Antiviral drug resistance in herpesviruses other than cytomegalovirus
Bibliographic record
Abstract
The discovery of acyclovir (ACV), a nucleoside analogue, more than 30 years ago, represents a milestone in the management of HSV and VZV infections. The modest activity of ACV against CMV prompted the development of another nucleoside analogue, ganciclovir, for the management of systemic and organ-specific CMV diseases. Second-line agents such as the pyrophosphate analogue foscarnet and the nucleotide analogue cidofovir have been approved subsequently. In contrast to ACV and ganciclovir, the latter drugs do not require selective phosphorylation by viral protein kinases to be converted into their active forms. Since the introduction of these antivirals, the emergence of drug-resistant mutants has been constantly reported particularly in severely immunocompromised patients such as bone marrow and solid organ transplant recipients as well as HIV-infected individuals. In this manuscript, we discuss the characteristics of the antiviral agents currently approved for the management of HSV, VZV and CMV diseases. In recent years, the resistance of CMV to antiviral drugs has been extensively reviewed. The emergence of antiviral drug resistance is also observed with other members of the Herpesviridae family, namely HSV-1, HSV-2, VZV and HHV-6, which are the focus of this review. More specifically, we describe the laboratory methods for assessing drug susceptibilities, the frequency and clinical significance of drug-resistant infections and their management.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".