Setting limits for N-nitrosamines in drugs: A defined approach based on read-across and structure-activity relationship for N-nitrosopiperazine impurities.

This paper describes DARAN (Defined Approach for Risk Assessment of New Nitrosamines), an new defined approach that uses lines of reasoning based on structure-activity relationship (SAR) patterns and Read-Across (RAx) to set transparent and acceptable limits for new N-nitrosamines for which no toxicological data exist. We selected the compound 1-methyl-4-nitrosopiperazine (MeNP) as a target to calculate a new acceptable limit on the basis of a more transparent and scientifically reasoned RAx. We used publicly available databases and datasets to retrieve experimental in vitro mutagenicity and in vivo carcinogenicity data for N-nitrosopiperazine compounds and to form the chemical category for an RAx. We carried out SAR analyses to try to understand patterns and to obtain interpretable inferences of variation in carcinogenic potency among the N-nitrosopiperazines compounds and their differences with the potent nitrosamines NDMA (N-nitrosodimethylamine) and NDEA (N-nitrosodiethylamine). To estimate an acceptable limit for the target MeNP, we used the scientifically based hypotheses and the evidence lines of about the influence of structural attributes for a robust RAx. On the basis of the criteria proposed in the Assessment Report EMA/369136/20202 and by using the SAR hypotheses obtained by the analysis, we obtained a robust RAx, scientifically supported assumptions, which resulted in TD50 values predicted from the closest structurally related compounds and a worst-case approach.

Analysis of the levels of metal(loid)s in environmental compartments in Ireland towards a screening measure for potential relative risk using open-source datasets.

Several metals and metalloids (metal(loid)s) have been identified as potential pollutants. Naturally occurring background levels and anthropogenic sources (direct or indirect) contribute to the baseline concentration of metal(loid)s in the environment. Recorded metal(loid)s in various environmental media (soil, water, sediment) were evaluated from existing databases. The first database is the national soil database or Soil Geochemical Atlas of Ireland (SGAI). The second one is a higher resolution Tellus project database created by the Geological Survey Ireland. This study focussed on 16 metal(loid)s: As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Sb, Sn, U, V, and Zn. A Risk Quotient (RQ) and Integrated Risk Quotient (IRQ) were used to analyse individual and combined effects of selected metal(loid)s due to the potential ingestion by humans through the food chain. The results revealed that in a few locations of Ireland, the predicted environmental concentration (PEC) of As, Cd, Pb and Zn in the soil was higher than the threshold values resulting in an RQ exceedance of 1. The RQ values of metal(loid)s corresponding to the water, and sediment samples suggest minimal exceedance of threshold values. The exceedance of the IRQ values (>2) for the soil, water, and sediment samples is 32.3 %, 14.8 %, and 1.3 %, respectively. Regions along the East coast of Ireland may pose a higher potential relative risk compared to other parts of Ireland. This research suggests the need for in-depth risk assessment studies on Cd, As and Pb, which showed elevated levels. Furthermore, it is essential to understand the fate of metal(loid)s and their risk assessment to inform regulations around metal(loid)s where intervention may be required.

The changing face of nanomaterials: Risk assessment challenges along the value chain.

Risk assessment (RA) of manufactured nanomaterials (MNM) is essential for regulatory purposes and risk management activities. Similar to RA of "classical" chemicals, MNM RA requires knowledge about exposure as well as of hazard potential and dose response relationships. What makes MNM RA especially challenging is the multitude of materials (which is expected to increase substantially in the future), the complexity of MNM value chains and life cycles, the accompanying possible changes in material properties over time and in contact with various environmental and organismal milieus, and the difficulties to obtain proper exposure data and to consider the proper dose metric. This article discusses these challenges and also critically overviews the current state of the art regarding MNM RA approaches.

Towards a nanospecific approach for risk assessment.

In the current paper, a new strategy for risk assessment of nanomaterials is described, which builds upon previous project outcomes and is developed within the FP7 NANoREG project. NANoREG has the aim to develop, for the long term, new testing strategies adapted to a high number of nanomaterials where many factors can affect their environmental and health impact. In the proposed risk assessment strategy, approaches for (Quantitative) Structure Activity Relationships ((Q)SARs), grouping and read-across are integrated and expanded to guide the user how to prioritise those nanomaterial applications that may lead to high risks for human health. Furthermore, those aspects of exposure, kinetics and hazard assessment that are most likely to be influenced by the nanospecific properties of the material under assessment are identified. These aspects are summarised in six elements, which play a key role in the strategy: exposure potential, dissolution, nanomaterial transformation, accumulation, genotoxicity and immunotoxicity. With the current approach it is possible to identify those situations where the use of nanospecific grouping, read-across and (Q)SAR tools is likely to become feasible in the future, and to point towards the generation of the type of data that is needed for scientific justification, which may lead to regulatory acceptance of nanospecific applications of these tools.

Risk factors and assessment strategies for the evaluation of human or environmental risk from metal(loid)s - A focus on Ireland.

