1. Pierre-Louis Toutain AAVM Congress - Ottawa June 2004
NATIONAL
VETERINARY
S C H O O L
T O U L O U S E
UMR 181 Physiopathologie & Toxicologie Expérimentales
Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine
Pierre-Louis Toutain
AAVM Congress - Ottawa June 2004
11/10/2018

2. Co-workers Academia Industry Horse study Pig study Horse study
A. Bousquet-Mélou
M. Doucet
D. Concordet
M. Peyrou
Pig study
J. del Castillo
V. Laroute
P. Sanders
M. Laurentie
H. Morvan
Industry
Horse study
Vetoquinol (France)
M. Schneider
Pig study
SOGEVAL (France)
C. Zemirline
P. Pomie
VIRBAC (France)
E. Bousquet
INTERVET (germany)
E Thomas

3. Schentag et al. Annals of Pharmacotherapy, 30: 1029-1031
"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:

4. Dosage regimen and prevention of resistance
Many factors can contribute to the development of bacteria resistance
the most important risk factor is repeated exposure to suboptimal antibiotic concentrations
dosage regimen should minimize the likelihood of exposing pathogens to sublethal drug levels

5. Ranking (Low, Medium, High) of extent of antibiotic drug use in animal based on duration and method of administration
Individual Groups or pens Flocks, herds
animal of animal of animals
Duration
Short <6 days L M H
Medium 6-21 d L M H
Long >21 days M H H

6. What is the contribution of the kineticist to the prudent use of antibiotics
To assist the clinicians designing an optimal dosage regimen
To ensure that the selected antibiotic reach the site of infection at an appropriate effective concentration, for an adequate duration and for all (or most) animals under treatment to guarantee a cure (clinical, bacteriological) and without favoring antibioresistance

7. The application of population pharmacokinetic modelling to optimize antibiotic therapy

8. How to ensure that a dosage regimen minimizes the likelihood of exposing pathogens to sub-clinical drug levels
Individual animals
groups or pens vs flocks/herds
 population approach

9. Reminder Traditional vs populational PK/PD approaches
What is PK/PD for antibiotics and how to determine a dosage regimen using PK/PD predictors
see P. Lees presentation

10. Traditional veterinary PK
Study performed in experimental setting
elaborate design
limited number of animals
rich data
Data analysis: two stages
1- modelling individuals  samples of individual estimates
Cl, Vss, F%, t1/2
2- statistical analysis
mean - SD
search for difference between subgroups (ANOVA), for associations (regression…)

11. Limits of traditional PK
Experimental conditions
may be not representative of the real world
consider variability as a nuisance
Data analysis
variance and covariance often badly estimated and explained
Solution: the population approach

12. How to determine a dosage regimen using PK/PD predictors

13. Dose titration PK/PD Black box Dose Response PK PD Response Dose
Plasma
concentration

14. The main goal of a PK/PD trial in veterinary pharmacology
To be an alternative to dose-titration studies to discover an optimal dosage regimen (will be presented by P. Lees)

15. Contributions of the PK/PD approach to the population determination of a dosage regimen
The separation of PK and PD variabilities

16. PK/PD variabilities for antibiotics
Consequence for dosage determination PK PD
Effect
Dose
BODY
Pathogens
Plasma concentration
Physiological/constitutional variables
Breed, sex, age
Kidney function
Liver function...
Clinical covariables
pathogens susceptibility (MIC)
disease severity or duration
PK/PD population approach

17. PK/PD predictors of efficacy
T>MIC : penicillins, cephalosporins, macrolides, oxazolidinones
T>CMI
Cmax/MIC
Cmax/MIC : aminoglycosides
AUIC =
AUC
MIC
Units = Time (h)
AUIC (or 24h AUC/MIC) : quinolones, tetracyclines, ketolides, azithromycins, streptogramins
Cmax
Concentrations
MIC
Time
24h

18. AUIC: an attempt to combine PK and PD properties of antibiotics
Capacity to eliminate the drug
AUC
MIC
Dose / Clearance
MIC90 or MIC50
AUIC # = = critical breakpoint value
Fixed endpoint related to Emax and EC50 PD
Application : fluoroquinolones

