A Repurposed Duo: Empagliflozin-Metformin Triggers a Metabolic Crisis in Cervical Cancer by Disrupting the Acyl-CoA/CoA Ratio via Dual Inhibition of PPAT and CPT1A
Bibliographic record
Abstract
Objective: This study aimed to assess the cytotoxic, selective, and synergistic effects of an Empagliflozin-Metformin mixture against HeLa cervical cancer cells and to investigate its underlying mechanism of action, especially through metabolic disruption. Materials and Methods: The cytotoxic effects of Empagliflozin, Metformin, carboplatin, and their mixture were evaluated in HeLa and human foreskin fibroblast (HFF) cells using the MTT assay at 24 and 72 hours. The Combination Index (CI) and Dose Reduction Index (DRI) were calculated to assess drug interactions. Metabolic disruption was analyzed by measuring the intracellular Acyl-CoA/CoA ratio. Molecular docking simulations were performed to predict binding affinities for key metabolic enzymes, Phosphopantetheine Adenylyl Transferase (PPAT) and Carnitine Palmitoyl Transferase 1A (CPT1A). Results: The mixture showed clear, time-dependent cytotoxic effects on HeLa cells, with an IC₅₀ of 207.5 µg/ml at 72 hours, which is significantly lower than the IC₅₀ values of either agent alone. The combination exhibited high selectivity toward malignant cells, with a Selectivity Index of over 4.82, and demonstrated strong synergistic interactions, as indicated by a combination index (CI) of less than 1.0. Additionally, a dose-dependent increase in the Acyl-CoA/CoA ratio was observed (5.02 ± 0.41 at 1000 µg/ml), indicating considerable metabolic stress. Molecular docking analyses showed strong binding affinities for both drugs to PPAT and CPT1A, with docking scores of -7.4 and -8.7 kcal/mol for Empagliflozin and -5.2 and -5.1 kcal/mol for metformin, respectively. Suggesting dual inhibitory effects on CoA biosynthesis and fatty acid oxidation. Conclusion: This study identifies the combination of Empagliflozin and Metformin as a promising candidate for repurposing as a therapeutic agent in cervical cancer. The therapeutic potential is supported by the well-characterized pharmacokinetics and established safety profiles of both agents, which are extensively used in the management of diabetes. The observed synergistic effect facilitates effective cytotoxicity at lower concentrations, thereby potentially minimizing adverse effects and enhancing the translational prospects of this metabolic-targeting strategy in clinical oncology trials.
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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) | 0.000 | 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".