How to establish a dosage regimen for a sustainable use of antibiotics in veterinary medicine

1. 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

2. EMEA "Points to consider" July 2000 • Inadequate dosing of antibiotics is probably an important reason for misuse and subsequent risk of resistance

3. EMA/CVMP 2015

4. EMA guideline 2015

5. The dosage regimen

6. The three endpoints to consider in veterinary medicine • Efficacy in animal • No promotion of resistance in animal (target pathogen) • No promotion of resistance in man

7. What are the elements of a dosage regimen • The dose • The dosing interval • The treatment duration • When to start • When to finish

8. How to find and confirm a dose (dosage regimen)

9. The dose-titration

10. Dose titration: principle

11. Dose titration require: an infectious model • 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

12. Endpoints • Efficacy evaluation should be based on clinical and bacteriological response as determined by appropriate clinical and bacteriological assessment

13. Dose titration: design • Usually three levels of dosage of the veterinary medicinal product should be tested, preferably using the final formulation. • Control group (dose=0) compulsory

14. Selected dose Parallel design for antibiotics: Statistical model Response NS • The null hypothesis • placebo = D1 = D2 = D3 • The statistical linear model • Yj = wj + j • Conclusion • D3 = D2 > D1 > Placebo * * Dose 1 2 3 Placebo

15. Dose titration: statistics • Statistical comparisons between different treatment groups and the negative control group • it is acknowledged that dose determination lack of power

16. The parallel design • 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.

17. Dose titration & PK/PD • Dose titration can be used to establish the critical value of the PK/PD index

18. Dose titration and critical value of the PK/PD index POC Response • Surrogate: AUC/MIC Dose 1 2 3 Placebo

19. Placebo effect Dependent variable sensitivity 2 parameters: a (placebo effect) & b (slope of the exposure-effect curve) Probability of cure (POC) • Logistic regression can be used to link measures of drug exposure (AUC/MIC) to the probability of a clinical success Independent variable

20. Dose determination using a PK/PD approach

21. 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

22. EMA and the PK/PD approach • 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.

23. EMA and the PK/PD approach • 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

24. Demonstration of applicability of PK/PD concepts to determine a dosage regimen for tulathromycin in the calf

25. 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 Histophilussomni;

26. PD properties(Zoetis)

27. PK parameters in cattle(Zoetis) • Clearance of a medium value (3mL/kg/min), • The long terminal half-life (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 MIC90

28. 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

29. The PK/PD issue for macrolides (triamilides): plasma concentration lower than MICs • Good clinical efficacy and bacteriological cure with macrolides is achievable with plasma concentrations (much) lower, than the in vitro MICs for major lung pathogens MIC Cmax=0.5µg/mL ≤ to MIC90

30. 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 concentration-effect relationship for macrolides.

31. Aims of the study • 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

32. In vitro investigations

33. MIC in MHB vs. calf serum25%,50%,75% and 100%

34. 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

35. The serum effect 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.

36. What PK/PD index to select? • 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

37. What is the size of the PK/PD index (AUC/MIC) for tulathromycin

38. Time-kill studies

39. Killing curves in serum to compute the PK/PD index breakpoint Multiple of MIC For a bactericidal effect (MH & PM): Breakpoint≈ 24h

40. Data analysis

41. Tulathromycin concentrations to achieve a bacteriostatic, bactericidal or an eradication effect for M haemolytica & P multocida as estimated from killing curves Results expressed for a typical MIC as obtained either in MHB (2000ng/ml) or for the same strain in serum (40ng/mL)

42. In vivo investigation

43. PK investigations

44. 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 • Distribution of field MIC for susceptible strains • Zoetis • M. haemolytica n = 2233 • P. multocida n=2483 • MC Simulation of 1040 PK • Distribution of clearances (log normal) • 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

45. 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).

46. Population modeling: structural model

47. The Between subject variability (BSV) was modeled using an exponential model

48. Model of the residual variance

49. Model with covariates • The only considered covariate was the health status, a categorical covariate with two levels (0=pneumonia and 1=control condition). • Actually not significant

50. Tulathromycin PK: Visual Predictive Check: Observed plasma concentration (ng/ml) vs. time (h) and observed and predicted quantiles. No difference between healthy and pneumonic calves