Study of Statin- and Loratadine-Induced Muscle Pain Mechanisms Using Human Skeletal Muscle Cells
Bibliographic record
Abstract
Many drugs can cause unexpected muscle disorders, often necessitating the cessation of an effective medication. Inhibition of monocarboxylate transporters (MCTs) may potentially lead to perturbation of l-lactic acid homeostasis and muscular toxicity. Previous studies have shown that statins and loratadine have the potential to inhibit l-lactic acid efflux by MCTs (MCT1 and 4). The main objective of this study was to confirm the inhibitory potentials of atorvastatin, simvastatin (acid and lactone forms), rosuvastatin, and loratadine on l-lactic acid transport using primary human skeletal muscle cells (SkMC). Loratadine (IC50 31 and 15 µM) and atorvastatin (IC50 ~130 and 210 µM) demonstrated the greatest potency for inhibition of l-lactic acid efflux at pH 7.0 and 7.4, respectively (~2.5-fold l-lactic acid intracellular accumulation). Simvastatin acid exhibited weak inhibitory potency on l-lactic acid efflux with an intracellular lactic acid increase of 25–35%. No l-lactic acid efflux inhibition was observed for simvastatin lactone or rosuvastatin. Pretreatment studies showed no change in inhibitory potential and did not affect lactic acid transport for all tested drugs. In conclusion, we have demonstrated that loratadine and atorvastatin can inhibit the efflux transport of l-lactic acid in SkMC. Inhibition of l-lactic acid efflux may cause an accumulation of intracellular l-lactic acid leading to the reported drug-induced myotoxicity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".