How to establish a dosage regimen for a sustainable use of antibiotics in veterinary medicine
Pierre-Louis Toutain,
Ecole Nationale Vétérinaire
INRA & National veterinary School of Toulouse, France
Wuhan 09/10/2015
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EMEA "Points to consider" July 2000
• Inadequate dosing of antibiotics is probably an important reason for misuse and subsequent risk of resistance
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• Efficacy in animal
• No promotion of resistance in animal
(target pathogen)
• No promotion of resistance in man
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What are the elements of a dosage regimen
–When to start
–When to finish
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How to find and confirm a dose
(dosage regimen)
Dose determination
Dose titration
Experimental infectious model
Well controlled, naturally infected animals
PK/PD
Dose confirmation
Dose confirmation
Dose confirmation
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ECVPT Toulouse 2009 -
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Dose titration: principle
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• Where possible, experimentally induced infections should be used in the dose-determination studies
– If no experimental model is available and study conditions are well controlled, naturally infected animals can be used
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• Efficacy evaluation should be based on clinical and bacteriological response as determined by appropriate clinical and bacteriological assessment
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• Usually three levels of dosage of the veterinary medicinal product should be tested, preferably using the final formulation.
– Control group (dose=0) compulsory
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Parallel design for antibiotics:
Statistical model
Response
NS
*
*
Selected dose
Placebo 1 2 3
Dose
• The null hypothesis
– placebo = D1 = D2 = D3
• The statistical linear model
– Yj =  wj
+
 j
• Conclusion
– D3 = D2 > D1 > Placebo
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• Statistical comparisons between different treatment groups and the negative control group
• it is acknowledged that dose determination lack of power
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• Advantages
– easy to execute
– total study lasts over one period
– approved by Authorities
• Disadvantages
– "local information" (response at a given dose does not provide any information about another dose)
– no information about the distribution of the individual patient's dose response.

• Dose titration can be used to establish the critical value of the PK/PD index
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Dose titration and critical value of the
PK/PD index
POC
Response
Placebo 1 2 3
Dose
• Surrogate: AUC/MIC
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Probability of cure (POC)
• Logistic regression can be used to link measures of drug exposure (AUC/MIC) to the probability of a clinical success
POC

1
 e a
 bf
1

AUC MIC

Dependent variable
Placebo effect sensitivity
Independent variable
2 parameters: a
(placebo effect) & b
(slope of the exposure-effect curve)
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Dose determination using a
PK/PD approach
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It has been developed surrogates indices (predictors) of antibiotic efficacy taking into account MIC (PD) and exposure antibiotic metrics (PK)
Practically, 3 indices cover all situations:
• AUC/MIC
• Time>MIC
• Cmax/MIC
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• PK/PD relationship may be used to support dose regimen selection
• In circumstances in which it is not feasible to generate extensive clinical efficacy data (e.g. in rare types of infections or against rare types of pathogens, including multidrug resistant pathogens that are rarely encountered) PK/PD analyses may also provide important supportive information on the potential efficacy of the test antibacterial agent.
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• It is acknowledged that the PK/PD analyses will be based on PK data obtained from healthy or experimentally infected animals . Nevertheless, the sponsor is encouraged to collect PK data from naturally diseased animals using population kinetic models.
• Knowledge of kinetic variability considerably increases the value of the PK/PD analysis
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Demonstration of applicability of PK/PD concepts to determine a dosage regimen for tulathromycin in the calf
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Tulathromycin
(Draxxin, Zoetis)
A semi-synthetic macrolide antibiotic of the subclass triamilide
Comprises a 13-member ring compound (10%) and a 15-member ring compound (90%)
• Molecular Weight Tul A: 806. [g/mol]
• at a pH of 7.4 the logD is -1.34,
• pKa values 8.00, 9.17 and 9.72
Treatment and control (metaphylaxia) of bovine respiratory disease
(BRD) associated with M.haemolytica, P.multocida, and Histophilus
somni;
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PD properties
(Zoetis)
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PK parameters in cattle
(Zoetis)
• Clearance of a medium value (3mL/kg/min),
• The long terminal halflife (90 h) is explain by a very large volume of
(11.1 L/kg)
• Bioavailability of 91% after SQ dosing in calves.
• Plasma protein binding : about 40%
Cmax=0.5µg/mL ≤ to MIC
90
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The dose was determined in clinics
• 1.25, 2.5 and 5mg/kg were tested
– 1.25mg/kg: success of 76.9%
– 2.5mg/kg: success of 86.8%
– 5.0mg/kg : no additional benefit
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The PK/PD issue for macrolides (triamilides): plasma concentration lower than MICs
Cmax=0.5µg/mL ≤ to MIC
90
MIC
• Good clinical efficacy and bacteriological cure with macrolides is achievable with plasma concentrations
(much) lower, than the in vitro MICs for major lung pathogens
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The issue for macrolides
(triamilides):
• PK/PD concepts to macrolides has been challenged rather than the validity of the in vitro MIC data obtained in matrices optimised for bacterial growth, as in
Mueller Hinton Broth (MHB).
– This has led some authors and Authorities to claim that there is no plasma concentrationeffect relationship for macrolides.
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• To generate the appropriate PK and PD data for tulathromycin for M. haemolytica and P. multocida in calves to show that it is possible to establish therapeutically relevant in vivo PK/PD relationships for a macrolide as for any AMD
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MIC in MHB vs. calf serum
25%,50%,75% and 100%
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Determination of a scaling factor between MHB and serum
• Minimum Inhibitory Concentrations (MIC) were approximately 50 times lower in calf serum than in Mueller Hinton Broth.
• A scaling factor of 50 was used to transform epidemiological data (MIC) into relevant MIC concentration for a dosage regimen determination
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For azithromycin (closely related to tulathromycin) the presence of 40% serum during the MIC test decreased
MICs by 26-fold for serum-resistant Escherichia coli and
15-fold for Staphylococcus aureus.
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• Time>MIC or AUC/MIC?
• Semi-mechanistic model predict that when the 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.
• We used AUC/MIC
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Killing curves in serum to compute the
PK/PD index breakpoint

