QSAR Toolbox Research Articles

Keratose Self-Cross-Linked Wound Dressing for Iron Sequestration in Chronic Wounds.

Chronic wound diseases affect a large part of the world population, and therefore, novel treatments are becoming fundamental. People with chronic wounds show high iron and protease levels due to genetic disorders or other comorbidities. Since it was demonstrated that iron plays an important role in chronic wounds, being responsible for oxidative processes (ROS generation), while metalloproteinases prevent wound healing by literally "eating" the growing skin, it is crucial to design an appropriate wound dressing. In this paper, a novel bioactive dressing for binding iron in chronic wounds has been produced. Wool-derived keratose wound dressing in the form of films has been prepared by casting an aqueous solution of keratoses. These films are water-soluble; therefore, in order to increase their stability, they have been made insoluble through a thermal cross-link treatment. Fourier transform infrared (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analyzer (TGA) analyses clarified the structure and the properties of the keratose wound dressing films. The capability of this new biomaterial in iron sequestration has been investigated by testing the adsorption of Fe3+ by inductively coupled plasma-optical emission spectrometry (ICP-OES). The results suggest that the keratose cross-linked films can adsorb a large amount of iron (about 85% of the average amount usually present in chronic wounds) following pseudo-second-order kinetics and an intraparticle diffusion model, thus opening new perspectives in chronic wound care. Furthermore, the QSAR Toolbox was applied for conducting in silico tests and for predicting the chemical behavior of the C-Ker-film. All of the data suggest that the keratose bioactive dressing can significantly contribute to wound healing by mechanisms such as iron depletion, acting as a radical scavenger, diminishing the proteolytic damage, acting as a substrate in place of skin, and, finally, promoting tissue regeneration.

Advancing the use of new approach methodologies for assessing teratogenicity: Building a tiered approach

Many New Approach Methodologies (NAMs) have been developed for the safety assessment of new ingredients. Research into reproductive toxicity and teratogenicity is a particularly high priority, especially given their mechanistic complexity. Forty-six non-teratogenic and 39 teratogenic chemicals were screened for teratogenic potential using the in silico DART model from the OECD QSAR Toolbox; the devTox quickPredict™ (devTox assay) test and the Zebrafish Embryotoxicity Test (ZET). The sensitivity and specificity were 94.7% and 84.1%, respectively, for the DART tree (83 chemicals), 86.1% and 35.6% for the devTox (81 chemicals) and 77.8% and 76.7% for the ZET (57 chemicals). Fifty-three chemicals were tested in all three assays and when results were combined and based on a “2 out of 3 rule”, the sensitivity and specificity were 96.0% and 71.4%, respectively. The specificity of the devTox assay for a sub-set of 43 chemicals was increased from 26.1% to 82.6% by incorporating human plasma concentrations into the assay interpretation. When all 85 chemicals were assessed in a decision tree approach, there was an excellent predictivity and assay robustness of 90%. In conclusion, all three models exhibited a good sensitivity and specificity, especially when outcomes from all three were combined or used in “2 out of 3” or a tiered decision tree approach. The latter is an interesting predictive approach for evaluating the teratogenic potential of new chemicals. Future investigations will extend the number of chemicals tested, as well as explore ways to refine the results and obtain a robust Integrated Testing Strategy to evaluate teratogenic potential.

Bazı Azo Boyalarının QSAR Yöntemi ve Daphnia Magna ile Akut Toksisite Testi ile İncelenmesi

Azo dyes represent the most important class of textile dyes. Biotransformation of azo dyes can release aromatic amines. It is well known that some aromatic amines have genotoxic and/or carcinogenic properties. In this study, it was aimed to investigate the effects of azo dyes, which are composed of aromatic amines, and are widely used in the textile industry, on the environment using acute water toxicity test. The acute water toxicity test using Daphnia Magna was investigated experimentally and theoretically. The OECD QSAR Toolbox program, which has recently attracted attention in theoretical studies and is also used in international regulations, has been preferred. As a result of the studies, it was seen that the experimental and theoretical results gave parallel results. This study showed that theoretical studies can be substituted for experimental studies in order to reduce cost, time, and animal mortality in acute toxicity tests on Daphnia Magna. In addition, according to the results of this study, it was found by experimental and theoretical methods that Basic Yellow 28, Disperse Blue 291, and Disperse Brown 27-1 azo dye are ecotoxicologically toxic.

