Introduction to population PKPD modelling in paediatric ... · Introduction to population PKPD modelling in paediatric clinical pharmacology Catherijne Knibbe, Oscar Della Pasqua,

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Introduction to population PKPDIntroduction to population PKPD modellingmodellinginin paediatricpaediatric clinical pharmacologyclinical pharmacology

Catherijne KnibbeCatherijne Knibbe, Oscar, Oscar Della PasquaDella Pasqua,, Meindert DanhofMeindert Danhof

Leiden/AmsterdamCenter for Drug Research

Division of Pharmacology

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What is the problem?What is the problem?

• Drugs dosing in children is largely empirical

• Frequent under-and overdosing problems

• Efficacy and safety of drugs, in particular in (premature) newborns is largely unknown

Body weight is used for dose adjustment instead of the PKPD relationships

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PKPD MODELLING: What is it?PKPD MODELLING: What is it?

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• How to identify a safe and effective dosing regimen in children in different age groups? – First time in kids (early drug development)

– Change in indication or age group, including neonates (clinical practice)

• Which factor(s) should be used to adjust the dose for the individual child in different age groups?

– dosing recommendation in the label

Clinical QuestionsClinical Questions

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PaediatricPaediatric Research IssuesResearch Issues

Unbalanced vs balanced designs:– 100 observations for subject A

– 1 observation for subject B

Sparse vs. serial data:– 2 measurements per subject

0

20

40

60

80

100

120

0 6 12 18 24

Population approachPopulation approach

Simultaneous analysis of all available data:

PK and/or PD parameters are simultaneously estimated taking into account differences between patients

1. POPULATION PK and/or PD parameters (fixed effects)

2. Inter-individual variability

3. Residual error

0

20

40

60

80

100

120

0 6 12 18 24

Con

cent

ratio

n (m

g/L)

TIme (hr)

Predicted

Observed

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20

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80

100

120

0 6 12 18 24

ID=1 (pred)ID=1 (obs)ID=2 (pred)ID=2 (obs)ID=3 (pred)ID=3 (obs)

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Population PKPDPopulation PKPD modelling modelling

Inter-individual variability

Residual error

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• Applicable to sparse and unbalanced data sets (neonates, children, etc)

••• Scientific basis for study/trial simulations, Scientific basis for study/trial simulations, Scientific basis for study/trial simulations, dose adjustment or labeling extensions in dose adjustment or labeling extensions in dose adjustment or labeling extensions in other populations other populations other populations (intra and interspecies)(intra and interspecies)(intra and interspecies)

••• Covariate analysis for identification of Covariate analysis for identification of Covariate analysis for identification of predictors of variability in PK and PD predictors of variability in PK and PD predictors of variability in PK and PD (genetics, body weight, age, interactions etc)(genetics, body weight, age, interactions etc)(genetics, body weight, age, interactions etc)

Population PK/PDPopulation PK/PD modelling modelling

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Prop

ofol

con

cent

ratio

n (m

g/l)

0

1

2

0 200 400 600

0

1

2

0 200 400 6000

1

2

0 200 400 600

0

1

2

0 200 400 600

0

1

2

0 200 400 6000

1

2

0 200 400 600

Best

Worst

Median

Ventilated childrenVentilated children (1(1--55 yrsyrs)) following following cardiac surgerycardiac surgery in the ICUin the ICU

Children Adults

Cl (l/min)

(ml/kg/min) 35*

2.3

28*

V1 (l)

(l/kg)

12

0.78*

21

0.26*

Q (l/min)

(l/kg/min)

0.35

23

1.4

18

V2 (l)

(l/kg)

24

1.54

139

1.88

Knibbe et al., Br J Clin Pharmacol 2002

6 samples of 2506 samples of 250 ulul per childper child6 children6 children

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• Scientific basis for study/trial simulations, dose adjustment or labeling extensions in other populations (intra and interspecies)

••• Covariate analysis for identification of Covariate analysis for identification of Covariate analysis for identification of predictors of variability in PK and PD predictors of variability in PK and PD predictors of variability in PK and PD (genetics, body weight, age, interactions etc)(genetics, body weight, age, interactions etc)(genetics, body weight, age, interactions etc)


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PropofolPropofol in nonin non--ventilated childrenventilated children

