Population PK/PD and the rational design of an antimicrobial dosage regimen in veterinary medicine

(1)

01/29/22 Ottawa Juin 2004 - 1

Population PK/PD and the rational design of an antimicrobial dosage

regimen in veterinary medicine

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

(2)

Ottawa Juin 2004 - 2

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

(3)

"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

(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)

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

Ranking (Low, Medium, High) of extent of

antibiotic drug use in animal based on duration

and method of administration

(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

Dose Response

Black box

PK/PD

Dose Response

PK PD

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 PK/PD variabilities for antibiotics

• Consequence for dosage determination

PK PD

Dose Plasma

concentration

Effect

BODY Pathogens

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

MIC Cmax

Concentrations

24h Time

Cmax/MIC

• Cmax/MIC : aminoglycosides

AUIC = AUC MIC Units = Time (h)

• AUIC (or 24h AUC/MIC) : quinolones, tetracyclines, ketolides, azithromycins, streptogramins

• T>MIC : penicillins, cephalosporins, macrolides, oxazolidinones

T>CMI

(18)

AUIC: an attempt to combine PK and PD properties of antibiotics

AUIC #

= = Dose / Clearance

critical breakpoint value

MIC

₉₀

or MIC

₅₀

PD

PK Capacity to eliminate

the drug

• Fixed endpoint related to Emax and EC₅₀

AUC MIC

Application : fluoroquinolones

(19)

Computation of dose using a PK/PD predictor

– Dose = x x Clearance (24h) AUIC 24h

MIC fu x F%

Free fraction

bioavailability

PK

Breakpoint to PD be achieved

(20)

Computation of dose using a PK/PD predictor

– Dose = x x Clearance AUIC 24h

MIC F%

PK PK Breakpoint to PD

be achieved

(average) (average)

MIC₅₀ : average MIC₉₀

(pop) average

(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

oral

Dose gut flora

Target biophase F%

Ingested dose

Experimental setting Field conditions

Therapeutic window

Undesirable concentration MIC₉₀

Side effects

Suboptimal exposure

 resistance

Selection of resistance

MIC gut flora

Resistance:

pathogens of interest

1-F%

Resistance: zoonotic, commensal

(23)

Variability and the likelihood of resistance

Field conditions

Therapeutic window

Undesirable concentration MIC₉₀

Side effects

Suboptimal exposure

 resistance

Selection of resistance

MIC gut flora Ingested dose

Experimental setting

Resistance:

pathogens of interest

gut flora

1-F%

Resistance: zoonotic, commensal

oral

Target biophase F%

Dose

(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

– t

_1/2

= 7.56 ± 1.99 h

(27)

Marbofloxacin in horses: PK/PD integration (oral route)

• Value of efficacy index (AUIC_24h) 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)

 “average” PK/PD indexAUIC_24h = 155 ± 21 Cmax/MIC = 31 ± 4.5

(28)

 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

Population PK approach for

marbofloxacin in horses: objective

(29)

 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)

Materials and Methods (1)

(30)

AUC imprecision

Sampling design

4 samples

5 samples

• 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

Sampling windows:

30min windows centred around 1.5, 3, 5, 7 and 19.5 h

post-administration

Sampling design selection

Materials and Methods (2)

(31)

• PK model : -

biexponential equation

- parameterisation in volumes of distribution and clearances

i Vc, Vc

i

C,

μ η

η  Model 1 : no covariate

i Cl, i

i 2

i 1

Cl

i

μ θ Age θ Weight Sex Disease η Cl

Log      

i

 

Model 2 : with covariates for body clearance

Materials and Methods (3)

(32)

• 52 horses, 253 blood samples

0.001 0.01 0.1 1 10

0 4 8 12 16 20 24

Time (h)

Marbofloxacin (g/mL)

Results: conventional vs pop

kinetics

(33)

0 0.5 1 1.5 2 2.5

observed concentrations

(g/mL)

predicted concentrations (g/mL)

Clearance (pop)

population mean = 3.88 mL/kg/min

Inter-individual variability CV(%) = 50 %

Variability: model without covariable

(34)

0 0.5 1 1.5 2 2.5

observed concentrations

(g/mL)

predicted concentrations (g/mL)

Without covariable With covariables

Variability: model with covariables

(35)

Covariables for body clearance expressed in

L.kg^-1.h^-1

Weight P=0.001 Age

Sex Disease

NS NS NS

R

²

= 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

Variability: explicative covariable

(36)

-3 -2 -1 0

0 200 400 600

Body weight (kg)

Ln (Clearance)

0 0.2 0.4 0.6

0 200 400 600

Body weight (kg)

Clearance (L/kg/h)

Marbofloxacin: the body weight is a covariable

Allometric relationship with an allometric exponent >1

(37)

• Marbofloxacin clearance in horses

0.233 50

0.19 - 0.246 18 - 21

Population trial Classical trials * Mean

(L.kg^-1.h^-1)

CV

(%)

*

Carretero 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

Discussion

(38)

 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

Conclusion

(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 12-15 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

n = 215

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

-5 0 5 10 15 20 25 30

Time (h)

Concentrations mg/mL

Doxycycline

(44)

PK doxycyclin variability analysis

(45)

Doxycycline : sex effect

Time (h)

Doxycycline

Sexe 0 Sexe 1

(46)

Doxycycline : body temperature effect

Rectal temperature

Doxycycline

(47)

Doxycycline : disease effect

Time (h)

Concentrations (µg/mL) ^healthydiseased

(48)

Variability analysis: AUC vs. body weight

Distribution of AUC [0, 24 h] with weight

0 5 10 15 20

20 40 60 80 100 120

BW (kg)

AUC (mg h mL-1)

(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 AUC

_0-24

/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

AUC [0, 24 h] Distribution

0 2 4 6 8 10 12 14 16

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 AUC (µg.h.mL^-1)

Fréquences (%)

MIC Distribution (simulation)

0 5 10 15 20 25 30

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 CMI (µg/mL)

% de germes

(54)

AUC distribution

AUC [0, 24 h] Distribution

0 2 4 6 8 10 12 14 16

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 AUC (mg.h.mL^-1)

Frequences (%)

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)

MIC (µg/mL)

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)

• 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

0%

20%

40%

60%

80%

90%

100%

0 24 48 72 96 120 144 168

AUC/MIC ratio (h)

% of pigs above a given AUC/MIC ratio

5 mg/kg 10 mg/kg 20 mg/kg

bacteriostatic

Doses

(67)

Doxycycline : selection of an empirical (initial) dose for Actinobacillus pleuropneumoniae

0%

20%

40%

60%

80%

100%

0 24 48 72

AUC/CMI ratio (h)

5mg/kg 10 mg/kg 20 mg/kg

bacteriostatic

Doses

(68)

Doxycycline : selection of an empirical (initial) dose for Streptococcus suis

0%

20%

40%

60%

80%

100%

0 24 48 72 96 120 144 168

5 mg/kg 10 mg/kg 20 mg/kg

Doses

(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)

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)

• 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

• 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

Experimental

• 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

Number of data points Trial

Population n=215

Experimental n=15 to 19

0 4 6 12 24 Time (h)

0.0 0.5 1.0 1.5

DOXYCYCLINE (µg/mL)

(88)

Doxycycline concentration variability:

population vs experimental trial for time 6h post-administration

0 1 2 3

0.0 0.5 1.0 1.5

DOXYCYCLINE (µg/mL)

Number of data points 1: Population n=215 2: Experimental n=16 3: Experimental n=64