5’- <i>S</i> -(3-aminophenyl)-5’-thioadenosine, a novel chemoprotective agent for reducing toxic side effects of fluorouracil in treatment of MTAP-deficient cancers
Bibliographic record
Abstract
Abstract Background Nucleobase analogue (NBA) drugs are effective chemotherapeutics, but their clinical use is limited by severe side effects. Compelling evidence suggests the use of S -methyl-5’-thioadenosine (MTA) can selectively reduce NBA toxicity on normal tissues while maintaining the efficacy of NBAs on methylthioadenosine phosphorylase (MTAP)-deficient cancers. However, we found that MTA induced hypothermia at its effective dose, limiting its translational potential. We intended to find an MTA analogue that can exert MTA function while minimize the undesired side effects of MTA. Thus, such an analogue can be used in combination with NBAs in selectively targeting MTAP-deficient cancers. Methods We screened a library of MTA analogues for the following criteria: 1) being substrates of MTAP; 2) selectively protection on MTAP-expressing cells from NBA toxicity using MTAP -isogenic cell lines; 3) ability to protect the host from NBA toxicity without hypothermic effect; and 4) lack of interference on the tumor-suppressive effect of NBA in mice bearing MTAP-deficient tumors. Results We identified 5’- S -(3-aminophenyl)-5’-thioadenosine (m-APTA) that did not induce hypothermia at the effective doses. We demonstrated that m-APTA could be converted to adenine by MTAP. Consequently, m-APTA selectively protected mouse hosts from 5-FU-induced toxicity (i.e. anemia); yet it did not interfere with the drug efficacy on MTAP-deficient bladder cancers. In silico docking studies revealed that, unlike MTA, m-APTA interact inefficiently with adenosine A 1 receptor, providing a plausible explanation of the superior safety profile of m-APTA. Conclusion m-APTA can significantly improve the translational potential of the NBA toxicity reduction strategy in selectively targeting MTAP-deficient cancers.
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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) | 0.001 | 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".