Precision Medicine in Substance Use Disorders: Integrating Behavioral, Environmental, and Biological Insights.
Authors
Affiliations (4)
Affiliations (4)
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands. Electronic address: [email protected].
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, the Netherlands; Nijmegen Institute for Scientist-Practitioners in Addiction (NISPA), Nijmegen, the Netherlands.
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, location Academic Medical Center, Amsterdam, the Netherlands; Department of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands.
Abstract
Substance use disorders (SUD) are chronic, relapsing conditions marked by high variability in treatment response and frequent relapse. This variability arises from complex interactions among behavioral, environmental, and biological factors unique to each individual. Precision medicine, which tailors treatment to patient-specific characteristics, offers a promising avenue to address these challenges. This review explores key factors influencing SUD, including severity, comorbidities, drug use motives, polysubstance use, cognitive impairments, and biological and environmental influences. Advanced neuroimaging, such as MRI and PET, enables patient subtyping by identifying altered brain mechanisms, including reward, relief, and cognitive pathways, and striatal dopamine D<sub>2/3</sub> receptor binding. Pharmacogenetic and epigenetic studies uncover how variations in dopaminergic, serotoninergic, and opioidergic systems shape treatment outcomes. Emerging biomarkers, such as neurofilament light chain, offer non-invasive relapse monitoring. Multifactorial models integrating behavioral and neural markers outperform single-factor approaches in predicting treatment success. Machine learning refines these models, while longitudinal and preclinical studies support individualized care. Despite translational hurdles, precision medicine offers transformative potential for improving SUD treatment outcomes.