Updated full range of Eliciting Dose values for Cow's milk for use in food allergen risk assessment.

Access to Eliciting Doses (ED) for allergens enables advanced food allergen risk assessment. Previously, the full ED range for 14 allergenic foods, including milk, and recommendations for their use were provided (Houben et al., 2020). Additional food challenge studies with cow's milk-allergic patients added 247 data points to the original dataset. Using the Stacked Model Averaging statistical method for interval-censored data on the 697 individual NOAELs and LOAELs for milk generated an updated full ED distribution. The ED01 and ED05, the doses at which 1% and 5% of the milk-allergic population would be predicted to experience any objective allergic reaction, were 0.3 and 3.2 mg milk protein for the discrete and 0.4 mg and 4.3 mg milk protein for the cumulative dose distribution, respectively. These values are slightly higher but remain within the 95% confidence interval of previously published EDs. We recommend using the updated EDs for future characterization of risks of exposure of milk-allergic individuals to milk protein. This paper contributes to the discussion on the Reference Dose for milk in the recent Ad hoc Joint FAO/WHO Expert Consultation on Risk Assessment of Food Allergens. It will also benefit harmonization of food allergen risk assessment and risk management globally.

A systematic comparison of food intake data of the United States and the Netherlands for food allergen risk assessment.

National population-based food consumption surveys are used in food allergen risk assessment. It would be beneficial if food intake data is interchangeable between countries to bridge potential gaps present in national survey data, which is only possible when risk assessment outcomes for comparable food product groups between countries are fairly similar. Additionally, merged food intake data would enable risk assessments that cover scenarios for various countries, if based on the most critical situation. Therefore, we systematically compared risk assessment outcomes for a broad range of food groups based on United States and Dutch population food consumption survey data. We calculated risks for 14 allergenic foods for 9 concentrations (1-10,000 ppm) to assess comparability. Depending on the assumed allergen concentration, risk assessment outcomes for 20% (10 out of 49) food groups differed considerably. We consider the number of potentially relevant risk differences too high to conclude that food intake data from the US and The Netherlands can be used interchangeably. To allow risk assessments that cover scenarios for several countries, we recommend development and use of a food intake dataset based on the highest intake levels for each food group of the involved countries to facilitate risk management efforts and harmonization.

Full range of population Eliciting Dose values for 14 priority allergenic foods and recommendations for use in risk characterization.

Previously, we published selected Eliciting Dose (ED) values (i.e. ED01 and ED05 values) for 14 allergenic foods, predicted to elicit objective allergic symptoms in 1% and 5%, respectively, of the allergic population (Remington et al., 2020). These ED01 and ED05 values were specifically presented and discussed in the context of establishing Reference Doses for allergen management and the calculation of Action Levels for Precautionary Allergen Labeling (PAL). In the current paper, we publish the full range of ED values for these allergenic foods and provide recommendations for their use, specifically in the context of characterizing risks of concentrations of (unintended) allergenic proteins in food products. The data provided in this publication give risk assessors access to full population ED distribution information for 14 priority allergenic foods, based on the largest threshold database worldwide. The ED distributions were established using broad international consensus regarding suitable datapoints and methods for establishing individual patient's NOAELs and LOAELs and state of the art statistical modelling. Access to these ED data enables risk assessors to use this information for state-of-the-art food allergen risk assessment. This paper contributes to a harmonization of food allergen risk assessment and risk management and PAL practices.

Seeking Windows of Opportunity to Shape Lifelong Immune Health: A Network-Based Strategy to Predict and Prioritize Markers of Early Life Immune Modulation.

A healthy immune status is strongly conditioned during early life stages. Insights into the molecular drivers of early life immune development and function are prerequisite to identify strategies to enhance immune health. Even though several starting points for targeted immune modulation have been identified and are being developed into prophylactic or therapeutic approaches, there is no regulatory guidance on how to assess the risk and benefit balance of such interventions. Six early life immune causal networks, each compromising a different time period in early life (the 1st, 2nd, 3rd trimester of gestations, birth, newborn, and infant period), were generated. Thereto information was extracted and structured from early life literature using the automated text mining and machine learning tool: Integrated Network and Dynamical Reasoning Assembler (INDRA). The tool identified relevant entities (e.g., genes/proteins/metabolites/processes/diseases), extracted causal relationships among these entities, and assembled them into early life-immune causal networks. These causal early life immune networks were denoised using GeneMania, enriched with data from the gene-disease association database DisGeNET and Gene Ontology resource tools (GO/GO-SLIM), inferred missing relationships and added expert knowledge to generate information-dense early life immune networks. Analysis of the six early life immune networks by PageRank, not only confirmed the central role of the "commonly used immune markers" (e.g., chemokines, interleukins, IFN, TNF, TGFB, and other immune activation regulators (e.g., CD55, FOXP3, GATA3, CD79A, C4BPA), but also identified less obvious candidates (e.g., CYP1A2, FOXK2, NELFCD, RENBP). Comparison of the different early life periods resulted in the prediction of 11 key early life genes overlapping all early life periods (TNF, IL6, IL10, CD4, FOXP3, IL4, NELFCD, CD79A, IL5, RENBP, and IFNG), and also genes that were only described in certain early life period(s). Concluding, here we describe a network-based approach that provides a science-based and systematical method to explore the functional development of the early life immune system through time. This systems approach aids the generation of a testing strategy for the safety and efficacy of early life immune modulation by predicting the key candidate markers during different phases of early life immune development.

