The role of MCT1 and MCT4 in drug‐induced muscle disorders
Bibliographic record
Abstract
Background Muscle injury, including myopathy, is a common side effect of some drugs, and particularly of statins. We hypothesized that these side effects are related to intracellular accumulation of lactic acid through monocarboxylate transporters, MCT1 and MCT4. Our objective was to investigate whether the transport of lactic acid is modulated by statins and other acidic drugs. Methods Cell lines used as models for MCT1 and MCT4 were Hs578T and MDA‐MB‐231, respectively. The cells were incubated with [ 14 C]lactic acid at 37°C and the intracellular concentration of radioactive lactic acid was measured. Inhibition studies were conducted by co‐incubating the cells with different concentrations of acidic drugs (e.g. statins, gemfibrozil, irbesartan, losartan, valsartan, loratadine, ibuprofen, naproxen, and salicylic acid) and lactic acid. Results Atorvastatin and loratadine showed the highest inhibition with IC 50 of 20 μM and 2μM, respectively. Fluvastatin and irbesartan showed an intermediate inhibition with IC 50 of 100–1000 μM, while drugs such as lovastatin and NSAID showed low or no inhibition (IC 50 >;1000 μM). Conclusions These results imply that atorvastatin and other acidic drugs can lead to the accumulation of lactic acid due to the blockage of MCT1 and MCT4. Further studies are required to link the intracellular accumulation of lactic acid and drug‐induced muscle pain. Research supports; CIHR and HSF
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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) | 0.002 | 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".