Letermovir and Tacrolimus Drug–Drug Interaction in a Pediatric Kidney Transplant Patient: A Case Report and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: Cytomegalovirus (CMV) viremia is one of the most common opportunistic infections following solid organ transplant (SOT) in pediatric patients and can be associated with significant morbidity. Valganciclovir is the preferred antiviral medication used for CMV prophylaxis in high-risk kidney transplant recipients, but its use is limited by myelosuppression. Letermovir is an alternative antiviral medication with lower rates of neutropenia. Letermovir has been implicated in many potential drug-drug interactions, including in combination with tacrolimus. METHODS: This report describes the case of a 6-year-old girl, 2 months post-kidney transplant, at risk for CMV, who received letermovir for CMV prophylaxis after developing neutropenia while receiving valganciclovir. Her maintenance immunosuppression included tacrolimus. This report describes the drug-drug interaction of letermovir and tacrolimus, the dose adjustments required, and summarizes the available literature on the interaction. RESULTS: While on concurrent letermovir and tacrolimus, the patient required a 36% tacrolimus dose reduction and additional therapeutic drug level monitoring. CONCLUSION: This case adds to the current limited data on the interaction of letermovir and tacrolimus in the pediatric SOT population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".