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Read-across is an alternative or supplementary method to the use of test results (including test results from tests employing laboratory animals) for chemical hazard assessment. Its use by both the industrial and the regulatory communities has increased markedly over the past decade. A seminal issue for the regulatory acceptance of read-across predictions is reducing uncertainty in the prediction by evaluating similarity among the chemicals participating in the read-across. To assist in demonstrating and documenting this similarity, a “category consistency” functionality has been added to the OECD QSAR Toolbox. This module (semi-) automatically collects and displays information key to establishing physicochemical, structural and mechanistic similarities, as well as any experimental data associated with the endpoint under consideration. The Toolbox user can then employ this suite of information to draw a conclusion on the consistency of the recorded similarity measurements of the category members. The report writer provides documentation of the Toolbox information supporting the conclusion. The implementation of “category consistency” functionality is illustrated with a case example.

The Read-Across Assessment Framework (RAAF) was developed by the European Chemicals Agency (ECHA) as an internal tool providing a framework for a consistent, structured and transparent assessment of grouping of chemicals and read-across. Following a RAAF-based evaluation, also developers and users of read-across predictions outside ECHA can judge whether their read-across rationale is sufficiently robust from a regulatory perspective. The aim of this paper is to describe the implementation of RAAF functionalities in the OECD QSAR Toolbox report. These can be activated in the prediction report after performing a readacross prediction. Once the user manually selects the appropriate scenario, the RAAF assessment elements appear and are automatically aligned with the suitable category elements of the Toolbox report. Subsequently, these are evaluated as part of the category consistency assessment functionality. The implementation of the RAAF functionality is illustrated in practice with two examples.

Toxicological understanding and computational possibilities progress every year. The OECD QSAR Toolbox, an in silico system for predicting toxicity of chemicals, is regularly updated with up to data knowledge and functionalities. This expansion has made the Toolbox increasingly complex and difficult to use for non-experts. Recent activities addressed this issue and enhanced the user-friendliness of the system. In a streamlining exercise, we developed automated and standardized workflows for acute aquatic toxicity and skin sensitization. These endpoints have been chosen based on understanding of the mechanisms of action, availability of experimental data and regulatory relevance. The workflows are designed to produce rational and reliable predictions, or no prediction if acceptance criteria are not met. The automated workflow is a predefined algorithm that generates a prediction without further interaction with the user after activation. The standardised workflow uses a similar algorithm but leaves to the user the key choices among a selection of suitable options. In the present work, we described in detail the algorithms for both the automated and standardized workflows. Using examples, the rationale behind each step of the two processes is explained.

Both industry and regulatory agencies use structure-activity relationships and read-across as toxicity assessment methods alternative to animal experimentation. A prerequisite for acceptance of the outcome of such in silico predictions is establishing the mechanistic similarity (i.e. a common mode of (toxicological) action) among the target substance(s) and the source substance(s). To assist in assessing this similarity, an “Alert performance” functionality has been added to the OECD QSAR Toolbox Version 4.0. This functionality is designed to provide information for evaluating the mechanistic and/or structural similarity among the analogues forming a category. This “Alert performance” informs the user on whether the identified alert(s) in the target substance or its metabolites is linked to consistent effects for the endpoint of interest for other substances triggering the same alert and for which experimental data are available. In this respect, calculation of alert performance shows the predictivity of a given alert to the selected endpoint. The predictivity of alerts is context-dependent and is affected by the structure of target chemical, targeted endpoint, selected databases, data usage and data scale. The use of alerts with high performances will lead to the formation of categories of chemicals showing similar toxicological effects, thus reducing the uncertainty when filling data gaps. This paper illustrates the workflow of the alert performance module and addresses several questions that could arise during its applications.