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Tulathromycin concentrations to achieve a bacteriostatic, bactericidal or an eradication effect for M haemolytica & P
multocida as estimated from killing curves
Effect M Haemolytica
(MHB)
MIC=2000ng/mL
M Haemolytica
(Serum)
(MIC=40ng/mL)
P Multocida
(MHB)
(MIC=2000ng/mL)
P Multocida
(serum)
(MIC=40ng/mL)
Bacteriostatic
Bactericidal
3log reduction
4log reduction
1420±520
1740±680
2060±880
32.8±24.4
41.6±12.0
53.6±19.2
1520±960
1960±1500
28.8±15.4
37.6±3.2
Results expressed for a typical MIC as obtained either in MHB
(2000ng/ml) or for the same strain in serum (40ng/mL)
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Healthy calves: n=10
Calf pneumonia model: n=16
Population (NLME) PK model in Phoenix typical values of plasma clearance/F and Between Subject variability
Effect of illness status
•
• MC Simulation of 1040 PK
Distribution of clearances (log normal)
• Distribution of field MIC for susceptible strains
• Zoetis
• M. haemolytica n = 2233
P. multocida n=2483
• PK/PD cutoff values
(for the establishment of breakpoint values of
Antimicrobial Susceptibility Testing)
Dose prediction with Monte Carlo computation
Derivation of doses for TAR 90%
Calculation of TAR for current dose
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Disposition of tulathromycin
Unbalanced data spaghetti plots of the disposition curves of tulathromycin over 336h after a single dose administration of tulathromycin by the SQ route (2.5mg/kg) in control calves
(red curves; n=10) and in calves with an experimental plmonary condition (black curves; n=16).
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Population modeling: structural model
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The Between subject variability (BSV) was modeled using an exponential model
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Model of the residual variance
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• The only considered covariate was the health status, a categorical covariate with two levels
(0=pneumonia and 1=control condition).
• Actually not significant
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Visual Predictive Check: Observed plasma concentration (ng/ml) vs. time (h) and observed and predicted quantiles.
No difference between healthy and pneumonic calves
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• (see presentation on the establishment of a clinical cutoff for AST)
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pkpd
• We have to compute the percentage of calves able to achieve some target values for the PK/PD index (here
AUC/MIC) and that, for the different possible MICs encountered in calves.
– In order to compute these critical values the pop model was used (without covariate) to generate a large vector of AUC using a Monte
Carlo tool.
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PK/PD cut-offs for tulathromycin (ng/mL )
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• The adequacy of the current dosage of tulathromycin (2.5 mg/kg) was explored by computing population doses covering different TAR i.e. different percentages of the population to assess the ability of a
PK/PD model to validate or not the current dose for tulathromycin.
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Determination of a dose when the PK/PD index is AUC/MIC
Breakpoint value e.g. 24h
PD
Dose

