In silico Evaluation of the Inhibitory Potential of Novel Hybrid Efflux Pump Inhibitors against Mycobacterium tuberculosis
Bibliographic record
Abstract
Tuberculosis (TB) is an infectious condition caused by Mycobacterium tuberculosis. One of the key steps towards introducing infectious disease in control is the creation and application of antibiotics. M. tuberculosis strain is multidrug resistant, which is a major threat to TB control. It develops multidrug resistance (MDR) by using efflux pumps (EPs) and other associated systems that can reduce the efficacy of the drug. Several techniques are currently developed to overcome the efflux-mediated resistance and the development of efflux pump inhibitors (EPIs) is one of them. The current study aims to provide an in-silico evaluation of biologically validated hybrid efflux pump inhibitors (HEPIs) with different M. tuberculosis EP proteins. Twelve different HEPIs were identified through literature review. Docking analysis was used to examine the role of HEPI inhibition against 5 MDR EPs. Additionally, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of all hybrid EP inhibitors were assessed. Molecular docking indicated that several HEPIs, specifically 5, 7, and 11, showed persistent higher binding affinities across multiple proteins, with docking scores comparable to or better than already known inhibitors. The predicted ADMET profiles suggested that most inhibitors had good oral bioavailability and adequate safety margins. To conclude, these HEPIs have the ability to effectively inhibit TB in human beings. In this regard, HEPI-5, HEPI-7, and HEPI-11 were determined as the most promising inhibitors because of their high binding affinities and positive ADMET profiles. Although experimental validation is essential to confirm their therapeutic relevance, these findings highlight their potential as novel EPI scaffolds. However, some of their pharmacological properties are not appropriate for human beings.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".