0 200 400 600 800 1000 1200 time (min)

0.0

0.5

1.0

1.5

2.0Pr

opof

ol c

once

ntra

tion

(mg/

l)Knibbeobserved concRigby-JonesSchuttlercurrent study

Peeters MYM et al., Anesthesiology 2006 ; 104(3):466-474

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PropofolPropofol inin nonventilatednonventilated childrenchildren

0 200 400 600 800 1000 1200 time (min)

0.0

0.5

1.0

1.5

2.0Pr

opof

ol c

once

ntra

tion

(mg/

l)Knibbeobserved concRigby-JonesSchuttlercurrent study

Peeters MYM et al., Anesthesiology 2006 Mar; 104(3):466-474

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COMFORT-B6 behaviour items• alertness

• Calmness/agitation

• Respiratory response / crying

• Physical movement

• Muscle tone

• Facial tension

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NonNon--agitated childrenagitated children

0 200 400 600 800 1000 1200 1400time (min)

6

12

18

24

30C

OM

FOR

T-B

0

0.2

0.8

0.6

0.4

Prop

ofol

con

cent

ratio

n (m

g/l)non-agitated, median performance

no propofol

1.0

20.00 h 07.00 h

Peeters et al., Anesthesiology, March 2006

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0 200 400 600 800 1000 1200 1400time (min)

6

12

18

24

30

CO

MFO

RT-

B

0

0.2

0.8

0.6

0.4

Prop

ofol

con

cent

ratio

n (m

g/l)B) agitated, median performance

start propofol 18 mg/h

8.6 kg

24 mg/h

1.0


propofolpropofol

Agitated childrenAgitated children

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Model based advisedModel based advised propofolpropofol dose dose 30 mg/h for a postoperative child of 10 kg30 mg/h for a postoperative child of 10 kg

0 200 400 600 800 1000 1200 1400time (min)

6

10

14

18

22C

OM

FOR

T-B

0.0

0.2

0.4

0.6

0.8

1.0

Pro

pofo

l con

cent

ratio

n (m

g/l)


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• Scientific basis for study/trial simulations, dose adjustment or labeling extensions in other populations (intra and interspecies)

• Covariate analysis for identification of predictors of variability in PK and PD (genetics, body weight, age, interactions etc)


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Body weight or age?Body weight or age?

0 400 800 1200PCA

0

5000

10000

15000

BWS

ageage

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Identification of potential covariates (body weight, gender, age, renal function, PGx etc).

Graphical evaluation of each covariate versus• The individual post-hoc PK or PD parameter estimate

• the weighted residuals

Statistical evaluation using standard techniques1. Change in objective function 2. Standard error of the additional parameter3. Improvement of individual fits4. Diagnostics: B) observed versus model-predicted

Covariate analysisCovariate analysis

Krekels et al, Expert Opin. Pharmacother. (2007) 8(12):1787-1800Peeters MY et al., Anesthesiology, March 2006 and Dec 2006, CP&T March 2008

2020

When more than one significant covariate for the simple model is found, the covariate-adjusted model with the largest decrease in objection function is chosen as a basis to explore the influence of additional covariates sequentially with the use of the same criteria

Forward inclusion and backward deletion



2121

Nature of the influence of the covariate – preferably non-empirical (mechanism/physiologically

based)– Consider the possibility of potential extrapolation or

interpolation

Validation confirms the influence of thecovariates



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MorphineMorphine PK inPK in ChildrenChildren

Supported by a grant of the Sophia Stichting voorWetenschappelijk Onderzoek

500 3000 5500 8000 10500 13000 15500 18000BWS

0.0

0.4

0.8

1.2

CL

Body weight

Cle

aran

ce

• 250 children:– 70 premature neonates,

– 60 neonates,

– 60 < ½ yr,

– 30 < 1 yr,

– 30 < 3 yr

• 1-4 samples/24 h/pt

• BW median 2.8 kg

2310-1.0 100.0 101.0 102.0 103.02 3 4 5 6 2 3 4 5 6 2 3 4 5 6 2 3 4 5 6 2 3

PNA

-1.0

-0.5

0.0

0.5

ET1

Influence of post natal age >/< 10 dInfluence of post natal age >/< 10 d

Independent of gestational time or body weight at birth

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Formation clearance to M3glucuronideFormation clearance to M3glucuronideobserved versus modelobserved versus model--predictedpredicted

