Transcript Population PK/PD and the rational design of an

NATIONAL
VETERINARY
SCHOOL
UMR 181 Physiopathologie &
Toxicologie Expérimentales
TOULOUSE
Population PK/PD and the rational
design of an antimicrobial dosage
regimen in veterinary medicine
Pierre-Louis Toutain
AAVM Congress - Ottawa June 2004
7/16/2015
Ottawa Juin 2004 - 1
Co-workers
• Academia
– Horse study
•
•
•
•
A. Bousquet-Mélou
M. Doucet
D. Concordet
M. Peyrou
– Pig study
•
•
•
•
•
•
J. del Castillo
V. Laroute
D. Concordet
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
Ottawa Juin 2004 - 2
"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
Ottawa Juin 2004 - 3
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
Ottawa Juin 2004 - 4
Ranking (Low, Medium, High) of extent of
antibiotic drug use in animal based on duration
and method of administration
Duration
Short <6 days
Medium 6-21 d
Long >21 days
Individual
animal
Groups or pens
of animal
Flocks, herds
of animals
L
L
M
M
M
H
H
H
H
Ottawa Juin 2004 - 5
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
Ottawa Juin 2004 - 6
The application of population
pharmacokinetic modelling to
optimize antibiotic therapy
Ottawa Juin 2004 - 7
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
Ottawa Juin 2004 - 8
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
Ottawa Juin 2004 - 9
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…)
Ottawa Juin 2004 - 10
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
Ottawa Juin 2004 - 11
How to determine a
dosage regimen using
PK/PD predictors
Ottawa Juin 2004 - 12
Dose titration
Dose
Response
Black box
PK/PD
PK
PD
Response
Dose
Plasma
concentration
Ottawa Juin 2004 - 13
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)
Ottawa Juin 2004 - 14
Contributions of the PK/PD
approach to the population
determination of a dosage regimen
The separation of PK and PD variabilities
Ottawa Juin 2004 - 15
PK/PD variabilities for antibiotics
• Consequence for dosage determination
PK
Dose
BODY
PD
Plasma
concentration
Physiological/constitutional
variables
•Breed, sex, age
•Kidney function
•Liver function...
Pathogens
Effect
Clinical covariables
• pathogens susceptibility (MIC)
• disease severity or duration
PK/PD population approach
Ottawa Juin 2004 - 16
PK/PD predictors of efficacy
• T>MIC : penicillins, cephalosporins, macrolides, oxazolidinones
• Cmax/MIC : aminoglycosides
• AUIC (or 24h AUC/MIC) : quinolones, tetracyclines,
ketolides, azithromycins, streptogramins
Cmax
Concentrations
Cmax/MIC
AUC
AUIC =
MIC
Units = Time (h)
MIC
T>CMI
24h
Time
Ottawa Juin 2004 - 17
AUIC: an attempt to combine PK and
PD properties of antibiotics
Capacity to eliminate
the drug
PK
AUC
AUIC #
MIC
Dose / Clearance
=
= critical breakpoint value
MIC90 or MIC50
• Fixed endpoint related to Emax and EC50
PD
Application : fluoroquinolones
Ottawa Juin 2004 - 18
Computation of dose using a
PK/PD predictor
PD
Breakpoint to
be achieved
– Dose =
(24h)
AUIC
x
24h
MIC
x Clearance
fu x F%
bioavailability
Free fraction
PK
Ottawa Juin 2004 - 19
Computation of dose using a
PK/PD predictor
PD
Breakpoint to
be achieved
– Dose =
AUIC
average
(pop)
24h
MIC50 : average
MIC90
PK (average)
x
MIC
x Clearance
F%
PK (average)
Ottawa Juin 2004 - 20
Dispersion of variance around the
mean may be the most relevant
parameter to predict a population
dosage regimen for antibiotics
Ottawa Juin 2004 - 21
Variability and the likelihood of resistance
Ingested dose
Selection of resistance
Experimental setting
MIC gut flora
Field conditions
oral
Dose
gut flora
1-F%
Resistance: zoonotic, commensal
F%
Target biophase
Resistance:
pathogens of interest
Side effects
Therapeutic
window
Undesirable
concentration
MIC90
Suboptimal exposure
 resistance
Ottawa Juin 2004 - 22
Variability and the likelihood of resistance
Ingested dose
Selection of resistance
Experimental setting
MIC gut flora
Field conditions
oral
Dose
gut flora
Resistance: zoonotic, commensal
F%
Target biophase
Resistance:
