A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy
Open accessAbstract
Considering the variability in individual responses to opioids and the growing concerns about opioid addiction, prescribing opioids for postoperative pain management after spine surgery presents significant challenges. Therefore, this study undertook a novel pharmacogenomics-based in silico investigation of FDA-approved opioid medications. The DrugBank database was employed to identify all FDA-approved opioids. Subsequently, the PharmGKB database was utilized to filter through all variant annotations associated with the relevant genes. In addition, the dpSNP ( https://www.ncbi.nlm.nih.gov/snp/ ), a publicly accessible repository, was used. Additional analyses were conducted using STRING-MODEL (version 12), Cytoscape (version 3.10.1), miRTargetLink.2, and NetworkAnalyst (version 3). The study identified 125 target genes of FDA-approved opioids, encompassing 7019 variant annotations. Of these, 3088 annotations were significant and pertained to 78 genes. During variant annotation assessments (VAA), 672 variants remained after filtration. Further in-depth filtration based on variant functions yielded 302 final filtered variants across 56 genes. The Monoamine GPCRs pathway emerged as the most significant signaling pathway. Protein–protein interaction (PPI) analysis revealed a fully connected network comprising 55 genes. Gene–miRNA Interaction (GMI) analysis of these 55 candidate genes identified miR-16-5p as a pivotal miRNA in this network. Protein–Drug Interaction (PDI) assessment showed that multiple drugs, including Ibuprofen, Nicotine, Tramadol, Haloperidol, Ketamine, l -Glutamic Acid, Caffeine, Citalopram, and Naloxone, had more than one interaction. Furthermore, Protein–Chemical Interaction (PCI) analysis highlighted that ABCB1, BCL2, CYP1A2, KCNH2, PTGS2, and DRD2 were key targets of the proposed chemicals. Notably, 10 chemicals, including carbamylhydrazine, tetrahydropalmatine, Terazosin, beta-methylcholine, rubimaillin, and quinelorane, demonstrated dual interactions with the aforementioned target genes. This comprehensive review offers multiple strong, evidence-based in silico findings regarding opioid prescribing in spine pain management, introducing 55 potential genes. The insights from this report can be applied in exome analysis as a pharmacogenomics (PGx) panel for pain susceptibility, facilitating individualized opioid prescribing through genotyping of related variants. The article also points out that African Americans represent an important group that displays a high catabolism of opioids and suggest the need for a personalized therapeutic approach based on genetic information. Graphical Abstract:
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These genetic insights point to new therapeutic targets for pain management and addiction prevention and recovery.
“These genetic insights point to new therapeutic targets for pain management and addiction prevention and recovery.26,28–32”
From Beyond Stigma–Integrating Neurobiology, Genetics, and Awareness Integration Theory in Addiction Recovery (2026) · cites this paper as [29]
The Results (Signaling Pathway Assessment; page unavailable) prioritize genes with potential as drug targets, and the Abstract proposes using genetic findings to personalize opioid prescribing for spine pain. The Introduction links genetic profiles to opioid-dependence risk, but the paper does not identify new therapeutic targets specifically for addiction prevention or recovery. (LLM, conf=0.84)
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Blum, K., Gold, M., Jafari, N., Lewandrowski, K.-U., Sharafshah, A., Mohankumar, K., Lorio, M., Bowirrat, A., Pinhasov, A., Thanos, K., et al. (2026) · Psychology Research and Behavior Management · doi:10.2147/prbm.s561157 · Cited by 1 · see citing works
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Self-citation
- Authors
- Lewandrowski, K.-U., Sharafshah, A., Elfar, J., Schmidt, S. L., Blum, K., & Wetzel, F. T.
- Affiliations
- Hospital Universitário Gaffrée e Guinle (BR)
- Guilan University of Medical Sciences (IR)
- University of Arizona (US)
- Universidade Federal do Rio de Janeiro (BR)
- Wright State University (US)
- Bassett Healthcare Network (US)
- Corresponding author
- Lewandrowski, Kai-Uwe
- Venue
- Cellular and Molecular Neurobiology
- Published
- 2024-05-27
- Volume
- 44
- Issue
- 1
- Pages
- 47-47
- ISSN
- 0272-4340 (ISSN-L)
- 1573-6830
- PMC
- PMC11129978
- OpenAlex
- W4399050803
- Cited by
- 16
- References
- 75
- Citations by year
- 2026: 6 · 2025: 10
- FWCI
- 6.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cellular and Molecular Neurobiology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute on Minority Health and Health Disparities (R41MD012318)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacogenetics and Drug Metabolism
- Health Sciences › Medicine › Anesthesiology and Pain Medicine › Pain Management and Opioid Use
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Opioid Use Disorder Treatment
- Keywords
- opioid prescribing, spinal surgery, personalized pain management, genetic variants, miR-16-5p, drug-protein interactions, opioid addiction risk, pain susceptibility, drug repurposing, P-glycoprotein, CYP1A2
- MeSH terms
- Analgesics, Opioid, Computer Simulation, Humans, Pain, Postoperative, Postoperative Pain, Pharmacogenetics, Spine, Precision Medicine, Pain Management
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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