Evidence map›Paper›PMID 41114134›Full record

ArticleFrontiers in plant science2025

Combining AI and new genomic techniques to 'fine-tune' plants: challenges in risk assessment.

Matthias Juhas, Bernd Rodekohr, Andreas Bauer-Panskus, Christoph Then

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Matthias JuhasTestbiotech e.V., Munich, Germany.
Bernd RodekohrAurelia Foundation, Berlin, Germany.
Andreas Bauer-PanskusTestbiotech e.V., Munich, Germany.
Christoph ThenTestbiotech e.V., Munich, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using new genomic techniques (NGTs) to 'fine-tune' plants typically involves changing just a small number of nucleotides. These small interventions can, nevertheless, lead to effects that go beyond the known plant characteristics, caused by genotypes previously unknown in the breeders' gene pool. The EU is currently discussing a proposal for the future regulation of NGT plants. In essence, the European Commission is proposing that NGT plants with less than 20 deletions, insertions or substitutions should in future no longer undergo mandatory risk assessment. NGT plants up to this threshold would be classified as Category 1 NGT, and therefore treated as equivalent to conventionally-bred plants. Plants in this category would not be subject to mandatory environmental risk assessment. The question thus arises of whether any of these Category1 NGT plants considered, in fact, have novel environmentally hazardous characteristics. Based on our findings from horizon scanning and to exemplify regulatory challenges, we used publicly available generative AI with the aim to design 'fine-tuned' NGT plants that would very likely require environmental risk assessment, but would nevertheless meet the specific the criteria for Category 1 NGT plants. As a proof of principle, we designed a genetic blueprint for an insecticidal maize plant, which could subsequently be developed using NGTs. There are several reasons why these insecticidal NGT plants should be subject to environmental risk assessment prior to being approved for cultivation. For example, they could be toxic to non-target species, cause resistance in pest insects, or show unintended genetic and phenotypic changes. In summary, there is no scientifically justifiable threshold of a certain number of mutations up to which NGT effects could be assumed to be of the same category as conventionally bred plants. Therefore, it is essential that the future regulatory concept is not based on such thresholds. On the contrary, future regulation should be science based and include case-by-case and step-by-step risk assessment, traceability and monitoring requirements to secure the future of food production and to protect biodiversity.

Indexed as

artificial intelligencecis-regulatory elementsgenetically engineered plantsgenetic engineeringgenome editingGMO regulationnew genomic techniques (NGT)risk assessment

Identifiers

PMID41114134
PMCPMC12528024

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Registered trials

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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.