Testing therapeutics in cell-based assays: Factors that influence the apparent potency of drugs
Bibliographic record
Abstract
Identifying effective antivirals for treating Ebola virus disease (EVD) and minimizing transmission of such disease is critical. A variety of cell-based assays have been developed for evaluating compounds for activity against Ebola virus. However, very few reports discuss the variable assay conditions that can affect the results obtained from these drug screens. Here, we describe variable conditions tested during the development of our cell-based drug screen assays designed to identify compounds with anti-Ebola virus activity using established cell lines and human primary cells. The effect of multiple assay readouts and variable assay conditions, including virus input, time of infection, and the cell passage number, were compared, and the impact on the effective concentration for 50% and/ or 90% inhibition (EC50, EC90) was evaluated using the FDA-approved compound, toremifene citrate. In these studies, we show that altering cell-based assay conditions can have an impact on apparent drug potency as measured by the EC50. These results further support the importance of developing standard operating procedures for generating reliable and reproducible in vitro data sets for potential 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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".