A Pharmacogenomics-Based In Silico Investigation of Opioid Prescribing in Post-operative Spine Pain Management and Personalized Therapy

Abstract

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.

Keywords: Drug; Opioid; Pharmacogenomics; Spine pain management; Variant.

Fig. 1

STRING-MODEL of 126 extracted targets of FDA-approved drugs from DrugBank visualized by a PPI enrichment p-value lower than 1.0e−16

Fig. 2

The Cytoscape output of 55 genes showing Monoamine GPCRs as the most significant pathway with a p-value of 3.78e−13

Fig. 3

The concentric model of 55 genes adjusted with strong validated evidences illustrated by miRTargetLink2 representing the most important genes and miRNAs in the candidate opioid gene list

Fig. 4

The PDIs of 55 genes in a Fruchterman–Reingold network showing the most potential drugs in association with the PGx of Opioid prescription in Spine pain managements

Fig. 5

Linear Bi/Tripartite model of PCIs visualized by NetworkAnalyst

Fig. 6

PRISMA workflow of reviewed studies. *These studies did not include the opioids prescription in surgical operations. **These publications were discarded for Reason 1: opioids in surgery were not their main focus; and Reason 2: they did not about orthopedic surgeries and opioid prescription