Impact of Hepatic CYP3A4 Ontogeny Functions on Drug-Drug Interaction Risk in Pediatric Physiologically-Based Pharmacokinetic/Pharmacodynamic Modeling: Critical Literature Review and Ivabradine Case Study.

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Tytuł:: Impact of Hepatic CYP3A4 Ontogeny Functions on Drug-Drug Interaction Risk in Pediatric Physiologically-Based Pharmacokinetic/Pharmacodynamic Modeling: Critical Literature Review and Ivabradine Case Study.
Autorzy:: Lang J; Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Vincent L; Centre de Pharmacocinétique et Métabolisme, Technologie Servier, Orléans, France.
Chenel M; Clinical Pharmacokinetics and Pharmacometrics, Institut de Recherches Internationales Servier, Suresnes, France.
Ogungbenro K; Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Galetin A; Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK.
Źródło:: Clinical pharmacology and therapeutics [Clin Pharmacol Ther] 2021 Jun; Vol. 109 (6), pp. 1618-1630. Date of Electronic Publication: 2020 Dec 31.
Typ publikacji:: Journal Article; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Publication: 2015- : Hoboken, NJ : Wiley
Original Publication: St. Louis : C.V. Mosby
MeSH Terms:: Drug Interactions*
Cardiotonic Agents/*pharmacokinetics
Cytochrome P-450 CYP3A/*genetics
Ivabradine/*pharmacokinetics
Liver/*enzymology
Adolescent ; Antifungal Agents/adverse effects ; Antifungal Agents/pharmacokinetics ; Cardiotonic Agents/adverse effects ; Child ; Child, Preschool ; Cytochrome P-450 CYP3A Inducers ; Cytochrome P-450 CYP3A Inhibitors/pharmacokinetics ; Drug-Related Side Effects and Adverse Reactions/epidemiology ; Drug-Related Side Effects and Adverse Reactions/genetics ; Humans ; Infant ; Infant, Newborn ; Ivabradine/adverse effects ; Ketoconazole/adverse effects ; Ketoconazole/pharmacokinetics ; Pediatrics
References:: Nestorov, I. Whole body pharmacokinetic models. Clin Pharmacokinet. 42, 883-908 (2003).
Jones, H. & Rowland-Yeo, K. Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst. Pharmacol. 2, 1-12 (2013).
Tsamandouras, N., Rostami-Hodjegan, A. & Aarons, L. Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data: Parameter estimation in PBPK models. Br. J. Clin. Pharmacol. 79, 48-55 (2015).
Rostami-Hodjegan, A. Reverse translation in PBPK and QSP: going backwards in order to go forward with confidence. Clin. Pharmacol. Ther. 103, 224-232 (2018).
Lang, J., Vincent, L., Chenel, M., Ogungbenro, K. & Galetin, A. Simultaneous ivabradine parent-metabolite PBPK/PD modelling using a bayesian estimation method. AAPS J. 22, 129 (2020).
Takita, H., Scotcher, D., Chinnadurai, R., Kalra, P.A. & Galetin, A. Physiologically-based pharmacokinetic modelling of creatinine-drug interactions in the chronic kidney disease population. CPT Pharmacometrics Syst. Pharmacol. (2020). https://doi.org/10.1002/psp4.12566.
Grimstein, M. et al. Physiologically based pharmacokinetic modeling in regulatory science: an update from the U.S. food and drug administration’s office of clinical pharmacology. J. Pharm. Sci. 108, 21-25 (2019).
Shebley, M. et al. Physiologically based pharmacokinetic model qualification and reporting procedures for regulatory submissions: a consortium perspective. Clin. Pharmacol. Ther. 104, 88-110 (2018).
Yoshida, K., Budha, N. & Jin, J.Y. Impact of physiologically based pharmacokinetic models on regulatory reviews and product labels: frequent utilization in the field of oncology. Clin. Pharmacol. Ther. 101, 597-602 (2017).