The prediction of hydrolysis and biodegradation of Novichoks using in silico toxicology methods

Novichoks constitute a relatively new class of nerve agents of extreme toxicity that we have had the opportunity to experience three times already. After the first case (Salisbury, UK), a public debate about Novichoks began, which resulted in the realisation of the nature of these chemicals. From a social security point of view, examining their properties, especially toxicological and environmental aspects, are crucial. After the CWC (Chemical Warfare Agent) list update, the candidate structures for the Novichoks may be over 10,000 compounds. It would be extremely laborious to conduct experimental research for each. Understanding their environmental persistence and health hazards is an essential national issue. Moreover, due to the high risk posed by contact with hazardous Novichok substances, in silico research was applied to estimate hydrolysis and biodegradation safely. The present study elucidates, using QSAR models, the environmental fate of the Novichoks studied (n = 17). The results indicate that Novichoks released into the environment hydrolyse at various rates, from extremely fast (<1 day) to very slow (more than a year). Furthermore, ultimate biodegradation from weeks to months is expected for most compounds, which classifies them as relatively difficult biodegradable. Applying reliable in silico methods (QSAR Toolbox and EPI Suite) for predicting various parameters is crucial to prepare for the upcoming usage of Novichoks.

An integrated in silico-in vitro investigation to assess the skin sensitization potential of 4-Octylphenol

One of the major challenges in chemical toxicity testing is the possibility to protect human health against adverse effects with non-animal methods. In this paper, 4-Octylphenol (OP) was tested for skin sensitization and immunomodulatory effects using an integrated in silico-in vitro test approach. In silico tools (QSAR TOOLBOX 4.5, ToxTree and VEGA) were used together with several in vitro tests including HaCaT cells (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA and expression of genes TNF, IL1A, IL6 and IL8 by RT- qPCR), RHE model (quantification of IL-6; IL-8; IL-1α and IL-18 by ELISA) and THP-1 activation assay (CD86/CD54 expression and IL-8 release). Additionally, the immunomodulatory effect of OP was investigated using lncRNAs MALAT1 and NEAT1 expression and LPS-induced THP-1 activation (CD86/CD54 expression and IL-8 release). The in silico tools predicted OP as a sensitizer. In vitro tests are also concordant with the in silico prediction. OP increased IL-6 expression (HaCaT cells); IL-18 and IL-8 expressions (RHE model). An irritant potential was also shown by a great expression of IL-1α (RHE model); and increased expression of CD54 marker and IL-8 in THP-1 cells. Immunomodulatory effects of OP were demonstrated by the downregulation of NEAT1, MALAT1 (epigenetic markers), IL6 and IL8; and an increase in LPS-induced CD54 and IL-8 expressions. Overall, results indicate that OP is a skin sensitizer, being positive in three key events of the AOP for skin sensitization, also showing immunomodulatory effects.

The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data.

In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.

Are we ready to combat the ecotoxicity of COVID-19 pharmaceuticals? An in silico aquatic risk assessment