Post natal age < 10 dPost natal age < 10 d Post natal age > 10 dPost natal age > 10 d

0 2500 5000 7500 10000 12500 15000 17500BWS

0.0

0.1

0.2

0.3

0.4

0.5

0.6

PNA > 10 days

0 1000 2000 3000 4000BWS

0.00

0.02

0.04

0.06

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Validation of sparse data studiesValidation of sparse data studies

• Diagnostics(e.g. observed versus model-predicted)

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• Diagnostics (e.g. observed versus model-predicted)

• Bootstrap resampling– repeated random sampling to produce another data set

(same size but different combination of individuals)– Compare parameters (250 times) with estimates from the original

data set

• Visual predictive check – Simulation with final estimates and compare the distribution of the

observations with the simulated distribution– Plot of the time course of the observations and prediction interval for

the simulated values

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• Diagnostics (e.g. observed versus model-predicted)

• Bootstrap re-sampling – repeated random sampling to produce another data set

(same size but different combination of individuals)– Compare parameters (250 times) with estimates from the original

data set

• Visual predictive check – Simulation with final estimates and compare the distribution of the

observations with the simulated distribution– Plot of the time course of the observations and prediction interval for

the simulated values

• Normalised Prediction Discrepancy Errors (NPDE)

1) Brendel et al. Pharm. Res. 23(9); 2036-49 (2006)

Points to considerPoints to consider

• Use of the population approach (nonlinearmixed effects modelling) in all phases of the investigation

• Validation of population PKPD models

• Infrastructure for data sharing

• Neonates deserve further attention

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NeonatesNeonates,, young infantsyoung infants are different!are different!

0 1 10 1002 3 4 5 6 7 8 2 3 4 5 6 7 8 2 3 4 5 6 7

Bodyweight (kg)

0.001

0.010

0.100

1.000

9

234579

234579

234579

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Cle

aran

ce (L

/min

)

R

RRRRRR

R

RR

R

RRR

RR

RRRR

RRRR

CC

C

CC

C

AA

A

A

AA

A

A

AAA

AAA

AAA AAA AA

A

A

N

NN

NN

NNN N

NN

DDD

D DDD D

DDDD

D DI I II IIII I

III

II

I II II

II

I

N

N

N

N

N

N

NN

N

N

NNNN

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• Population PK-PD modelling (or non linear mixed effects modelling) should be the PRIMARY ANALYSIS METHOD in paediatric drug development and dosing studies

• Population PK-PD models can be also be developed based on data from PREVIOUS CLINICAL STUDIES (retrospective studies/meta analyses)

• Dosing regimen based on VALIDATED POPULATION PK-PD MODELS should be included in the LABEL of drugs

ConclusionsConclusions

• University-Industry consortium with 6 industrial partners(Eli Lilly, GSK, Johnson & Johnson, Organon, Nycomed, Pfizer)

• Unique infrastructure for data management, data analysis and reporting: sharing of data, models and biological system specific information

• Emphasis on key factors in drug discovery and development– Translational pharmacology (efficacy and safety)– Developmental pharmacology (pediatrics, elderly)– Disease system analysis

Mechanism-based PK-PD modeling platform

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MultidisciplinaryMultidisciplinary,, multicentremulticentre researchresearch

Dr. M. vanDr. M. van DijkDijkDr. R.N. vanDr. R.N. van SchaikSchaikProf. dr. J.N. van denProf. dr. J.N. van den AnkerAnkerProf. dr. D.Prof. dr. D. TibboelTibboel

E.E. KrekelsKrekelsDr. J. DeDr. J. De JonghJonghDr. O. DellaDr. O. Della PasquaPasquaProf. Dr. M.Prof. Dr. M. DanhofDanhof

Dr. M.Y.M.Dr. M.Y.M. PeetersPeetersDr. C.A.J.Dr. C.A.J. KnibbeKnibbeSupported by NWO/Supported by NWO/VeniVeni

Leiden/AmsterdamCenter for Drug Research

Division of Pharmacology

Documents

Introduction to population PKPD modelling in paediatric ... · Introduction to population PKPD modelling in paediatric clinical pharmacology Catherijne Knibbe, Oscar Della Pasqua,