University of Pittsburgh researchers have used a novel 3D computational model to discover compounds targeting the transient receptor potential vanilloid type 1 (TRPV1), a heat-activated cation channel protein contributing to inflammation and pain.
Description
Human TRPV1 (hTRPV1) has been linked to neurogenesis and conditions like migraine and inflammatory bowel disease. Targeting hTRPV1 could be a novel therapeutic approach to managing pain, but the lack of a 3D structure has hindered much research on developing antagonists. A computational model of hTRPV1 and in silico screening has been developed to identify potential antagonists and understand the conformational changes of hTRPV1 following binding with antagonists and agonists. Understanding the structure of hTRPV1, potential antagonists/agonists, and conformational changes can optimize a therapeutic approach against pain and inflammation, dramatically improving the lives of chronically ill patients.
Applications
1. Management of chronic and acute pain, including neuropathic pain.
2. Inflammatory conditions, including arthritis and inflammatory bowel disease.
Advantages
Adequate pain treatment strategies, including neuropathic pain, remain an unmet clinical need. This novel approach targets a key receptor involved in pain, hTRPV1, using computational modelling and in silico screening. Following the development of an accurate model, the predicted binding pockets in hTRPV1 have been used to identify novel antagonists. The computational model developed for hTRPV1 allows for identifying therapeutic antagonists/agonists and understanding the interactions with hTRPV1 could revolutionize the treatment of pain and inflammation-related conditions.
Invention Readiness
A validated computer model of hTRPV1 has been developed based on a low-resolution 3D structure of rat TRPV1 (rTRPV1), molecular dynamic simulations, and energy minimizations. This model was tested by screening a panel of 1000 compounds containing 10 known antagonists and agonists to hTRPV1 with a 100% hit rate on compounds already known to interact with hTRPV1. The model identified active sites and provided insight into the conformational changes in hTRPV1 upon antagonist/agonist binding. Using a virtual docking screen of over 15,000 compounds, several new potential inhibitors of hTRPV1 were identified, which have now progressed to bioassay and animal testing.
IP Status
https://patents.google.com/patent/US11339122B2
