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A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use of interlaboratory comparisons or round-robins (RRs), in which a common test material and standard operating procedure is provided to all participants, who measure the specific endpoint and return their data for statistical comparison to demonstrate the reproducibility of the method. While there is currently no standard approach for the comparison of modelling approaches, consensus modelling is emerging as a “modelling equivalent” of a RR. We demonstrate here a novel approach to evaluate the performance of different models for the same endpoint (nanomaterials’ zeta potential) trained using a common dataset, through generation of a consensus model, leading to increased confidence in the model predictions and underlying models. Using a publicly available dataset, four research groups (NovaMechanics Ltd. (NovaM)-Cyprus, National Technical University of Athens (NTUA)-Greece, QSAR Lab Ltd.-Poland, and DTC Lab-India) built five distinct machine learning (ML) models for the in silico prediction of the zeta potential of metal and metal oxide-nanomaterials (NMs) in aqueous media. The individual models were integrated into a consensus modelling scheme, enhancing their predictive accuracy and reducing their biases. The consensus models outperform the individual models, resulting in more reliable predictions. We propose this approach as a valuable method for increasing the validity of nanoinformatics models and driving regulatory acceptance of in silico new approach methodologies for the use within an “Integrated Approach to Testing and Assessment” (IATA) for risk assessment of NMs.

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure-activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50 mg/kg/day. These values are in good agreement with findings fromstudies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118 mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment. [ABSTRACT FROM AUTHOR]

This article aims to provide a comprehensive critical, yet readable, review of general interest to the chemistry community on molecular similarity as applied to chemical informatics and predictive modeling with a special focus on read-across (RA) and read-across structure–activity relationships (RASAR). Molecular similarity-based computational tools, such as quantitative structure–activity relationships (QSARs) and RA, are routinely used to fill the data gaps for a wide range of properties including toxicity endpoints for regulatory purposes. This review will explore the background of RA starting from how structural information has been used through to how other similarity contexts such as physicochemical, absorption, distribution, metabolism, and elimination (ADME) properties, and biological aspects are being characterized. More recent developments of RA's integration with QSAR have resulted in the emergence of novel models such as ToxRead, generalized read-across (GenRA), and quantitative RASAR (q-RASAR). Conventional QSAR techniques have been excluded from this review except where necessary for context. [ABSTRACT FROM AUTHOR]

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure–toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs. [ABSTRACT FROM AUTHOR]

Case studies are needed to demonstrate the use of human‐relevant New Approach Methodologies in cosmetics ingredient safety assessments. For read‐across assessments, it is crucial to compare the target chemical with the most appropriate analog; therefore, reliable analog selection should consider physicochemical properties, bioavailability, metabolism, as well as the bioactivity of potential analogs. To complement in vitro bioactivity assays, we evaluated the suitability of three potential analogs for the UV filters, homosalate and octisalate, according to their in vitro ADME properties. We describe how technical aspects of conducting assays for these highly lipophilic chemicals were addressed and interpreted. There were several properties that were common to all five chemicals: they all had similar stability in gastrointestinal fluids (in which no hydrolysis to salicylic occurred); were not substrates of the P‐glycoprotein efflux transporter; were highly protein bound; and were hydrolyzed to salicylic acid (which was also a major metabolite). The main properties differentiating the chemicals were their permeability in Caco‐2 cells, plasma stability, clearance in hepatic models, and the extent of hydrolysis to salicylic acid. Cyclohexyl salicylate, octisalate, and homosalate were identified suitable analogs for each other, whereas butyloctyl salicylate exhibited ADME properties that were markedly different, indicating it is unsuitable. Isoamyl salicylate can be a suitable analog with interpretation for octisalate. In conclusion, in vitro ADME properties of five chemicals were measured and used to pair target and potential analogs. This study demonstrates the importance of robust ADME data for the selection of analogs in a read‐across safety assessment. We evaluated the suitability of three potential analogues homosalate and octisalate (UV filters), according to their in vitro ADME properties. Technical aspects of testing these highly lipophilic chemicals were addressed. Main differentiating properties were plasma stability, hepatic clearance, and hydrolysis to salicylic acid. Cyclohexyl salicylate, octisalate, and homosalate were identified suitable analogues for each other, whereas butyloctyl salicylate exhibited ADME properties that were markedly different, as a unsuitable analogue. Isoamyl salicylate can be a suitable analogue with interpretation for octisalate. [ABSTRACT FROM AUTHOR]

Introduction: We performed an exposure-based Next Generation Risk Assessment case read-across study using New Approach Methodologies (NAMs) to determine the highest safe concentration of daidzein in a body lotion, based on its similarities with its structural analogue, genistein. Two assumptions were: (1) daidzein is a new chemical and its dietary intake omitted; (2) only in vitro data were used for daidzein, while in vitro and legacy in vivo data for genistein were considered. Methods: The 10-step tiered approach evaluating systemic toxicity included toxicokinetics NAMs: PBPK models and in vitro biokinetics measurements in cells used for toxicogenomics and toxicodynamic NAMs: pharmacology profiling (i.e., interaction with molecular targets), toxicogenomics and EATS assays (endocrine disruption endpoints). Whole body rat and human PBPK models were used to convert external doses of genistein to plasma concentrations and in vitro Points of Departure (PoD) to external doses. The PBPK human dermal module was refined using in vitro human skin metabolism and penetration data. Results: The most relevant endpoint for daidzein was from the ERa assay (Lowest Observed Effective Concentration was 100 ± 0.0 nM), which was converted to an in vitro PoD of 33 nM. After application of a safety factor of 3.3 for intra-individual variability, the safe concentration of daidzein was estimated to be 10 nM. This was extrapolated to an external dose of 0.5 µg/cm2 for a body lotion and face cream, equating to a concentration of 0.1%. Discussion: When in vitro PoD of 33 nM for daidzein was converted to an external oral dose in rats, the value correlated with the in vivo NOAEL. This increased confidence that the rat oral PBPK model provided accurate estimates of internal and external exposure and that the in vitro PoD was relevant in the safety assessment of both chemicals. When plasma concentrations estimated from applications of 0.1% and 0.02% daidzein were used to calculate bioactivity exposure ratios, values were >1, indicating a good margin between exposure and concentrations causing adverse effects. In conclusion, this case study highlights the use of NAMs in a 10-step tiered workflow to conclude that the highest safe concentration of daidzein in a body lotion is 0.1%. [ABSTRACT FROM AUTHOR]

Substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs) are commonly found in the environment. However, assessing their human toxicological risk is challenging due to their variable composition and many constituents. Metal naphthenate salts are one such category of UVCBs that are the reaction products of naphthenic acids with metals to form complex mixtures. Metal naphthenates are often found or used in household and industrial materials with potential for human exposure, but very few of these materials have been evaluated for causing human health hazards. Herein, we evaluate metal naphthenates using predictions derived from read-across and quantitative structure–activity/property relationship (QSAR/QSPR) models. Accordingly, we first built a computational chemistry library by enumerating the structures of naphthenic acids and derived 11,850 QSAR-acceptable structures; then, we used open and commercial in silico tools on these structures to predict a set of physicochemical properties and toxicity endpoints. We then compared the QSAR/QSPR predictions with available experimental data on naphthenic acids to provide a more complete picture of the contributions of the components to the toxicity profiles of metal naphthenate mixtures. The available systematic acute oral toxicity values (LD50) and QSAR LD50 predictions of all the naphthenic acid components indicated low concern for toxicity. The point of departure predictions for chronic repeated dose toxicity for the naphthenic acid components using QSAR models developed from studies on rats ranged from 25 to 50 mg/kg/day. These values are in good agreement with findings from studies on copper and zinc naphthenates, which had no observed adverse effect levels of 30 and 118 mg/kg/day, respectively. Hence, this study demonstrates how published in silico approaches can be used to identify the potential components of metal naphthenates for further testing, inform groupings of UVCBs such as naphthenates, as well as fill the data gaps using read-across and QSAR models to inform risk assessment.

