Evidence map›Paper›PMID 40993956›Full record

ArticleCurrent neuropharmacology2026

PGx-Based

Alireza Sharafshah, Panayotis K Thanos, Albert Pinhasov, Abdalla Bowirrat, Colin Hanna, Kai-Uwe Lewandrowski, Christopher Rowan, Igor Elman, Mark S Gold, Catherine A Dennen and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Current neuropharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Alireza SharafshahCellular and Molecular Research Center, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
Panayotis K ThanosDepartment of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel.
Albert PinhasovDepartment of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel.
Abdalla BowirratDepartment of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel.
Colin HannaBehavioral Neuropharmacology and Neuroimaging Laboratory on Addictions, Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biosciences, State University of New York at Buffalo, Buffalo, NY, USA.
Kai-Uwe LewandrowskiDepartment of Orthopaedics, Fundación Universitaria Sanitas Bogotá, D.C., Bogotá, Colombia.
Christopher RowanBehavioral Neuropharmacology and Neuroimaging Laboratory on Addictions, Clinical Research Institute on Addictions, Department of Pharmacology and Toxicology, Jacobs School of Medicine and Biosciences, State University of New York at Buffalo, Buffalo, NY, USA.
Igor ElmanDepartment of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel.
Mark S GoldDepartment of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
Catherine A DennenDepartment of Family Medicine, Jefferson Health Northeast, Philadelphia, PA, USA.
Edward J ModestinoBrain & Behavior Laboratory, Department of Psychology, Curry College, Milton, MA, USA.
Rajendra D BadgaiyanDepartment of Psychiatry, School of Medicine, Case Western Reserve University, Cleveland, OH., USA.
David BaronDivision of Addiction Research & Education, Center for Sports, Exercise, and Mental Health, Western University of Health Sciences, Pomona, CA., USA.
Brian FuehrleinYale University School of Medicine, Yale-New Haven Hospital, New Haven, Connecticut, United States.
Ashim GuptaFuture Biologics, Lawrenceville, GA, 30043, USA.
Jean Lud CadetMolecular Neuropsychiatry Research Branch, NIH National Institute on Drug Abuse, Baltimore, MD, 20892, USA.
Aryeh R PollackDivision of Clinical Neurology, The Blum Institute of Neurogenetics & Behavior, Austin, TX., USA.
Jag KhalsaDepartment of Medicne, School of Medicine, Maryland University, Baltimore, MD., USA.
Milan MakaleDepartment of Radiation Medicine, Oncology, and Applied Sciences, UC San Diego, La Jolla, CA, USA.
Alexander P L LewandrowskiDepartment of Biological Sciences, Dornsife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, CA., USA.
Kenneth BlumDepartment of Molecular Biology, Adelson School of Medicine, Ariel University, Ariel, Israel.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionOur team conducted a pharmacogenomics (PGx) analysis to evaluate the interactions between cocaine, glucose metabolism, and functional connectivity using in-depth silico PGx methods.

methodsUtilizing PharmGKB, we extracted PGx annotations related to cocaine, glucose, and dopamine (raw data). After filtering, we refined a list of 49 unrepeated, brain-expressed genes and examined their interactions in a protein-protein interaction (PPI) network through STRING-MODEL, identifying top candidate genes.

resultsTargeting key protein-coding genes with the highest connectivity, we identified COMT, DRD2, and SLC6A3, along with their 17 connected genes. A deep dive into gene-miRNA interactions (GMIs) using NetworkAnalyst revealed that COMT, DRD2, and hsa-miR-16-5p have multiple interactions with OPRM1 and BDNF. Enrichment analysis via Enrichr confirmed that this refined set of 17 impacts dopamine function and are interactive with dopaminergic pathways. Notably, Substance Use disorders (SUD) were the most significant manifestation predicted for the interplays among these genes. DISCUSSION: Reviewing all PGx annotations for the 17 genes, we found 4,665 PGx entries, among which 1,970 were significant, with a p-value above 0.045. These were ultimately filtered down to 32 potential PGx annotations excluded in association with "Cocaine," "Glucose or Diabetes," and "Dopamine". Accordingly, 12 Pharmacogenes represented 32 PGx-associated with Cocaine, Glucose, and Dopamine, including

conclusionThis in silico PGx analysis demonstrates strong, validated connections based on prior published data and robust computational predictions. Among the findings, the COMT gene was found to be the best-scoring gene here.

Indexed as

Cocaine-Related DisordersDopamineGlucosePharmacogeneticsCocaineComputer SimulationHumansMicroRNAsCocaineDopamineGlucoseMicroRNAscocaine use disorderdopaminergic systemepigeneticsgeneticsglucose metabolismPharmacogenomicspre-addictionreward deficiency syndrome (RDS)reward dysregulation

Identifiers

PMID40993956
PMCPMC13647659

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.