Showcasing the potential of PBPK modeling to inform drug-drug interactions specific recommendations for pediatric labeling: omeprazol case example
Leyanis Rodriguez-Vera(1)*; Ana Alarcia Lacalle(2)*, Parsshava Mehta(1), Amira E. Soliman(1), Saima Subhani(3), Jorge Duconge(4), Viera Lukacova(3), and Valvanera Vozmediano(1)
(1) Department of Pharmaceutics, Center for Pharmacometrics and Systems Pharmacology, University of Florida, Orlando, Florida, USA. (2) University of the Basque Country, Vitoria-Gasteiz, Spain. (3) Simulation Plus, Inc. Lancaster, CA, USA (4) Pharmaceutical Sciences Department. School of Pharmacy. Medical Sciences Campus, PR, USA. * Contributed equally to this work
Introduction: Exposure-matching approach is commonly agreed to bridge efficacy labeling information from adults to pediatrics when it is possible to consider similar disease progression and response to intervention [1]. In such cases, same effect of drug-drug interactions (DDI) on drug exposure is presumed between both populations. As a consequence, drug labels usually do not contain specific dose adjustment recommendations in DDI scenarios in pediatrics. However, age-dependent changes of the metabolic pathways may impact the effect of DDIs leading to special dosing needs in pediatrics taking interacting medications. A physiologically based PK (PBPK) strategy can be applied as tool to explore ontogenic-driven differences on enzymatic contribution to metabolism [2], evaluate the impact of potential differences on DDIs in pediatrics and elaborate more informative pediatric labels.
Objectives: The objective of this study was to demonstrate the validity of PBPK modelling to support pediatric label information on DDI scenarios using omeprazole (OMP), a compound with complex metabolism, as model drug.
Methods: Omeprazole exhibits time dependent non-linear PK. In adults, the main metabolic pathway is the CYP2C19 accounting for ~70% of the total metabolism. A full adult PBPK model was developed in GastroPlusTM version 9.8.2 software for omeprazole and its metabolites hydroxy omeprazole and omeprazole sulphone (doses of 20, 40 and 90 mg [3, 4]). Competitive inhibition and mechanism-based inactivation (MBI) were integrated into the metabolic pathways to explain observed PK profiles at multiple doses [5, 6]. Once, verified, the adult PBPK model was extrapolated to pediatric patients (neonates to adolescents). The incorporation of CYP3A7 based metabolism and autoinhibition mechanisms were needed to capture pediatric observations from literature [4, 7, 8, 9]. The model was then applied to evaluate the contribution of each individual enzyme to the overall metabolism and the impact of CYP3A4 DDIs in adults and pediatrics of different ages via simulation.
Results: The model was able to predict the shape and magnitude of the observed profiles for both parent and metabolites in adults and pediatrics. The final PBPK model included the competitive inhibition and MBI of drug metabolizing enzymes (CYP2C19 and CYP3A4) by OMP and its metabolites. The total metabolism gradually increases with a maximum percentage metabolized of 98.4% in adults. Metabolism via CYP3A7 decreases with the age being negligible after 9-10 months of age. CYP2C9 and CYP3A4 are the major metabolic pathways in children less than 2 years after which CYP2C19 starts being the major one accounting for 68% of the metabolism at the age of 6 years. The interaction of omeprazole with strong CYP3A4 inhibitors, such as itraconazole, led to not significant increase of OMP exposure in adults. However, the drug-driven phenoconversion in pediatrics due to the blockade of the CYP3A4 led to an almost 2-fold increase in children < 2 years highlighting the need of specific DDI recommendations in younger pediatrics.
Conclusions: OMP and its metabolites are mainly metabolized via CYP2C19. Considering that CYP2C19 is highly polymorphic, a genotype-guided dose could be beneficial in children from 6 years old up to adults. The interaction OMP- strong CYP3A4 inhibitor may lead to significant increase of drug exposure and, thus, safety. Proton pump inhibitors have been associated with adverse infection events and even a higher risk of fracture risk in pediatrics. The integration of the mechanisms of drug metabolism and enzyme ontogeny, points to the need of age-based evaluation of DDI. PBPK modeling and simulation can be used to develop more elaborated pediatric labels by virtually testing DDIs scenarios in populations in which clinical DDI studies are unrealistic.
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