drug-sensitive bacteria;

PKPD model for resistance (persisters) and predicted bacterial time-kill curves B1, compartment with drug sensitive bacteria; B3, compartment with non growing, drug-insensitive bacteria

The mechanism-based models The mechanism-based model can be used for many purposes to test mechanistic hypotheses, to predict untested doses and complicated dosing regimens (PK mimicking in vivo situations, drug combinations, duration of treatment, etc).

Classical PK/PD indices vs. semi-mechanistic models

Classical PK/PD indices vs. semi-mechanistic models These semi-mechanistic models are able to predict the classical PK/PD indices and their breakpoint values.

Classical PK/PD indices vs. semi-mechanistic models However, they also predict that when the AM half-life is short, the best predictor is always T>MIC and when the half-life is long, the best predictor is always AUC/MIC whatever the antibiotic. For long-acting formulations AUC/MIC is likely an universal PK/PD index This would greatly facilitate many tasks such as finding an optimal dosage regimen and fixing sound clinical breakpoints for susceptibility testing.

How to mitigate emergence of resistance: practical aspects

How to combat resistance Early initiation of AM therapy Short duration Combination therapy with 2 antibiotics

The different uses of antibiotics in veterinary medicine Disease health Antibiotic consumption Metaphylaxis (Control) Prophylaxis (prevention) Growth promotion Therapy High Pathogen load Only a risk factor No Small NA

amoxicillin & cefquinome marbofloxacin, amoxicillin & cefquinome

Low dose and gut microbiota In addition this strategy of an early low-dose treatment will be considered as the best in terms of public health to minimize the unselective impact of most AM used in veterinary medicine on the gut microbiota.

Duration of therapy

Reducing exposure by reducing the duration of therapy and prophylaxis With rare exceptions (e.g. bacteremia due to S. aureus, endocarditis, osteomyelitis), there is no evidence to support most of the traditional 10–14-day courses of antibiotics, which are based more on conventional wisdom than strong evidence. Short-course therapy for urinary tract infection, acute otitis media, tonsillopharyngitis, sinusitis and pneumonia is slowly gaining support (MASTIN study group, 2002; Lutters and Vogt, 2002).

The one-shot therapy The so-called one-shot therapy is the veterinary option to minimize the treatment duration. Using PK/PD mechanistic models, it was shown that the killing rate of concentration dependent AM was dose-dependent and the goal of the one-shot high dose therapy is to kill as rapidly as possible the target pathogens or at least a sufficient fraction of the initial load to allow the host natural defenses to eradicate the remnant bacterial population.

Increasing exposure trough combination therapy

What is the clinical value of AM combination?

Conclusion (1) The optimization of dosing regimens should be accomplished by choosing the dose and schedule that results that will achieve the microbiological and clinical outcome desired while simultaneously suppressing emergence of resistance on both the target and commensal flora. Early treatment and short duration are currently the two best options

Conclusions (2) To combat resistance and the public health impact, the best strategy is likely to be to develop new antibiotics that are selective for the target pathogens and not impacting the gut microbiota (green antibiotics). While waiting for this new generation of veterinary antibiotics, we have to revisit the current dosage regimens of antibiotics (dose, dosing interval and treatment duration)

Conclusion (3) This reevaluation should take into account the different possible clinical conditions that are faced in veterinary medicine (curative, metaphylaxia and prophylaxia) and consider that a single regimen does not fit all. As veterinary medicine is resources-limited and cannot test experimentally all possible situations and hypotheses, veterinary pharmacologists should explore more deeply the class of these so-called mechanism-based models and their regulatory acceptance should be rapidly considered.