Management of Cytomegalovirus Infections in the Era of the Novel Antiviral Players, Letermovir and Maribavir
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cytomegalovirus (CMV) infections may increase morbidity and mortality in immunocompromised patients. Until recently, standard antiviral drugs against CMV were limited to viral DNA polymerase inhibitors (val)ganciclovir, foscarnet and cidofovir with a risk for cross-resistance. These drugs may also cause serious side effects. This narrative review provides an update on new antiviral agents that were approved for the prevention and treatment of CMV infections in transplant recipients. Letermovir was approved in 2017 for CMV prophylaxis in CMV-seropositive adults who received an allogeneic hematopoietic stem cell transplant. Maribavir followed four years later, with an indication in the treatment of adult and pediatric transplant patients with refractory/resistant CMV disease. The target of letermovir is the CMV terminase complex (constituted of pUL56, pUL89 and pUL51 subunits). Letermovir prevents the cleavage of viral DNA and its packaging into capsids. Maribavir is a pUL97 kinase inhibitor, which interferes with the assembly of capsids and the egress of virions from the nucleus. Both drugs have activity against most CMV strains resistant to standard drugs and exhibit favorable safety profiles. However, high-level resistance mutations may arise more rapidly in the UL56 gene under letermovir than low-grade resistance mutations. Some mutations emerging in the UL97 gene under maribavir can be cross-resistant with ganciclovir. Thus, letermovir and maribavir now extend the drug arsenal available for the management of CMV infections and their respective niches are currently defined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it