Drug Resistance Assessed in a Phase 3 Clinical Trial of Maribavir Therapy for Refractory or Resistant Cytomegalovirus Infection in Transplant Recipients
Bibliographic record
Abstract
BACKGROUND: This drug resistance analysis of a randomized trial includes 234 patients receiving maribavir and 116 receiving investigator-assigned standard therapy (IAT), where 56% and 24%, respectively, cleared cytomegalovirus DNA at week 8 (treatment responders). METHODS: Baseline and posttreatment plasma samples were tested for mutations conferring drug resistance in viral genes UL97, UL54, and UL27. RESULTS: At baseline, genotypic testing revealed resistance to ganciclovir, foscarnet, or cidofovir in 56% of patients receiving maribavir and 68% receiving IAT, including 9 newly phenotyped mutations. Among them, 63% (maribavir) and 21% (IAT) were treatment responders. Detected baseline maribavir resistance mutations were UL27 L193F (n = 1) and UL97 F342Y (n = 3). Posttreatment, emergent maribavir resistance mutations were detected in 60 (26%) of those randomized to maribavir, including 49 (48%) of 103 nonresponders and 25 (86%) of the 29 nonresponders where viral DNA initially cleared then rebounded while on maribavir. The most common maribavir resistance mutations were UL97 T409M (n = 34), H411Y (n = 26), and C480F (n = 21), first detected 26 to 130 (median 56) days after starting maribavir. CONCLUSIONS: Baseline maribavir resistance was rare. Drug resistance to standard cytomegalovirus antivirals did not preclude treatment response to maribavir. Rebound in plasma cytomegalovirus DNA while on maribavir strongly suggests emerging drug resistance. CLINICAL TRIALS REGISTRATION: NCT02931539.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".