Effect of major tumor metabolites on release of doxorubicin from Doxil – implications for precision nano-medicine.
Bibliographic record
Abstract
<img src=” https://s3.amazonaws.com/production.scholastica/article/17956/large/prnano_562020_ga.jpg?1605542414”> Our previous studies demonstrate that ammonia induces doxorubicin release from Doxil® in a concentration-dependent manner. Because ammonia that results from glutaminolysis is continu-ously generated in tumors at high enough concentration to induce doxorubicin release of Doxil in tumors, this may explain why doxorubicin release in interstitial tumor fluids is much faster and higher than in the plasma, in which release is minimal. This unique activity of tumor ammo-nia may explain, at least in part, the therapeutic efficacy of Doxil, which in practice does not re-lease doxorubicin in animal and human plasma. Our current study aims to evaluate if tumor-specific metabolites other than ammonia, such as lactate and pyruvate, are also involved in dox-orubicin release from Doxil. Also, we studied levels of ammonia in other mouse organs. Our data shows that these other metabolites do not affect doxorubicin release. Furthermore, using the Metabolic gEne RApid Visualizer database (MERAV), we computationally explored the relation-ships of glutaminase (GLS) 1 and 2, glutamate dehydrogenase (GLUD)1 and 2, as well as gluta-mine transporters regulating the glutaminolysis levels found in different cancers. These glutami-nolysis levels could not be achieved without the upregulation of glutamine transporters. Indeed, our queries to MERAV showed that SLC38A1, SLC38A2, and especially SLC38A6, are upregu-lated in cancerous tissues. We discuss how the information on the upregulation of enzymes re-lated to glutaminolysis could be used for “precision medicine” to determine if Doxil is an ap-propriate choice for a specific cancer patient. Our computational exploration shows that glutami-nolysis is heightened in some cancerous tissues as compared to their normal counterparts, but not in all cases. It would be possible and perhaps advantageous to test individual patient tissues to determine glutaminolysis, and therefore likely, ammonium levels in cancerous tissues, and to use this to ascertain if Doxil would be a good treatment choice.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".