Pharmacological Treatments for Methamphetamine Use Disorder: Current Status and Future Targets.
Justin R YatesPublished in: Substance abuse and rehabilitation (2024)
The illicit use of the psychostimulant methamphetamine (METH) is a major concern, with overdose deaths increasing substantially since the mid-2010s. One challenge to treating METH use disorder (MUD), as with other psychostimulant use disorders, is that there are no available pharmacotherapies that can reduce cravings and help individuals achieve abstinence. The purpose of the current review is to discuss the molecular targets that have been tested in assays measuring the physiological, the cognitive, and the reinforcing effects of METH in both animals and humans. Several drugs show promise as potential pharmacotherapies for MUD when tested in animals, but fail to produce long-term changes in METH use in dependent individuals (eg, modafinil, antipsychotic medications, baclofen). However, these drugs, plus medications like atomoxetine and varenicline, may be better served as treatments to ameliorate the psychotomimetic effects of METH or to reverse METH-induced cognitive deficits. Preclinical studies show that vesicular monoamine transporter 2 inhibitors, metabotropic glutamate receptor ligands, and trace amine-associated receptor agonists are efficacious in attenuating the reinforcing effects of METH; however, clinical studies are needed to determine if these drugs effectively treat MUD. In addition to screening these compounds in individuals with MUD, potential future directions include increased emphasis on sex differences in preclinical studies and utilization of pharmacogenetic approaches to determine if genetic variances are predictive of treatment outcomes. These future directions can help lead to better interventions for treating MUD.
Keyphrases
- smoking cessation
- physical activity
- current status
- gene expression
- drug induced
- oxidative stress
- genome wide
- machine learning
- high glucose
- endothelial cells
- big data
- diabetic rats
- mesenchymal stem cells
- dna methylation
- autism spectrum disorder
- high throughput
- artificial intelligence
- risk assessment
- climate change
- bone marrow
- working memory
- deep learning