SORRY, THE EVENT HAS BEEN CANCELED

Neuroscience Seminar Series - First Year Presentation

Title: Assessing the Analgesic Potency of Veraguamides on Mechanical Hypersensitivity in Mouse Model

Speaker:
Ana Sofia Carreon
Student, UT Dallas

Opioids are commonly prescribed to treat chronic pain . However, there is an ongoing crisis regarding excessive opiate usage that can lead to adverse side effects, therefore, the search for alternative analgesics is critically urgent. Scientists have discovered that the Sigma-2 receptor, later identified as being identical to the transmembrane protein, TMEM97, could reduce mechanical hypersensitivity in rodent models when the receptor is inhibited. Previous studies have observed that the presence of TMEM97 is necessary for the ligand FEM-1689 to produce anti-nociceptive effects in a Spared Nerve Injury model. We have collected marine cyanobacteria compounds, known as the veraguamides, that have shown non-toxic affinity to TMEM97 in non-cancerous cells, and have shown potential to be used as alternative analgesics to opiates. To test the sensitivity and efficacy of our analgesics, we utilized a Complete Freund’s Adjuvant (CFA) inflammatory paw injury model in both female and male adult wildtype mice, which possess the TMEM97 protein. Mechanical hypersensitivity was assessed using a 50% withdrawal latency von Frey test throughout various time points per experimental group. With this model, we observed the analgesic efficacy of three analogs of Veraguamide, FEM-1689, and a negative control, post-CFA injury. We found that veraguamides demonstrate a small ability to reduce mechanical hypersensitivity in wildtype mice. With these findings, we can further understand the role of TMEM97 in treating inflammatory pain with novel analgesics. Future studies will test the efficacy of veraguamides in TMEM97 knockout mice to understand the role of TMEM97 in pain targeting. 

On December 6, 2024 at 1:00 - 1:30PM
In-person in CRA 12.110.

This will also be available on MS Teams.
Meeting ID: 269 223 059 147
Passcode: xAAaar