Salem F, Johnson TN and Rostami-Hodjegan A
The University of Manchester1, Manchester UK, Simcyp Limited2, Sheffield UK
Objectives: The aims of this study are to:
1) Deconvolute components of clearance based on known ontogenies and explore any age-dependence of fraction metabolised (fm) using iv clearance (CL) data
2) Compare performance of three existing CYP3A ontogeny models in prediction of observed MDZ CL
3) Produce a novel ontogeny function for CYP3A based on MDZ CLint using deconvoluted CLiv
Methods: MDZ CLiv values were collected from the literature. Unbound intrinsic clearance (CLuint) was calculated by a retrograde approach. Three CYP3A ontogeny models proposed by Bjorkman[1], Edington[2] and Johnson[3] were used within Simcyp v11 to simulate paediatric populations to predict MDZ CL in neonates, infants, children and adolescents. The ratio of CLint in paediatric to CLint adults was used from deconvolution stage to derive a new ontogeny function for CYP3A. CL predictions from these models were compared with those of Anderson (allometrically scaled to 70 kg by 0.75 exponent)[4]. Two assumptions for MPPGL ontogeny (fixed MPPGL value in all ages vs the Barter[5] model) were studied.
Results: At birth, MDZ fm by CYP3A4, CYP3A7 and UGT1A4 was calculated to be 82%, 8% and 10%. Edington ontogeny model overpredicted MDZ CL up to 100 weeks post menstrual age. Comparison between simulated and observed CL showed that Johnson model, with average 5% difference with observed CLiv, was the best CYP3A ontogeny model and was also the best predictor of CL in neonates and infants. Using a fixed age-independent MPPGL value of 40 mg/g improved the CL predictions even further. A new model for ontogeny of CLint was successfully derived by deconvolution of CLiv using well stirred liver model assumptions.
Conclusions: Although the existing models performed well, the new model combines existing knowledge from clinical observations and could be used with more confidence to predict age dependent CL of other drugs where CYP3A has substantial role. Application of this model and deriving similar ontogeny models for other enzymes warrant further studies.
References:
[1] Bjorkman S (2005) Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs. Br J Clin Pharmacol 59:691-704.
[2] Edginton AN, Schmitt W, Voith B and Willmann S (2006) A mechanistic approach for the scaling of clearance in children.Clin Pharmacokinet 45:683-704.
[3] Johnson TN, Rostami-Hodjegan A and Tucker GT (2006) Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin Pharmacokinet 45:931-956.
[4] Anderson BJ and Larsson P (2011) A maturation model for midazolam clearance. Paediatr Anaesth 21:302-308.clearance. Paediatr Anaesth 21:302-308.
[5] Barter ZE, Chowdry JE, Harlow JR, Snawder JE, Lipscomb JC and Rostami-Hodjegan A (2008) Covariation of human microsomal protein per gram of liver with age: absence of influence of operator and sample storage may justify interlaboratory data pooling. Drug Metab Dispos 36:2405-2409.
Reference: PAGE 21 (2012) Abstr 2649 [www.page-meeting.org/?abstract=2649]
Poster: Paediatrics