ReviewEnvironmental entomology2018
When the Whole is Not Greater than the Sum of the Parts: A Critical Review of Laboratory Bioassay Effects Testing for Insecticidal Protein Interactions.
Review in Environmental entomology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Ecological risk assessment for Cry1Da_7, Cry1B.3 and Vip3Cb1 proteins expressed in MON 89151 cotton: an insect-protected cotton with targeted activity against Lepidoptera.Transgenic research · 2025Article
- A major forest insect pest, the pine weevil Hylobius abietis, is more susceptible to Diptera- than Coleoptera-targeted Bacillus thuringiensis strains.Pest management science · 2021Article
- TOXiTAXi: a web resource for toxicity of Bacillus thuringiensis protein compositions towards species of various taxonomic groups.Scientific reports · 2020Article
- Assessing the Potential for Interaction in Insecticidal Activity Between MON 87751 × MON 87701 Produced by Conventional Breeding.Environmental entomology · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many studies have been conducted to investigate synergism among insecticidal proteins; however, a consensus on minimal data requirements and interpretation is lacking. While some have concluded that all additive predictive-type models should be abandoned, we advocate that additivity models can remain useful as assessment tools and that an appropriately designed interaction study will never systematically underestimate the existence of synergism, irrespective of which additivity model (or none at all) may be used. To generate the most meaningful synergy assessment datasets in support of safety assessments, we highlight two beneficial steps to follow: (i) select a testing model which is the most consistent with current knowledge regarding the action of the insecticidal proteins and (ii) avoid using bioassay methods which may result in excess response heterogeneity. We also outline other experimental design elements to consider, which may be crucial for conducting future studies of this type. A contrast of underlying testing assumptions associated with the additivity models is provided, along with a comprehensive review of interaction data for Cry1, Cry2, Cry3, Cry9, and Vip3A insecticidal proteins. Our review captures four recurrent findings: i) experiments reporting synergistic interactions are a minority, ii) the degree of synergism reported is low in magnitude, iii) reported interactions are sometimes equivocal/inconclusive due to unconfirmed model assumptions or other bioassay challenges, and iv) due to biological response variation many of the reported interactions may be artefactual. A brief overview of the positioning of interaction testing data in safety assessments of GM food crops is also provided.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.