The RNA disruption assay is superior to conventional drug sensitivity assays in detecting cytotoxic drugs
Bibliographic record
Abstract
Conventional drug sensitivity assays used to screen prospective anti-cancer agents for cytotoxicity monitor biological processes associated with active growth and proliferation, used as proxies of cell viability. However, these assays are unable to distinguish between growth-arrested (but otherwise viable) cells and non-viable/dead cells. As a result, compounds selected based on the results of these assays may only be cytostatic, halting or slowing tumour progression temporarily, without tumour eradication. Because agents capable of killing tumour cells (cytotoxic drugs) are likely the most promising in the clinic, there is a need for drug sensitivity assays that reliably identify cytotoxic compounds that induce cell death. We recently developed a drug sensitivity assay, called the RNA disruption assay (RDA), which measures a phenomenon associated with tumour cell death. In this study, we sought to compare our assay's performance to that of current commonly used drug sensitivity assays (i.e, the clonogenic, the cell counting kit-8 and the Trypan blue exclusion assays). We found that RNA disruption occurred almost exclusively when total cell numbers decreased (cytotoxic concentrations), with little to no signal detected until cells had lost viability. In contrast, conventional assays detected a decrease in their respective drug sensitivity parameters despite cells retaining their viability, as assessed using a recovery assay. We also found that the RDA can differentiate between drug-sensitive and -resistant cells, and that it can identify agents capable of circumventing drug resistance. Taken together, our study suggests that the RDA is a superior drug discovery tool, providing a unique assessment of cell death.
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.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".