Read-Across for Rat Oral Gavage Repeated-Dose Toxicity for Short-Chain Mono-Alkylphenols: A Case Study

Short-chain mono-alkylphenols provide an example of where a category-approach to read-across may be used to estimate the repeated-dose endpoint for a number of derivatives. Specifically, the NOAELs of 50 mg/kg bw/d for mono-methylphenols based on a LOAEL of very low systemic toxicity can be read across with confidence to untested mono-alkylphenols in the category. These simple alkylphenols are non-reactive and exhibit an unspecific, reversible polar narcosis mode of toxic action. Briefly, polar narcotics act via unspecific, reversible interactions with biological membranes in a manner similar to cataleptic anaesthetics. The read-across premise includes rapid and complete absorption via the gastrointestinal tract, distribution in the circulatory system, first-pass Phase 2 metabolism in the liver, and elimination of sulphates and glucuronides in the urine. Thus, toxicokinetic parameters are considered to be similar and have the same toxicological significance. Five analogues have high quality experimental oral repeated-dose toxicity data (i.e., OECD TG 408 or OECD TG 422). These repeated-dose toxicity test results exhibit qualitative consistency in symptoms. Typical findings include decreased body weight and slightly increased liver and kidney weights which are generally without concurrent histopathological effects. The sub-chronic findings are quantitatively consistent with the No Observed Adverse Effect Level (NOAEL) of ≥ 50 mg/kg bw/d. Chemical similarity between the analogues is readily defined, and data uncertainty associated with the similarities in toxicokinetic properties, as well as toxicodynamic properties, are low. Uncertainty associated with mechanistic relevance and completeness of the read-across is low-to-moderate, largely because there is no adverse outcome pathway or intermediate event data. Uncertainty associated with mechanistic relevance and completeness of the read-across is reduced by the concordance of in vivo, in vitro, USEPA toxicity forecaster (ToxCast) results, as well as the in silico data. The rat oral repeated-dose NOAEL values for the source substances can be read across to fill the data gaps of the untested analogues in this category with uncertainty deemed equivalent to results from a TG 408 assessment

A comparison of bioclimatic conditions on Franz Josef Land (the Arctic) between the turn of the nineteenth to twentieth century and present day

The paper presents the variability of meteorological conditions: air temperature, wind speed and relative air humidity; and biometeorological indices: wind chill temperature, predicted clothing insulation and accepted level of physical activity on Franz Josef Land (in Teplitz Bay and Calm Bay) in the years 1899–1931. It employs meteorological measurements taken during four scientific expeditions to the study area. The analysis mainly covered the period October–April, for which the most complete data set is available. For that period of the year, which includes the part of the year with the Franz Josef Land’s coldest air temperatures, the range and nature of changes in meteorological and biometeorological conditions between historical periods and the modern period (1981–2010) were studied. The data analysis revealed that during the three oldest expeditions (which took place in the years 1899–1914), the biometeorological conditions in the study area were more harsh to humans than in the modern period (1981–2010) or similarly harsh. In contrast, during the 1930/1931 expedition, which represents the Early Twentieth CenturyWarming (ETCW), conditions were clearly more favourable (including predicted clothing insulation being 0.3 clo lower and 4.0 °C higher wind chill temperature than conditions observed nowadays)

In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This study has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, were developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given

Assessment and Reproducibility of Quantitative Structure-Activity Relationship Models by the Nonexpert

Model reliability is generally assessed and reported as an intrinsic component of QSAR publications; it can be evaluated using defined quality criteria such as the Organisation for Economic Cooperation and Development (OECD) Principles for the validation of QSARs. However, less emphasis is afforded to the assessment of model reproducibility, particularly by users who may wish to use model outcomes for decision making, but who are not QSAR experts. In this study we identified a range of QSARs in the area of absorption, distribution, metabolism and elimination (ADME) prediction and assessed their adherence to the OECD Principles, as well as investigating their reproducibility by scientists without expertise in QSAR. 85 papers were reviewed, reporting over 80 models for 31 ADME-related endpoints. Of these, 12 models were identified that fulfilled at least four of the five OECD Principles and three of these 12 could be readily reproduced. Published QSAR models should aim to meet a standard level of quality and be clearly communicated, ensuring their reproducibility, to progress the uptake of the models in both research and regulatory landscapes. A pragmatic workflow for implementing published QSAR models and recommendations to modellers, for publishing models with greater usability, are presented herein

Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds Of Toxicological Concern (TTC) for cancer potency