To fight COVID-19 with uncountable medications and bioproducts throughout the world has taken us to another challenge of ecotoxicity. The indiscriminate usage followed by improper disposal of unused antibacterials, antivirals, antimalarials, immunomodulators, angiotensin II receptor blockers, corticosteroids, anthelmintics, anticoagulants etc. can lead us to an unimaginable ecotoxicity in the long run. A series of studies already identified active pharmaceutical ingredients (APIs) of the mentioned therapeutic classes and their metabolites in aquatic bodies as well as in wastewater treatment plants. Therefore, an initial ecotoxicity assessment of the majorly used pharmaceuticals is utmost requirement of the present time. The present in silico risk assessment study is focused on the aquatic toxicity prediction of 81 pharmaceuticals where 77 are most-used pharmaceuticals for COVID-19 throughout the world based on the literature along with one drug nirmatrelvir [PF-07321332] approved for emergency use by US-FDA and three other molecules under clinical trial. The ecotoxicity of the studied compounds were predicted based on the three aquatic species fish, algae and crustaceans employing the highest quality QSAR models available from the literature as well as using ECOSAR and QSAR Toolbox. To compare the toxicity thresholds, we have also used 4 control pharmaceuticals based on the worldwide occurrence from river, lake, STP, WWTPs, influent and effluent followed by high reported aquatic toxicity over the years as per the literature. Based on the statistical comparison, we have proposed top 3 pharmaceuticals used for the COVID-19 most toxic to the aquatic environment. The study will provide confident predictions of aquatic ecotoxicity data related to abundant use of COVID-19 drugs. The major aim of the study is to fill up the aquatic ecotoxicity data gap of major medications used for COVID-19.

In Silico Predictability of Toxicity Parameters Using the OECD QSAR Toolbox of Some Components of Cannabis sativa

AbstractIn this paper, the quantitative structure‐activity relationship predictive models (QSAR) are presented: the prediction model for some of the main components of cannabis sativa, acute toxicity LD50 (n=45, r2=0.917, q2=0.902), chronic toxicity LOAEL (n=35, r2=0.911, q2=0.868), NOAEL(n=22, r2=0.964, q2=0.937), LOEL (n=26, r2=0.911, q2=0.863) and NOEL (n=25, r2=0.867, q2=0.802), in addition to carcinogenicity and mutagenicity effects. Only for the case of LD50, the values reported in the literature of the delta 9‐THC compound present in Cannabis sativa were taken into account and this model was applied to the other compounds present in the plant, which we consider the most relevant since they fell from the allowed domain for QSAR Toolbox to predict the LD50 of each of them, while the other additional endpoints were established according to the methodology that marks the QSAR Toolbox software. The application of these models and predictions allowed us to predict the permissible daily exposure (PDE) and acceptable daily intake (ADI) values of some of the cannabis components. This work aims to present important elements of the toxicology of some of the components of the plant that serve only as a reference and does not intend to favor or inhibit the use of cannabis for medicinal or recreational use.

Application of OECD QSAR Toolbox software for predicting the mutagenic effects of chemicals

Introduction. The introduction of new chemical products to the market requires a preliminary assessment of their danger to human health and the environment, including specific and long-term effects. Taking into account the medical and social significance of the consequences of exposure to chemicals with a mutagenic effect on the body, the study of genotoxicity / mutagenicity seems to be extremely relevant. In order to reduce the number of experiments performed on animals, calculation methods are being actively introduced into toxicological practice, in particular OECD QSAR Toolbox software. Therefore, the aim of our research was to study the possibility of using the OECD QSAR Toolbox software to predict the mutagenic effects of chemicals. Material and methods. OECD QSAR Toolbox software version 4.4.1., OECD guidelines on the assessment of the mutagenic effect. Results and discussion. We have developed the “Guidelines for predicting the mutagenic effects of chemicals”, which includes a detailed description and work with the program when predicting the mutagenic effects of chemicals. Based on the analysis of OECD documents on the assessment of the mutagenic potential of chemicals, seven most widely used tests were selected, which allow evaluating the effect on various test objects in the presence / absence of metabolic activation systems. For methods, indicators were selected that must be set to start the calculation in the QSAR Toolbox software. The most suitable databases and profilers were also indentified. Conclusion. Forecasting using the OECD QSAR Toolbox software is a screening method that involves performing several calculations using the results of various experimental studies for analogs, and subsequent expert interpretation of the data obtained.