19. Computation of dose using a PK/PD predictor
Dose = x x Clearance (24h) PD
Breakpoint to be achieved
AUIC
24h
MIC
fu x F%
bioavailability
Free fraction PK

20. Computation of dose using a PK/PD predictor
Dose = x x Clearance
MIC90
MIC50 : average PD
Breakpoint to be achieved PK
(average)
AUIC
24h
MIC F%
average
(pop) PK

21. Dispersion of variance around the mean may be the most relevant parameter to predict a population dosage regimen for antibiotics

22. Variability and the likelihood of resistance
Ingested dose
Experimental setting
Selection of resistance
MIC gut flora
Field conditions
oral
Dose gut flora
Target biophase
1-F%
Resistance: zoonotic, commensal F%
Side effects
Therapeutic window
Undesirable concentration
MIC90
Resistance: pathogens of interest
Suboptimal exposure  resistance

23. Variability and the likelihood of resistance
Ingested dose
1-F%
Selection of resistance
Experimental setting
MIC gut flora
Field conditions
gut flora
oral
Target biophase F%
Dose
Resistance: zoonotic, commensal
Side effects
Therapeutic window
Undesirable concentration
MIC90
Resistance: pathogens of interest
Suboptimal exposure  resistance

24. Examples of population approaches for antibiotics in veterinary medicine
Identification and explanation of PK variability
marbofloxacin in horse
Determining drug PK characteristics in tissues using sparse sampling
marbofoxacin in ocular fluid in dog
Dosage regimen determining
doxycyclin in pig

25. Marbofloxacin in horses
A. Bousquet-Mélou et al.

26. Marbofloxacin in horses: PK
A fluoroquinolone
No marketing authorization in horses
Conventional PK study
data analysis using the two-stage approach
clearance = 4.15 ± 0.75 mL/kg/min CV = 18%
Vss = 1.48 ± 0.3 L/kg
t1/2 = ± 1.99 h

27. Marbofloxacin in horses: PK/PD integration (oral route)
Value of efficacy index (AUIC24h) and Cmax/MIC calculated from PK parameters obtained after the administration of 2 mg/kg BW in 6 horses
MIC90 = µg/mL (enterobacteriaceae)
“average” PK/PD index
AUIC24h = 155 ± 21
Cmax/MIC = 31 ± 4.5

28. Population PK approach for marbofloxacin in horses: objective
To measure the interindividual variability of systemic exposure to marbofloxacin in horses
To identify covariates explaining a part of this variability
Body clearance
The only determinant of AUC

29. Materials and Methods (1)
Animals
patients from the Equine Clinic of the Veterinary School
healthy horses from the Riding School
Covariates record
demographic, physiological, disease
not all covariates presented
IV administration of marbofloxacin (2 mg.kg-1)
Nonlinear mixed-effects modelling
Kinepop software (D. Concordet)

30. Materials and Methods (2)
Sampling design selection
Number of samples per animal and selection of sampling times
D - optimal design to maximize the precision of AUC [0-24h]
previous informations : AUC[0-24h] Mean and Standard Deviation
Bousquet-Melou et al., Equine Vet J, 34, 2002
AUC imprecision
4 samples
5 samples
Sampling windows:
30min windows centred around
1.5, 3, 5, 7 and 19.5 h
post-administration
Sampling design

31. Materials and Methods (3)
PK model : - biexponential equation
- parameterisation in volumes of distribution and clearances
Statistical model : - lognormal distribution of PK parameters
Model 1 : no covariate
Model 2 : with covariates for body clearance

32. Results: conventional vs pop kinetics
52 horses, 253 blood samples 10
Bousquet-Melou et al., Equine Vet J, 34, 2002 1
Marbofloxacin (mg/mL)
0.1
0.01
0.001 4 8 12 16 20 24
Time (h)

33. Variability: model without covariable
Clearance (pop)
population mean = 3.88 mL/kg/min
Inter-individual variability
CV(%) = 50 %
0.5 1
1.5 2
2.5
observed concentrations
(mg/mL)
predicted concentrations

34. Variability: model with covariables
Without covariable
With covariables
0.5 1
1.5 2
2.5
predicted concentrations
(mg/mL)
observed concentrations
(mg/mL)

