Abstract LB-097: Whole transcriptome dose response profiling enables characterization of efficacy, metabolism, side effects and cytotoxicity in a single comprehensive assay
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,015 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».