Use of high-throughput, in vitro bioactivity data in setting a point-of-departure (POD) has the potential to accelerate the pace of human health safety evaluation by informing screening-level assessments. The primary objective of this work was to compare PODs based on high-throughput predictions of bioactivity, exposure predictions, and traditional hazard information for 448 chemicals. PODs derived from new approach methodologies (NAMs) were obtained for this comparison using the 50th (PODNAM, 50) and the 95th (PODNAM, 95) percentile credible interval estimates for the steady-state plasma concentration used in in vitro to in vivo extrapolation of administered equivalent doses. Of the 448 substances, 89% had a PODNAM, 95 that was less than the traditional POD (PODtraditional) value. For the 48 substances for which PODtraditional < PODNAM, 95, the PODNAM and PODtraditional were typically within a factor of 10 of each other, and there was an enrichment of chemical structural features associated with organophosphate and carbamate insecticides. When PODtraditional < PODNAM, 95, it did not appear to result from an enrichment of PODtraditional based on a particular study type (eg, developmental, reproductive, and chronic studies). Bioactivity:exposure ratios, useful for identification of substances with potential priority, demonstrated that high-throughput exposure predictions were greater than the PODNAM, 95 for 11 substances. When compared with threshold of toxicological concern (TTC) values, the PODNAM, 95 was greater than the corresponding TTC value 90% of the time. This work demonstrates the feasibility, and continuing challenges, of using in vitro bioactivity as a protective estimate of POD in screening-level assessments via a case study.

The REACH Regulation requires information on acute oral toxicity for substances produced or imported in quantities greater than one ton per year. When registering, animal testing should be used as last resort. The standard acute oral toxicity test requires use of animals. Therefore, the European Chemicals Agency examined whether alternative ways exist to generate information on acute oral toxicity. The starting hypothesis was that low acute oral toxicity can be predicted from the results of low toxicity in oral sub-acute toxicity studies. Proving this hypothesis would allow avoiding acute toxicity oral testing whenever a sub-acute oral toxicity study is required or available and indicates low toxicity. ECHA conducted an analysis of the REACH database and found suitable studies on both acute oral and sub-acute oral toxicities for 1,256 substances. 415 of these substances had low toxicity in the sub-acute toxicity study (i.e., NO(A)EL at or above the limit test threshold of 1,000 mg/kg). For 98% of these substances, low acute oral toxicity was also reported (i.e., LD50 above the classification threshold of 2,000 mg/kg). On the other hand, no correlation was found between lower NO(A)ELs and LD50. According to the REACH Regulation, this approach for predicting acute oral toxicity needs to be considered as part of a weight of evidence analysis. Therefore, additional sources of information to support this approach are presented. Ahead of the last REACH registration deadline, in 2018, ECHA estimates that registrants of about 550 substances can omit the in vivo acute oral toxicity study by using this adaptation.

REACH is a regulation of the European Union adopted to improve the safe use of chemicals with regard to human health and the environment. The safe use of chemicals can be achieved only if the hazard and the exposure of the substances are well characterized. Testing on animals has been traditionally the main tool for hazard assessment. For ethical and economic reasons, alternative ways of testing that do not use laboratory animals have been developed by different parties (regulatory agencies, researchers, industry) over the recent decades, and their proper use in hazard assessment is encouraged under REACH. In this chapter, we describe how (Q)SAR models and predictions are included into REACH and their adequate use promoted by the European Chemicals Agency (ECHA).

REACH is a regulation of the European Union adopted to improve the safe use of chemicals with regard to human health and the environment. The safe use of chemicals can be achieved only if the hazard and the exposure of the substances are well characterized. Testing on animals has been traditionally the main tool for hazard assessment. For ethical and economic reasons, alternative ways of testing that do not use laboratory animals have been developed by different parties (regulatory agencies, researchers, industry) over the recent decades, and their proper use in hazard assessment is encouraged under REACH. In this chapter, we describe how (Q)SAR models and predictions are included into REACH and their adequate use promoted by the European Chemicals Agency (ECHA).

