Ribociclib-Induced Hepatitis: A Case Report of Possible Autoimmune Hepatotoxicity
Bibliographic record
Abstract
Background: Hormone receptor-positive breast cancer is the most common subtype, accounting for approximately two-thirds of all breast cancer cases. In the metastatic setting, first-line treatment is comprised of endocrine therapy and a cyclin-dependent kinase 4 and 6 inhibitor. Ribociclib is the most used agent and has the highest risk of hepatotoxicity among the three currently available cyclin-dependent kinase 4 and 6 inhibitors. Case Presentation: We present a case of a 59-year-old postmenopausal female with metastatic hormone receptor-positive breast cancer who started treatment with fulvestrant and ribociclib after progression on anastrozole. After the third cycle, she developed grade 3 transaminitis. Ribociclib was held, and after no improvement in 28 days, she was treated with a 6-day course of prednisone 1 mg/kg, with significant improvement in her liver enzymes. Rechallenge with a lower dose of ribociclib (200 mg) was attempted; however, she again developed grade 3 transaminitis. This again required treatment with a short course of corticosteroids. Following normalization of her liver enzymes, she was rechallenged with abemaciclib with no recurrent hepatotoxicity. Conclusion: Ribociclib hepatotoxicity can be successfully treated with withdrawal of the medication and a short course of corticosteroids if liver enzymes do not improve following a 28-day withdrawal, highlighting a potential immune-mediated mechanism. Additionally, rechallenge with another cyclin-dependent kinase 4 and 6 inhibitor is a safe and effective strategy that should be considered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".