Clearance (per hours)
 fu

F %
AUC
MIC
BP

MIC
Bioavailability
Free fraction
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Computation of the dose with point estimates
(mean clearance and F%, MIC
90
)
BP: 24h
180mL/Kg/h
MIC
90
160ng/ng/mL
Dose

Clearance (per hours)

F %
AUC
MIC
BP

MIC
0.6mg/kg/day or 6.9mg/kg for 10 days

Dose distribution using Monte
Carlo Computation
• using actual MIC distributions of M. haemolytica and P. multocida , we have generated by Monte Carlo computation
(MCC) the population distribution of the tulathromycin doses to determine the corresponding TAR for the currently marketed dose of 2.5 mg/kg.
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Population distribution of doses
BP
24h
Where Dose is the amount of AMD to be administered to guarantee an activity (Actually to achieve a UC/MIC of 24h i.e SF=1) for tulathromycin over 10 days
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MIC distribution for M haemolytica & P multocida (2004-20010)
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• An add-in design to help
Excel spreadsheet modelers perform Monte
Carlo simulations
• Others features
– Search optimal solution (e.g. dose) by finding the best combination of decision variables for the best possible results

Population distributions of tulathromycin doses
Nominal dose of 2.5mg/kg
Dose mg/kg
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87 %
63 %
Population cumulative distributions of tulathromycin doses
87%
Dose=2.5mg/kg
Dose mg/kg
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• For a TAR of 90%, the computed dose for M. haemolytica was 5.3mg/kg, indicating that, with this dose, the average serum concentration of tulathromycin over the first 240 h following administration of this dose will be equal to the MIC of M. haemolytica in 90% of calves.
• For the nominal dose of 2.5mg/kg, the corresponding TAR for M. haemolytica was
65.9%. For a TAR of 90%, the computed dose for
P. multocida was 2.52 mg/kg and for the nominal dose of 2.50 mg/kg, the TAR was 87.2%.
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• Analyze the contribution of the different variables to the final result (predicted dose)
• Allow to detect the most important drivers of the model
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• To identify the main sources of variability amongst the factors controlling the dose
(plasma clearance, MICs or fu),
– P. multocida
• MIC :76.7%
• clearance (23.0%)
• fu was negligible.
– M. haemolytica ,
• MIC :70.3%
• Clearance: 29.2%
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1. The dosage of tulathromycin can be documented using standard PK/PD concepts;
2.
the recommended dose of tulathromycin (2.5mg/kg) is consistent with the computed population doses with computed TARs of 66% and 87% for a 2.50 mg/kg dose for M. haemolytica and P. multocida, respectively;
3. PK/PD cut-offs are consistent with the current BPs of
AST issued by the CLSI and EMA;
4.
the main source of variability to take into account to determine a dose for a given animal is of PD origin
(MIC) and not the actual tulathromycin exposure.
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• At least one dose confirmation study must be presented if the dose finding is based on in vitro PD data only.
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• The aim of dose-confirmation studies is to confirm the efficacy of the selected dosage regimen in individual animals (treatment claims) or groups of animals (including metaphylaxis claims) under controlled clinical conditions.
– These studies can be performed using experimental models of infections but well controlled studies using naturally infected animals are preferred
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• Dose confirmation studies may allow for the assessment of relapse among animals that were considered successfully treated at the time of primary efficacy assessment.
• The objective is to distinguish between relapse and re-infection ;
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• The recommended treatment duration could be justified on basis of the time course of disease progress.
• Typically during clinical trials
• For a single dose, OK/PD consideration can support the duration of treatment (as we have done for tulathromycin)
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"The design of appropriate dosage regimens may be the single most important contribution of clinical pharmacology to the resistance problem"
Schentag et al. Annals of Pharmacotherapy, 30: 1029-1031
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