High-Throughput Screening for Next-Generation Analgesics: Charting Non-Opioid Pain Targets and Assay Innovations

The growing burden of acute and chronic pain, coupled with the limitations of opioid-based therapies, has intensified the search for safer and more effective non-opioid analgesics. High-throughput screening (HTS) has emerged as a transformative strategy in analgesic drug discovery, enabling the rapid identification and characterization of novel compounds targeting peripheral pain pathways while minimizing central nervous system liabilities. This short communication highlights recent advances in HTS technologies and their role in accelerating the development of next-generation analgesics. We discuss the expanding landscape of non-opioid molecular targets, including voltage-gated sodium (NaV) channels, transient receptor potential (TRP) channels, non-opioid G protein-coupled receptors, and neuroimmune signalling pathways, alongside innovative screening platforms such as automated patch-clamp electrophysiology, human induced pluripotent stem cell (hiPSC)-derived nociceptor models, multi-electrode arrays, all-optical electrophysiology, and label-free mass spectrometry. We further examine the complementary roles of target-based and phenotypic screening approaches and consider the growing integration of artificial intelligence and machine learning in virtual screening, hit prioritization, and lead optimization. Collectively, these advances are reshaping the analgesic discovery pipeline by improving the physiological relevance, scalability, and predictive power of preclinical screening. As clinically validated non-opioid therapies continue to emerge, the convergence of advanced screening technologies and computational approaches offers a promising framework for the discovery of safer, more effective analgesics to address the global burden of pain.

High-throughput screening (HTS), Non-opioid analgesics, Pain drug discovery, Human induced pluripotent stem cells (hiPSCs), Phenotypic screening

1. Volkow, N. D., & McLellan, A. T. (2016). Opioid abuse in chronic pain—Misconceptions and mitigation strategies. The New England Journal of Medicine, 374(13), 1253–1263. https://doi.org/10.1056/NEJMra1507771 [Google Scholar] [Crossref]

2. Ciccarone, D. (2021). The rise of illicit fentanyls, stimulants and the fourth wave of the opioid overdose crisis. Current Opinion in Psychiatry, 34(4), 344–350. https://doi.org/10.1097/YCO.0000000000000717 [Google Scholar] [Crossref]

3. Kingwell, K. (2024). Nav1.8 inhibitor poised to provide opioid-free pain relief. Nature Reviews Drug Discovery. https://doi.org/10.1038/d41573-024-00203-3 [Google Scholar] [Crossref]

4. Fofie, C. K., Granja-Vazquez, R., Truong, V., Walsh, P., Price, T., Biswas, S., Dussor, G., Pancrazio, J., & Kolber, B. (2025). Profiling human iPSC-derived sensory neurons for analgesic drug screening using a multi-electrode array. Cell Reports Methods, 5(5), Article 101051. https://doi.org/10.1016/j.crmeth.2025.101051 [Google Scholar] [Crossref]

5. Insel, P. A., Sriram, K., Gorr, M. W., Wiley, S. Z., Michkov, A., Salmerón, C., Roth, A. L., & Wilderman, A. (2019). GPCRomics: An approach to discover GPCR drug targets. Trends in Pharmacological Sciences, 40(6), 378–387. https://doi.org/10.1016/j.tips.2019.04.001 [Google Scholar] [Crossref]

6. Jayakar, S., Shim, J., Jo, S., Bean, B. P., Singeç, I., & Woolf, C. J. (2021). Developing nociceptor-selective treatments for acute and chronic pain. Science Translational Medicine, 13(621), Article eabj9837. https://doi.org/10.1126/scitranslmed.abj9837 [Google Scholar] [Crossref]

7. Jones, J., Correll, D. J., Lechner, S. M., Jazic, I., Miao, X., Shaw, D., Simard, C., Osteen, J. D., Hare, B., Beaton, A., Bertoch, T., Buvanendran, A., Habib, A. S., Pizzi, L. J., Pollak, R. A., Weiner, S. G., Bozic, C., Negulescu, P., White, P. F., & Vertex Pain Study Group. (2023). Selective inhibition of Nav1.8 with VX-548 for acute pain. The New England Journal of Medicine, 389(5), 393–405. https://doi.org/10.1056/NEJMoa2209870 [Google Scholar] [Crossref]

8. Thornton, J. R., Gregoire, A. E., & Woolf, C. J. (2025). Screening of candidate analgesics using a patient-derived human iPSC model of nociception identifies putative compounds for therapeutic treatment. Clinical and Translational Medicine, 15(5), Article e70339. https://doi.org/10.1002/ctm2.70339 [Google Scholar] [Crossref]

9. McCoun, J., Tapley, T. L., & Indersmitten, T. (2025). Suzetrigine, a non-opioid Nav1.8 inhibitor with broad applicability for moderate-to-severe acute pain: A Phase 3 single-arm study for surgical or non-surgical acute pain. Journal of Pain Research, 18, 1569–1576. https://doi.org/10.2147/JPR.S509144 [Google Scholar] [Crossref]

10. Retamal, J. S., Ramírez-García, P. D., Shenoy, P. A., Poole, D. P., & Veldhuis, N. A. (2019). Internalized GPCRs as potential therapeutic targets for the management of pain. Frontiers in Molecular Neuroscience, 12, Article 273. https://doi.org/10.3389/fnmol.2019.00273 [Google Scholar] [Crossref]

