Modelling and Simulation Group, School of Pharmacy Pharmacokinetic design optimization in children and estimation of maturation parameters: example of.

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Presentation transcript:

Modelling and Simulation Group, School of Pharmacy Pharmacokinetic design optimization in children and estimation of maturation parameters: example of CYP 3A4 Marion Bouillon-Pichault, Vincent Jullien, Caroline Bazzoli, Gerard Pons, Michel Tod

Modelling and Simulation Group, School of Pharmacy INTRODUCTION PK in children is different to adults Pop PK –Pro  Less Blood Samples –Con  More patients

Modelling and Simulation Group, School of Pharmacy INTRODUCTION Different ages may have different PK parameters Predictions from a pop PK model should be limited to the age range study

Modelling and Simulation Group, School of Pharmacy INTRODUCTION Dilemma –what if we didn’t get all ages due to difficulty in recruiting

Modelling and Simulation Group, School of Pharmacy AIMS To determine whether including samples form children of specific ages in a PK study can be used to predict the PK profile throughout childhood –Theoretical 3A4 probe

Modelling and Simulation Group, School of Pharmacy BACKGROUND How can we model the differences in pk between children –Allometric Scaling –Maturation Function

Modelling and Simulation Group, School of Pharmacy ALLOMETRIC SCALING Taken from Anderson and Holford 2006

Modelling and Simulation Group, School of Pharmacy

INTRODUCTION Allometry can account for some of the SIZE related PK variability seen in Paediatrics; HOWEVER It does not take into account maturation of metabolic pathways

Modelling and Simulation Group, School of Pharmacy INTRODUCTION Taken from Anderson and Holford 2009

Modelling and Simulation Group, School of Pharmacy INTRODUCTION

Modelling and Simulation Group, School of Pharmacy Taken from sumpter and anderson 2006

Modelling and Simulation Group, School of Pharmacy INTRODUCTION

Modelling and Simulation Group, School of Pharmacy METHODS

Modelling and Simulation Group, School of Pharmacy Step 1 – Age optimisation

Modelling and Simulation Group, School of Pharmacy STEP 1 – Age Optimization Θ = 0.83 PNA 50 = 0.31

Modelling and Simulation Group, School of Pharmacy

Adapted from Johnson et al 2006

Modelling and Simulation Group, School of Pharmacy Taken from Jeffery et al 2003

Modelling and Simulation Group, School of Pharmacy Development of Enzyme Systems Taken from Burton et al, Applied pharmacokinetics and pharmacodynamics

Modelling and Simulation Group, School of Pharmacy Step 1 – Age Optimisation Proportional 30% Additive 5% BSV = 30% Initial estimates = ten different ages Number of patients fixed at 80

Modelling and Simulation Group, School of Pharmacy Step 2 – Post-dose time optimisation

Modelling and Simulation Group, School of Pharmacy Step 2 – Post-dose time optimisation PK model = 1 comp, first-order absorption and linear elimination Model based on midaz PK parameters –CL/F = 24 L/h –V/F = 66.1 L –Dose 250mcg/kg, 15000mcg for adults –Ka 1.5 h-1

Modelling and Simulation Group, School of Pharmacy Step 2 – Post-dose time optimisation Clearance and Volume change for different ages –Need to calculate values for each age specified in step 1

Modelling and Simulation Group, School of Pharmacy METHODS Calculating Clearance

Modelling and Simulation Group, School of Pharmacy METHODS

Modelling and Simulation Group, School of Pharmacy METHODS Calculating Volume of Distribution

Modelling and Simulation Group, School of Pharmacy METHODS BSV and Error models –BSV for Cl and V = 30% –BSV for Ka 100% Additive (10), Proportional (0.1) and Combined error models tested

Modelling and Simulation Group, School of Pharmacy Step 2 – Post-dose time optimisation

Modelling and Simulation Group, School of Pharmacy

METHODS AgeErrorSampling Times Optimal Age 1AdditiveSamp 1 Samp n+1 (optimised) ProportionalSamp 1 Samp n+1 (optimised) CombinedSamp 1 Samp n + 1 (optimised) Optimal Age n + 1AdditiveSamp 1 Samp n +1 (optimised) NB Each age has own set of values for structural parameters OPTIMISED SPARSE SAMPLING DATABASE

Modelling and Simulation Group, School of Pharmacy METHODS AgeErrorSampling Times Optimal Age 1AdditiveSamp 1 Samp n+1 (upto n=15) ProportionalSamp 1 Samp n+1 (upto n=15) CombinedSamp 1 Samp n + 1 (upto n=15) Optimal Age n + 1AdditiveSamp 1 Samp n +1 (upto n=15) OPTIMISED RICH PHARMACOKINETICS SAMPLING DATABASE

Modelling and Simulation Group, School of Pharmacy METHODS AgeErrorSampling Times 2 days old*AdditiveSamp 1 Samp n+1 (upto n=15) ProportionalSamp 1 Samp n+1 (upto n=15) CombinedSamp 1 Samp n + 1 (upto n=15) COMPLETE RICH PHARMACOKINETIC DATABASE * Whole process repeated for 400 ages between 2 days to adulthood

Modelling and Simulation Group, School of Pharmacy

METHODS AgeErrorSampling TimesConcentration 2 days oldAdditiveSamp 1Sim Con 1 Samp n+1 (upto n=15)Sim Conc n + 1 (upto n =15) Proportion al Samp 1Sim Con 1 Samp n+1 (upto n=15)Sim Conc n + 1 (upto n =15) CombinedSamp 1Sim Con 1 Samp n + 1 (upto n=15)Sim Conc n + 1 (upto n =15) COMPLETE RICH PHARMACOKINETIC DATABASE NB Each age has own set of values for structural parameters

Modelling and Simulation Group, School of Pharmacy METHODS

Modelling and Simulation Group, School of Pharmacy RESULTS Rich and Complete results not reported other than authors say they yielded different results

Modelling and Simulation Group, School of Pharmacy METHODS First 100 successful estimation of pk and maturation parameter recorded Success defined by minimisation successful and covariance step Calculate RMSE and MPE  unbiased and precise if <15%

Modelling and Simulation Group, School of Pharmacy RESULTS Using optimised sparse sampling –PK estimates good (RMSE <15) –MF estimates bad

Modelling and Simulation Group, School of Pharmacy RESULTS Additive Error Model

Modelling and Simulation Group, School of Pharmacy RESULTS Combined Error Model

Modelling and Simulation Group, School of Pharmacy RESULTS Proportional Error Model

Modelling and Simulation Group, School of Pharmacy DISCUSSION Reinforces aim – can we get away with only including certain ages and still yet models that describe pk across entire age range.

Modelling and Simulation Group, School of Pharmacy DISCUSSION Polymorphism, actual CYP model?? Theoretical 3A4 probe –Variability might be less with other CYP Plug for PFIM

Modelling and Simulation Group, School of Pharmacy DISCUSSION Do with think estimation of Maturation parameters was OK Actual suggested ages probably make it impractical Good suggestion for future work,  my project update