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Development of a construct-based risk assessment framework for genetic engineered crops.

M P Beker1, P Boari2, M Burachik3

  • 1Bayer SA, Ricardo Gutierrez 3652, CP 1605, Munro, Buenos Aires, Argentina.

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Summary
This summary is machine-generated.

Risk assessment (RA) for genetically engineered (GE) crops has evolved. A new approach for GE crops using similar genetic constructs focuses on construct similarity, considering biological function, exposure, and crop familiarity for safety evaluations.

Keywords:
Construct similarityFamiliarityGenetic engineeringProblem FormulationRisk assessment

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Area of Science:

  • Agricultural Biotechnology
  • Genetics and Genomics
  • Food Safety and Toxicology

Background:

  • Extensive experience with genetically engineered (GE) crop risk assessment (RA) since the 1990s has prompted global agencies to re-evaluate RA criteria.
  • A specific need for scientific discussion on GE crops developed with identical or similar genetic constructs was identified in Argentina.

Purpose of the Study:

  • To discuss and define a science-based evaluation approach for new transformation events in GE crops that utilize genetic constructs identical or similar to those previously assessed.
  • To cover both environmental and food safety aspects for these GE crops.

Main Methods:

  • Convened a tripartite working group under the International Life Sciences Institute (ILSI Argentina) to discuss evaluation approaches.
  • Defined a 'construct similarity' concept based on the biological function of introduced genes.
  • Considered new transformation events in the same or different species.

Main Results:

  • A construct similarity concept was defined, focusing on the biological function of introduced genes.
  • Key factors for a construct-based RA process were identified, including environmental and dietary exposure, familiarity with the crop and trait, and crop biology.

Conclusions:

  • A construct-based RA approach is feasible for GE crops using identical or similar genetic constructs.
  • This approach enhances scientific discussion and provides a framework for evaluating new transformation events efficiently and safely.