Environmental context: Pesticides are critical to agriculture and food production but require ecological risk assessments. Although most risk assessments require data from vertebrate animal testing, we have developed an approach to assess risk to fish, birds and mammals using other means. This approach could help to ensure protection of the environment while minimising animal testing. Rationale: Recent directives to reduce animal testing have implications for ecological risk assessment, as several vertebrate tests are used to support these assessments. Therefore, a modern approach was devised to address these key knowledge needs without the use of chemical-specific vertebrate testing. Methodology: An ecological risk assessment for a novel acetyl-coenzyme A carboxylase (ACCase) inhibitor herbicide was conducted using alternative lines of evidence. For fish, chemical toxicity distributions were constructed to quantify the probability of effects, and these distributions were compared with exposure estimates for a representative use in soybeans. The effect distributions were further refined based on invertebrate toxicity and partitioning behaviour. For birds and mammals, a joint probability curve was constructed by integrating chemical toxicity distributions and Kenaga exposure distributions. Results: The lines of evidence presented in this predictive risk assessment suggest the intended use of a new ACCase inhibitor is unlikely to affect fish, birds, or mammals. Exposure was unlikely to exceed effect estimates, regardless of whether they were derived based on chemical-read across, invertebrate toxicity, or partitioning behaviour. Discussion: Key knowledge needs for ecological risk assessment can be informed by lines of evidence that do not require animal testing. The present study demonstrates such an approach by comparing predicted exposure and effects, which are expected to be protective. This predictive approach can be extended to other active ingredients and chemical classes, as well as other taxonomic groups of interest. Future research should aim to integrate new approach methods in a predictive risk assessment framework. Environmental context. Pesticides are critical to agriculture and food production but require ecological risk assessments. Although most risk assessments require data from vertebrate animal testing, we have developed an approach to assess risk to fish, birds and mammals using other means. This approach could help to ensure protection of the environment while minimising animal testing. This article belongs to the collection NAMs in Environmental Chemistry and Toxicology. [ABSTRACT FROM AUTHOR]

The application of various in‐silico‐based approaches for the prediction of various properties of materials has been an effective alternative to experimental methods. Recently, the concepts of Quantitative structure‐property relationship (QSPR) and read‐across (RA) methods were merged to develop a new emerging chemoinformatic tool: read‐across structure‐property relationship (RASPR). The RASPR method can be applicable to both large and small datasets as it uses various similarity and error‐based measures. It has also been observed that RASPR models tend to have an increased external predictivity compared to the corresponding QSPR models. In this study, we have modeled the power conversion efficiency (PCE) of organic dyes used in dye‐sensitized solar cells (DSSCs) by using the quantitative RASPR (q‐RASPR) method. We have used relatively larger classes of organic dyes–Phenothiazines (n=207), Porphyrins (n=281), and Triphenylamines (n=229) for the modelling purpose. We have divided each of the datasets into training and test sets in 3 different combinations, and with the training sets we have developed three different QSPR models with structural and physicochemical descriptors and validated them with the corresponding test sets. These corresponding modeled descriptors were used to calculate the RASPR descriptors using a Java‐based tool RASAR Descriptor Calculator v2.0 (https://sites.google.com/jadavpuruniversity.in/dtc‐lab‐software/home), and then data fusion was performed by pooling the previously selected structural and physicochemical descriptors with the calculated RASPR descriptors. Further feature selection algorithm was employed to develop the final RASPR PLS models. Here, we also developed different machine learning (ML) models with the descriptors selected in the QSPR PLS and RASPR PLS models, and it was found that models with RASPR descriptors superseded in external predictivity the models with only structural and physicochemical descriptors: RMSEP reduced for phenothiazines from 1.16–1.25 to 1.07–1.18, for porphyrins from 1.60–1.79 to 1.45–1.53, for triphenylamines from 1.27–1.54 to 1.20–1.47. [ABSTRACT FROM AUTHOR]

Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results ($n{\rm{train}} = 53 - 62,{\rm{ }}{r^2}$ n train = 53 − 62 , r 2 = 0.614 to 0.754, $Q_{{\rm{LOO}}}^2$ Q L O O 2 = 0.501 to 0.703 and ${\rm{ }}Q_{\left({{\rm{F}}1} \right)}^2$ Q F 1 2 = 0.531 to 0.718, $Q_{\left({{\rm{F}}2} \right)}^2 = 0.523 - 0.713$ Q F 2 2 = 0.523 − 0.713), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity ($Q_{\left({{\rm{F}}1} \right)}^2 = 0.595 - 0.813, Q_{\left({{\rm{F}}2} \right)}^2 = 0.573 - 0.809$ Q F 1 2 = 0.595 − 0.813 , Q F 2 2 = 0.573 − 0.809) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides. [ABSTRACT FROM AUTHOR]

Structure-based grouping of chemicals for targeted testing and read-across is an efficient way to reduce resources and animal usage. For substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs), structure-based grouping is virtually impossible. Biology-based approaches such as metabolomics could provide a solution. Here, 15 steam-cracked distillates, registered in the EU through the Lower Olefins Aromatics Reach Consortium (LOA), as well as six of the major substance constituents, were tested in a 14-day rat oral gavage study, in line with the fundamental elements of the OECD 407 guideline, in combination with plasma metabolomics. Beyond signs of clinical toxicity, reduced body weight (gain), and food consumption, pathological investigations demonstrated the liver, thyroid, kidneys (males only), and hematological system to be the target organs. These targets were confirmed by metabolome pattern recognition, with no additional targets being identified. While classical toxicological parameters did not allow for a clear distinction between the substances, univariate and multivariate statistical analysis of the respective metabolomes allowed for the identification of several subclusters of biologically most similar substances. These groups were partly associated with the dominant (> 50%) constituents of these UVCBs, i.e., indene and dicyclopentadiene. Despite minor differences in clustering results based on the two statistical analyses, a proposal can be made for the grouping of these UVCBs. Both analyses correctly clustered the chemically most similar compounds, increasing the confidence that this biological approach may provide a solution for the grouping of UVCBs. [ABSTRACT FROM AUTHOR]

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering − an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

