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In vitro mammalian cell mutation assays based on transgenic reporters: A report of the International Workshop on Genotoxicity Testing (IWGT)

2019· review· en· W2935834115 on OpenAlexafffund
Paul A. White, Mirjam Luijten, Masayuki Mishima, Julie A. Cox, Joleen N. Hanna, Rebecca M. Maertens, Edwin Zwart

Bibliographic record

VenueMutation Research/Genetic Toxicology and Environmental Mutagenesis · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsHealth Canada
FundersRijksinstituut voor Volksgezondheid en MilieuHealth CanadaChugai Pharmaceutical
KeywordsIn vitro toxicologyTransgeneBiologyGenotoxicityComputational biologyIn vitroGeneticsGeneToxicityChemistry

Abstract

fetched live from OpenAlex

Chemical safety evaluations require assessment of genetic toxicity. Transgenic rodent (TGR) assays permit enumeration of mutations in chromosomally-integrated targets contained in shuttle vectors. In order to improve in vitro mutagenicity assessment, and to substantially reduce animal use, in vitro assays using transgenic reporters have been developed. These assays are based on cells derived from TGRs, or cells transfected with transgenic shuttle vectors containing a mutation target. As part of the 7th International Workshop on Genotoxicity Testing, an In Vitro Mammalian Cell Gene Mutation Assay working group reviewed all published information pertaining to in vitro transgene mutagenicity assays; the utility, advantages and disadvantages of the assays were evaluated and discussed. The review revealed that over 20 TGR-based in vitro assays have been used to assess the mutagenic activity of over 150 agents. Overall, the Working Group considered in vitro transgene mutagenicity assays pragmatic tools for the safety evaluation of new and existing substances. A formal SWOT (strengths, weaknesses, opportunities, threats) analysis revealed advantages including the use of established scoring protocols, avoidance of laborious clone isolation and enumeration, ability to use metabolically competent primary cells, ability to detect different types of genetic damage, large dynamic range, and complementarity to in vivo TGR endpoints. Disadvantages include lack of validation and little consistency in protocols, the use of specialised reagents, the time and effort required for mutant enumeration, the use of some cell lines that lack metabolic capacity, and the need for multiple assays to cover all mutational mechanisms. Several assays have been partially validated, indicating promising reliability, reproducibility and applicability domain. Once in vitro transgene mutagenicity assays have been more thoroughly validated, they are well placed to augment or replace existing in vitro mammalian cell mutagenicity assays, particularly in cases where the in vivo TGR mutation assay is intended for follow-up.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.358
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2019
Admission routes2
Has abstractyes

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