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Record W3095129474 · doi:10.33218/001c.17956

Effect of major tumor metabolites on release of doxorubicin from Doxil – implications for precision nano-medicine.

2020· article· en· W3095129474 on OpenAlexaff
Lisa Silverman, Yechezkel Barenholz

Bibliographic record

VenuePrecision Nanomedicine · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Victoria
FundersHebrew University of Jerusalem
KeywordsGlutaminolysisDoxorubicinGlutaminaseGlutamineDownregulation and upregulationChemistryCancer researchTransporterPharmacologyBiochemistryBiologyMedicineInternal medicineChemotherapyAmino acidGene

Abstract

fetched live from OpenAlex

<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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.288
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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