Introduction: We performed an exposure-based Next Generation Risk Assessment case read-across study using New Approach Methodologies (NAMs) to determine the highest safe concentration of daidzein in a body lotion, based on its similarities with its structural analogue, genistein. Two assumptions were: (1) daidzein is a new chemical and its dietary intake omitted; (2) only in vitro data were used for daidzein, while in vitro and legacy in vivo data for genistein were considered.Methods: The 10-step tiered approach evaluating systemic toxicity included toxicokinetics NAMs: PBPK models and in vitro biokinetics measurements in cells used for toxicogenomics and toxicodynamic NAMs: pharmacology profiling (i.e., interaction with molecular targets), toxicogenomics and EATS assays (endocrine disruption endpoints). Whole body rat and human PBPK models were used to convert external doses of genistein to plasma concentrations and in vitro Points of Departure (PoD) to external doses. The PBPK human dermal module was refined using in vitro human skin metabolism and penetration data.Results: The most relevant endpoint for daidzein was from the ERα assay (Lowest Observed Effective Concentration was 100 ± 0.0 nM), which was converted to an in vitro PoD of 33 nM. After application of a safety factor of 3.3 for intra-individual variability, the safe concentration of daidzein was estimated to be 10 nM. This was extrapolated to an external dose of 0.5 μg/cm2 for a body lotion and face cream, equating to a concentration of 0.1%.Discussion: When in vitro PoD of 33 nM for daidzein was converted to an external oral dose in rats, the value correlated with the in vivo NOAEL. This increased confidence that the rat oral PBPK model provided accurate estimates of internal and external exposure and that the in vitro PoD was relevant in the safety assessment of both chemicals. When plasma concentrations estimated from applications of 0.1% and 0.02% daidzein were used to calculate bioactivity exposure ratios, values were >1, indicating a good margin between exposure and concentrations causing adverse effects. In conclusion, this case study highlights the use of NAMs in a 10-step tiered workflow to conclude that the highest safe concentration of daidzein in a body lotion is 0.1%.

The read-across method is a popular data gap filling technique with developed application for multiple purposes, including regulatory. Within the US Environmental Protection Agency's (US EPA) New Chemicals Program under Toxic Substances Control Act (TSCA), read-across has been widely used, as well as within technical guidance published by the Organization for Economic Co-operation and Development, the European Chemicals Agency, and the European Center for Ecotoxicology and Toxicology of Chemicals for filling chemical toxicity data gaps. Under the TSCA New Chemicals Review Program, US EPA is tasked with reviewing proposed new chemical applications prior to commencing commercial manufacturing within or importing into the United States. The primary goal of this review is to identify any unreasonable human health and environmental risks, arising from environmental releases/emissions during manufacturing and the resulting exposure from these environmental releases. The authors propose the application of read-across techniques for the development and use of a framework for estimating the emissions arising during the chemical manufacturing process. This methodology is to utilize available emissions data from a structurally similar analogue chemical or a group of structurally similar chemicals in a chemical family taking into consideration their physicochemical properties under specified chemical process unit operations and conditions. This framework is also designed to apply existing knowledge of read-across principles previously utilized in toxicity estimation for an analogue or category of chemicals and introduced and extended with a concurrent case study.

Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the safety of 41 compounds to provide a Herbal flavour and belonging to different chemical groups, when used as sensory additives in feed for all animal species. Fourteen out of the 41 compounds were tested in tolerance studies in chickens for fattening, piglets, cattle for fattening and Atlantic salmon. No adverse effects were observed in the tolerance studies at 10‐fold the intended level. The Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) concluded that the 14 tested compounds were safe for these species at the proposed use level and conclusions were extrapolated to all animal species. For the remaining 27 compounds, read‐across from structurally similar compounds tested in tolerance trials and belonging to the same chemical group was applied. The FEEDAP Panel concluded that these 27 compounds were safe for all animal species at the proposed use level. No safety concern would arise for the consumer and the environment from the use of the 41 compounds up to the maximum proposed use level in feed. [ABSTRACT FROM AUTHOR]

O6-methylguanine-DNA methyltransferase (MGMT) constitutes an important cellular mechanism for repairing potentially cytotoxic DNA damage induced by guanine O6-alkylating agents and can render cells highly resistant to certain cancer chemotherapeutic drugs. A wide variety of potential MGMT inactivators have been designed and synthesized for the purpose of overcoming MGMT-mediated tumor resistance. We determined the inactivation potency of these compounds against human recombinant MGMT using [3H]-methylated-DNA-based MGMT inactivation assays and calculated the IC50 values. Using the results of 370 compounds, we performed quantitative structure–activity relationship (QSAR) modeling to identify the correlation between the chemical structure and MGMT-inactivating ability. Modeling was based on subdividing the sorted pIC50 values or on chemical structures or was random. A total of nine molecular descriptors were presented in the model equation, in which the mechanistic interpretation indicated that the status of nitrogen atoms, aliphatic primary amino groups, the presence of O-S at topological distance 3, the presence of Al-O-Ar/Ar-O-Ar/R..O..R/R-O-C=X, the ionization potential and hydrogen bond donors are the main factors responsible for inactivation ability. The final model was of high internal robustness, goodness of fit and prediction ability (R2pr = 0.7474, Q2Fn = 0.7375–0.7437, CCCpr = 0.8530). After the best splitting model was decided, we established the full model based on the entire set of compounds using the same descriptor combination. We also used a similarity-based read-across technique to further improve the external predictive ability of the model (R2pr = 0.7528, Q2Fn = 0.7387–0.7449, CCCpr = 0.8560). The prediction quality of 66 true external compounds was checked using the "Prediction Reliability Indicator" tool. In summary, we defined key structural features associated with MGMT inactivation, thus allowing for the design of MGMT inactivators that might improve clinical outcomes in cancer treatment. [ABSTRACT FROM AUTHOR]

In this study we present deimos, a computational methodology for optimal grouping, applied on the read‐across prediction of engineered nanomaterials' (ENMs) toxicity‐related properties. The method is based on the formulation and the solution of a mixed‐integer optimization program (MILP) problem that automatically and simultaneously performs feature selection, defines the grouping boundaries according to the response variable and develops linear regression models in each group. For each group/region, the characteristic centroid is defined in order to allocate untested ENMs to the groups. The deimos MILP problem is integrated in a broader optimization workflow that selects the best performing methodology between the standard multiple linear regression (MLR), the least absolute shrinkage and selection operator (LASSO) models and the proposed deimos multiple‐region model. The performance of the suggested methodology is demonstrated through the application to benchmark ENMs datasets and comparison with other predictive modelling approaches. However, the proposed method can be applied to property prediction of other than ENM chemical entities and it is not limited to ENMs toxicity prediction. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFAS) are synthetic chemicals in widespread use that have been shown to be toxic to wildlife and humans. Human serum albumin (HSA) is a known transport protein that binds PFAS at various sites, leading to bioaccumulation and long-term toxicity. In silico tools like quantitative structure-activity relationship (QSAR), read-across, and quantitative read-across structure-property relationship (q-RASPR) are proven techniques for modeling chemical toxicity based on experimental data which can be used to predict the toxicity of untested and new chemicals, while at the same time, help to identify the major features responsible for toxicity. Classification-based and regression-based QSAR models are employed in the present study to predict the binding affinities of 24 PFAS to HSA. Regression-based QSAR models revealed that the packing density index (PDI) and quantitative estimation of drug-likeness (QED) descriptors were both positively correlated with higher binding affinity, while the classification-based QSAR model showed the average connectivity index of order 4 (X4A) descriptor was inversely correlated with binding affinity. Whereas molecular docking studies suggested that PFAS with the highest binding affinity to HSA create hydrogen bonds with Arg348 and salt bridges with Arg348 and Arg485, PFAS with lower binding affinity either showed no interactions with either amino acid or only interactions with Arg348. Among the studied PFAS, perfluoroalkyl acids (PFAA) with large carbon chain length (>C10) have one of the lowest binding affinities, compared to PFAA with carbon chain length ranging from 7 to 9, which showed the highest affinity to HSA. Generalized Read-Across (GenRA) was used to predict toxicity outcomes for the top five highest binding affinity PFAS based on 10 structural analogs for each and found that all are predicted as being chronic to sub-chronically toxic to HSA. The developed in silico models presented in this work can provide a framework for designing PFAS alternatives, screening compounds currently in use, and for the study of PFAS mixture toxicity, which is an area of intense research. [ABSTRACT FROM AUTHOR]

