Unraveling Translational Insights into Systemic Multi-Organ Toxicity of Cytosine Arabinoside (Ara-C): A Systematic Review of Preclinical Animal Evidence
Bibliographic record
Abstract
Background/Objectives: Cytarabine (Ara-C) remains central to acute myeloid leukemia therapy but is limited by unpredictable systemic toxicities. Preclinical studies have long documented multi-organ injury, yet findings remain fragmented. This systematic review synthesizes animal evidence to clarify the spectrum, dose–response patterns, and mechanisms of cytarabine-induced toxicity. Methods: Following PRISMA 2020 guidelines and PROSPERO registration (CRD420251081384), a comprehensive search of PubMed, MEDLINE, Scopus, Cochrane Library and Embase identified eligible in vivo animal studies. Data extraction covered animal models, dosing regimens, routes of administration, histopathological and biochemical endpoints and mechanistic findings. Risk of bias and study quality were assessed using SYRCLE’s tool, CAMARADES checklist and an adapted Newcastle–Ottawa Scale, with reporting benchmarked against ARRIVE 2.0. Results: Eighty-one studies (1964–2024) were included. Cytarabine produced dose- and regimen-dependent toxicities across multiple organs. Neurotoxicity was most frequently reported, followed by intestinal mucositis, ocular injury, alopecia, hepatotoxicity, nephrotoxicity, and developmental anomalies. Mechanistic analyses consistently implicated oxidative stress, inflammatory cascades, apoptosis, and epigenetic dysregulation. Study quality was moderate, with frequent deficiencies in randomization, blinding, and sample-size justification, raising concerns about reproducibility. Cardiotoxicity, despite clinical relevance, was virtually absent from preclinical evaluation. Conclusions: Preclinical evidence suggests cytarabine’s systemic toxicity as a multifactorial process extending beyond rapidly proliferating tissues. While animal studies provide mechanistic insights, methodological weaknesses and translational gaps limit predictive value. Future research must adopt rigorous design, systematically assess underexplored toxicities, and integrate molecular profiling to identify biomarkers and protective strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".