CYP2B6 and ABCB1 genotypes predict methadone plasma exposure among patients on maintenance therapy against opioid addictions in Tanzania

Abstract

AimsMethadone maintenance therapy (MMT) exhibits significant variability in pharmacokinetics and clinical response, partly due to genetic variations. However, data from sub‐Saharan African populations are lacking. We examined plasma methadone variability and pharmacogenetic influences among opioid‐addicted Tanzanian patients.MethodsPatients attending MMT clinics (n = 119) in Tanzania were genotyped for common functional variants of the CYP3A4, CYP3A5, CYP2A6, CYP2B6, CYP2C19, CYP2D6, ABCB1, UGT2B7 and SLCO1B1 genotypes. Trough plasma concentrations of total methadone, S‐methadone (S‐MTD) and R‐methadone (R‐MTD), with their respective metabolites, 2‐ethylidene‐1,5‐dimethyl‐3,3‐diphenylpyrrolidine (EDDP), were quantified using liquid chromatography–tandem mass spectrometry (LC–MS/MS). The methadone‐to‐EDDP metabolic ratio (MMR) was used to categorize the phenotype.ResultsThe proportions of MMR‐predicted ultrarapid, extensive, intermediate and slow methadone metabolizer phenotypes were 2.5%, 58.2%, 23.7% and 15.6%, respectively. CYP2B6 genotype significantly correlated with S‐methadone (P = .006), total methadone (P = .03), and dose‐normalized methadone plasma concentrations (P = .001). Metabolic ratios of R‐methadone (R‐MTD/R‐EDDP), S‐methadone (S‐MTD/S‐EDDP), and total methadone (MMR) were significantly higher among patients homozygous for defective variants (*6 or *18) than heterozygous or CYP2B6*1/*1 genotypes (P < .001). The metabolic ratio for S‐MTD and total methadone was significantly higher among ABCB1c.3435T/T than in the C/C genotype. No significant effect of CYP2D6, CYP2C19, CYP3A4, CYP3A5, CYP2A6, UGT2B7 and SLCO1B1 genotypes on S‐methadone, R‐methadone, or total methadone was observed.ConclusionsApproximately one in six opioid‐addicted Tanzanian patients are methadone slow metabolizers, influenced by genetic factors. Both the CYP2B6 and ABCB1 genotypes are strong predictors of methadone metabolic capacity and plasma exposure. Further investigation is needed to determine their predictive value for methadone treatment outcomes and to develop genotype‐based dosing algorithms for safe and effective therapy.