Human cell-based test methods can be used to evaluate potential hazards of mixtures and products of petroleum refining ("unknown or variable composition, complex reaction products, or biological materials" substances, UVCBs). Analyses of bioactivity and detailed chemical characterization of petroleum UVCBs were used separately for grouping these substances; a combination of the approaches has not been undertaken. Therefore, we used a case example of representative high production volume categories of petroleum UVCBs, 25 lower olefin substances from low benzene naphtha and resin oils categories, to determine whether existing manufacturing-based category grouping can be supported. We collected two types of data: nontarget ion mobility spectrometry-mass spectrometry of both neat substances and their organic extracts and in vitro bioactivity of the organic extracts in five human cell types: umbilical vein endothelial cells and induced pluripotent stem cell-derived hepatocytes, endothelial cells, neurons, and cardiomyocytes. We found that while similarity in composition and bioactivity can be observed for some substances, existing categories are largely heterogeneous. Strong relationships between composition and bioactivity were observed, and individual constituents that determine these associations were identified. Overall, this study showed a promising approach that combines chemical composition and bioactivity data to better characterize the variability within manufacturing categories of petroleum UVCBs. [ABSTRACT FROM AUTHOR]

The neurotransmitter acetylcholine (ACh) plays a ubiquitous role in cognitive functions including learning and memory with widespread innervation in the cortex, subcortical structures, and the cerebellum. Cholinergic receptors, transporters, or enzymes associated with many neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), are potential imaging targets. In the present study, we have developed 2D-quantitative structure–activity relationship (2D-QSAR) models for 19 positron emission tomography (PET) imaging agents targeted against presynaptic vesicular acetylcholine transporter (VAChT). VAChT assists in the transport of ACh into the presynaptic storage vesicles, and it becomes one of the main targets for the diagnosis of various neurodegenerative diseases. In our work, we aimed to understand the important structural features of the PET imaging agents required for their binding with VAChT. This was done by feature selection using a Genetic Algorithm followed by the Best Subset Selection method and developing a Partial Least Squares- based 2D-QSAR model using the best feature combination. The developed QSAR model showed significant statistical performance and reliability. Using the features selected in the 2D-QSAR analysis, we have also performed similarity-based chemical read-across predictions and obtained encouraging external validation statistics. Further, we have also performed molecular docking analysis to understand the molecular interactions occurring between the PET imaging agents and the VAChT receptor. The molecular docking results were correlated with the QSAR features for a better understanding of the molecular interactions. This research serves to fulfill the experimental data gap, highlighting the applicability of computational methods in the PET imaging agents' binding affinity prediction. [ABSTRACT FROM AUTHOR]

Abstract Background The EU-project GRACIOUS developed an Integrated Approach to Testing and Assessment (IATA) to support grouping high aspect ratio nanomaterials (HARNs) presenting a similar inhalation hazard. Application of grouping reduces the need to assess toxicity on a case-by-case basis and supports read-across of hazard data from substances that have the data required for risk assessment (source) to those that lack such data (target). The HARN IATA, based on the fibre paradigm for pathogenic fibres, facilitates structured data gathering to propose groups of similar HARN and to support read-across by prompting users to address relevant questions regarding HARN morphology, biopersistence and inflammatory potential. The IATA is structured in tiers, allowing grouping decisions to be made using simple in vitro or in silico methods in Tier1 progressing to in vivo approaches at the highest Tier3. Here we present a case-study testing the applicability of GRACIOUS IATA to form an evidence-based group of multiwalled carbon nanotubes (MWCNT) posing a similar predicted fibre-hazard, to support read-across and reduce the burden of toxicity testing. Results The case-study uses data on 15 different MWCNT, obtained from the published literature. By following the IATA, a group of 2 MWCNT was identified (NRCWE006 and NM-401) based on a high degree of similarity. A pairwise similarity assessment was subsequently conducted between the grouped MWCNT to evaluate the potential to conduct read-across and fill data gaps required for regulatory hazard assessment. The similarity assessment, based on expert judgement of Tier 1 assay results, predicts both MWCNT are likely to cause a similar acute in vivo hazard. This result supports the possibility for read-across of sub-chronic and chronic hazard endpoint data for lung fibrosis and carcinogenicity between the 2 grouped MWCNT. The implications of accepting the similarity assessment based on expert judgement of the MWCNT group are considered to stimulate future discussion on the level of similarity between group members considered sufficient to allow regulatory acceptance of a read-across argument. Conclusion This proof-of-concept case-study demonstrates how a grouping hypothesis and IATA may be used to support a nuanced and evidence-based grouping of ‘similar’ MWCNT and the subsequent interpolation of data between group members to streamline the hazard assessment process.

The reduction and replacement of in vivo tests have become crucial in terms of resources and animal benefits. The read-across approach reduces the number of substances to be tested, exploiting existing experimental data to predict the properties of untested substances. Currently, several tools have been developed to perform read-across, but other approaches, such as computational workflows, can offer a more flexible and less prescriptive approach. In this paper, we are introducing a workflow to support analogue identification for read-across. The implementation of the workflow was performed using a database of azole chemicals with in vitro toxicity data for human aromatase enzymes. The workflow identified analogues based on three similarities: structural similarity (StrS), metabolic similarity (MtS), and mechanistic similarity (McS). Our results showed how multiple similarity metrics can be combined within a read-across assessment. The use of the similarity based on metabolism and toxicological mechanism improved the predictions in particular for sensitivity. Beyond the results predicting a large population of substances, practical examples illustrate the advantages of the proposed approach. [ABSTRACT FROM AUTHOR]

