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From Elsevier via Jisc Publications Router ; History: accepted 2019-07-25, epub 2019-08-01, issue date 2019-10-31 ; Article version: VoR ; Publication status: Published ; Funder: European Union; Grant(s): 681002 ; Funder: EFSA; Grant(s): NP/EFSA/AFSCO/2016/01 ; Abstract In silico methods and models are increasingly used for predicting properties of chemicals for hazard identification and hazard characterisation in the absence of experimental toxicity data. Many in silico models are available and can be used individually or in an integrated fashion. Whilst such models offer major benefits to toxicologists, risk assessors and the global scientific community, the lack of a consistent framework for the integration of in silico results can lead to uncertainty and even contradictions across models and users, even for the same chemicals. In this context, a range of methods for integrating in silico results have been proposed on a statistical or case-specific basis. Read-across constitutes another strategy for deriving reference points or points of departure for hazard characterisation of untested chemicals, from the available experimental data for structurally-similar compounds, mostly using expert judgment. Recently a number of software systems have been developed to support experts in this task providing a formalised and structured procedure. Such a procedure could also facilitate further integration of the results generated from in silico models and read-across. This article discusses a framework on weight of evidence published by EFSA to identify the stepwise approach for systematic integration of results or values obtained from these "non-testing methods". Key criteria and best practices for selecting and evaluating individual in silico models are also described, together with the means to combining the results, taking into account any limitations, and identifying strategies that are likely to provide consistent results.

Maize is the principal staple food/feed crop exposed to mycotoxins, and the co-occurrence of multiple mycotoxins and their metabolites has been well documented. This review presents the infection cycle, ecology, and plant-pathogen interactions of Aspergillus and Fusarium species in maize, and current knowledge on maize chain management to mitigate the occurrence of aflatoxins and fumonisins. Preventive actions include at pre-harvest, as part of cropping systems, at harvest, and at postharvest, through storage, processing, and detoxification to minimize consumer exposure. Preventive actions in the field have been recognized as efficient for reducing the entrance of mycotoxins into production chains. Biological control of Aspergillus flavus has been recognized to minimize contamination with aflatoxins. Post-harvest maize grain management is also crucial to complete preventive actions, and has been made mandatory in government food and feed legislation. ; This review was prepared as part of MYCHIF EFSA project (GP/EFSA/AFSCO/2016/01). Roberta Palumbo carried out this work within the PhD school Agrisystem of Università Cattolica del Sacro Cuore, Italy. This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. This paper was critically reviewed in collaboration with MycoKey project (Horizon 2020, Grant Agreement No. 678781). ...

Maize is the principal staple food/feed crop exposed to mycotoxins, and the co-occurrence of multiple mycotoxins and their metabolites has been well docu- mented. This review presents the infection cycle, ecology, and plant-pathogen interac- tions of Aspergillus and Fusarium species in maize, and current knowledge on maize chain management to mitigate the occurrence of aflatoxins and fumonisins. Preven- tive actions include at pre-harvest, as part of cropping systems, at harvest, and at post- harvest, through storage, processing, and detoxification to minimize consumer expo- sure. Preventive actions in the field have been recognized as efficient for reducing the entrance of mycotoxins into production chains. Biological control of Aspergillus flavus has been recognized to minimize contamination with aflatoxins. Post-harvest maize grain management is also crucial to complete preventive actions, and has been made mandatory in government food and feed legislation.

