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 1

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

3. EMA/CVMP 2015 3

4. EMA guideline 2015 4

5. The dosage regimen 5

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 6

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

8. Dose determination Dose titration Experimental infectious model Dose confirmation Well controlled, naturally infected animals Dose confirmation PK/PD Dose confirmation How to find and confirm a dose (dosage regimen) 8

9. ECVPT Toulouse 2009 - 9 The dose-titration

10. Dose titration: principle 10

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 11

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

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 13

14. Parallel design for antibiotics: Statistical model The null hypothesis –placebo = D1 = D2 = D3 The statistical linear model –Yj =  wj +  j Conclusion –D3 = D2 > D1 > Placebo Placebo Dose Response 123 * * NS Selected dose 14

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

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 17

18. Dose titration and critical value of the PK/PD index Surrogate: AUC/MIC Placebo Dose Response 123 POC 18

19. 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 Dependent variable Placebo effect sensitivity 2 parameters: a (placebo effect) & b (slope of the exposure-effect curve) 19

20. Dose determination using a PK/PD approach 2

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 21

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. 22

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 23

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

25. Tulathromycin (Draxxin, Zoetis) 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; A semi-synthetic macrolide antibiotic of the subclass triamilide Comprises a 13-member ring compound (10%) and a 15-member ring compound (90%) 25

26. PD properties (Zoetis) 26

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 MIC 90 27

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 28

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 Cmax=0.5µg/mL ≤ to MIC 90 MIC 29

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. 30

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 31

32. In vitro investigations 32

33. MIC in MHB vs. calf serum 25%,50%,75% and 100% 33

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 34

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. 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. 35

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 36

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

38. Time-kill studies 38

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

40. Data analysis 40

41. Tulathromycin concentrations to achieve a bacteriostatic, bactericidal or an eradication effect for M haemolytica & P multocida as estimated from killing curves EffectM Haemolytica (MHB) MIC=2000ng/mL M Haemolytica (Serum) (MIC=40ng/mL) P Multocida (MHB) (MIC=2000ng/mL) P Multocida (serum) (MIC=40ng/mL) Bacteriostatic1420±52032.8±24.41520±96028.8±15.4 Bactericidal 3log reduction 1740±68041.6±12.01960±150037.6±3.2 4log reduction2060±88053.6±19.2 Results expressed for a typical MIC as obtained either in MHB (2000ng/ml) or for the same strain in serum (40ng/mL) 41

42. In vivo investigation 42

43. PK investigations 43

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 typical values of plasma clearance/F and Between Subject variability Effect of illness status MC Simulation of 1040 PK Distribution of clearances (log normal) MC Simulation of 1040 PK Distribution of clearances (log normal) Dose prediction with Monte Carlo computation Distribution of field MIC for susceptible strains Zoetis M. haemolytica n = 2233 P. multocida n=2483 Distribution of field MIC for susceptible strains Zoetis M. haemolytica n = 2233 P. multocida n=2483 Derivation of doses for TAR 90% Calculation of TAR for current dose Derivation of doses for TAR 90% Calculation of TAR for current dose PK/PD cutoff values (for the establishment of breakpoint values of Antimicrobial Susceptibility Testing) PK/PD cutoff values (for the establishment of breakpoint values of Antimicrobial Susceptibility Testing) 44

45. Disposition of tulathromycin 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). 45 Unbalanced data

46. Population modeling: structural model 46

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

48. Model of the residual variance 48

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 49

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 50

51. PRED vs. Dependent Variable PRED (population predicted concentration ) vs observed concentrations (ng/mL). Data are evenly distributed about the line of identity, indicating an appropriate structural model could be found for most individuals 51

52. IPRED ( Individual predicted concentrations) vs. observed concentrations (ng/mL). IPRED were obtained by setting random effects to the 'post hoc' or empirical Bayesian estimate of the random effects for the individual from which the DV observation was made. Thus the plot shows observed vs fitted values of the model function. Here Data are evenly distributed about the line of identity, indicating no major bias in the random component of the model. IPRED were obtained by setting random effects to the 'post hoc' or empirical Bayesian estimate of the random effects for the individual from which the DV observation was made. Thus the plot shows observed vs fitted values of the model function. Here Data are evenly distributed about the line of identity, indicating no major bias in the random component of the model. 52

53. Computation of PK/PD cut- offs for Antimicrobial Susceptibility Testing (AST) (see presentation on the establishment of a clinical cutoff for AST) 53

54. Cutoff PK/PD (Co 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. 54

55. PK/PD cut-offs for tulathromycin (ng/mL ) 55

56. TAR (%) for tulathromycin 56

57. Population Dosage prediction 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. 57

58. Determination of a dose when the PK/PD index is AUC/MIC Breakpoint value e.g. 24h PD Free fraction Bioavailability 58

59. Computation of the dose with point estimates (mean clearance and F%, MIC 90 ) BP: 24h MIC 90 160ng/ng/mL 180mL/Kg/h 0.6mg/kg/day or 6.9mg/kg for 10 days

60. MonteCarlo-Orlando06 - 60 MIC 90 or MIC distribution? Distributions with the same MIC 90 % Patients with an AUIC > auic 60 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 708090100110120130140150 AUIC

61. 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. 61

62. Population distribution of doses 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 BP 24h BP 24h 62

63. MIC distribution for M haemolytica & P multocida (2004-20010) 63

64. 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

65. Population distributions of tulathromycin doses Dose mg/kg Nominal dose of 2.5mg/kg 65

66. Population cumulative distributions of tulathromycin doses Dose mg/kg 63 % Dose=2.5mg/kg 87% 66

67. Dose computation 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%. 67

68. Sensitivity analysis Analyze the contribution of the different variables to the final result (predicted dose) Allow to detect the most important drivers of the model 68

69. Sensitivity analysis 69

70. Sensitivity analysis 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% 70

71. Conclusions 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. 71

72. Dose confirmation 72

73. Dose-confirmation studies At least one dose confirmation study must be presented if the dose finding is based on in vitro PD data only. 73

74. Dose-confirmation studies 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 74

75. Dose-confirmation studies 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; 75

76. Determination of the duration of treatment 76

77. EMA and the duration of treatment 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) 77

78. General conclusion 78

79. "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 79