Predictive Factors for Methadone Maintenance Treatment Compliance: Exploring Resistance and Tolerance in Heroin Addiction

Abstract

Background: Methadone maintenance treatment (MMT) has been a cornerstone in heroin addiction management. However, its efficacy varies among individuals. The complex interplay of genetic backgrounds and demographic data could influence the response to MMT in heroin addiction. No previous adoption study has aimed to merge these findings into a potential pre-treatment screening tool. Objectives: This study aimed to investigate the combined influence of dopamine and opioid receptors and receptor endocytosis machinery genes, individual genetic backgrounds, and demographic data on the response to MMT in patients with heroin addiction. Methods: We enrolled 80 heroin addicts receiving MMT for 3 months alongside 80 healthy individuals in a comparative study. The approach utilized multinomial, linear, and binary logistic regression analyses to investigate the interplay of genetic factors (DRD1-5, opioid receptors [µ1, δ1, and κ1], DNM1L, RAB22A, and COMT), demographic independent variables, including, family history, heroin duration, age onset, heroin dose, and methadone dose, and clinical markers Subjective Opiate Withdrawal Scale (SOWS) with compliance with MMT protocols. Results: Results revealed that a positive family history and a higher level of heroin dose significantly predicted poor compliance to MMT. Additionally, the patients with lower expression levels of DRD2 and higher expression levels of DNM1L and COMT genes were at higher risk for poor compliance with the treatment. Conclusions: By utilizing a comprehensive dataset of gene expression profiles and demographic and clinical parameters, this study developed a regression model predicting resistance or response to methadone. This innovative approach seeks to bridge the gap between pharmacogenomics and clinical practice and offer a potential pre-treatment screening tool for personalized MMT strategies in opioid addiction management. The obtained findings hold intriguing promise for future research, potentially unlocking deeper insights into the underlying risk factors of addiction.