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Human health risk assessment for microbial pesticides in the EU: challenges and perspectives
Jimena Barrero-Canosa1, Julia Ebeling1, Elaine F Kenny2
1German Federal Institute for Risk Assessment (BfR), Berlin, Germany.
Abstract:
The risk assessment of microbial pesticides in the European Union (EU) is covered by a regulatory framework based on EU Regulation 1107/2009 and 546/2011 together with the data requirements in EU Regulation 283/2013 and 284/2013, Part B, respectively (all amended in 2022). Furthermore, several guidance documents specify the data requirements for the human health assessment. As in other regulatory contexts, the assessment of hazardous properties of a microbial plant protection product (PPP) can be based on in vivo data. In order to decrease the use of test animals, support high-throughput data generation with larger repetition, and to facilitate faster testing methods, New Approach Methodologies (NAMs) for this field need to be developed. Here we focus on the assessment of the potential pathogenicity/infectivity and the presence of transferable antimicrobial resistance (AMR) genes of a microorganism when utilised as the active substance (AS) in a PPP. For the purpose of risk assessment of microbial PPPs, NAMs developed in view of the Next Generation Risk Assessment (NGRA) for chemicals can be applied. However, major drawbacks in the ability to use existing NAMs in the risk assessment of microbial pesticides are the reliability of Adverse Outcome Pathway (AOP) generated data for humans and the practicability of in vitro methods to test living microorganisms. It must be emphasised that tests for risk assessment are only useful if the test interpretation is clearly defined. Without prior definition of the possible effects and their interpretation, including the possible outcome for risk assessment, the test has limited value, as the results may raise more questions than answers. Overall, the regulatory assessment of the human health effects of microbial pesticides used in PPP needs reliable and robust data. These data should ideally be presented by an applicant based on animal-free study setups together with thorough literature searches.
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