Predicting Paediatric Tobramycin Pharmacokinetics with Five Different Methods Joseph Standing,...

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different Methods

Joseph Standing, Elizabeth Greening, Victoria Holden, Susan Picton, Nicola Young, Henry Chrystyn,

Mats Karlsson

Uppsala Universitet, SwedenSt James’s University Hospital, Leeds, UK

University of Huddersfield, UK

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

“Children are not small adults”– Differences (Kearns 2003 NEJM)

“Children are just small adults”– Similarities (Anderson 2008 Ann Rev PT)

• When and how can adult PK data help with paediatric analysis?

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

• Aminoglycoside

• Mainly Gm-ve activity• Blind therapy in feb. neutropaenia

(in Leeds)

• Once daily dosing (Maglio 2002)

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

• Log P = -7.3 (DiCicco 2002)

• Freely soluble in water

• Renal elimination

• Narrow therapeutic index– Peaks >10mg/L (efficacy)

– AUC <100mg.hr/L (toxicity)

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

• ADULT INDEX (Aarons 1989 BJCP):

– 97 adults

– 322 observations

– 16-85yrs, – CrCl 10-166mL/min (Cockroft Gault)

– Median 2 samples per dose

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

• PAEDIATRIC INDEX:

– 112 children,

– 650 observations

– 1-16yrs – CrCl 16-173mL/min (Anderson 2008)

– Median 2 samples per dose

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

• PAEDIATRIC TEST:

– 54 children

– 110 observations

– 1-12yrs– CrCl 29-101mL/min (Anderson 2008)

– 2 samples per dose

Predicting Paediatric Tobramycin Pharmacokinetics with Five Different

Methods

Predict PAEDIATRIC TEST with:

1. ADULT INDEX

2. PAEDIATRIC INDEX

3. Pooled ADULT/PAEDS INDEX

4. PAEDIATRIC INDEX with NWPRIOR

5. PAEDIATRIC INDEX with TNPRIOR

Priors in NONMEM

• Use of prior knowledge (Gisleskog 2002)

• NWPRIOR = Normal / Wishart-1

– Fixed and random effects– No prior on residual variability

• TNPRIOR = Normal / Normal– Priors on all parameters

Aims

• Evaluate adult data to predict paediatric PK

• Choose model to recommend dosing in children

Overview

• Introduction

• Aims

• Method

• Results

• Conclusions

Method

• PK Model (NONMEMVI FOCEI)

–2 compartment

–CL scaled to CrCl

–VD and VP scaled to wt

–Q scaled to wt0.75

–BOV on F for each dose

–Proportional residual error(Aarons 2005 BJCP Editorial)

Method

1. Analyse each index dataset2. Take final parameter estimates,

run PAEDIATRIC TEST MAXEVAL = 0

OFVMeasures overall fit

Method

3. Calculate patient averaged % prediction errors

(PRED-OBS) x 100

PRED

(IPRED-OBS) x 100

IPRED

4. Reduce paediatric index to half, quarter, eighth original size

Overview

• Introduction

• Aims

• Method

• Results

• Conclusions

Results

• PAEDIATRIC TEST OFV with params from each method (MAXEVALS=0)

– Adults: 316.5

– Paeds: 295.6

– Pooled: 304.1

– NWPRIOR: 312.8

– TNPRIOR: 297.5

Results

Test Data Versus Paediatric Index Population Prediction (log scale as proportional residual error)

1

10

100

1 10 100

Population predicted conc (mg/L)

Ob

serv

ed t

ob

ram

ycin

co

nc

(mg

/L)

Patient averaged prediction error:

9.2%(-5.2,23.6)

Patient averaged individual prediction error:

3.9%(1.7,6.2)

Test Data Versus Paediatric Index Individual Prediction(log scale as proportional residual error)

1

10

100

1 10 100

Individual predicted conc (mg/L)

Ob

serv

ed t

ob

ram

ycin

co

nc

(mg

/L)

PAEDIATRIC TEST predicted with PAEDATRIC INDEX

Results – Interim Summary

• PAEDIATRIC INDEX best at predicting PAEDIATRIC TEST

• What happens when paediatric data are less informative?– 56, 28 or 14 children in INDEX

