Precision medicine in substance use disorders: Integrating behavioral, environmental, and biological insights

Cyprien G.J. Guerrin, Debbie R.M. Tesselaar, Jan Booij, Arnt F.A. Schellekens, Judith R. Homberg
Neuroscience & Biobehavioral Reviews. 2025-09-01; 176: 106311
DOI: 10.1016/j.neubiorev.2025.106311

Guerrin CGJ(1), Tesselaar DRM(2), Booij J(3), Schellekens AFA(2), Homberg JR(4).

Author information:
(1)Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition,
and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands.
Electronic address: .
(2)Department of Psychiatry, Radboud University Medical Center, Nijmegen, the
Netherlands; Nijmegen Institute for Scientist-Practitioners in Addiction
(NISPA), Nijmegen, the Netherlands.
(3)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.
(4)Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition,
and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands.

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 D2/3 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.