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds

CheS-Mapper - Chemical Space Mapping and Visualization in 3D

Analyzing chemical datasets is a challenging task for scientific researchers in the field of chemoinformatics. It is important, yet difficult to understand the relationship between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects. To that respect, visualization tools can help to better comprehend the underlying correlations. Our recently developed 3D molecular viewer CheS-Mapper (Chemical Space Mapper) divides large datasets into clusters of similar compounds and consequently arranges them in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kind of features, like structural fragments as well as quantitative chemical descriptors. These features can be highlighted within CheS-Mapper, which aids the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. As a final function, the tool can also be used to select and export specific subsets of a given dataset for further analysis

Mechanistically driven identification of novel structural alerts for mitochondrial toxicity

Mitochondrial toxicity is a problem of growing concern in modern society, resulting in several serious adverse health effects including cardiac failure, hepatotoxicity, and neurodegenerative disorders. Adverse outcome pathways provide models for structuring a mechanistic understanding of toxicology, but most existing in silico models for prediction of mitochondrial toxicity do not account for the mechanism of action of potential toxicants. In a recent study by Hallinger et al., the Seahorse respirometric assay provides experimental data relating to the mechanism of action of several mitochondrial toxins. This makes possible, for the first time, the development of structural alerts for mitochondrial toxicity linked to mechanisms. By using a maximal common substructure searcher and Bayesian statistical analysis, we have discovered 11 alerts associated with different mechanisms of action. Eight of these are completely novel. By incorporating the mechanisms into the structural alert, more information can be gained about the molecular initiating events involved and build toward more complete adverse outcome pathways for mitochondrial toxicity.Unilever Clare College, Cambridg

A multi-data set comparison of the vertical structure of temperature variability and change over the Arctic during the past 100 years

We compare the daily, interannual, and decadal variability and trends in the thermal structure of the Arctic troposphere using eight observation-based, vertically resolved data sets, four of which have data prior to 1948. Comparisons on the daily scale between historical reanalysis data and historical upper-air observations were performed for Svalbard for the cold winters 1911/1912 and 1988/1989, the warm winters 1944/1945 and 2005/2006, and the International Geophysical Year 1957/1958. Excellent agreement is found at mid-tropospheric levels. Near the ground and at the tropopause level, however, systematic differences are identified. On the interannual time scale, the correlations between all data sets are high, but there are systematic biases in terms of absolute values as well as discrepancies in the magnitude of the variability. The causes of these differences are discussed. While none of the data sets individually may be suitable for trend analysis, consistent features can be identified from analyzing all data sets together. To illustrate this, we examine trends and 20-year averages for those regions and seasons that exhibit large sea-ice changes and have enough data for comparison. In the summertime Pacific Arctic and the autumn eastern Canadian Arctic, the lower tropospheric temperature anomalies for the recent two decades are higher than in any previous 20-year period. In contrast, mid-tropospheric temperatures of the European Arctic in the wintertime of the 1920s and 1930s may have reached values as high as those of the late 20th and early 21st centuries

The role of a molecular informatics platform to support next generation risk assessment

Chemoinformatics has been successfully employed in safety assessment through various regulatory programs for which information from databases, as well as predictive methodologies including computational methods, are accepted. One example is the European Union Cosmetics Products Regulations, for which Cosmetics Europe (CE) research activities in non-animal methods have been managed by the Long Range Science Strategy (LRSS) program. The vision is to use mechanistic aspects of existing non-animal methods, as well as New Approach Methodologies (NAMs), to demonstrate that safety assessment of chemicals can be performed using a combination of in silico and in vitro data. To this end, ChemTunes•ToxGPS® has been adopted as the foundation of the safety assessment system and provides a platform to integrate data and knowledge, and enable toxicity predictions and safety assessments, relevant to cosmetics industries. The ChemTunes•ToxGPS® platform provides chemical, biological, and safety data based both on experiments and predictions, and an interactive/customizable read-across platform. The safety assessment workflow enables users to compile qualified data sources, quantify their reliabilities, and combine them using a weight of evidence approach based on decision theory. The power of this platform was demonstrated through a use case to perform a safety assessment for Perilla frutescens through the workflows of threshold of toxicological concern (TTC), in silico predictions (QSAR and structural rules) and quantitative read-across (qRAX) assessment for overall safety. The system digitalizes workflows within a knowledge hub, exploiting advanced in silico tools in this age of artificial intelligence. The further design of the system for next generation risk assessment (NGRA) is scientifically guided by interactions between the workgroup and international regulatory entities