Results• Reduced no. children in index (56, 28, 14)• Mean OFV from 5 random samples

Adult only TEST OFV: 316.5

OFV of Paediatric Test Data versus Number of Children in Index Data

290

295

300

305

310

315

320

325

330

335

0 20 40 60 80 100 120

Number of children in index dataset

Tes

t O

FV Paediatric Index

Pooled Index

NWPRIOR Index

TNPRIOR Index

Results - Summary

• Bias in adult predictions

• As imprecision rises (with fewer children), adult bias becomes less important

How to predict whether it is worth including adult data?

Results

• For each INDEX dataset:

• Case deletion diagnostic (CDD)

– 14 children, remove 1

– Estimate remaining 13

– Evaluate OFV for removed child

– Repeat for all, sum OFVs

Results

• CDD result:

1. TNPRIOR: 227.0

2. Paeds: 254.5

3. NWPRIOR: 332.8

4. Pooled: 368.1OFV of Paediatric Test Data versus Number of Children in Index Data

290

295

300

305

310

315

320

325

330

335

0 20 40 60 80 100 120

Number of children in index dataset

Tes

t O

FV Paediatric Index

Pooled Index

NWPRIOR Index

TNPRIOR Index

ResultsFinal Model

• Pooled paediatric INDEX and TEST:

All Paediatric Data Population Predictions versus Observations (log scale)

0

1

10

100

0 1 10 100

Population predicted tobramycin conc (mg/L)

Ob

serv

ed t

ob

ram

ycin

co

nc

(mg

/L)

All Paediatric Data Individual Predictions versus Observations (log scale)

0

1

10

100

0 1 10 100

Individual predicted tobramycin conc (mg/L)O

bse

rved

to

bra

myc

in c

on

c (m

g/L

)

ResultsAll Paediatric Data Conditional Weighted Residual

Error versus Time After Dose

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 14

Time after dose (hr)

CW

RE

S

All Paediatric Data Conditional Weighted Residual Error versus Population Predictions

-5

-4

-3

-2

-1

0

1

2

3

4

5

0 60

Population predicted tobramycin conc (mg/L)

CW

RE

S

Overview

• Introduction

• Aims

• Method

• Results

• Conclusions

Conclusions

• Best prediction of paed PK was with paed PK!

• Whether to add adult data depends on relative informativeness

(CDD could help with this)

• Model for dose recommendation (+ TDM) developed

Acknowledgements

Patients who took part

• Leon Aarons - adult data from:

http://www.rfpk.washington.edu/

• Uppsala colleagues

• Pfizer for postdoc funding (JS)

References• Aarons L, Vozeh S Wenk M, Weiss P, Follath F. British Journal of Clinical

Pharmacology, 1989;28:305-14. Raw data from: http://www.rfpk.washington.edu/

• Aarons L. 2005. Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed. British Journal of Clinical Pharmacology, 60:581-3. Anderson BJ & Holford NHG. Annual Review of Pharmacology & Toxicology, 2008;48:12.1-12.30.

• DiCicco M, Duong T, Chu A, Jansen SA. 2002. J Mat Res B Appl Biomater, 65:137-49.

• Gisleskog PO, Karlsson MO, Beal SL. 2002. Journal of Pharmacokinetics and Pharmacodynamics, 29:473-505.

• Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE. 2003. New England Journal of Medicine, 349:1157-67.

• Maglio D, Nightingale CH, Nicolau DP. 2002. International Journal of Antimicrobial Agants, 19:341-8.

• Martindale. 2007. Martindale, the complete drug reference. 35th Edition, Pharmaceutical Press, London, UK.

Extra Slides

NWPRIOR Degrees of Freedom

Give DOF for each ETA prior

SE(2) = 2 (2/(N-1))½

2 = variance (ETA)

N = DOF

CrCl Estimation from SeCr

• Aarons used Cockroft Gault: = 150-age*wt (+/-10% m/f)

SeCr

• “Anderson Holford” in children= CPR

SeCr

Results