Cole, S., Hay, J.L., Luzon, E., Nordmark, A. & Rusten, I.S. European regulatory perspective on pediatric physiologically based pharmacokinetic models. Int. J. Pharmacokinet. 2, 113-124 (2017).
Wagner, C. et al. Predicting the effect of cytochrome P450 inhibitors on substrate drugs: analysis of physiologically based pharmacokinetic modeling submissions to the US Food and Drug Administration. Clin. Pharmacokinet. 54, 117-127 (2015).
Bi, Y. et al. Role of model-informed drug development in pediatric drug development, regulatory evaluation, and labeling. J. Clin. Pharmacol. 59(suppl. 1), S104-S111 (2019).
Templeton, I.E., Jones, N.S. & Musib, L. Pediatric dose selection and utility of PBPK in determining dose. AAPS J. 20, 31 (2018).
Salerno, S.N., Burckart, G.J., Huang, S.-M. & Gonzalez, D. Pediatric drug-drug interaction studies: barriers and opportunities. Clin. Pharmacol. Ther. 105, 1067-1070 (2019).
Verscheijden, L.F.M., Koenderink, J.B., Johnson, T.N., de Wildt, S.N. & Russel, F.G.M. Physiologically-based pharmacokinetic models for children: starting to reach maturation? Pharmacol. Ther. 211, 107541 (2020).
Johnson, T.N., Rostami-Hodjegan, A. & Tucker, G.T. Prediction of the clearance of eleven drugs and associated variability in neonates, infants and children. Clin. Pharmacokinet. 45, 931-956 (2006).
Hayton, W.L. Maturation and growth of renal function: Dosing renally cleared drugs in children. AAPS PharmSci. 2, 22-28 (2000).
Allegaert, K. & van der Anker, J. Ontogeny of phase I metabolism of drugs. J. Clin. Pharmacol. 59(suppl. 1), S33-S41 (2019).
Edginton, A.N., Schmitt, W., Voith, B. & Willmann, S. A mechanistic approach for the scaling of clearance in children. Clin Pharmacokinet. 45, 683-704 (2006).
Cristea, S., Krekels, E.H.J., Rostami-Hodjegan, A., Allegaert, K. & Knibbe, C.A.J. The influence of drug properties and ontogeny of transporters on pediatric renal clearance through glomerular filtration and active secretion: a simulation-based study. AAPS J. 22, 87 (2020).
Salem, F., Johnson, T.N., Abduljalil, K., Tucker, G.T. & Rostami-Hodjegan, A. A re-evaluation and validation of ontogeny functions for cytochrome P450 1A2 and 3A4 based on in vivo data. Clin. Pharmacokinet. 53, 625-636 (2014).
Upreti, V.V. & Wahlstrom, J.L. Meta-analysis of hepatic cytochrome P450 ontogeny to underwrite the prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling. J. Clin. Pharmacol. 56, 266-283 (2016).
US Food and Drug Administration.Corlanor (ivabradine) Oral Solution. Clinical review NDA 209664 <https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/209964Orig1s000MedR.pdf > (2019).
Ragueneau, I., Laveille, C., Jochemsen, R., Resplandy, G., Funck-Brentano, C. & Jaillon, P. Pharmacokinetic-pharmacodynamic modeling of the effects of ivabradine, a direct sinus node inhibitor, on heart rate in healthy volunteers. Clin. Pharmacol. Ther. 64, 192-203 (1998).
Peigné, S., Fouliard, S., Decourcelle, S. & Chenel, M. Model-based approaches for ivabradine development in paediatric population, part II: PK and PK/PD assessment. J Pharmacokinet. Pharmacodyn. 43, 29-43 (2016).
Duffull, S.B. & Aarons, L. Development of a sequential linked pharmacokinetic and pharmacodynamic simulation model for ivabradine in healthy volunteers. Eur. J. Pharm. Sci. 10, 275-284 (2000).
Dokoumetzidis, A. & Aarons, L. Proper lumping in systems biology models. IET Syst. Biol. 3, 40-51 (2009).