In silico prediction of persistent, mobile, and toxic pharmaceuticals (PMT): A case study in São Paulo Metropolitan Region, Brazil

Computational modelling (in silico) methods based on quantitative structure-activity relationship ((Q)SAR) models, are powerful tools for the assessment of the potential “persistency, mobility, and toxicity” (PMT) of pharmaceuticals compounds. Moreover, the use of (Q)SAR models, is recommended by European Union’s REACH Regulation. In this context, the aims of this research were estimating, for the first time and based by REACH guidelines, the PMT potentials of 115 most sold pharmaceuticals in São Paulo Metropolitan Region (a megacity with 21 million of Brazilian), through five (Q)SAR updated models, namely: the OPERA QSAR; the VEGA QSAR (Version 1.1.5); the EPI Suite (Version 4.11); the ECOSAR (Version, 2.0); and the QSAR Toolbox (Version 4.5). This study prioritized the in-silico predictions from the OPERA and the VEGA, because both QSARs can generate reliable predictions, i.e., they have detailed information about the applicability domains. In silico predictions were performed considering ten endpoints: (i) Molecular weight (MW); (ii) “STP total removal”: Sewage Treatment Plant; (iii) Octanol-water partition coefficient (KOW); (iv) Predicted ready biodegradability; (v) Soil organic adsorption coefficient (KOC); (vi) “Short-term and long-term ecological assessments”; (vii) “Carcinogenicity”; (viii) “Mutagenicity”; (ix) “Estrogen receptor binding”; (x) “Cramer decision tree”. The main results showed that: (a) These 115 pharmaceuticals cover a wide range of so-called small molecules (range from 100 to 600 MW); (b) In STP, a predicted removal lower than 10 % was found for 76 pharmaceuticals; (c) Additionally, 101 chemicals has low (Log KOW <2.5), or medium sorption potential (2.5< log KOW <4.0); (d) Ultimately, 36 PPCPs were considered “persistent” after a weight-of-evidence assessment. In addiction, 17 among these 36 persistent chemicals, were classified as “very mobile” in water (log KOC <3). Finally, only three among 17 PPCPs, namely ciprofibrate, fluconazole and metoclopramide, exhibited one or more toxic characteristics (described in items vi – x). These results it will be possible to alert about the potential risks arising from the indiscriminate disposal of these PPCPs along the water sources of this Brazilian mega metropolis.

SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization.

Skin sensitization is an important regulatory endpoint associated with allergic contact dermatitis. Recently, several adverse outcome pathway (AOP)-based alternative methods were developed to replace animal testing for evaluating skin sensitizers. The AOP-based assays were further integrated as a two-out-of-three method with good predictivity. However, the acquisition of experimental data is resource-intensive. In contrast, an integrated testing strategy (ITS) capable of maximizing the usage of laboratory data from AOP-based and in silico methods was developed as defined approaches (DAs) to both hazard and potency assessment. There are currently two in silico models, namely Derek Nexus and OECD QSAR Toolbox, evaluated in the OECD Testing Guideline No. 497. Since more advanced machine learning algorithms have been proposed for skin sensitization prediction, it is therefore desirable to evaluate their performance under the ITS framework. This study evaluated the performance of a new ITS DA (ITS-SkinSensPred) adopting a transfer learning-based SkinSensPred model. Results showed that the ITS-SkinSensPred has similar or slightly better performance compared to the other ITS models. SkinSensPred-based ITS is expected to be a promising method for assessing skin sensitization.

How to resolve inconclusive predictions from defined approaches for skin sensitisation in OECD Guideline No. 497