Metabolism is a critical aspect of toxicokinetics. It is crucial to take it into account when evaluating similarity among the chemicals participating in a read-across prediction. Lack of considerations on metabolism represents a major source of uncertainty in a read-across justification. To better assist users in applying metabolic considerations during data gap filling, the metabolism/transformations functionality in the OECD QSAR Toolbox has been upgraded in version 4. Analyses of metabolic similarity are specific to a toxic endpoint and dependent on data availability. By using optimized features, the metabolic simulators available in Toolbox can predict the generation of possible metabolites and take them into account for read-across. In the traditional downstream approach, metabolic information in the Toolbox is used to identify the formation of (a)biotic products of known toxicity and read-across their toxicity to the parent chemical. By a stepwise protocol the user may explore knowledge embedded in the Toolbox to identify source analogues that have the same pattern of metabolic activation as the target. Furthermore, following the upstream approach, precursors of the chemicals could be also searched in the Toolbox databases. Five scenarios, highlighting the applications of the metabolism in chemical assessment within the Toolbox, are illustrated.

The CORAL (CORrelation And Logic) software can be used to build up the quantitative structure–property/activity relationships (QSPR/QSAR) with optimal descriptors calculated with the simplified molecular input line entry system. We used the CORAL to build up QSAR for toxicity of psychotropic drugs (phenothiazines, antidepressants, and anxiolitics). The acute toxicity data of drugs to mouse (oral LD50, mg/kg) were taken in the literature. These values were converted into pLD50, i.e., negative decimal logarithm of the LD50 expressed in mmol/kg. The statistical quality of the models for “invisible” validation set (compounds which are not involved in building up model) is quite good.

The views expressed in this article are solely those of the authors, and the content of the article does not represent an official position of the European Chemicals Agency.

The bioconcentration factor (BCF) is an important bioaccumulation hazard assessment metric in many regulatory contexts. Its assessment is required by the REACH regulation (Registration, Evaluation, Authorization and Restriction of Chemicals) and by CLP (Classification, Labeling and Packaging). We challenged nine well-known and widely used BCF QSAR models against 851 compounds stored in an ad-hoc created database. The goodness of the regression analysis was assessed by considering the determination coefficient (R(2)) and the Root Mean Square Error (RMSE); Cooper's statistics and Matthew's Correlation Coefficient (MCC) were calculated for all the thresholds relevant for regulatory purposes (i.e. 100L/kg for Chemical Safety Assessment; 500L/kg for Classification and Labeling; 2000 and 5000L/kg for Persistent, Bioaccumulative and Toxic (PBT) and very Persistent, very Bioaccumulative (vPvB) assessment) to assess the classification, with particular attention to the models' ability to control the occurrence of false negatives. As a first step, statistical analysis was performed for the predictions of the entire dataset; R(2)>0.70 was obtained using CORAL, T.E.S.T. and EPISuite Arnot-Gobas models. As classifiers, ACD and logP-based equations were the best in terms of sensitivity, ranging from 0.75 to 0.94. External compound predictions were carried out for the models that had their own training sets. CORAL model returned the best performance (R(2)ext=0.59), followed by the EPISuite Meylan model (R(2)ext=0.58). The latter gave also the highest sensitivity on external compounds with values from 0.55 to 0.85, depending on the thresholds. Statistics were also compiled for compounds falling into the models Applicability Domain (AD), giving better performances. In this respect, VEGA CAESAR was the best model in terms of regression (R(2)=0.94) and classification (average sensitivity>0.80). This model also showed the best regression (R(2)=0.85) and sensitivity (average>0.70) for new compounds in the AD but not present in the training set. However, no single optimal model exists and, thus, it would be wise a case-by-case assessment. Yet, integrating the wealth of information from multiple models remains the winner approach.

REACH, the most ambitious chemical legislation in the world, provides unprecedented opportunities for medicinal chemists. Companies must report (eco)toxicological information about their chemicals, disseminated to the public domain by the European Chemicals Agency after their registration. The availability of this wealth of new toxicological data, together with the promotion of REACH of in silico methods, appoints medicinal chemists to a leading role in the regulatory hazard assessment process. In fact, Quantitative Structure-Activity Relation ship (QSAR) models and predictive toxicology have been applied in drug design and development for decades. Here, we discuss toxicological endpoints and areas where further development is needed to provide an enlightened appraisal of this attractive new opportunity.