Elevated human exposure to metals and metalloids (metal(loid)s) may lead to acute sickness and pose a severe threat to human health. The human body is exposed to metal(loid)s principally through food, water, supplements, and (occasionally) air. There are inherent background levels of many metal(loid)s in regional soils as a consequence of geological sources. Baseline levels coupled with anthropogenic sources such as regional application of biosolids may lead to increased levels of certain metal(loid)s in soil, leading to potential transfer to water sources and potential uptake by plants. The latter could potentially transfer into the feed-to-food chain, viz. grazing animals, and bio-transfer to food products resulting in human exposure. This study addresses health concerns due to excessive intake of metal(loid)s by conducting a traditional review of peer-reviewed journals between 2015 and 2019, secondary references and relevant websites. The review identified the most researched metal(loid)s as Cu, Zn, Pb, Cd, Ni, Cr, As, Hg, Mn, Fe in the environment. The potential uptake of metal(loid)s by plants (phytoavailability) is a function of the mobility/retainability of metal(loid)s in the soil, influenced by soil geochemistry. The most critical parameters (including soil pH, soil organic matter, clay content, cation exchange capacity, the capability of decomposition of organic matter by microbes, redox potential, ionic strength) influencing metal(loid)s in soil are reviewed and used as a foundation to build a framework model for ranking metal(loid)s of concern. A robust quantitative risk assessment model is recommended for evaluating risk from individual metal(loid)s based on health-based indices (Daily Dietary Index (DDI), No Observed Adverse Effect Level (NOAEL), and Lowest Observed Adverse Effect Level (LOAEL)). This research proposes a risk assessment framework for potentially harmful metal(loid)s in the environment and highlights where regulation and intervention may be required.

A Bayesian inference approach to quantify average pathogen loads in farmyard manure and slurry using open-source Irish datasets.

Farm-to-fork quantitative microbial risk assessments (QMRA) typically start with a preliminary estimate of initial concentration (Cinitial) of microorganism loading at farm level, consisting of an initial estimate of prevalence (P) and the resulting pathogen levels in animal faeces. An average estimation of the initial concentration of pathogens can be achieved by combining P estimates in animal populations and the levels of pathogens in colonised animals' faeces and resulting cumulative levels in herd farmyard manure and slurry (FYM&S). In the present study, 14 years of data were collated and assessed using a Bayesian inference loop to assess the likely P of pathogens. In this regard, historical and current survey data exists on P estimates for a number of pathogens, including Cryptosporidium parvum, Mycobacterium avium subspecies paratuberculosis (MAP), Salmonella spp., Clostridium spp., Campylobacter spp., pathogenic E. coli, and Listeria monocytogenes in several species (cattle, pigs, and sheep) in Ireland. The results revealed that Cryptosporidium spp. has potentially the highest mean P (Pmean) (25.93%), followed by MAP (15.68%) and Campylobacter spp. (8.80%) for cattle. The Pmean of E. coli is highest (7.42%) in pigs, while the Pmean of Clostridium spp. in sheep was estimated to be 7.94%. Cinitial for Cryptosporidium spp., MAP., Salmonella spp., Clostridium spp., and Campylobacter spp. in cattle faeces were derived with an average of 2.69, 4.38, 4.24, 3.46, and 3.84 log10 MPN g -1, respectively. Average Cinitial of Cryptosporidium spp., Salmonella spp., Clostridium spp., and E. coli in pig slurry was estimated as 1.27, 3.12, 3.02, and 4.48 log10 MPN g -1, respectively. It was only possible to calculate the average Cinitial of Listeria monocytogenes in sheep manure as 1.86 log10 MPN g -1. This study creates a basis for future farm-to-fork risk assessment models to base initial pathogen loading values for animal faeces and enhance risk assessment efforts.

Urban Seismic Risk Assessment by Integrating Direct Economic Loss and Loss of Statistical Life: An Empirical Study in Xiamen, China.

The growing densities of human and economic activities in cities lead to more severe consequences when a catastrophe such as an earthquake occurs. This study on urban seismic risk evaluation is carried out from the perspective of the direct loss caused by disasters in urban areas, including the measurement of both the expected direct economic loss and loss of life in the face of characteristic earthquakes. Aiming to estimate, quantify and visualize the earthquake risk in each unit of urban space, this research proposes to assess urban seismic risk by integrating the direct economic loss and the loss of statistical life in a disaster, with consideration of diverse earthquake frequencies. Empirical research of the proposed assessment framework and corresponding models is then conducted to measure urban seismic risk in Xiamen, China. Key findings of the case study include the expected direct economic losses and the expected number of deaths in three characteristic earthquakes, their estimated spatial distributions, the average loss of the value of a statistical life (VSL) of one average local resident and the overall seismic risk distributions in Xiamen.

Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions.

This study presented a qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Accordingly, an exploratory and applied research design was employed in this study. The research sample included 15 experienced staff working in main and related positions in Neyr Perse Company. After reviewing the literature and the Project Management Body of Knowledge (PMBOK), 32 risk factors were identified and their number reduced to 17 risks using the expert opinions via the fuzzy Delphi technique run through three stages. The results of the confirmatory factor analysis showed that all risks were confirmed by the members of the research sample. Then the identified risks were structured and ranked using fuzzy DEMATEL and fuzzy ANP techniques. The final results of the study showed that the political and economic sanctions had the highest weight followed by foreign investors' attraction and the lack of regional infrastructure.