Editorial: In vitro toxicogenomics (TGx) in hazard and risk assessment
Bibliographic record
Abstract
In vitro toxicogenomics (TGx) in hazard and risk assessment Toxicogenomics (TGx) involves the application of transcriptomics to study how cells and organisms respond to environmental and chemical exposures by measuring changes in gene expression.TGx technologies have proven to be valuable tools in human health and environmental risk assessment.Specifically, TGx data provides a wealth of mechanistic insight to support a weight-of-evidence approach when establishing links between exposure and adverse outcomes.Furthermore, short-term in vivo studies using animals as models have supported the derivation of transcriptomic points-of-departure (PODs), i.e., the dose levels expected to lead to chronic adverse outcomes, that serve as surrogates of PODs measured using traditional apical toxicological endpoints in longer-term studies (Thomas et al., 2013;Zhou et al., 2017;Gwinn et al., 2020;Johnson et al., 2020).However, given the concerted international efforts to reduce the use of animals in toxicity testing, it is anticipated that animal studies will become less frequent over time (Kavlock et al., 2018).Thus, there is a pressing need to build confidence in non-animal TGx approaches and promote alternative testing strategies that take advantage of higher throughput in vitro systems.This shift towards New Approach Methodologies (NAMs), including novel in vitro TGx methods, presents an opportunity to accelerate the pace of chemical risk assessment and establish a next-generation risk assessment strategy to protect humans and the environment from emerging chemicals of concern.This Research Topic presents a collection of articles highlighting recent in vitro TGx advancements to establish the utility of both TGx methods and data for hazard and risk assessment.High-throughput transcriptomic biomarkers can predict specific adverse outcomes and establish PODs at which key molecular events occur following exposures.In the studies by Buick et al. and Fortin et al., a previously developed transcriptomic biomarker referred to as TGx-DDI (Li et al., 2015; 2017) was used to classify chemicals tested in human lymphoblastoid TK6 cells as either DNA damage-inducing (DDI) or non-DDI.Buick et al. investigated three anti-infective agents that had conflicting results in previous reports: nitrofurantoin (NIT), metronidazole (MTZ), and novobiocin (NOV).The TGx-DDI biomarker suggested that NIT and MTZ are unlikely to be DDI in human cells, while NOV was classified as DDI consistent with its ability to inhibit DNA topoisomerase II.Similarly, Fortin et al. used TGx-DDI alongside other genotoxicity NAMs in an in vitro only
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.038 | 0.029 |
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".