Abstract LB-097: Whole transcriptome dose response profiling enables characterization of efficacy, metabolism, side effects and cytotoxicity in a single comprehensive assay
Bibliographic record
Abstract
Dose response assays are used throughout drug discovery and are key to comparing efficacy and safety, however, because of assay complexity and poor quantitative repeatability, gene expression-based dose response assays have not been successfully developed and adopted. Measuring dose response changes in cellular signaling pathways is challenging, as multiple pathways interact in a complex inter-connected manner. Benchmark dose (BMD) approaches allow researchers to quantitatively break down the complexity in an analyzable stepwise format for use in basic research, medicinal chemistry and toxicology. A BMD is calculated utilizing methods similar to an EC50 but does not differentiate between induction and suppression. By using this approach, it is possible to calculate a “convergent” BMD for each gene by fitting the data to a panel of kinetic models including Hill, Power, Exponential, and Linear. A software package, BMDExpress 2.0, has been developed by a consortium of EPA, NTP, Health Canada, and others, to facilitate the use of BMD calculations. The software can filter the data according to user defined parameters for the ~20,000 genes measured by the TempO-Seq® whole transcriptome targeted gene expression assay (Yeakley et al, PLOSone, 2017) which is an addition-only assay that uses crude cell lysates rather than extracted RNA. This enables high sample throughput from a minimal number of cells, carried out manually or with standard automation hardware in 96-well microplates, without the need of new equipment. The software determines a BMD value which can be a % change or change relative to SD, such as a BMD1SD. By tracking how individual genes respond to increasing doses, it is possible to differentiate between the onset of efficacy vs drug metabolism, side effects, and cytotoxicity. Simultaneously, it allows the identification of specific molecular pathways that are modulated by a given compound. We collected whole transcriptome dose response from Choline Fenofibrate treated HepG2 cells, computed the BMD1SD for each gene using BMDExpress, and generated accumulation plots that allowed monitoring of modulated genes and pathways in a dose-response manner. Genes and pathways associated with the known mechanistic efficacy of Fenofibrate were modulated at the lowest BMD1SD, followed by genes associated with its metabolism, and then by genes associated with side effects, then cytotoxicity at higher doses, indicated by a sudden, coordinated change in expression levels in a large number of genes. We carried out connectivity mapping to identify key molecular targets, by comparing BMD1SD values to the conventional mapping based on fold change. Our results demonstrate that high quality whole transcriptome dose response data generated by TempO-Seq® assay can be used to distinguish not only a therapeutic window of efficacy vs side effects but also factor in drug metabolism and cytotoxicity. Furthermore, gene expression BMD1SD analysis can identify novel target genes based on connectivity mapping.Citation Format: Megha Raghunathan, Elliot Imler, Christy Trejo, Peter Shepard, Bruce Seligmann. Whole transcriptome dose response profiling enables characterization of efficacy, metabolism, side effects and cytotoxicity in a single comprehensive assay [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-097.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".