Functional comparison of human and murine equilibrative nucleobase transporter 1
Bibliographic record
Abstract
6-Mercaptopurine (6-MP) maintenance therapy is the mainstay for various types of leukemia and inflammatory bowel disease. 6-MP is associated with numerous adverse effects including gastrointestinal intolerance, myelotoxicity, and hepatotoxicity. This can lead to therapy discontinuation which is associated with a higher risk of relapse. Drug transporter expression is a known factor contributing to patient variability in drug response and toxicity. We have established that the SLC43A3-encoded equilibrative nucleobase transporter 1 (ENBT1) mediates the transport of 6-MP into human lymphocytes and human embryonic kidney 293 (HEK293) cell lines transfected with SLC43A3. ENBT1 is known to be expressed in the gastrointestinal tract, bone marrow, and the liver. However, the relationship between ENBT1 and 6-MP-associated adverse events, and its pharmacokinetics, is unknown. To validate the use of mouse models (e.g. slc43a3-null mice) for exploring this relationship, we assessed the functional similarities between human and murine ENBT1 using HEK293 cells transfected with the respective SLC43A3/slc43a3 constructs, and the leukemia cell lines MOLT-4 (human) and L1210 (murine). Based on in silico analyses of structural similarities between transporters, we hypothesized that human and murine ENBT1 will have similar 6-MP transport/inhibition kinetics and a similar impact on 6-MP-induced cytotoxicity. We show herein that mslc43a3-encoded mouse ENBT1 transports both [3H]6-MP and [3H]adenine with kinetics similar to those of hSLC43A3-encoded human ENBT1. Both are also similarly distributed in mouse and human tissues. Therefore, data obtained from mouse models where ENBT1 is disrupted or modified may provide clinically relevant insights on its roles in modulating the actions of 6-MP.
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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".