Yu, L.X. & Amidon, G.L. A compartmental absorption and transit model for estimating oral drug absorption. Int. J. Pharm. 186, 119-125 (1999).
Gertz, M., Houston, J.B. & Galetin, A. Physiologically based pharmacokinetic modeling of intestinal first-pass metabolism of CYP3A4 substrates with high intestinal extraction. Drug Metab. Dispos. 39, 1633-1642 (2011).
Valentin, J. Basic anatomical and physiological data for use in radiological protection: reference values: ICRP Publication 89. Ann ICRP 32, 1-277 (2002).
Edginton, A.N., Schmitt, W. & Willmann, S. Development and evaluation of a generic physiologically based pharmacokinetic model for children. Clin. Pharmacokinet. 45, 1013-1034 (2006).
Wendling, T., Dumitras, S., Ogungbenro, K. & Aarons, L. Application of a Bayesian approach to physiological modelling of mavoglurant population pharmacokinetics. J. Pharmacokinet. Pharmacodyn. 42, 639-657 (2015).
Tsamandouras, N. et al. Development and application of a mechanistic pharmacokinetic model for simvastatin and its active metabolite simvastatin acid using an integrated population PBPK approach. Pharm. Res. 32, 1864-1883 (2015).
Björkman, S. 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 (2005).
Zane, N.R. & Thakker, D.R. A physiologically based pharmacokinetic model for voriconazole disposition predicts intestinal first-pass metabolism in children. Clin. Pharmacokinet. 53, 1171-1182 (2014).
Lee, C.M., Zane, N.R., Veal, G. & Thakker, D.R. Physiologically based pharmacokinetic models for adults and children reveal a role of intracellular tubulin binding in vincristine disposition. CPT Pharmacometrics Syst. Pharmacol. 8, 759-768 (2019).
T’jollyn, H. et al. Physiologically based pharmacokinetic predictions of tramadol exposure throughout pediatric life: an analysis of the different clearance contributors with emphasis on CYP2D6 maturation. AAPS J. 17, 1376-1387 (2015).
Emoto, C., Fukuda, T., Johnson, T.N., Adams, D.M. & Vinks, A.A. Development of a pediatric physiologically based pharmacokinetic model for sirolimus: applying principles of growth and maturation in neonates and infants. CPT Pharmacometrics Syst. Pharmacol. 4, 127-134 (2015).
Ginsburg, C.M., McCracken, G.H. & Olsen, K. Pharmacology of ketoconazole suspension in infants and children. Antimicrob. Agents Chemother. 23, 787-789 (1983).
Bardare, M., Tortorano, A.M., Pietrogrande, M.C. & Viviani, M.A. Pharmacokinetics of ketoconazole and treatment evaluation in candidal infections. Arch. Dis. Child. 59, 1068-1071 (1984).
Salem, F., Rostami-Hodjegan, A. & Johnson, T.N. Do children have the same vulnerability to metabolic drug-drug interactions as adults? A critical analysis of the literature. J. Clin. Pharmacol. 53, 559-566 (2013).
Johnson, T.N., Cleary, Y., Parrott, N., Reigner, B., Smith, J.R. & Toovey, S. Development of a physiologically based pharmacokinetic model for mefloquine and its application alongside a clinical effectiveness model to select an optimal dose for prevention of malaria in young Caucasian children. Br. J. Clin. Pharmacol. 85, 100-113 (2019).
Emoto, C. et al. A theoretical physiologically-based pharmacokinetic approach to ascertain covariates explaining the large interpatient variability in tacrolimus disposition. CPT Pharmacometrics Syst. Pharmacol. 8, 273-284 (2019).
Adiwidjaja, J., Boddy, A.V. & McLachlan, A.J. Implementation of a physiologically based pharmacokinetic modeling approach to guide optimal dosing regimens for imatinib and potential drug interactions in paediatrics. Front. Pharmacol. 10, 1672 (2020).