35. Variability: explicative covariable
Covariables for body clearance expressed in L.kg-1.h-1
Age
Sex
Disease NS
Weight
P=0.001
R2 = 0.33
The body weight explains about 33%
of marbofloxacin clearance variability
Note: dose was 2 mg/kg BW i.e. already scaled to BW

36. Marbofloxacin: the body weight is a covariable-3 -2 -1
200
400
600
Body weight (kg)
Ln (Clearance)
0.2
0.4
0.6
200
400
600
Body weight (kg)
Clearance (L/kg/h)
Allometric relationship with an allometric exponent >1

37. Discussion Marbofloxacin clearance in horses Influence of body weight
Population trial
Classical trials *
Mean (L.kg-1.h-1)
0.233
CV (%) 50
* Carretero et al., Equine Vet J, 34, 2002
Bousquet-Melou et al., Equine Vet J, 34, 2002
Influence of body weight
In the range of observed weights : about 3-fold variation in body clearance expressed per kilogram

38. Conclusion
High interindividual variability of marbofloxacin body clearance in horses
Underestimated in classical PK trials
Influence of body weight
Consequences on systemic exposure
Clinical relevance for efficacy and resistance ?
Current trial
Multicentric experiment (Montreal, Toulouse, Utrecht, Vienna)
Increased number of covariates
Further trials
Assessment of variability of PD origin

39. Population PK/PD determination of a dosage regimen for an antiobiotic

40. Objectives
Document, with population PK/PD approach, the dosage regimen for antibiotics in pig
Ultimate goal : make recommendations
to determine a dosage regimen
to establish MIC breakpoints
to establish PK/PD predictor breakpoints

41. Population trial (INRA/SOGEVAL/CTPA) J. del Castillo et al.
Antibiotic: doxycyclin
Britain (2 settings)
215 pigs (30 to 110 kg BW)
oral (soup)
pens of pigs (unit of treatment)

42. Population trial Decision of treatment : metaphylaxis
prevalence of disease>10% (tachypnee, body temperature > 40°C)
Treatments :
Doxycyclin (5 mg/kg) or
Doxycyclin + paracetamol (15 mg/kg)
2 meals apart from 24h
Measure of covariables (rectal temperature /clinical signs etc.)
Blood samplings (4 or 5 after the 2nd dose)
Dosage HPLC (doxy, paracetamol+metabolite)

43. PK Variability
Doxycycline
n = 215

44. PK doxycyclin variability analysis

45. Doxycycline : sex effect
Time (h)

46. Doxycycline : body temperature effect
Rectal temperature

47. Doxycycline : disease effect
healthy
diseased
Concentrations (µg/mL)
Time (h)

48. Variability analysis: AUC vs. body weight

49. How to make use of PK/PD population knowledge to predict how well will doxycyclin perform clinically?

50. The use of MonteCarlo simulation
Dose selection at the population level
Determination of breakpoints:
PK/PD
MIC

51. Material and Method
PK/PD analysis was performed using Monte Carlo simulations
The method accounts for the variability in PK as well as MIC data to determine the probability of reaching a target AUC0-24/MIC ratio

52. Data analysis PK : non linear mixed effect model
seek to explain the variability by covariables
Computation of AUC and statistical establishment of distribution
PK/PD: MonteCarlo approach to assess the distribution of the PK/PD endpoint

53. Dosage regimen: application of PK/PD concepts
The 2 sources of variability : PK and PD
PK: exposure
PD: MIC
Distribution of PK/PD surrogates (AUC/MIC)
Monte-Carlo approach

54. AUC distribution
Under-exposure ?

55. Microbiological data Intervet, Virbac, AFSSA
Streptococcus suis (n=180)
Actinobacillus pleuropneumoniae (n=110)
Pasteurella multocida (n=206)
Haemophilus (n=25)

56. MIC distribution: Actinobacillus pleuropneumoniae (n=106)40 35 30
Pathogens % 25 20
INTERMEDIATE 15 10
SUSCEPTIBLE
RESISTANT 5
0.25
0.5 1 2 4 8
MIC
(µg/mL)

