Predicting Paediatric Tobramycin Pharmacokinetics with Five Different Methods Joseph Standing, Elizabeth Greening, Victoria Holden, Susan Picton, Nicola Young, Henry Chrystyn, Mats Karlsson Uppsala Universitet, Sweden St 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 mL/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 mL/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 mL/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 –V D and V P scaled to wt –Q scaled to wt 0.75 –BOV on F for each dose –Proportional residual error (Aarons 2005 BJCP Editorial)

Method 1.Analyse each index dataset 2.Take final parameter estimates, run PAEDIATRIC TEST MAXEVAL = 0 OFV Measures 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: –Paeds: –Pooled:304.1 –NWPRIOR:312.8 –TNPRIOR:297.5

Results Patient averaged prediction error: 9.2%(-5.2,23.6) Patient averaged individual prediction error: 3.9%(1.7,6.2) 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

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: Paeds: NWPRIOR: Pooled:368.1

Results Final Model Pooled paediatric INDEX and TEST:

Results

Overview Introduction Aims Method Results 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: 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: Raw data from: Aarons L Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed. British Journal of Clinical Pharmacology, 60: Anderson BJ & Holford NHG. Annual Review of Pharmacology & Toxicology, 2008;48: DiCicco M, Duong T, Chu A, Jansen SA J Mat Res B Appl Biomater, 65: Gisleskog PO, Karlsson MO, Beal SL Journal of Pharmacokinetics and Pharmacodynamics, 29: Kearns GL, Abdel-Rahman SM, Alander SW, Blowey DL, Leeder JS, Kauffman RE New England Journal of Medicine, 349: Maglio D, Nightingale CH, Nicolau DP International Journal of Antimicrobial Agants, 19: Martindale 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