European Union Reference Laboratory (EURL) for Honeybee Health ; International audience ; Bees are exposed to a wide range of multiple chemicals "chemical mixtures" from anthropogenic (e.g. plant protection products or veterinary products) or natural origin (e.g. mycotoxins, plant toxins). Quantifying the relative impact of multiple chemicals on bee health compared with other environmental stressors (e.g. varroa, viruses, and nutrition) has been identified as a priority to support the development of holistic risk assessment methods. Here, extensive literature searches and data collection of available laboratory studies on combined toxicity data for binary mixtures of pesticides and non-chemical stressors has been performed for honey bees (Apis mellifera), wild bees (Bombus spp.) and solitary bee species (Osmia spp.). From 957 screened publications, 14 publications provided 218 binary mixture toxicity data mostly for acute mortality (lethal dose: LD50) after contact exposure (61%), with fewer studies reporting chronic oral toxicity (20%) and acute oral LC50 values (19%). From the data collection, available dose response data for 92 binary mixtures were modelled using a Toxic Unit (TU) approach and the MIXTOX modelling tool to test assumptions of combined toxicity i.e. concentration addition (CA), and interactions (i.e. synergism, antagonism). The magnitude of interactions was quantified as the Model Deviation Ratio (MDR). The CA model applied to 17% of cases while synergism and antagonism were observed for 72% (MDR > 1.25) and 11% (MDR < 0.83) respectively. Most synergistic effects (55%) were observed as interactions between sterol-biosynthesis-inhibiting (SBI) fungicides and insecticide/acaricide. The mechanisms behind such synergistic effects of binary mixtures in bees are known to involve direct cytochrome P450 (CYP) inhibition, resulting in an increase in internal dose and toxicity of the binary mixture. Moreover, bees are known to have the lowest number of CYP copies and other detoxification enzymes in the insect kingdom. In the light of these findings, occurrence of these binary mixtures in relevant crops (frequency and concentrations) would need to be investigated. Addressing this exposure dimension remains critical to characterise the likelihood and plausibility of such interactions to occur under field realistic conditions. Finally, data gaps and further work for the development of risk assessment methods to assess multiple stressors in bees including chemicals and non-chemical stressors in bees are discussed.

European Union Reference Laboratory (EURL) for Honeybee Health ; International audience ; Bees are exposed to a wide range of multiple chemicals "chemical mixtures" from anthropogenic (e.g. plant protection products or veterinary products) or natural origin (e.g. mycotoxins, plant toxins). Quantifying the relative impact of multiple chemicals on bee health compared with other environmental stressors (e.g. varroa, viruses, and nutrition) has been identified as a priority to support the development of holistic risk assessment methods. Here, extensive literature searches and data collection of available laboratory studies on combined toxicity data for binary mixtures of pesticides and non-chemical stressors has been performed for honey bees (Apis mellifera), wild bees (Bombus spp.) and solitary bee species (Osmia spp.). From 957 screened publications, 14 publications provided 218 binary mixture toxicity data mostly for acute mortality (lethal dose: LD50) after contact exposure (61%), with fewer studies reporting chronic oral toxicity (20%) and acute oral LC50 values (19%). From the data collection, available dose response data for 92 binary mixtures were modelled using a Toxic Unit (TU) approach and the MIXTOX modelling tool to test assumptions of combined toxicity i.e. concentration addition (CA), and interactions (i.e. synergism, antagonism). The magnitude of interactions was quantified as the Model Deviation Ratio (MDR). The CA model applied to 17% of cases while synergism and antagonism were observed for 72% (MDR > 1.25) and 11% (MDR < 0.83) respectively. Most synergistic effects (55%) were observed as interactions between sterol-biosynthesis-inhibiting (SBI) fungicides and insecticide/acaricide. The mechanisms behind such synergistic effects of binary mixtures in bees are known to involve direct cytochrome P450 (CYP) inhibition, resulting in an increase in internal dose and toxicity of the binary mixture. Moreover, bees are known to have the lowest number of CYP copies and other detoxification enzymes in the insect kingdom. In the light of these findings, occurrence of these binary mixtures in relevant crops (frequency and concentrations) would need to be investigated. Addressing this exposure dimension remains critical to characterise the likelihood and plausibility of such interactions to occur under field realistic conditions. Finally, data gaps and further work for the development of risk assessment methods to assess multiple stressors in bees including chemicals and non-chemical stressors in bees are discussed.