Hazardous aromatic compounds have a high probability of entering the surrounding environment, posing a threat to humans and other habitats. It is, thus, necessary to estimate the toxicity of these chemicals as a preventive measure before being marketed. The present study involves the development of in silico-based predictive 2D-QSTR models using a PLS regression approach for the exploration of the structural features responsible for the toxicity of T. pyriformis using simple and easily interpretable 2D descriptors employing a dataset containing 892 chemicals. The developed model was extensively validated by evaluating the model's reliability and predictability using internationally accepted external and internal validation metrics. We have also used the "Intelligent Consensus Predictor" tool and Read-Across software, which shows better results for test set compounds as compared with individual PLS models. We have also validated the models using a set of 383 external set compounds which are not used for the development of models. The results suggested that molecules with aliphatic aldehyde groups are much more toxic to the protozoan whereas chemicals containing C–N fragments at the topological distance 3 & 4, polar and alcoholic groups are less toxic to the protozoan. In conclusion, the developed QSTR and Read-Across models might be extremely beneficial as guides for researchers to estimate the toxicity profile of novel compounds against T. pyriformis. [Display omitted] [ABSTRACT FROM AUTHOR]

The purpose of this study was to use chemical similarity evaluations, transcriptional profiling, in vitro toxicokinetic data, and physiologically based pharmacokinetic (PBPK) models to support read-across for a series of branched carboxylic acids using valproic acid (VPA), a known developmental toxicant, as a comparator. The chemicals included 2-propylpentanoic acid (VPA), 2-ethylbutanoic acid, 2-ethylhexanoic acid (EHA), 2-methylnonanoic acid, 2-hexyldecanoic acid, 2-propylnonanoic acid (PNA), dipentyl acetic acid or 2-pentylheptanoic acid, octanoic acid (a straight chain alkyl acid), and 2-ethylhexanol. Transcriptomics was evaluated in 4 cell types (A549, HepG2, MCF7, and iCell cardiomyocytes) 6 h after exposure to 3 concentrations of the compounds, using the L1000 platform. The transcriptional profiling data indicate that 2- or 3-carbon alkyl substituents at the alpha position of the carboxylic acid (EHA and PNA) elicit a transcriptional profile similar to the one elicited by VPA. The transcriptional profile is different for the other chemicals tested, which provides support for limiting read-across from VPA to much shorter and longer acids. Molecular docking models for histone deacetylases, the putative target of VPA, provide a possible mechanistic explanation for the activity cliff elucidated by transcriptomics. In vitro toxicokinetic data were utilized in a PBPK model to estimate internal dosimetry. The PBPK modeling data show that as the branched chain increases, predicted plasma Cmax decreases. This work demonstrates how transcriptomics and other mode of action-based methods can improve read-across. [ABSTRACT FROM AUTHOR]

Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the safety of 27 compounds to provide a milky‐vanilla flavour belonging to different chemical groups, when used as sensory additives in feed for all animal species. Fifteen of the 27 compounds were tested in tolerance studies in chickens for fattening, piglets and cattle for fattening. No adverse effects were observed in the tolerance studies at 10‐fold the intended level. The Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) concluded that the 15 tested compounds were safe for these species at the proposed use level and conclusions were extrapolated to all animal species. For the remaining 12 compounds, read‐across from structurally similar compounds tested in tolerance trials and belonging to the same chemical group was applied. The FEEDAP Panel concluded that these 12 compounds were safe for all animal species at the proposed use level. No safety concern would arise for the consumer from the use of the 27 compounds up to the highest levels considered safe for target animals. No new data were submitted on the safety for the user that would allow the FEEDAP Panel to change its previous conclusion for 5‐methylhept‐2‐en‐4‐one [07.139], 5‐methylfurfural [13.001] and 4‐phenylbut‐3‐en‐2‐one [07.024]. The concentrations considered safe for the target species are unlikely to have detrimental effects on the environment for all the compounds. [ABSTRACT FROM AUTHOR]

Soil invertebrates serve as an outstanding biological indicator of the terrestrial ecosystem and overall soil quality, considering their high sensitivity when compared to other indicators of soil quality. In this study, the available soil ecotoxicity data (pEC50) against the soil invertebrate Folsomia candida (C. name: Springtail) (n = 45) were collated from the database of ECOTOX (cfpub.epa.gov/ecotox) and subjected to QSAR analysis using 2D descriptors. Four partial least squares (PLS) models were built based on the features selected through genertic algorithm followed by the best subset selection. These four models were then used as inputs for Intelligent Consensus Predictor version 1.2 (PLS version) to get the final consensus predictions, using the best selection of predictions (compound-wise) from four "qualified" individual models. Both internal and external validations metrics of the consensus predictions are well- balanced and within the acceptable range as per the OECD criteria. The consensus model was found to be better than the previous developed models for this endpoint. Predictions were also made using the Chemical Read-across approach, which showed even better external validation metric values than the consensus predictions. From the selected features in the QSAR models, it has been found out that molecular weight and presence of a di-thiophosphate group, electron donor groups, and polyhalogen substitutions have a significant impact on the soil ecotoxicity. The soil ecotoxicological risk assessment of organic chemicals can therefore be prioritized by these features. The models developed from diverse structural organic compounds can be applied to any new query compound for data gap filling. [ABSTRACT FROM AUTHOR]

Read-across, an alternative approach for hazard assessment, has been widely adopted when in vivo data are unavailable for chemicals of interest. Read-across is enabled via in silico tools such as quantitative structure activity relationship (QSAR) modeling. In this study, the current status of structure activity relationship (SAR)-based read-across applications in the Republic of Korea (ROK) was examined considering both chemical risk assessments and chemical registrations from different sectors, including regulatory agencies, industry, and academia. From the regulatory perspective, the Ministry of Environment (MOE) established the Act on Registration and Evaluation of Chemicals (AREC) in 2019 to enable registrants to submit alternative data such as information from read-across instead of in vivo data to support hazard assessment and determine chemical-specific risks. Further, the Ministry of Food and Drug Safety (MFDS) began to consider read-across approaches for establishing acceptable intake (AI) limits of impurities occurring during pharmaceutical manufacturing processes under the ICH M7 guideline. Although read-across has its advantages, this approach also has limitations including (1) lack of standardized criteria for regulatory acceptance, (2) inconsistencies in the robustness of scientific evidence, and (3) deficiencies in the objective reliability of read-across data. The application and acceptance rate of read-across may vary among regulatory agencies. Therefore, sufficient data need to be prepared to verify the hypothesis that structural similarities might lead to similarities in properties of substances (between source and target chemicals) prior to adopting a read-across approach. In some cases, additional tests may be required during the registration process to clarify long-term effects on human health or the environment for certain substances that are data deficient. To improve the quality of read-across data for regulatory acceptance, cooperative efforts from regulatory agencies, academia, and industry are needed to minimize limitations of read-across applications. [ABSTRACT FROM AUTHOR]

