New promising targets to control neuropathic pain

Cellular and Molecular Machinery of Neuropathic Pain: an Emerging Insight

Purpose of Review

Neuropathic pain (NP) has been ubiquitously characterized by lesion and its linked somatosensory system either the central nervous system (CNS) or peripheral nervous system (PNS) This PNS episode is the most prevalent site of NP origin and is found to be associated with afferent nerve fibers carrying pain signals from injured/trauma site to the CNS including the brain. Several kinds of pharmacotherapeutic drugs shuch as analgesics, anti-convulsants, and anti-depressants are being employed for the its possible interventions. The NP has been a great interest to follow different pathophysiological mechanisms which are often considered to correlate with the metabolic pathways and its mediated disease. There is paucity of knowledge to make such mechanism via NP.

Recent Finding

Most notably, recent pandemic outbreak of COVID-19 has also been reported in chronic pain mediated diabetes, inflammatory disorders, and cancers. There is an increasing incidence of NP and its complex mechanism has now led to identify the possible investigations of responsible genes and proteins via bioinformatics tools. The analysis might be more instrumental as collecting the genes from pain genetic database, analyzing the variants through differential gene expression (DEG) and constructing the protein–protein interaction (PPI) networks and thereby determining their upregulating and downregulating pathways.

Summary

This review sheds a bright light towards several mechanisms at both cellular and molecular level, correlation of NP-mediated disease mechanism and possible cell surface biomarkers (receptors), and identified genes could be more promising for their pharmacological targets.

Prioritizing Pain-Associated Targets with Machine Learning

While hundreds of genes have been associated with pain, much of the molecular mechanisms of pain remain unknown. As a result, current analgesics are limited to few clinically validated targets. Here, we trained a machine learning (ML) ensemble model to predict new targets for 17 categories of pain. The model utilizes features from transcriptomics, proteomics, and gene ontology to prioritize targets for modulating pain. We focused on identifying novel G-protein-coupled receptors (GPCRs), ion channels, and protein kinases because these proteins represent the most successful drug target families. The performance of the model to predict novel pain targets is 0.839 on average based on AUROC, while the predictions for arthritis had the highest accuracy (AUROC = 0.929). The model predicts hundreds of novel targets for pain; for example, GPR132 and GPR109B are highly ranked GPCRs for rheumatoid arthritis. Overall, gene-pain association predictions cluster into three groups that are enriched for cytokine, calcium, and GABA-related cell signaling pathways. These predictions can serve as a foundation for future experimental exploration to advance the development of safer and more effective analgesics.