Peigné, S., Bouzom, F., Brendel, K., Gesson, C., Fouliard, S. & Chenel, M. Model-based approaches for ivabradine development in paediatric population, part I: study preparation assessment. J. Pharmacokinet. Pharmacodyn. 43, 13-27 (2016).
Maharaj, A.R. & Edginton, A.N. Physiologically based pharmacokinetic modeling and simulation in pediatric drug development. CPT Pharmacometrics Syst. Pharmacol. 3, 1-13 (2014).
Brussee, J.M. et al. Characterization of intestinal and hepatic CYP3A-mediated metabolism of midazolam in children using a physiological population pharmacokinetic modelling approach. Pharm. Res. 35, 182 (2018).
Salem, F., Johnson, T.N., Barter, Z.E., Leeder, J.S. & Rostami-Hodjegan, A. Age related changes in fractional elimination pathways for drugs: Assessing the impact of variable ontogeny on metabolic drug-drug interactions. J. Clin. Pharmacol. 53, 857-865 (2013).
US Food and Drug Administration. Guidance for industry: clinical drug interaction studies - cytochrome P450 enzyme- and transporter-mediated drug interactions <https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-drug-interaction-studies-cytochrome-p450-enzyme-and-transporter-mediated-drug-interactions> (2020).
Substance Nomenclature:: 0 (Antifungal Agents)
0 (Cardiotonic Agents)
0 (Cytochrome P-450 CYP3A Inducers)
0 (Cytochrome P-450 CYP3A Inhibitors)
3H48L0LPZQ (Ivabradine)
EC 1.14.14.1 (Cytochrome P-450 CYP3A)
EC 1.14.14.55 (CYP3A4 protein, human)
R9400W927I (Ketoconazole)
Entry Date(s):: Date Created: 20201207 Date Completed: 20210910 Latest Revision: 20210910
Update Code:: 20240104
DOI:: 10.1002/cpt.2134
PMID:: 33283268
: Czasopismo naukowe

Clinical assessment of drug-drug interactions (DDIs) in children is not a common practice in drug development. Therefore, physiologically-based pharmacokinetic (PBPK) modeling can be beneficial for informing drug labeling. Using ivabradine and its metabolite (both cytochrome P450 3A4 enzyme (CYP3A4) substrates), the objectives were (i) to scale ivabradine-metabolite adult PBPK/PD to pediatrics, (ii) to predict the DDIs with a strong CYP3A4 inhibitor, and (iii) to compare the sensitivity of children to DDIs using two CYP3A4 hepatic ontogeny functions: Salem and Upreti. A scaled parent-metabolite PBPK/PD model from adults to children satisfactorily predicted pharmacokinetics (PK) and pharmacodynamics (PD) in 74 children (0.5-18 years) regardless of CYP3A4 hepatic ontogeny function applied. However, using the Salem ontogeny, mean predicted parent and metabolite area under the concentration-time curve over 12 hours (AUC 12h ) and heart rate change from baseline were 2-fold, 1.5-fold, and 1.4-fold higher in young children (0.5-3 years old) compared with Upreti ontogeny, respectively. Despite these differences, choice of appropriate hepatic CYP3A4 ontogeny was challenging due to sparse PK and PD data. Different sensitivity to ivabradine-ketoconazole DDIs was simulated in young children relative to adults depending on the choice of hepatic CYP3A4 ontogeny. Predicted ivabradine and metabolite AUC DDI /AUC control were 2-fold lower in the youngest children (0.5-1 year old) compared with adults (Salem function). In contrast, the Upreti function predicted comparable ivabradine DDIs across all age groups, although predicted metabolite AUC DDI/ AUC control was 1.3-fold higher between the youngest children and adults. In the case of PD, differences in predicted DDIs were minor across age groups and between both functions. Current work highlights the importance of careful consideration of hepatic CYP3A4 ontogeny function and implications on labeling recommendations in the pediatric population.
(© 2020 The Authors. Clinical Pharmacology & Therapeutics © 2020 American Society for Clinical Pharmacology and Therapeutics.)

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