Updated population minimal eliciting dose distributions for use in risk assessment of 14 priority food allergens.

Food allergy and allergen management are important global public health issues. In 2011, the first iteration of our allergen threshold database (ATDB) was established based on individual NOAELs and LOAELs from oral food challenge in roughly 1750 allergic individuals. Population minimal eliciting dose (EDp) distributions based on this dataset were published for 11 allergenic foods in 2014. Systematic data collection has continued (2011-2018) and the dataset now contains over 3400 data points. The current study provides new and updated EDp values for 14 allergenic foods and incorporates a newly developed Stacked Model Averaging statistical method for interval-censored data. ED01 and ED05 values, the doses at which 1%, and respectively 5%, of the respective allergic population would be predicted to experience any objective allergic reaction were determined. The 14 allergenic foods were cashew, celery, egg, fish, hazelnut, lupine, milk, mustard, peanut, sesame, shrimp (for crustacean shellfish), soy, walnut, and wheat. Updated ED01 estimates ranged between 0.03 mg for walnut protein and 26.2 mg for shrimp protein. ED05 estimates ranged between 0.4 mg for mustard protein and 280 mg for shrimp protein. The ED01 and ED05 values presented here are valuable in the risk assessment and subsequent risk management of allergenic foods.

A Family of Water-Immiscible, Dipolar Aprotic, Diamide Solvents from Succinic Acid.

Three dipolar aprotic solvents were designed to possess high dipolarity and low toxicity: N,N,N',N'-tetrabutylsuccindiamide (TBSA), N,N'-diethyl-N,N'-dibutylsuccindiamide (EBSA), and N,N'-dimethyl-N,N'-dibutylsuccindiamide (MBSA). They were synthesized catalytically by using a K60 silica catalyst in a solventless system. Their water immiscibility stands out as an unusual and useful property for dipolar aprotic solvents. They were tested in a model Heck reaction, metal-organic framework syntheses, and a selection of polymer solubility experiments in which their performances were found to be comparable to traditional solvents. Furthermore, MBSA was found to be suitable for the production of an industrially relevant membrane from polyethersulfone. An integrated approach involving in silico analysis based on available experimental information, prediction model outcomes and read across data, as well as a panel of in vitro reporter gene assays covering a broad range of toxicological endpoints was used to assess toxicity. These in silico and in vitro tests suggested no alarming indications of toxicity in the new solvents.

Residential exposure to RF-EMF from mobile phone base stations: Model predictions versus personal and home measurements.

INTRODUCTION: Geospatial models have been demonstrated to reliably and efficiently estimate RF-EMF exposure from mobile phone base stations (downlink) at stationary locations with the implicit assumption that this reflects personal exposure. In this study we evaluated whether RF-EMF model predictions at the home address are a good proxy of personal 48h exposure. We furthermore studied potential modification of this association by degree of urbanisation. METHOD: We first used an initial NISMap estimation (at an assumed height of 4.5m) for 9563 randomly selected addresses in order to oversample addresses with higher exposure levels and achieve exposure contrast. We included 47 individuals across the range of potential RF-EMF exposure and used NISMap to re-assess downlink exposure at the home address (at bedroom height). We computed several indicators to determine the accuracy of the NISMap model predictions. We compared residential RF-EMF model predictions with personal 48h, at home, and night-time (0:00-8:00AM) ExpoM3 measurements, and with EME-SPY 140 spot measurements in the bedroom. We obtained information about urbanisation degree and compared the accuracy of model predictions in high and low urbanised areas. RESULTS: We found a moderate Spearman correlation between model predictions and personal 48h (rSp=0.47), at home (rSp=0.49), at night (rSp=0.51) and spot measurements (rSp=0.54). We found no clear differences between high and low urbanised areas (48h: high rSp=0.38, low rSp=0.55, bedroom spot measurements: high rSp=0.55, low rSp=0.50). DISCUSSION: We achieved a meaningful ranking of personal downlink exposure irrespective of degree of urbanisation, indicating that these models can provide a good proxy of personal exposure in areas with varying build-up.