Novel Method Based on Real-Time Cell Analysis for Drug Susceptibility Testing of Herpes Simplex Virus and Human Cytomegalovirus
Bibliographic record
Abstract
The plaque reduction assay (PRA) is the gold standard phenotypic method to determine herpes simplex virus (HSV) and human cytomegalovirus (HCMV) susceptibilities to antiviral drugs. However, this assay is subjective and labor intensive. Here, we describe a novel antiviral phenotypic method based on real-time cell analysis (RTCA) that measures electronic impedance over time. The effective drug concentrations that reduced by 50% (EC50s) the cytopathic effects induced by HSV-1 and HCMV were evaluated by both methods. The EC50s of acyclovir and foscarnet against a reference wild-type (WT) HSV-1 strain in Vero cells were, respectively, 0.5 μM and 32.6 μM by PRA and 0.8 μM and 93.6 μM by RTCA. The EC50 ratios for acyclovir against several HSV-1 thymidine kinase (TK) mutants were 101.8×, 73.4×, 28.8×, and 35.4× (PRA) and 18.0×, 52.0×, 5.5×, and 87.8× (RTCA) compared to those for the WT. The EC50 ratios for acyclovir and foscarnet against the HSV-1 TK/DNA polymerase mutant were 182.8× and 9.7× (PRA) and >125.0× and 10.8× (RTCA) compared to the WT. The EC50s of ganciclovir and foscarnet against WT HCMV strain AD169 in fibroblasts were, respectively, 1.6 μM and 27.8 μM by PRA and 5.0 μM and 111.4 μM by RTCA. The EC50 ratios of ganciclovir against the HCMV UL97 mutant were 3.8× (PRA) and 8.2× (RTCA) compared to those for the WT. The EC50 ratios of ganciclovir and foscarnet against the HCMV UL97/DNA polymerase mutant were 17.1× and 12.1× (PRA) and 14.7× and 4.6× (RTCA) compared to those for the WT. RTCA allows objective drug susceptibility testing of HSV and HCMV and could permit high-throughput screening of new antivirals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".