TKPlate 1.0: An Open-access platform for toxicokinetic and toxicodynamic modelling of chemicals to implement new approach methodologies in chemical risk assessment.

This publication is linked to the following EFSA Supporting Publications articles: http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8441/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8440/full, http://onlinelibrary.wiley.com/doi/10.2903/sp.efsa.2023.EN-8437/full.

Investigating combined toxicity of binary mixtures in bees: Meta-analysis of laboratory tests, modelling, mechanistic basis and implications for risk assessment.

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.

Risk assessment of African swine fever in the south-eastern countries of Europe.

The European Commission requested EFSA to estimate the risk of spread of African swine fever (ASF) and to identify potential risk factors (indicators) for the spread of ASF, given introduction in the south-eastern countries of Europe (region of concern, ROC), namely Albania, Bosnia and Herzegovina, Croatia, Greece, Kosovo, Montenegro, North Macedonia, Serbia and Slovenia. Three EU Member States (MS) - Croatia, Greece and Slovenia - were included in the ROC due to their geographical location and ASF-free status. Based on collected information on potential risk factors (indicators) for each country and the relevant EU regulations in force, the estimated probability of spread of ASF within the ROC within one year after introduction into the ROC was assessed to be very high (from 66% to 100%). This estimate was determined after considering the high number of indicators present in most of the countries in the ROC and the known effect that these indicators can have on ASF spread, especially those related to the structure of the domestic pig sector, the presence of wild boar and social factors. The presence of indicators varies between countries in the ROC. Each country is at risk of ASF spread following introduction; however, some countries may have a higher probability of ASF spread following introduction. In addition, the probability of ASF spread from the ROC to EU MSs outside the ROC within one year after introduction of ASF in the ROC was estimated to be very low to low (from 0% to 15%). This estimate was based on the comparison of the indicators present in the ROC and the already affected countries in south-eastern Europe, such as Bulgaria and Romania, where there was no evidence of ASF spread to other EU MS within one year.

One Health Economics to confront disease threats.

Global economic impacts of epidemics suggest high return on investment in prevention and One Health capacity. However, such investments remain limited, contributing to persistent endemic diseases and vulnerability to emerging ones. An interdisciplinary workshop explored methods for country-level analysis of added value of One Health approaches to disease control. Key recommendations include: 1. systems thinking to identify risks and mitigation options for decision-making under uncertainty; 2. multisectoral economic impact assessment to identify wider relevance and possible resource-sharing, and 3. consistent integration of environmental considerations. Economic analysis offers a congruent measure of value complementing diverse impact metrics among sectors and contexts.

Clasificación de dengue hemorrágico utilizando árboles de decisión en la fase temprana de la enfermedad / Classification of dengue hemorrhagic fever using decision trees in the early phase of the disease

Introducción: el dengue es una enfermedad viral con comportamiento epidémico, a su inicio no es posible saber qué pacientes evolucionarán desfavorablemente, sin embargo, pueden presentar signos de alarma que anuncian deterioro clínico. Objetivo: aplicar la técnica de árboles de decisión a la búsqueda de signos de alarma de gravedad en la fase temprana de la enfermedad. Métodos: la muestra de estudio la constituyeron 230 pacientes ingresados con dengue en el Instituto de Medicina Tropical "Pedro Kourí" en 2001. Las variables consideradas para la clasificación fueron los signos, síntomas y exámenes de laboratorio al tercer día de evolución de la enfermedad. Se aplicó el algoritmo de árboles de clasificación y regresión utilizando el índice de Gini. Se consideraron diferentes matrices de pérdida para mejorar la sensibilidad. Resultados: el algoritmo ARC, correspondiente a la mejor pérdida, tuvo una sensibilidad de 98,68 por ciento y error global de 0,36. Sin considerar pérdida, el árbol resultante obtuvo una sensibilidad de 74 por ciento con un error de 0,25. En ambos casos las variables de mayor importancia fueron plaqueta y hemoglobina. Conclusiones: se proponen reglas de decisión con alta sensibilidad y valor predictivo negativo de utilidad en la práctica clínica. Las variables de laboratorio resultan tener mayor importancia que las clínicas para discriminar las formas clínicas de dengue.

Introduction: dengue is a viral disease with endemic behavior. At the beginning of the illness it is not possible to know which patients will have an unfavorable evolution and develop a severe form of dengue. However, some warning symptoms and signs may be present. Objective: to apply decision tree techniques to the exploration of signs of severity in the early phase of the illness. Methods: the study sample was made up of 230 patients admitted with dengue to "Pedro Kourí" Institute of Tropical Medicine in 2001. The variables considered for the classification were the signs, symptoms and laboratory exams on the third day of evolution of the illness. The algorithm of classification and regression trees using the Gini's index was applied. Different loss matrices to improve the sensitivity were considered. Results: the algorithm CART, corresponding to the best loss, had a sensitivity of 98.68 percent and global error of 0.36. Without considering loss, it obtained its sensitivity reached 74 percent with an error of 0.25. In both cases, the most important variables were platelets and hemoglobin. Conclusions: the study submitted rules of decision with high sensitivity and negative predictive value of utility in the clinical practice. The laboratory variables resulted more important from the informational viewpoint than the clinical ones to discriminate clinical forms of dengue.