(T-054) The effect of CYP2B6 genotype on the clearance and autoinduction of efavirenz in healthy subjects and the subsequent impact on efavirenz exposure.
Tuesday, November 12, 2024
7:00 AM – 5:00 PM MST
Blessed Aruldhas, PhD – Professor, Pharmacology and Clinical Pharmacology, Christian Medical College; Zeruesenay Desta, PhD – Professor, Division of Clinical Pharmacology, Indiana University School of Medicine; Brandon Gufford, PharmD, PhD – Adjunct Assistant Professor, Division of Clinical Pharmacology, Indiana University School of Medicine; Jessica Lu, MS – Research Analyst, Division of Clinical Pharmacology, Indiana University School of Medicine; Ingrid Metzger, PhD – Assistant Professor, Pharmacy, Universidade de Brasilia
Senior Principal Scientist Metrum Research Group, United States
Disclosure(s):
Michael Heathman, M.S.: No financial relationships to disclose
Objectives: The antiretroviral drug efavirenz (EFV) is metabolized primarily by CYP2B6, to form 8-OH EFV, and also by CYP2A6, to form 7-OH EFV. Upon chronic administration, EFV induces both CYP2B6 and CYP2A6, leading to autoinduction of its metabolism. Variability in the extent of autoinduction contributes to the variability of EFV pharmacokinetics (PK), which in turn determines clinical response, adverse events, and drug interactions. The objective of this analysis was to quantify the impact of CYP2B6 metabolizer status on EFV autoinduction and subsequent exposure, using a population PK modeling approach.
Methods: Samples (n=4594) from 135 healthy volunteers were collected up to 144 hours post-dose following a single 600 mg dose of EFV and after once daily treatment with 600 mg/day for 17 days. EFV and its 8-OH and 7-OH metabolites were quantified using LC/MS/MS. CYP2B6 genotype was obtained and phenotype classified as normal (NM), intermediate (IM), or poor metabolizer (PM). A population PK model was developed for EFV and its metabolites using NONMEM.[1]
Results: Two-compartment models were used to describe the PK of EFV and its metabolites. EFV absorption was described using a sequential zero- and first-order process. The central volume of both metabolites were set equal to that of EFV, due to lack of identifiability. Exponential inter-individual variability was incorporated on all PK parameters. Residual unexplained variability was described using a proportional model for all three analytes. Independent enzyme turnover models were used to characterize the autoinduction of CYP2B6 and CYP2A6. The enzyme models were parameterized in terms of maximum induction, concentration of half-maximal induction, and enzyme turnover rate. The effect of CYP2B6 phenotype was evaluated on formation rate of 8-OH EFV and maximum induction of CYP2B6. IM and PM status were found to decrease the formation rate of 8-OH EFV by 22% and 30%, respectively. IM and PM status were found to decrease the maximum induction of CYP2B6 by 36% and 81%, respectively. Simulations were conducted to explore the effect of CYP2B6 metabolizer status on EFV exposure. The simulations showed that EFV concentrations in NM and IM subjects peak after 5-7 days and then decline, while concentrations in PM subjects continue to accumulate for approximately two weeks. The commonly accepted minimum effective concentration of EFV in adults is 1 μg/mL at 12 hours post-dose, while concentrations of over 4 μg/mL are associated with CNS side effects.[2] The model predicted that steady-state EFV concentrations 12 hours post-dose exceed 4 μg/mL in more than 70% of PM subjects.
Conclusions: CYP2B6 polymorphisms significantly attenuate the autoinduction of EFV clearance, while also directly impacting clearance. Accounting for these differences in autoinduction between CYP2B6 polymorphisms may allow better understanding of optimal treatment regimens in patients with IM or PM status.
Citations: [1] SL Beal, LB Sheiner, AJ Boeckmann, RJ Bauer, eds. NONMEM 7.5 Users Guides. ICON plc; 1989–2020. https://nonmem.iconplc.com/nonmem750. [2] Marzolini C, Telenti A, Decosterd LA, Greub G, Biollaz J, Buclin T. Efavirenz plasma levels can predict treatment failure and central nervous system side effects in HIV-1-infected patients. AIDS. 2001;15(1):71-75. Available at: https://www.ncbi.nlm.nih.gov/pubmed/11192870.