Effect of Cholesterol on Cellular Uptake of Cancer Drugs Pirarubicin and Ellipticine
Bibliographic record
Abstract
The cell membrane is a major barrier for drug transport. Given that many cancer drugs must passively cross the cell membrane, understanding drug-membrane interactions is crucial. We used fluorescence-activated cell sorting to investigate how cholesterol influences the transport of the cancer drugs ellipticine and pirarubicin across cell membranes. We showed that cholesterol depletion helped pirarubicin cross the membranes of nonsmall cell lung carcinoma and Chinese hamster ovary cells. In contrast, the uptake of ellipticine was not strongly influenced by cholesterol depletion. To study the microscopic origins of these observations, atomistic molecular dynamics simulations were performed. Doxorubicin (similar in structure to pirarubicin) and ellipticine were simulated in model membranes of POPC and POPC with 40 mol % cholesterol. Atomistic free energy calculations for the translocation of a single ellipticine and doxorubicin across the lipid bilayers qualitatively matched the experiment results. The free energy barrier for doxorubicin crossing the bilayer was strongly increased when cholesterol was present, while for ellipticine the barrier remained similar with and without cholesterol. Molecular dynamics simulations showed that the different hydrogen-bonding propensities of the two drugs are likely the major factor for the different behaviors. The qualitative agreement between cell experiments and atomistic computer simulations illustrates the potential to link observed biological phenomena and single molecule mechanisms of actions. Our results suggest that the traditional understanding of drug permeation and the influence of cholesterol on the small molecule transport is naïve and needs to be re-examined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".