The official journal of
EUROPAD - European Opiate Addiction Treatment Association
WFTOD - World Federation for the Treatment of Opioid Dependence
Editor: Icro Maremmani, MD - Pisa, Italy, EU
Associate Editors:
Thomas Clausen, MD - Oslo, Norway
Pier Paolo Pani, MD - Cagliari, Italy, EU
Marta Torrens, MD - Barcelona, Spain, EU
Statistical Editor:
Mario Miccoli, PhD - Pisa, Italy, EU

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Heroin Addiction and Related Clinical Problems: 2023, 25, N2 (pages: 29 - 36)

CYP genetic polymorphism, pharmacokinetics of methadone, and the biggest risks

Jovanovic M., Bozovic N., Rancic N., Pjevac A., Rascanin S., and Stojkovic M.

Summary: Background The metabolism of methadone is quite complex and not yet fully understood. Current understanding indicates that multiple CYP 450 enzyme systems likely contribute to the N-demethylation of methadone, and the metabolic pathways may differ in different individuals. Aim: To examine the potential side effects of methadone and the association between them and CYP polymorphism. Methods: Electronic database (Medline, PubMed, Cochrane library and Embase) and key website search using search terms related to the topic. Results: The significant genetic variability of metabolizing methadone affects its application in determining the adequate dose and the therapy safety. Polymorphisms in CYP2B6 may contribute to metabolic rate, while polymorphisms in CYP2D6 appear not to do so. Inhibition of CIP2B6 significantly reduces the metabolism of both isomers with a threefold increase in plasma (S)-methadone. The influence of genetic polymorphism on QTC interval prolongation showed that methadone causes prolongation of the QTc interval in a dose-dependent manner. It can cause serious side effects such as TdP (Torsades de pointes) and respiratory depression. Conclusions: To increase the safety of patients on methadone treatment, the authors recommend that doctors carefully pay attention to the risk factors associated with the long QT interval syndrome, through regular evaluations of cardiac function and systematic ECGs, especially among patients with a history of heart disease. The progress of science points to introducing new control mechanisms, such as specific biomarkers. Artificial intelligence and machine learning are promising tools for predicting treatment outcomes.


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