57. MIC distribution Pasteurella multocida (n=205)40 35 30
Pathogens % 25 20 15 10
SUSCEPTIBLE 5
0.0625
0.125
0.25
0.5 1 2 4
MIC ( m
g/mL)

58. MIC distribution Streptococcus suis (n=180)
Bimodal distribution 35 30 25
Pathogens %
INTERMEDIATE 20
RESIST.
SUSCEPTIBLE 15 10 5
0.0313
0.0625
0.125
0.5 1 2 4 8 16 32
CMI ( m
g/mL)

59. Statistical distribution of PK/PD predictors
Question: what is the percentage of a pig population to achieve a given value of the PK/PD predictor for a given dose of doxycyclin for a:
Empirical (initial) antibiotherapy (pathogen known, MIC unknown but distribution known)
Targeted antibiotherapy (MIC known)

60. Doctor or Regulator
In clinical therapy, we would like to give optimal dose to each individual patient for the particular disease
individualized therapy (targeted antibiotherapy)
In new drug assessment / development, we would like to know the overall probability for a population of an appropriate response to a given drug and proposed regimen
population-based recommendations (empirical antibiotherapy)
H. Sun, ISAP-FDA workshop 1999

61. Population PK/PD: applications
Individualisation  doctor
Recommandation  regulator

62. Breakpoint to be achieved (AUC/MIC) (h)
Doxycycline (5 mg/kg) : empirical vs targeted antibiotherapy for Pasteurella multocida
100%
Empirical antibiotherapy
Targeted antibiotherapy (MIC = 0.25 µg/mL)
80%
60%
% of pigs above the breakpoint
40%
20% 0% 24 48 72 96
120
144
168
192
bacteriostatic
Breakpoint to be achieved (AUC/MIC) (h)

63. Breakpoint to be achieved (AUC/MIC) (h)
Doxycycline (5 mg/kg): empirical vs targeted antibiotherapy for Actinobacillus pleuropneumoniae
100%
Empirical (MIC unknown)
80%
Targeted (MIC = 0.5 µg/mL)
60%
% of pigs above the breakpoints
40%
20%
Breakpoint to be achieved (AUC/MIC) (h) 0% 24 48 72
Bacteriostatic

64. Breakpoint to be achieved (AUC/MIC) (h)
Doxycycline (5 mg/kg) : empirical vs targeted antibiotherapy for Streptococcus suis
100%
80%
Empirical antibiotherapy
Targeted antibiotherapy (MIC = 16 µg/mL)
60%
% of pigs above the breakpoint
40%
20% 0% 24 48 72 96
120
144
168
192
Breakpoint to be achieved (AUC/MIC) (h)
bacteriostatic

65. Population dose determination
Question: what is the doxycycline dose to be administered to achieve a given AUC/MIC ratio for a given percentage of the pig population ? (e.g. 90%)

66. Doxycycline : selection of an empirical (initial) dose for Pasteurella multocida
Doses
100%
5 mg/kg
90%
80%
10 mg/kg
20 mg/kg
60%
% of pigs above a given AUC/MIC ratio
40%
20% 0% 24 48 72 96
120
144
168
bacteriostatic
AUC/MIC ratio (h)

67. Doxycycline : selection of an empirical (initial) dose for Actinobacillus pleuropneumoniae
Doses
100%
5mg/kg
80%
10 mg/kg
20 mg/kg
60%
% of pigs above a given AUC/MIC ratio
40%
20% 0% 24 48 72
bacteriostatic
AUC/CMI ratio (h)

68. Doxycycline : selection of an empirical (initial) dose for Streptococcus suis
Doses
100%
5 mg/kg
80%
10 mg/kg
20 mg/kg
60%
% of pigs above a given AUC/MIC ratio
40%
20% 0% 24 48 72 96
120
144
168
AUC/MIC ratio (h)

69. Determination of MIC breakpoints by standard developing organizations using population approach

70. Determination of MIC breakpoints
Current situation
PK information is badly taken into account
population approach

71. Dose fixed (marketing authorization) breakpoint to achieve determined:
Determination (or revision) of the clinical MIC breakpoint for a given drug against a given pathogen
Dose fixed (marketing authorization)
breakpoint to achieve determined:
T>MIC >80% of the dosage interval
or AUC/MIC = 100h
computation of the critical MIC value for which T>MIC (or other PK/PD indices) are in excess of 90% (or other %) of subjects.

