Pharmacokinetics and drug-likeness of anticancer phytoconstituents: ADMET, Molecular docking, and Simulation studies
Bibliographic record
Abstract
Abstract Breast cancer (BC) is the most commonly diagnosed cancer in women around the world. Several genetic mutations tend to induce the risk of BC progression. SPDEF (Sam pointed domain containing ETS transcription factor) is a prostate-derived ETS factor that maintains homeostasis, differentiation of epithelial tissues, and heritable alterations in cancer. Plant secondary metabolites like flavonoids, terpenoids, and alkaloids have shown anticarcinogenic effect in several literatures. Therefore, in this study the SPDEF protein was used as potential breast cancer therapeutic target. Pharmacokinetic properties (ADMET), drug-likeness, and molecular docking of the fifteen phytoconstituents were assessed against SPDEF protein by various in silico approaches. The results showed that genistein, 2-hydroxychalcone, ajoene, and allicin had no toxicity. As per toxicological endpoints prediction study, the median lethal dosage (LD50) values vary from 159 to 3919 mg/Kg. All of the phytoconstituents derivatives taken into account in this investigation, are projected to be good candidates for P-glycoprotein (p-gp). Using in silico methods, the fifteen phytoconstituents identified from the different plants were predicted for their inhibitory actions against SPDEF protein, suggesting their breast cancer therapeutic potential. Silibinin, codonolactone and genistein have showed the lowest binding energy (-7.7, -6.1 and -6.1 kcal/mol) respectively and predicted to have the best inhibitory effect against SPDEF protein. We selected these three phytoconstituents for molecular dynamic simulation at 200 ns. In a comparison analysis, the Silibinin-receptor complex structure qualifies for the maximum parameters. The predictions about the pharmacokinetic properties of these phytoconstituents would form the basis for future in vivo, and in vitro experiments to identify the most appropriate therapeutic compound.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".