In-silico investigation of curcumin drug-likeness, gene-targets and prognostic relevance of the targets in panels of human cancer cohorts
Bibliographic record
Abstract
Despite advancements in diagnostic and standard treatment modalities, cancer survival rate remains disappointing globally. It has however, been recognized that exploring the therapeutic properties of secondary metabolite from natural products may alleviate the problems of drug resistance and toxicity that besiege the conventional therapies, and hence improve the overall prognosis of cancer patient. To this end curcumin, a polyphenolic natural compound has been widely studied for it anticancer activities in in vitro and in vivo models. Computational technology has significantly improved the success rate of drug discovery and development, hence, it has become a widely explore tool in drug candidate identification. In this study we used computational approached to identify 12 genes that are potential druggable candidate for curcumin. The genes identified were found to be enriched in cancer and drug resistance associated signaling pathways. Interestingly, the top 3 identified genes; Microtubule-associated protein tau (MAPT), Toll-like receptor 9 (TLR9) and Tyrosyl-DNA phosphodiesterase 1 (TDP1) were observed to be over expressed in multiple cancer cohorts and were associated with poor prognoses of the patients. Curcumin has good physicochemical, bioavailability and ADMET properties. Importantly, it met the Lipinski's Rule of 5 for drug likeness and thus worthy of further in vitro and in vivo confirmation studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".