pathogens of interest
Side effects
Therapeutic
window
Undesirable
concentration
MIC90
Suboptimal exposure
 resistance
Ottawa Juin 2004 - 23
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
Ottawa Juin 2004 - 24
Marbofloxacin in horses
A. Bousquet-Mélou et al.
Ottawa Juin 2004 - 25
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 = 7.56 ± 1.99 h
Ottawa Juin 2004 - 26
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 = 0.027 µg/mL (enterobacteriaceae)
AUIC24h = 155 ± 21
Cmax/MIC = 31 ± 4.5
 “average” PK/PD index
Ottawa Juin 2004 - 27
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
Ottawa Juin 2004 - 28
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)
Ottawa Juin 2004 - 29
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
Sampling windows:
30min windows centred around
1.5, 3, 5, 7 and 19.5 h
post-administration
5 samples
Sampling design
Ottawa Juin 2004 - 30
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

 N0, ω
 N0, ω
 N0, ω
Log VC,i  μ Vc  ηVc, i
ηVC  N 0, ω2 Vc
Log Vp,i  μ Vp  ηVp, i
ηVp
Log Cl i  μ Cl  ηCl,i
ηCl
Log Cl d,i  μ Cld  ηCld ,i
ηCld
2
2
Vp
Cl
2



Cld

Model 2 : with covariates for body clearance
Log Cli  μ Cl  θ1  Age i  θ 2  Weight i  Sex i  Disease i  ηCl,i
Ottawa Juin 2004 - 31
Results: conventional vs pop
kinetics
Marbofloxacin (mg/mL)
• 52 horses, 253 blood samples
10
1
0.1
0.01
0.001
0
4
8
12 16
Time (h)
20
24
Bousquet-Melou et al., Equine Vet J, 34, 2002
Ottawa Juin 2004 - 32
Variability: model without covariable
2.5
population mean = 3.88 mL/kg/min
2
(mg/mL)
predicted concentrations
Clearance (pop)
Inter-individual variability
CV(%) = 50 %
1.5
1
0.5
0
0
0.5
1
1.5
2
2.5
observed concentrations
(mg/mL)
Ottawa Juin 2004 - 33
Variability: model with covariables
(mg/mL)
predicted concentrations
Without covariable
With covariables
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
0
0.5
1
1.5
2
2.5
0
0.5
1
1.5
2
2.5
observed concentrations
(mg/mL)
Ottawa Juin 2004 - 34
Variability: explicative covariable
Covariables for body clearance expressed in L.kg-1.h-1
Age
NS
Disease
NS
Sex
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
Ottawa Juin 2004 - 35
Marbofloxacin: the body weight is a
covariable
Body weight (kg)
200
400
600
0
-1
-2
-3
Clearance (L/kg/h)
Ln (Clearance)
0
0.6
0.4
0.2
0
0
Allometric relationship with
an allometric exponent >1
200
400
600
Body weight (kg)
Ottawa Juin 2004 - 36
Discussion
• Marbofloxacin clearance in horses
Population trial
Mean (L.kg-1.h-1)
CV (%)
Classical trials *
0.233
0.19 - 0.246
50
18 - 21
* 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
Ottawa Juin 2004 - 37
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
Ottawa Juin 2004 - 38
Population PK/PD
determination of a dosage
regimen for an antiobiotic
Ottawa Juin 2004 - 39
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
Ottawa Juin 2004 - 40
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 12-15 pigs (unit of treatment)
Ottawa Juin 2004 - 41
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)Ottawa Juin 2004 - 42
PK Variability
1.6
Doxycycline
Concentrations mg/mL
1.4
n = 215
1.2
1
0.8
0.6
0.4
0.2
0
-5
0
5
10
15
20
25
30
Time (h)
Ottawa Juin 2004 - 43
PK doxycyclin variability analysis
Ottawa Juin 2004 - 44
Doxycycline : sex effect
Doxycycline
Sexe 0
Sexe 1
Time (h)
Ottawa Juin 2004 - 45
Doxycycline
Doxycycline : body temperature effect
Rectal temperature
Ottawa Juin 2004 - 46
Concentrations (µg/mL)
Doxycycline : disease effect
healthy
diseased
Time (h)
Ottawa Juin 2004 - 47
Variability analysis: AUC vs. body weight
Distribution of AUC [0, 24 h] with weight
AUC (mg h mL-1)
20
15
10
5
0
20
40
60
80
100
120
BW (kg)
Ottawa Juin 2004 - 48
How to make use of PK/PD
population knowledge to predict
how well will doxycyclin perform
clinically?