Dimethyltin dichloride is used as the putative toxophore for dimethyltin bis-alkylthio esters in a read-across approach. Recent chemical and toxicological investigations challenges this read across as data on dioctyltin bis(2-ethylhexyl thioglycolate) and dibutyltin bis(2-ethylhexyl thioglycolate) showed the dialkyltin thioglycolates do not generate dialkyltin dichloride. Results obtained by 119Sn-NMR spectroscopy demonstrated that dimethyltin bis(2-ethylhexyl thioglycolate), the smallest commercially manufactured dialkyltin thioester molecule of this kind, hydrolyzed to dimethyltin chloro-(2-ethylhexyl) thioglycolate under simulated gastric conditions. These studies did not detect dimethyltin dichloride. Dimethyltin bis(2-ethylhexyl thioglycolate) was administered orally to timed-pregnant Wistar-Han rats in an Arachis oil vehicle at 5, 10, and 25 mg/kg/day [Gestation Day 6 (GD6) through GD20] with no maternal deaths observed. At 25 mg/kg/day treatment statistically significant reductions occurred in feed consumption (−9%), maternal body weight (−2.4%) and adjusted maternal weight gain (−68%). There were no adverse gestational findings. Maternal thymus weight was significantly reduced in rats at 25 mg/kg in the absence of changes in hormone levels of T3, T4 or TSH. There were no effects on fetal growth, no dose-dependent pattern of external, visceral, or skeletal malformations and no toxicologically relevant increase in anatomical variations at any dose group. Based on the obtained experimental data it is concluded that dimethyltin bis(2-ethylhexyl thioglycolate) forms dimethyltin chloro-(2-ethylhexyl thioglycolate), not dimethyltin dichloride, in the stomach environment at pH 1.2, and dimethyltin bis(2-ethylhexyl thioglycolate) was not teratogenic nor fetotoxic in rats. The maternal NOAEL was 10 mg/kg/day, and the developmental NOAEL was 25 mg/kg/day, the high dose. The maternal LOAEL was 25 mg/kg/day based on decreased food consumption, lower adjusted mean body weight gain and reduced maternal thymus weight.

The objective of this work was to use transcriptional profiling to assess the biological activity of structurally related chemicals to define their biological similarity and with that, substantiate the validity of a read-across approach usable in risk assessment. Two case studies are presented, one with 4 short alkyl chain parabens: methyl (MP), ethyl (EP), butyl (BP), and propylparaben (PP), as well as their main metabolite, p-hydroxybenzoic acid (pHBA) with the assumption that propylparaben was the target chemical; and a second one with caffeine and its main metabolites theophylline, theobromine and paraxanthine where CA was the target chemical. The comprehensive transcriptional response of MCF7, HepG2, A549 and ICell cardiomyocytes was evaluated (TempO-Seq) after exposure to vehicle-control, each paraben or pHBA, CA or its metabolites, at 3 non-cytotoxic concentrations, for 6 h. Differentially expressed genes (FDR ≥0.05, and fold change ±1.2≥) were identified for each chemical, at each concentration, and used to determine similarities. Each of the chemicals is able to elicit changes in the expression of a number of genes, as compared to controls. Importantly, the transcriptional profile elicited by each of the parabens shares a high degree of similarity across the group. The highest number of genes commonly affected was between butylparaben and PP. The transcriptional profile of the parabens is similar to the one elicited by estrogen receptor agonists, with BP being the closest structural and biological analogue for PP. In the CA case, the transcriptional profile elicited of all four methylxanthines had a high degree of similarity across the cell types, with CA and theophylline being the most active. The most robust response was obtained in the cardiomyocytes with the highest transcriptional profile similarity between CA and TP. The transcriptional profile of the methylxanthines is similar to the one elicited by inhibitors of phosphatidylinositol 3-kinase as well as other kinase inhibitors. Overall, our results support the approach of incorporating transcriptional profiling in well-designed in vitro tests as one robust stream of data to support biological similarity driven read-across procedures and strengthening the traditional structure-based approaches useful in risk assessment.

Read-across applies the principle of similarity to identify the most similar substances to represent a given target substance in data-poor situations. However, differences between the target and the source substances exist. The present study aims to screen and assess the effect of the key components in a molecule which may escape the evaluation for read-across based only on the most similar substance(s) using a new open-access software: Virtual Extensive Read-Across (VERA). VERA provides a means to assess similarity between chemicals using structural alerts specific to the property, pre-defined molecular groups and structural similarity. The software finds the most similar compounds with a certain feature, e.g., structural alerts and molecular groups, and provides clusters of similar substances while comparing these similar substances within different clusters. Carcinogenicity is a complex endpoint with several mechanisms, requiring resource intensive experimental bioassays and a large number of animals; as such, the use of read-across as part of new approach methodologies would support carcinogenicity assessment. To test the VERA software, carcinogenicity was selected as the endpoint of interest for a range of botanicals. VERA correctly labelled 70% of the botanicals, indicating the most similar substances and the main features associated with carcinogenicity. [ABSTRACT FROM AUTHOR]

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 5.9 million deaths documented so far since the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. The current research aims to identify potential anti-SARS-CoV-2 drugs using a computational approach and repurpose them if possible. In the present study, we have collected a set of 44 FDA-approved drugs of different classes from a previously published literature with their potential antiviral activity against COVID-19. We have employed both regression- and classification-based quantitative structure–activity relationship (QSAR) modeling to identify critical chemical features essential for anticoronaviral activity. Multiple models with the consensus algorithm were employed for the regression-based approach to improve the predictions. Additionally, we have employed a machine learning-based read-across approach using Read-Across-v3.1 available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home and linear discriminant analysis for the efficient prediction of potential drug candidate for COVID-19. Finally, the quantitative prediction ability of different modeling approaches was compared using the sum of ranking differences (SRD). Furthermore, we have predicted a true external set of 98 pharmaceuticals using the developed models for their probable anti-COVID activity and their prediction reliability was checked employing the "Prediction Reliability Indicator" tool available from https://dtclab.webs.com/software-tools. Though the present study does not target any protein of viral interaction, the modeling approaches developed can be helpful for identifying or screening potential anti-coronaviral drug candidates. [ABSTRACT FROM AUTHOR]

Quantitative structure–activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure–activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability. [ABSTRACT FROM AUTHOR]

Human cell-based test methods can be used to evaluate potential hazards of mixtures and products of petroleum refining (“unknown or variable composition, complex reaction products, or biological materials” substances, UVCBs). Analyses of bioactivity and detailed chemical characterization of petroleum UVCBs were used separately for grouping these substances; a combination of the approaches has not been undertaken. Therefore, we used a case example of representative high production volume categories of petroleum UVCBs, 25 lower olefin substances from low benzene naphtha and resin oils categories, to determine whether existing manufacturing-based category grouping can be supported. We collected two types of data: nontarget ion mobility spectrometry-mass spectrometry of both neat substances and their organic extracts and in vitro bioactivity of the organic extracts in five human cell types: umbilical vein endothelial cells and induced pluripotent stem cell-derived hepatocytes, endothelial cells, neurons, and cardiomyocytes. We found that while similarity in composition and bioactivity can be observed for some substances, existing categories are largely heterogeneous. Strong relationships between composition and bioactivity were observed, and individual constituents that determine these associations were identified. Overall, this study showed a promising approach that combines chemical composition and bioactivity data to better characterize the variability within manufacturing categories of petroleum UVCBs.