In June 2021 the Organisation for Economic Co-operation and Development published Guideline No. 497 on Defined Approaches for Skin Sensitisation (DASS GL). There are two DAs published, known as the 2o3 and the ITS. The 2o3 uses two concordant results from either the DPRA, KeratinoSens™, or the h-CLAT assays to predict hazard (sensitiser/non-sensitiser). The ITS applies a score to results from the DPRA, the h-CLAT and an in silico model to predict United Nations Globally Harmonized System (GHS) sub-categories (1A/1B/Not Classified). The ITS can use Derek Nexus as the in silico model (known as ITSv1) or use OECD QSAR Toolbox (known as ITSv2).As limitations of the individual in chemico/in vitro assays and in silico predictions are carried through to the DAs, inconclusive predictions are possible for chemicals with results in the borderline range, and chemicals with out of domain results. However, these inconclusive predictions can be resolved by applying a weight of evidence approach.Herein, four case studies are presented, each ‘inconclusive’ for skin sensitisation potential according to both DAs. A weight of evidence approach was applied to each using a robust scientific approach to provide a conclusive prediction, where possible, based on several additional, non-animal lines of evidence.

Application of Defined Approaches for Skin Sensitization to Agrochemical Products

Skin sensitization testing is a regulatory requirement for safety evaluations of pesticides in multiple countries. Globally harmonized test guidelines that include in chemico and in vitro methods reduce animal use, but no single assay is recommended as a complete replacement for animal tests. Defined approaches (DAs) that integrate data from multiple non-animal methods are accepted; however, the methods that comprise them have been evaluated using monoconstituent substances rather than mixtures or formulations. To address this data gap, we tested 27 agrochemical formulations in the direct peptide reactivity assay (DPRA), the KeratinoSens™ assay, and the human cell line activation test (h-CLAT). These data were used as inputs to evaluate three DAs for hazard classification of skin sensitization potential and two DAs for potency categorization. When compared to historical animal results, balanced accuracy for the DAs for predicting in vivo skin sensitization hazard (i.e., sensitizer vs. nonsensitizer) ranged from 56 to 78%. The best performing DA was the “2 out of 3 (2o3)” DA, in which the hazard classification was based on two concordant results from the DPRA, KeratinoSens, or h-CLAT. The KE 3/1 sequential testing strategy (STS), which uses h-CLAT and DPRA results, and the integrated testing strategy (ITSv2), which uses h-CLAT, DPRA, and an in silico hazard prediction from OECD QSAR Toolbox, had balanced accuracies of 56–57% for hazard classification. Of the individual test methods, KeratinoSens had the best performance for predicting in vivo hazard outcomes. Its balanced accuracy of 81% was similar to that of the 2o3 DA (78%). For predicting potency categories defined by the United Nations Globally Harmonized System of Classification and Labelling of Chemicals (GHS), the correct classification rate of the STS was 52% and that of the ITSv2 was 43%. These results demonstrate that non-animal test methods have utility for evaluating the skin sensitization potential of agrochemical formulations as compared to animal reference data. While additional data generation is needed, testing strategies such as DAs anchored to human biology and mechanistic information provide a promising approach for agrochemical formulation testing.

Application of the OECD QSAR Toolbox software for calculating the parameters of acute aquatic toxicity of chemicals

Introduction. The Organization for Economic Cooperation and Development (OECD) has developed the QSAR Toolbox software, which allows predicting the properties of chemicals including (eco)toxic based on the structure of the substance using mathematical statistics methods. The purpose of this work was to study the applicability of the OECD QSAR Toolbox software for calculating the acute aquatic toxicity parameters (LC50 and EC50) of chemicals necessary, for example, to determine the hazard class of chemical products according to GOST 32419-2013 “Classification of chemical products. General requirements” or to prepare a safety data sheet for products. Materials and methods. The OECD QSAR Toolbox software version 4.4.1 (current for August 2021), documents, manuals and webinars of the OECD, the European Chemical Agency (ECHA), the Laboratory of Mathematical Chemistry of the University of Burgas, Bulgaria (the main software developer), articles. Discussion of the results. The OECD QSAR Toolbox software version 4.4.1 allows calculating the acute aquatic toxicity parameters (LC50, EC50) of chemicals using trend analysis and read across, as well as automated and standardized workflows. About 50 chemicals with experimental data of LC50 and EC50 belonging to different hazard classes according to GOST 32419-2013, with different functional groups in the structure of the molecule, were selected for testing. Calculated values of LC50 and EC50 of chemicals were compared with the experimental data. Conclusion. The OECD QSAR Toolbox software version 4.1.1 can be successfully used to calculate the acute toxicity parameters LC50, Pimephales promelas, 96 h; LC50 (EC50), Actinopterygii, 96 h and LC50, Daphnia magna, 48 h for a wide range of organic compounds, but is not applicable for inorganic substances, organometallic compounds, polymer molecules, chemicals containing metal ions.

