Novel Methodology to Identify TRPV1 Antagonists Independent of Capsaicin Activation
Bibliographic record
Abstract
TRPV1 was originally characterized as an integrator of various noxious stimuli such as capsaicin, heat, and protons. TRPV1-null mice exhibit a deficiency in sensing noxious heat stimuli, suggesting that TRPV1 is one of the main heat sensors on nociceptive primary afferent neurons and a candidate target for heat hypersensitivity in chronic pain. Several different potent and selective TRPV1 antagonists have been developed by more than 50 companies since the characterization of the receptor in 1997. A consequence of this competitive interest is the crowding of patentable chemical space, because very similar in vitro screening assays are used. To circumvent this issue and to expand our understanding of TRPV1 biology, we sought to take advantage of recent advancements in automated patch-clamp technology to design a novel screening cascade. This SAR-driving assay identified novel modulators that blocked the depolarization-induced activation of outwardly-rectifying TRPV1 currents independent of agonist stimulation, and we correlated the pharmacology to three other innovative assays for higher-throughput screening. Ultimately, we have identified a screening paradigm that would have good predictive value for future TRPV1 drug discovery projects and novel chemical space with a higher probability of gaining intellectual property coverage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".