11. Dunlop, J., Bowlby, M., Peri, R., Vasilyev, D., & Arias, R. (2008). High-throughput electrophysiology: An emerging paradigm for ion-channel screening and physiology. Nature Reviews Drug Discovery, 7(4), 358–368. https://doi.org/10.1038/nrd2552 [Google Scholar] [Crossref]

12. Dallas, M. L., & Bell, D. (2024). Advances in ion channel high throughput screening: Where are we in 2023? Expert Opinion on Drug Discovery, 19(3), 331–337. https://doi.org/10.1080/17460441.2023.2294948 [Google Scholar] [Crossref]

13. Bell, D. C., & Dallas, M. L. (2018). Using automated patch clamp electrophysiology platforms in pain-related ion channel research: Insights from industry and academia. British Journal of Pharmacology, 175(12), 2312–2321. https://doi.org/10.1111/bph.13916 [Google Scholar] [Crossref]

14. Zhang, H., & Cohen, A. E. (2017). Optogenetic approaches to drug discovery in neuroscience and beyond. Trends in Biotechnology, 35(7), 625–639. https://doi.org/10.1016/j.tibtech.2017.04.002 [Google Scholar] [Crossref]

15. Liu, P. W., Zhang, H., Werley, C. A., Pichler, M., Ryan, S. J., Lewarch, C. L., Jacques, J., Grooms, J., Ferrante, J., Li, G., Zhang, D., Bremmer, N., Barnett, A., Chantre, R., Elder, A. E., Cohen, A. E., Williams, L. A., Dempsey, G. T., & McManus, O. B. (2024). A phenotypic screening platform for chronic pain therapeutics using all-optical electrophysiology. Pain, 165(4), 922–940. https://doi.org/10.1097/j.pain.0000000000003090 [Google Scholar] [Crossref]

16. Odawara, A., Matsuda, N., & Suzuki, I. (2019). Electrophysiological pain responses in cultured human iPSC-derived sensory neurons using high-throughput multi-electrode array system. Frontiers in Cellular Neuroscience, 13, Article 38. https://doi.org/10.3389/conf.fncel.2018.38.00060 [Google Scholar] [Crossref]

17. Takáts, Z., Wiseman, J. M., Gologan, B., & Cooks, R. G. (2005). Desorption electrospray ionization mass spectrometry for high-throughput analysis of pharmaceutical samples in the ambient environment. Analytical Chemistry, 77(24), 8115–8124. https://doi.org/10.1021/ac050989d [Google Scholar] [Crossref]

18. Wleklinski, M., Loren, B. P., Ferreira, C. R., Jaman, Z., Avramova, L., Sobreira, T. J. P., Thompson, D. H., & Cooks, R. G. (2018). High throughput reaction screening using desorption electrospray ionization mass spectrometry. Chemical Science, 9(6), 1647–1653. https://doi.org/10.1039/c7sc04606e [Google Scholar] [Crossref]

19. Moffatt, J. G., Vincent, F., Lee, J. A., Eder, J., & Prunotto, M. (2017). Opportunities and challenges in phenotypic drug discovery: An industry perspective. Nature Reviews Drug Discovery, 16(8), 531–543. https://doi.org/10.1038/nrd.2017.111 [Google Scholar] [Crossref]

20. Swinney, D. C., & Anthony, J. (2011). How were new medicines discovered? Nature Reviews Drug Discovery, 10(7), 507–519. https://doi.org/10.1038/nrd3480 [Google Scholar] [Crossref]

21. Woolf, C. J. (2010). Overcoming obstacles to developing new analgesics. Nature Medicine, 16(11), 1241–1247. https://doi.org/10.1038/nm.2230 [Google Scholar] [Crossref]

22. Vincent, F., Nueda, A., Lee, J., Schenone, M., Prunotto, M., & Mercola, M. (2022). Phenotypic drug discovery: Recent successes, lessons learned and new directions. Nature Reviews Drug Discovery, 21(12), 899–914. https://doi.org/10.1038/s41573-022-00472-w [Google Scholar] [Crossref]

23. Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., McConkey, B. J., Mitchell, J. S., & Schneider, G. (2020). Rethinking drug design in the artificial intelligence era. Nature Reviews Drug Discovery, 19(5), 353–364. https://doi.org/10.1038/s41573-019-0050-3 [Google Scholar] [Crossref]

24. Gorgulla, C., Boeszoermenyi, A., Wang, Z. F., Fischer, P. D., Coote, P. W., Das, K. M. P., Malets, Y. S., Radchenko, D. S., Moroz, Y. S., Scott, D. A., & Arthanari, H. (2020). An open-source drug discovery platform enables ultra-large virtual screens. Nature, 580(7805), 663–668. https://doi.org/10.1038/s41586-020-2117-z [Google Scholar] [Crossref]

25. Sadybekov, A. A., Sadybekov, A. V., Liu, Y., Iliopoulos-Tsoutsouvas, C., Huang, X. P., Pickett, J., Houser, B., Patel, N., Tran, N. K., Tong, F., Zvonok, N., Jain, M. K., Savych, O., Radchenko, D. S., Nikas, S. P., Petasis, N. A., Moroz, Y. S., Roth, B. L., Makriyannis, A., & Katritch, V. (2022). Synthon-based ligand discovery in virtual libraries of over 11 billion compounds. Nature, 601(7893), 452–459. https://doi.org/10.1038/s41586-021-04220-9 [Google Scholar] [Crossref]

26. Walters, W. P., & Murcko, M. (2020). Assessing the impact of generative AI in drug design. Nature Biotechnology, 38(2), 143–145. https://doi.org/10.1038/s41587-020-0418-2 [Google Scholar] [Crossref]

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