Rational design and synthesis of novel diphenyl ether derivatives as antitubercular agents
Bibliographic record
Abstract
Sidhartha S Kar,1 Varadaraj Bhat G,1 Praveen PN Rao,2 Vishnu P Shenoy,3 Indira Bairy,4 G Gautham Shenoy1 1Department of Pharmaceutical Chemistry, Manipal College of Pharmaceutical Sciences, Manipal University, Manipal, India; 2School of Pharmacy, Health Sciences Campus, University of Waterloo, Waterloo, ON, Canada; 3Department of Microbiology, Kasturba Medical College, Manipal, 4Melaka-Manipal Medical College, Manipal University, Manipal, India Abstract: A series of triclosan mimic diphenyl ether derivatives have been synthesized and evaluated for their in vitro antitubercular activity against Mycobacterium tuberculosis H37Rv. The binding mode of the compounds at the active site of enoyl-acyl carrier protein reductase of M. tuberculosis has been explored. Among them, compound 10b was found to possess antitubercular activity (minimum inhibitory concentration =12.5 µg/mL) comparable to triclosan. All the synthesized compounds exhibited low levels of cytotoxicity against Vero and HepG2 cell lines, and three compounds 10a, 10b, and 10c had a selectivity index more than 10. Compound 10b was also evaluated for log P, pKa, human liver microsomal stability, and % protein binding, in order to probe its druglikeness. Based on the antitubercular activity and druglikeness profile, it may be concluded that compound 10b could be a lead for future development of antitubercular drugs. Keywords: dihenyl ether, tuberculosis, cytotoxicity, druglikeness
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".