In this study, we utilized an innovative quantitative read-across (RA) structure-activity relationship (q-RASAR) approach to predict the bioconcentration factor (BCF) values of a diverse range of organic compounds, based on a dataset of 575 compounds tested using Organisation for Economic Co-operation and Development Test Guideline 305 for bioaccumulation in fish. Initially, we constructed the q-RASAR model using the partial least squares regression method, yielding promising statistical results for the training set (R2 =0.71, Q2 LOO =0.68, mean absolute error [MAE] training =0.54). The model was further validated using the test set (Q2 F1 =0.77, Q2 F2 =0.75, MAE test =0.51). Subsequently, we explored the q-RASAR method using other regression-based supervised machine-learning algorithms, demonstrating favourable results for the training and test sets. All models exhibited R2 and Q2 F1 values exceeding 0.7, Q2 LOO values greater than 0.6, and low MAE values, indicating high model quality and predictive capability for new, unidentified chemical substances. These findings represent the significance of the RASAR method in enhancing predictivity for new unknown chemicals due to the incorporation of similarity functions in the RASAR descriptors, independent of a specific algorithm. [Display omitted] • We have used the q-RASAR method for the prediction of the BCF values of organic chemicals. • The bioaccumulation data in fish tested according to OECD Test Guideline 305 were used. • The models were satisfactorily experimentally validated using an external set of compounds. [ABSTRACT FROM AUTHOR]

The migration of primary aromatic amines (PAAs) from food contact materials raises significant public health concerns. In this study, the migration levels of 26 PAAs were analyzed in 242 nylon cooking utensils using ultra-performance liquid chromatography-tandem mass spectrometry. A total of 18 PAAs were detected, of which 14 were quantified, with 4,4′-diaminodiphenylmethane (4,4′-MDA) and aniline being the most prevalent ones. The compliance rates for nylon kitchenware were similar under both legislation of European Commission (76.9%) and Chinese legislation (77.9%). Probabilistic non-carcinogenic and carcinogenic risk assessment were conducted using Monte Carlo simulation, with read-across approach applied to fill the gap of toxicity data. The hazard quotients for 18 PAAs were calculated, revealing that 17 PAAs (excluding 4,4′-MDA) had acceptable hazard quotients (<1). Lifetime cancer risks for 17 PAAs were determined, with 15 PAAs (excluding benzidine and 4,4′-MDA) showing acceptable cancer risks (<10−4). The study suggests that the non-carcinogenic and carcinogenic health risks posed by PAAs migrating from FCMs can be effectively mitigated by promptly identifying non-compliant products and reducing exposure to high-risk PAAs such as 4,4′-MDA and benzidine. Enhancing the understanding of PAA hazard characterization and implementing measures to minimize health risks associated with PAA migration from FCMs is hence recommended. [Display omitted] • Migration levels of 26 PAAs determined in 242 nylon cooking utensils using a UPLC-MS/MS system. • Probabilistic health risks for PAAs were assessed via Monte Carlo simulation with read-across approaches adopted. • Non-carcinogenic and carcinogenic health risks of PAAs could be controlled by limiting the exposure to high-concern PAAs. [ABSTRACT FROM AUTHOR]

Quantitative Structure-Activity Relationship (QSAR) analysis greatly enhances the development and research of pesticides. This study employed Multiple Linear Regression (MLR), machine learning (ML), and read-across (RA) approaches to investigate the combined effects of binary mixtures of fungicides on Macrophomina phaseolina. Using the Fixed Ratio Ray Design (FRRD) method, 75 binary mixtures of six frequently used fungicides were generated, with many exhibiting additive interactions as indicated by the Concentration Addition (CA) and Independent Action (IA) models. The QSAR analysis revealed that Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models were the most effective, outperforming the Least Squares Kernel (LSK), MLR, and RA methods. SVR achieved an outstanding R2 of 0.95 and Q 2 LMO of 0.81, whereas GPR demonstrated values of 0.93 and 0.81 for the same metrics. Internal and external validation confirmed the reliability and generalizability of these models, suggesting they could be applied to a wider array of data. Moreover, Molecular Dynamics (MD) simulations showed that the effects of the fungicides are linked to physiological mechanisms rather than intermolecular interactions within their formulations. This study establishes a robust framework for creating potent fungicide combinations that improve disease management efficacy while promoting environmental sustainability and reducing the chemical load to mitigate negative impacts. [Display omitted] • A total of 75 binary mixtures were evaluated for their combined fungicidal effectiveness against M. phaseolina. • ML-based QSAR models were developed to predict the fungicidal activity. • The SVR and GPR models demonstrated optimal performance, achieving R2 values of 0.95 and 0.93, respectively. • Molecular Dynamics simulations were performed to elucidate the molecular interactions involved. • These models can be used to create more effective fungicide mixtures at reduced dosages. [ABSTRACT FROM AUTHOR]

Background: The EU-project GRACIOUS developed an Integrated Approach to Testing and Assessment (IATA) to support grouping high aspect ratio nanomaterials (HARNs) presenting a similar inhalation hazard. Application of grouping reduces the need to assess toxicity on a case-by-case basis and supports read-across of hazard data from substances that have the data required for risk assessment (source) to those that lack such data (target). The HARN IATA, based on the fibre paradigm for pathogenic fibres, facilitates structured data gathering to propose groups of similar HARN and to support read-across by prompting users to address relevant questions regarding HARN morphology, biopersistence and inflammatory potential. The IATA is structured in tiers, allowing grouping decisions to be made using simple in vitro or in silico methods in Tier1 progressing to in vivo approaches at the highest Tier3. Here we present a case-study testing the applicability of GRACIOUS IATA to form an evidence-based group of multiwalled carbon nanotubes (MWCNT) posing a similar predicted fibre-hazard, to support read-across and reduce the burden of toxicity testing. Results: The case-study uses data on 15 different MWCNT, obtained from the published literature. By following the IATA, a group of 2 MWCNT was identified (NRCWE006 and NM-401) based on a high degree of similarity. A pairwise similarity assessment was subsequently conducted between the grouped MWCNT to evaluate the potential to conduct read-across and fill data gaps required for regulatory hazard assessment. The similarity assessment, based on expert judgement of Tier 1 assay results, predicts both MWCNT are likely to cause a similar acute in vivo hazard. This result supports the possibility for read-across of sub-chronic and chronic hazard endpoint data for lung fibrosis and carcinogenicity between the 2 grouped MWCNT. The implications of accepting the similarity assessment based on expert judgement of the MWCNT group are considered to stimulate future discussion on the level of similarity between group members considered sufficient to allow regulatory acceptance of a read-across argument. Conclusion: This proof-of-concept case-study demonstrates how a grouping hypothesis and IATA may be used to support a nuanced and evidence-based grouping of 'similar' MWCNT and the subsequent interpolation of data between group members to streamline the hazard assessment process. [ABSTRACT FROM AUTHOR]

The demand for nutrients and new technologies has increased with population growth. The agro-technological revolution with metal oxide engineered nanoparticles (MeOx ENPs) has the potential to reform the resilient agricultural system while maintaining the security of food. When utilized extensively, MeOx ENPs may have unintended toxicological effects on both target and non-targeted species. Since limited information about nanopesticides' pernicious effects is available, in silico modeling can be done to explore these issues. Hence, in the present work, we have applied computational modeling to explore the influence of metal oxide nanoparticles on the toxicity of bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells to bridge the data gap relating to the toxicity of MeOx NPs. Initially, partial least squares (PLS) regression models were developed applying the Small Dataset Modeler software () using four datasets having effective concentration (EC50%) as the endpoints and employing only periodic table descriptors. To further explore the predictions, we applied a read-across approach using the descriptors selected in the QSAR models. Also, the inter-endpoint cytotoxicity relationship modeling (quantitative toxicity-toxicity relationship or QTTR) was conducted. It was found that the result obtained by nano-read-across provided a similar level of accuracy as provided by QSAR. The information derived from the PLS models of both the cell lines suggested that metal cation formation, and bond-forming capacity influence the toxicity whereas the presence of metal has an influential impact on the ecotoxicological effects. Thus, it is feasible to design safe nanopesticides that could be more effective than conventional analogs. [ABSTRACT FROM AUTHOR]