72. Doxycycline (5 mg/kg) : MIC breakpoint for Actinobacillus pleuropneumoniae to achieve a given AUC/MIC ratio for 90% of pig
MIC = µg/mL
100%
90%
MIC = µg/mL
80%
MIC = 0.25 µg/mL
60%
% of pigs above the breakpoint
40%
20% 0% 24 48 72 96
120
144
168
192
216
240
bacteriostatic
Breakpoint AUC/MIC (h)

73. Doxycycline (5 mg/kg): MIC breakpoint for Streptococcus suis to achieve a given AUC/MIC ratio
100%
MIC = 0.5µg/mL
90%
MIC = µg/mL
80%
MIC = µg/mL
60%
% of pigs above a given AUC/MIC ratio
40%
20% 0% 24 48 72 96
120
144
168
192
Bacteriostatic
Breakpoint AUC/CMI (h)

74. Doxycycline(5 mg/kg) : MIC breakpoints for Pasteurella multocida to achieve a given AUC/MIC ratio
MIC = µg/mL
MIC = µg/mL
100%
MIC = 0.25 µg/mL
90%
80%
60%
% de pc avec une AUC/CMI> seuil
40%
20% 0% 24 48 72 96
120
144
168
192
Bacteriostatic
AUC/MIC ratio (h)

75. Determination of PK/PD predictor breakpoints
For drug dosage prediction, not only PK/PD index that determine the effect but also its magnitude must be determined
Prospective or retrospective approach using clinical data

76. Conclusion For practitioners
to adjust the dosage regimen for a given animal (or a given breed…)
flexible dosage regimen
For drug companies and authorities
a general framework to propose an empirical (initial) dosage regimen
For standards-developing organizations
MIC breakpoints

77. Experimental vs population studies

78. Experimental
Population

79. Experimental vs. population approach
Two questions regarding experimental approach
What is its validity (clinical relevance)
What about variability

80. Drug administration, social behavior and the dose
Experimental
Individually controlled by the investigator (restricted, tubing…)
The nominal dose is guaranteed to all individuals
Field
related to individual feeding behavior (fever, anorexia)
group effect (hierarchy, dominance) or other behavior
Dose actually ingested can be much higher or much lower than the nominal dose

81. The pathology Experimental Field
Standardised experimental • Spontaneous disease
infectious model

82. Animal selection Experimental Population
Highly selected (as homogeneous as possible) body weight, sex, age...
Population
Representative of the target population different breed, age, pathological conditions…

83. Study design Experimental experimental, restrictive
artificial (temperature, light…)
Population
Observational
natural (e.g. field)
Difference
Power,
inference space
interaction with environment behavior

84. Experimental vs population approach: the status of variability
viewed as a nuisance that has to be overcome
Population
recognized as an important feature that should be identified, measured and explained (covariables)

85. Experimental vs population approach Accuracy and variability
In current experimental practices, major determinant of drug disposition (PK) or of drug effect (PD) can be modified, altered or suppressed
GLP is not synonymous to good science !

86. Advantage of field population kinetics over classical experimental setting
Experimental environment
healthy animals selected for homogeneity inter-individual variability is viewed as a nuisance
conditions rigidly standardized
artificial conditions
Real world / clinical setting
patients representative of target population
variability (inter & intra-individual, inter-occasion) is an important feature that should be identified and measured
seek for explaining variability by identifying factors of demographic pathophysiology

87. Doxycycline concentration variability: population vs experimental trial
1.5
1.0
Number of data points
Trial
Population n=215
Experimental n=15 to 19
DOXYCYCLINE (µg/mL)
0.5
0.0 4 6 12 24
Time (h)

88. Doxycycline concentration variability: population vs experimental trial for time 6h post-administration
1.5
Number of data points
1: Population n=215
2: Experimental n=16
3: Experimental n=64
1.0
DOXYCYCLINE (µg/mL)
0.5
0.0 1 2 3