Pyrazoloquinazolinecarboxilate analogues inhibit nerve growth factor in vitro
Bibliographic record
Abstract
NGF is known to regulate the development and survival of select populations of neurons via its binding/activation of the TrkA and p75 NTR receptors. However, NGF dysregulation can result in debilitating pathologies. Thus, the identification of small molecules which inhibit NGF signaling have significant therapeutic potential. PD 90780, Ro 08‐2750, and ALE 0540 are small molecules that have been reported to bind and inhibit NGF activity. Importantly, the docking site of these compounds is hypothesized to occur at the loop I/IV cleft of NGF ‐ a region which is required for efficient and selective binding of a neurotrophin to its receptor(s). Molecular modeling predicts these molecules share conserved molecular features and have the ability to bind and modify the molecular topology of NGF. In order to understand the binding mechanism, we synthesized a pyrazoloquinazolinecarboxilate (PQC) analogue series and tested each compound in an NGF‐dependent PC12 cell differentiation assay. In vitro data confirms that the PQC analogues functionally inhibit NGF's agonist effects on PC12 cell differentiation. The results of this study provide evidence to refine the docking mode of PQC‐like compounds for the purposes of inhibiting NGF in vitro . In addition, we identified series analogue PQC 083 which is markedly better than previously described NGF antagonists.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".