modelling and simulation group, school of pharmacy pharmacokinetic design optimization in children...

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


INTRODUCTION

• PK in children is different to adults

• Pop PK – Pro Less Blood Samples– Con More patients


INTRODUCTION

• Different ages may have different PK parameters

• Predictions from a pop PK model should be limited to the age range study


INTRODUCTION

• Dilemma

–what if we didn’t get all ages due to difficulty in recruiting


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


BACKGROUND

• How can we model the differences in pk between children

– Allometric Scaling

– Maturation Function


ALLOMETRIC SCALING

Taken from Anderson and Holford 2006


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


INTRODUCTION

Taken from Anderson and Holford 2009


INTRODUCTION


Taken from sumpter and anderson 2006


INTRODUCTION


METHODS


Step 1 – Age optimisation


STEP 1 – Age Optimization

Θ = 0.83 PNA50 = 0.31


Adapted from Johnson et al 2006


Taken from Jeffery et al 2003


Development of Enzyme Systems

Taken from Burton et al, Applied pharmacokinetics and pharmacodynamics


Step 1 – Age Optimisation

• Proportional 30%

• Additive 5%

• BSV = 30%

• Initial estimates = ten different ages

• Number of patients fixed at 80


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



• Clearance and Volume change for different ages – Need to calculate values for each age

specified in step 1


METHODS

• Calculating Clearance


METHODS


METHODS

• Calculating Volume of Distribution


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


METHODS

Age Error Sampling Times

Optimal Age 1 Additive Samp 1

Samp n+1 (optimised)

Proportional Samp 1

Samp n+1 (optimised)

Combined Samp 1

Samp n + 1 (optimised)

Optimal Age n + 1 Additive Samp 1

Samp n +1 (optimised)

NB Each age has own set of values for structural parameters

OPTIMISED SPARSE SAMPLING DATABASE


METHODS


Optimal Age 1 Additive Samp 1

Samp n+1 (upto n=15)

Proportional Samp 1


Combined Samp 1

Samp n + 1 (upto n=15)

Optimal Age n + 1 Additive Samp 1

Samp n +1 (upto n=15)

OPTIMISED RICH PHARMACOKINETICS SAMPLING DATABASE


METHODS


2 days old* Additive Samp 1


Proportional Samp 1


Combined Samp 1

Samp n + 1 (upto n=15)

COMPLETE RICH PHARMACOKINETIC DATABASE

* Whole process repeated for 400 ages between 2 days to adulthood


METHODS

Age Error Sampling Times Concentration

2 days old Additive Samp 1 Sim Con 1

Samp n+1 (upto n=15) Sim Conc n + 1 (upto n =15)

Proportional

Samp 1 Sim Con 1

Samp n+1 (upto n=15) Sim Conc n + 1 (upto n =15)

Combined Samp 1 Sim 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


METHODS


RESULTS

Rich and Complete results not reported other than authors say they yielded different results


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%


RESULTS

• Using optimised sparse sampling– PK estimates good (RMSE <15)– MF estimates bad


RESULTS

Additive Error Model


RESULTS

Combined Error Model


RESULTS

Proportional Error Model


DISCUSSION

• Reinforces aim

– can we get away with only including certain ages and still yet models that describe pk across entire age range.


DISCUSSION

• Polymorphism, actual CYP model??

• Theoretical 3A4 probe– Variability might be less with other CYP

• Plug for PFIM


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

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