Mouse Models as a Translational Platform for the Development of New Therapeutic Agents in Multiple Myeloma
Bibliographic record
Abstract
Mouse models of multiple myeloma (MM) are basic tools for translational research and play a fundamental role in the development of new therapeutics against plasma cell malignancies. All available models, including transplantable murine tumors in syngenic mice, xenografts of established human cell lines in immunocompromised mice and transgenic models that mirror specific steps of MM pathogenesis, have demonstrated some weaknesses in predicting clinical results, particularly for new drugs targeting the human bone marrow microenvironment (huBMM). The recent interest to models recapitulating the in vivo growth of primary MM cells in a human (SCID-hu) or humanized (SCID-synth-hu) host recipient has provided powerful platforms for the investigation of new compounds targeting MM and/or its huBMM. Here, we review and discuss strengths and weaknesses of the key in vivo models that are currently utilized in the MM preclinical investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".