Ottawa Juin 2004 - 49
The use of MonteCarlo simulation
• Dose selection at the population level
• Determination of breakpoints:
– PK/PD
– MIC
Ottawa Juin 2004 - 50
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
Ottawa Juin 2004 - 51
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
Ottawa Juin 2004 - 52
Dosage regimen: application of
PK/PD concepts
The 2 sources of variability : PK and PD
PK: exposure
PD: MIC
AUC [0, 24 h] Distribution
MIC Distribution (simulation)
16
30
25
12
% de germes
Fréquences (%)
14
10
8
6
20
15
10
4
5
2
0
0
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
AUC (µg.h.mL-1)
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Distribution of PK/PD surrogates (AUC/MIC)
Monte-Carlo approach
1
1.1 1.2 1.3
CMI (µg/mL)
Ottawa Juin 2004 - 53
AUC distribution
AUC [0, 24 h] Distribution
16
Frequences (%)
14
12
10
8
6
4
2
0
3
4
5
6
7
8
Under-exposure ?
9 10 11 12 13 14 15 16 17 18 19 20
AUC (mg.h.mL-1)
Ottawa Juin 2004 - 54
Microbiological data
Intervet, Virbac, AFSSA
•
•
•
•
Streptococcus suis (n=180)
Actinobacillus pleuropneumoniae (n=110)
Pasteurella multocida (n=206)
Haemophilus (n=25)
Ottawa Juin 2004 - 55
MIC distribution:
Actinobacillus pleuropneumoniae (n=106)
35
30
INTERMEDIATE
Pathogens %
40
25
20
15
10
SUSCEPTIBLE
5
RESISTANT
0
0.25
0.5
1
2
4
8
MIC (µg/mL)
Ottawa Juin 2004 - 56
Pathogens %
MIC distribution
Pasteurella multocida (n=205)
40
35
30
25
20
15
10
5
0
SUSCEPTIBLE
0.0625 0.125 0.25
0.5
1
2
4
MIC (m g/mL)
Ottawa Juin 2004 - 57
MIC distribution
Streptococcus suis (n=180)
Bimodal distribution
30
INTERMEDIATE
Pathogens %
35
25
20
SUSCEPTIBLE
15
RESIST.
10
5
0
0.0313 0.0625 0.125
0.5
1
2
4
8
16
32
CMI ( m g/mL)
Ottawa Juin 2004 - 58
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)
Ottawa Juin 2004 - 59
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
Ottawa Juin 2004 - 60
Population PK/PD: applications
• Individualisation  doctor
• Recommandation
 regulator
Ottawa Juin 2004 - 61
% of pigs above the breakpoint
Doxycycline (5 mg/kg) : empirical vs targeted
antibiotherapy for Pasteurella multocida
Empirical antibiotherapy
Targeted antibiotherapy (MIC = 0.25 µg/mL)
100%
80%
60%
40%
20%
0%
0
24 48 72
bacteriostatic
96
120 144 168 192
Breakpoint to be achieved
(AUC/MIC) (h)
Ottawa Juin 2004 - 62
% of pigs above the breakpoints
Doxycycline (5 mg/kg): empirical vs targeted
antibiotherapy for Actinobacillus pleuropneumoniae
100%
Empirical (MIC unknown)
80%
Targeted (MIC = 0.5 µg/mL)
60%
40%
20%
Breakpoint to be achieved
(AUC/MIC) (h)
0%
0
24
48
72
Bacteriostatic
Ottawa Juin 2004 - 63
% of pigs above the breakpoint
Doxycycline (5 mg/kg) : empirical vs targeted
antibiotherapy for Streptococcus suis
100%
80%
Empirical antibiotherapy
Targeted antibiotherapy (MIC = 16 µg/mL)
60%
40%
20%
0%
0
24
48
bacteriostatic
72
96
120
144
168
192
Breakpoint to be achieved
(AUC/MIC) (h)
Ottawa Juin 2004 - 64
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%)
Ottawa Juin 2004 - 65
% of pigs above a given AUC/MIC ratio
Doxycycline : selection of an empirical
(initial) dose for Pasteurella multocida
Doses
100%
90%
80%
5 mg/kg
60%
20 mg/kg
10 mg/kg
40%
20%
0%
0
24
48
bacteriostatic
72
96
120
144
168
AUC/MIC ratio (h)
Ottawa Juin 2004 - 66
% of pigs above a given AUC/MIC ratio
Doxycycline : selection of an empirical (initial)
dose for Actinobacillus pleuropneumoniae
Doses
100%
5mg/kg
10 mg/kg
80%
20 mg/kg
60%
40%
20%
0%
0
24
bacteriostatic
48
72
AUC/CMI ratio (h)
Ottawa Juin 2004 - 67
% of pigs above a given AUC/MIC ratio
Doxycycline : selection of an empirical
(initial) dose for Streptococcus suis
Doses
100%
5 mg/kg
80%
10 mg/kg
60%
20 mg/kg
40%
20%
0%
0
24
48
72
96
120
144
168
AUC/MIC ratio (h)
Ottawa Juin 2004 - 68
Determination of MIC
breakpoints by standard
developing organizations using
population approach
Ottawa Juin 2004 - 69
Determination of MIC breakpoints
• Current situation
– PK information is badly taken into account
 population approach
Ottawa Juin 2004 - 70
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.