Assessment of uncertainty and credibility of predictions by the OECD QSAR Toolbox automated read-across workflow for predicting acute oral toxicity

The platform of OECD Toolbox version 4.5 was used for building an automated decision tree for filling data gaps for rat acute oral toxicity (AOT) by read-across (RA). Our previous publications have described the workflow of the AOT tree and conducted verification and validation studies on it. The overall uncertainty in the AOT workflow is low as the similarity in mechanistic probability, metabolism and 2D structure are maximized in the RA analogue selection process. The endpoint, rat oral LD50, is well-defined and has universal regulatory acceptance. Since OECD test guidelines are followed in generating the database, the data are widely recognized to be of the highest quality. The credibility of the workflow is high as it meets the critical factors of being based on confirmed assumptions, having demonstrated concordance and consistency, permitting the ability to explain AOT-related mechanisms and modes of action, and being simple in design. Additionally, the Z-score and probability distribution methods of assessing the uncertainty of a particular RA are discussed. Two examples of numerical and classification uncertainty are presented. These cases represent the extremes observed in a series of target chemical-based predictions that the authors observed when testing the workflow. The reliability and relevance associated with the workflow are high. However, the completeness and weights-of-evidence varied markedly among possible RA scenarios and particular target substances.

EDTox: an R Shiny application to predict the endocrine disruption potential of compounds.

PurposeEndocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new classifiers based on new available toxicogenomic data. This functionality helps users to explore the ED potential of chemicals in new, untested exposure scenarios.Availability and implementationThis tool is available as web application (www.edtox.fi) and stand-alone software on GitHub and Zenodo (https://doi.org/10.5281/zenodo.5817093).Supplementary information Supplementary data are available at Bioinformatics online.

The QSAR Toolbox automated read-across workflow for predicting acute oral toxicity: II. Verification and validation

As modules in the QSAR Toolbox become automated, operator control is diminished. Therefore, it becomes more crucial that evidence be provided that reveals the software meets the specification intended by the developers (i.e., verification) and the resulting software meets the user's expectations (i.e., validation). Recently an automated computerised decision tree for acquiring the most appropriate data for rat acute oral toxicity by read-across was published. The present study examines the dependability of this automated workflow as implemented in the Toolbox Version 4.5, mainly the logic behind its courses of action and the reliability of its predictions. These factors can impact the acceptance of a prediction from the module. The automated workflow offers a systematic means of transparently reporting read-across predictions. Protein-binding alerts and acute oral toxicity-specific alerts, or lack of, are fundamental to identifying the correct scenario from the fifteen possibilities, either with or without considering metabolic simulation. Reducing the primary group of possible source analogues to the best analogues by the systematic use of structure-based profilers is fundamental to identifying the correct potency. The verification demonstrates the automatic workflow meets the expectations of the developers. The external validation establishes the automated system will likely meet the user's requirements.

In silico assessment of genotoxicity. Combinations of sensitive structural alerts minimize false negative predictions for all genotoxicity endpoints and can single out chemicals for which experimentation can be avoided