Following a request from the European Commission, the EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) was asked to deliver a scientific opinion on the supplementary information submitted on the safety of 37 compounds belonging to different chemical groups, when used as sensory additives (flavourings) in feed for all animal species formerly assessed by the Panel in the context of the re‐evaluation of these feed additives. The FEEDAP Panel concludes that ethyl oleate [09.192] and benzyl cinnamate [09.738] are safe at the proposed use level of 5 mg/kg complete feed for all animal species, the consumer and the environment; ethyl salicylate [09.748] is safe up to the maximum proposed use level of 5 mg/kg complete feed for all animal species and the consumer. No new data were submitted on the safety for the user that would allow the FEEDAP Panel to change its previous conclusion for 26 out of the 37 compounds under assessment. The use of 4‐terpinenol [02.072], linalyl butyrate [09.050], linalyl formate [09.080], linalyl propionate [09.130], linalyl isobutyrate [09.423], isopulegol [02.167] and 1,2‐dimethoxy‐4‐(prop‐1‐enyl)‐benzene [04.013] as flavouring additives at the proposed use level of 5 mg/kg in feed for all animal species is considered safe for the environment. The use of 3‐methyl‐2‐cyclopenten‐1‐one [07.112] at 0.5 mg/kg and methyl dihydrojasmonate [09.520] at 5 mg/kg in feed for all animal species except marine animals is considered safe for the environment. [ABSTRACT FROM AUTHOR]

The aim of this paper was to investigate the current status of read-across approaches in the Republic of Korea, Japan, and China in terms of applications and regulatory acceptance. In the Republic of Korea, over the last 6 years, approximately 8% of safety data records used for chemical registrations were based upon read-across, and a guideline published on the use of read-across results in 2017. In Japan, read-across is generally accepted for screening hazard classification of toxicological endpoints according to the Chemical Substances Control Law (CSCL). In China, read-across data, along with data from other animal alternatives are accepted as a data source for chemical registrations, but could be only considered when testing is not technically feasible. At present, read-across is not widely used for chemical registrations and regulatory acceptance of read-across may differ among countries in Asia. With consideration of the advantages and limitations of read-across, it is expected that read-across may soon gradually be employed in Asian countries. Thus, regulatory agencies need to prepare for this progression. [ABSTRACT FROM AUTHOR]

Abstract Risk assessment of newly synthesised chemicals is a prerequisite for regulatory approval. In this context, in silico methods have great potential to reduce time, cost, and ultimately animal testing as they make use of the ever-growing amount of available toxicity data. Here, KnowTox is presented, a novel pipeline that combines three different in silico toxicology approaches to allow for confident prediction of potentially toxic effects of query compounds, i.e. machine learning models for 88 endpoints, alerts for 919 toxic substructures, and computational support for read-across. It is mainly based on the ToxCast dataset, containing after preprocessing a sparse matrix of 7912 compounds tested against 985 endpoints. When applying machine learning models, applicability and reliability of predictions for new chemicals are of utmost importance. Therefore, first, the conformal prediction technique was deployed, comprising an additional calibration step and per definition creating internally valid predictors at a given significance level. Second, to further improve validity and information efficiency, two adaptations are suggested, exemplified at the androgen receptor antagonism endpoint. An absolute increase in validity of 23% on the in-house dataset of 534 compounds could be achieved by introducing KNNRegressor normalisation. This increase in validity comes at the cost of efficiency, which could again be improved by 20% for the initial ToxCast model by balancing the dataset during model training. Finally, the value of the developed pipeline for risk assessment is discussed using two in-house triazole molecules. Compared to a single toxicity prediction method, complementing the outputs of different approaches can have a higher impact on guiding toxicity testing and de-selecting most likely harmful development-candidate compounds early in the development process.

The glucocorticosteroid, or glucocorticoid (GC), system is largely conserved across vertebrates and plays a central role in numerous vital physiological processes including bone development, immunomodulation, and modification of glucose metabolism and the induction of stress-related behaviours. As a result of their wide-ranging actions, synthetic GCs are widely prescribed for numerous human and veterinary therapeutic purposes and consequently have been detected extensively within the aquatic environment. Synthetic GCs designed for humans are pharmacologically active in non-mammalian vertebrates, including fish, however they are generally detected in surface waters at low (ng/L) concentrations. In this review, we assess the potential environmental risk of synthetic GCs to fish by comparing available experimental data and effect levels in fish with those in mammals. We found the majority of compounds were predicted to have insignificant risk to fish, however some compounds were predicted to be of moderate and high risk to fish, although the dataset of compounds used for this analysis was small. Given the common mode of action and high level of inter-species target conservation exhibited amongst the GCs, we also give due consideration to the potential for mixture effects, which may be particularly significant when considering the potential for environmental impact from this class of pharmaceuticals. Finally, we also provide recommendations for further research to more fully understand the potential environmental impact of this relatively understudied group of commonly prescribed human and veterinary drugs.

Background: An Integrated Testing and Assessment Strategy (ITS) for aquatic toxicity of 16 thiochemicals to be registered under REACH revealed 12 data gaps, which had to be filled by experimental data. These test results are now available and offer the unique opportunity to subject previous estimates obtained by read-across (analogue and category approaches) to an external validation. The case study thiochemicals are so-called difficult substances due to instability and poor water solubility, challenging established ITS. Results: The new experimental data confirm the previous predictions of acute aquatic toxicity with the new test results indicating a 2–5 times lower toxicity than previously predicted. The previous predictions thus are conservative and close to the new experimental results. The good agreement can be attributed to the fact that we had limited the extrapolations to narrow chemical groups with similar SH-group reactivities. The new experimental data further strengthen and externally validate the existing trends based on similarity in chemical structures, mode of action (MoA), water solubility and stability of source and target compounds in aquatic media. Based on the new experimental data, reliable revised PNECs could be derived and the REACH requirements for these thiochemicals are largely fulfilled. Appropriately adapted ITS are therefore able to reduce in vivo tests with fish even for difficult substances and replace them with alternative information. Conclusions: Both experimental and alternative information for difficult substances such as thiochemicals that are rapidly transformed in water are subject to considerable uncertainty. For example, the use of either nominal, initial or time-weighted average concentrations contributes to the variability of aquatic toxicity data. In the case of these thiochemicals, a weight-of-evidence (WoE) approach to determining aquatic toxicity based on nominal and time-weighted average concentrations may be the most appropriate choice to reflect environmental conditions. Overall, uncertainties in historical test results and alternative information, here from read-across, have to be considered in relation to how much uncertainty is acceptable for environmental protection on the one hand and how much certainty is technically feasible on the other. [ABSTRACT FROM AUTHOR]