Ottawa Juin 2004 - 71
% of pigs above the breakpoint
Doxycycline (5 mg/kg) : MIC breakpoint for
Actinobacillus pleuropneumoniae to achieve a given
AUC/MIC ratio for 90% of pig
MIC = 0.0625 µg/mL
100%
90%
80%
MIC = 0.125 µg/mL
MIC = 0.25 µg/mL
60%
40%
20%
0%
0
24
48
bacteriostatic
72
96
120 144 168 192 216 240
Breakpoint AUC/MIC (h)
Ottawa Juin 2004 - 72
% of pigs above a given AUC/MIC ratio
Doxycycline (5 mg/kg): MIC breakpoint for
Streptococcus suis to achieve a given
AUC/MIC ratio
100%
90%
80%
MIC = 0.5µg/mL
MIC = 0.125 µg/mL
MIC = 0.0625 µg/mL
60%
40%
20%
0%
0
24
48
Bacteriostatic
72
96
120
144
168
192
Breakpoint AUC/CMI (h)
Ottawa Juin 2004 - 73
Doxycycline(5 mg/kg) : MIC breakpoints for
Pasteurella multocida to achieve a given
AUC/MIC ratio
MIC = 0.0625 µg/mL
% de pc avec une AUC/CMI> seuil
MIC = 0.125 µg/mL
MIC = 0.25 µg/mL
100%
90%
80%
60%
40%
20%
0%
0
24
48
Bacteriostatic
72
96
120
144
168
192
AUC/MIC ratio (h)
Ottawa Juin 2004 - 74
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
Ottawa Juin 2004 - 75
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
Ottawa Juin 2004 - 76
Experimental vs population
studies
Ottawa Juin 2004 - 77
Experimental
Population
Ottawa Juin 2004 - 78
Experimental vs. population
approach
Two questions regarding
experimental approach
• What is its validity
(clinical relevance)
• What about variability
Ottawa Juin 2004 - 79
Drug administration, social behavior
and the dose
Experimental
Field
• Individually controlled by
the investigator
(restricted, tubing…)
• related to individual feeding
behavior (fever, anorexia)
• group effect (hierarchy,
dominance) or other behavior
• The nominal dose is
guaranteed to all
individuals
• Dose actually ingested can be
much higher or much lower than
the nominal dose
Ottawa Juin 2004 - 80
The pathology
Experimental
• Standardised experimental
infectious model
Field
• Spontaneous disease
Ottawa Juin 2004 - 81
Animal selection
Experimental
Population
• Highly selected (as
homogeneous as
possible) body weight,
sex, age...
• Representative of the target
population different breed, age,
pathological conditions…
Ottawa Juin 2004 - 82
Study design
Experimental
Population
Difference
• experimental,
restrictive
• Observational
• Power,
inference space
• artificial
(temperature,
light…)
• natural (e.g. field)
• interaction with
environment
behavior
Ottawa Juin 2004 - 83
Experimental vs population approach:
the status of variability
Experimental
Population
• viewed as a nuisance
that has to be
overcome
• recognized as an important
feature that should be identified,
measured and explained
(covariables)
Ottawa Juin 2004 - 84
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
Ottawa Juin 2004 - 85
Advantage of field population kinetics
over classical experimental setting
• Experimental
environment
– healthy animals selected
for homogeneity interindividual variability is
viewed as a nuisance
– conditions rigidly
standardized
– artificial conditions
• Real world / clinical setting
– patients representative of target
population
– variability (inter & intraindividual, inter-occasion) is an
important feature that should
be identified and measured
– seek for explaining variability
by identifying factors of
demographic pathophysiology
Ottawa Juin 2004 - 86
Doxycycline concentration variability:
population vs experimental trial
DOXYCYCLINE (µg/mL)
1.5
1.0
Number of data points
Trial
Population n=215
Experimental n=15 to 19
0.5
0.0
0
4
6
12
24
Time (h)
Ottawa Juin 2004 - 87
Doxycycline concentration variability:
population vs experimental trial for time 6h
post-administration
DOXYCYCLINE (µg/mL)
1.5
Number of data points
1: Population n=215
2: Experimental n=16
3: Experimental n=64
1.0
0.5
0.0
0
1
2
3
Ottawa Juin 2004 - 88