Genotoxicity assessment of chemicals has a crucial role in most regulations. Due to labor, time, cost, and animal welfare issues, attention is being given to (Q)SAR methods. A strategic application of alternative methods is to first use a sequence of conservative (very sensitive) (Q)SARs and/or in vitro models to arrive at the conclusion that no further testing is necessary for negatives, and to use mechanistically based, Weight-Of-Evidence approach to evaluate the chemicals showing positive results. The ICH M7 guideline to detect DNA-reactive impurities in drugs follows these lines (recommending solely (Q)SAR in step 1). However, ICH M7 focuses only on Ames test. Here a large database of more than 6000 chemicals positive in at least one endpoint (in vitro gene mutations or chromosomal aberrations, in vivo micronucleus, aneugenicity) were analyzed with structural alerts implemented in the OECD QSAR Toolbox, resulting in maximum 3% false negatives. These promising results indicate that it may be possible to extend the approach to the whole range of genotoxicity endpoints required by regulations. Since structural alerts may generate false positives, cautious follow-up of positives is recommended (with e.g., statistically based QSARs, read across of similar chemicals, expert judgement, and experimentation when necessary).

Integrating publicly available information to screen potential candidates for chemical prioritization under the Toxic Substances Control Act: A proof of concept case study using genotoxicity and carcinogenicity

The Toxic Substances Control Act (TSCA) became law in the U.S. in 1976 and was amended in 2016. The amended law requires the U.S. EPA to perform risk-based evaluations of existing chemicals. Here, we developed a tiered approach to screen potential candidates based on their genotoxicity and carcinogenicity information to inform the selection of candidate chemicals for prioritization under TSCA. The approach was underpinned by a large database of carcinogenicity and genotoxicity information that had been compiled from various public sources. Carcinogenicity data included weight-of-evidence human carcinogenicity evaluations and animal cancer data. Genotoxicity data included bacterial gene mutation data from the Salmonella (Ames) and Escherichia coli WP2 assays and chromosomal mutation (clastogenicity) data. Additionally, Ames and clastogenicity outcomes were predicted using the alert schemes within the OECD QSAR Toolbox and the Toxicity Estimation Software Tool (TEST).The evaluation workflows for carcinogenicity and genotoxicity were developed along with associated scoring schemes to make an overall outcome determination. For this case study, two sets of chemicals, the TSCA Active Inventory non-confidential portion list available on the EPA CompTox Chemicals Dashboard (33,364 chemicals, ‘TSCA Active List’) and a representative proof-of-concept (POC) set of 238 chemicals were profiled through the two workflows to make determinations of carcinogenicity and genotoxicity potential. Of the 33,364 substances on the ‘TSCA Active List’, overall calls could be made for 20,371 substances. Here 46.67%% (9507) of substances were non-genotoxic, 0.5% (103) were scored as inconclusive, 43.93% (8949) were predicted genotoxic and 8.9% (1812) were genotoxic. Overall calls for genotoxicity could be made for 225 of the 238 POC chemicals. Of these, 40.44% (91) were non-genotoxic, 2.67% (6) were inconclusive, 6.22% (14) were predicted genotoxic, and 50.67% (114) genotoxic. The approach shows promise as a means to identify potential candidates for prioritization from a genotoxicity and carcinogenicity perspective.

Automated read-across workflow for predicting acute oral toxicity: I. The decision scheme in the QSAR toolbox

A decision-scheme outlining the steps for identifying the appropriate chemical category and subsequently appropriate tested source analog(s) for data gap filling of a target chemical by read-across is described. The primary features used in the grouping of the target chemical with source analogues within a database of 10,039 discrete organic substances include reactivity mechanisms associated with protein interactions and specific-acute-oral-toxicity-related mechanisms (e.g., mitochondrial uncoupling). Additionally, the grouping of chemicals making use of the in vivo rat metabolic simulator and neutral hydrolysis. Subsequently, a series of structure-based profilers are used to narrow the group to the most similar analogues. The scheme is implemented in the OECD QSAR Toolbox, so it automatically predicts acute oral toxicity as the rat oral LD50 value in log [1/mol/kg]. It was demonstrated that due to the inherent variability in experimental data, classification distribution should be employed as more adequate in comparison to the exact classification. It was proved that the predictions falling in the adjacent GSH categories to the experimentally-stated ones are acceptable given the variation in experimental data. The model performance estimated by adjacent accuracy was found to be 0.89 and 0.54 while based on R2. The mechanistic and predictive coverages were >0.85.