Design of novel 4-maleimidylphenyl-hydrazide molecules displaying anti-inflammatory properties: Refining the chemical structure
Bibliographic record
Abstract
The discovery of new drugs possessing multiple biological properties in a single molecular entity is a subject of considerable scrutiny by the scientific community. This strategy can lead to better drug candidates for the treatment of many diseases, including cancer. In our quest for a more efficient bladder cancer treatment, we recently identified a compound readily accessible from para-aminobenzoic acid that showed anti-inflammatory, anti-metastatic as well as anticancer activities. This unique compound called DAB-1 can reduce the size of a tumor in an animal model by 90% within 25 days without apparent side effects. Its structure was modified to provide the molecule 2, a second-generation molecule named DAB-2-28, with enhanced in vitro and in vivo biological properties compared to DAB-1. The prospect of lead optimization is significant. This manuscript describes the synthesis of 2 as well as several higher analogs and reports on their anti-inflammatory activity in addition to their in vitro biological potential against bladder cancer. Amongst the results, it was discovered that the substitution pattern on the hydrazide core significantly affects the anti-inflammatory potential of the molecules. In fact, all the mono-acylated hydrazide derivatives 1, 5, 7, 9 were highly effective inhibiting the production of NO measured by the Griess reagents. By using the MTT assay, the same products displayed slightly lower toxicity (average 90% cell viability) on murine bladder cancer MB49-I cells in comparison to the reference DAB-1 molecule (85%). The best mono-acylated derivative 1 showed about 83% NO production inhibition level in relation to the relative number of viable/proliferating cells, the results are disclosed herein.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
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".