Summary The REACH (Registration, Evaluation, Authorization and restriction of Chemicals) and BPR (Biocidal Product Regulation) regulations strongly promote the use of non-animal testing techniques to evaluate chemical risk. This has renewed the interest towards alternative methods such as QSAR in the regulatory context. The assessment of bioconcentration factor (BCF) required by these regulations is expensive, in terms of costs, time, and laboratory animal sacrifices. Herein, we present QSAR models based on the ANTARES dataset, which is a large collection of known and verified experimental BCF data. Among the models developed, the best results were obtained from a nine-descriptor highly predictive model. This model was derived from a training set of 608 chemicals and challenged against a validation and blind set containing 152 and 76 chemicals, respectively. The model’s robustness was further controlled through several validation strategies and the implementation of a multi-step approach for the applicability domain. Suitable safety margins were used to increase sensitivity. The easy interpretability of the model is ensured by the use of meaningful biokinetics descriptors. The satisfactory predictive power for external compounds suggests that the new models could represent a reliable alternative to the in vivo assay, helping the registrants to fulfill regulatory requirements in compliance with the ethical and economic necessity to reduce animal testing.

Water-solubility is an important physicochemical property in pharmaceutical and environmental studies. We assessed the performance of five predictive computer models: ACD/PhysChem History, ADMET Predictor, T.E.S.T., EPI Suite-WSKOWWIN and EPI Suite-WATERNT; two of them are commercial, the others are free. We used more than 4000 compounds with experimental values to evaluate the models, considering the chemicals inside and outside the applicability domain of the models, those used to build up the model (training set) and those not present in it (prediction set). We also evaluated their ability to predict continuous solubility values, and solubility classes. Overall, considering the whole data set, some models gave a good statistical performance, with R(2) up to 0.88.

Leading QSAR models provide supporting documentation in addition to a predicted toxicological value. Such information enables the toxicologist to explore the properties of chemical substances as well as to review and to increase the reliability of toxicity predictions. This article focuses on the use of this information in practice. We explore the supporting documentation provided by the EPISuite, T.E.S.T. and VEGA platforms when evaluating the bioconcentration factor (BCF) of three example compounds. Each compound presents a different challenge: to recognize high reliability, analyze complex evidence of reliability, and recognize uncertainty. In each case, we first describe and discuss the supporting documentation provided by the QSAR platforms. We then discuss the judgments on reliability across sectors from 28 toxicologists who used this supporting information and commented on the process. The article demonstrates both the use of QSAR models as tools to reduce or replace in vivo testing, and the need for scientific expertise and rigor in their use.

QSAR (Quantitative Structure Activity Relationship) models can be a valuable alternative method to replace or reduce animal test required by REACH. In particular, some endpoints such as bioconcentration factor (BCF) are easier to predict and many useful models have been already developed. In this paper we describe how to integrate two popular BCF models to obtain more reliable predictions. In particular, the herein presented integrated model relies on the predictions of two among the most used BCF models (CAESAR and Meylan), together with the Applicability Domain Index (ADI) provided by the software VEGA. Using a set of simple rules, the integrated model selects the most reliable and conservative predictions and discards possible outliers. In this way, for the prediction of the 851 compounds included in the ANTARES BCF dataset, the integrated model discloses a R(2) (coefficient of determination) of 0.80, a RMSE (Root Mean Square Error) of 0.61 log units and a sensitivity of 76%, with a considerable improvement in respect to the CAESAR (R(2)=0.63; RMSE=0.84 log units; sensitivity 55%) and Meylan (R(2)=0.66; RMSE=0.77 log units; sensitivity 65%) without discarding too many predictions (118 out of 851). Importantly, considering solely the compounds within the new integrated ADI, the R(2) increased to 0.92, and the sensitivity to 85%, with a RMSE of 0.44 log units. Finally, the use of properly set safety thresholds applied for monitoring the so called "suspicious" compounds, which are those chemicals predicted in proximity of the border normally accepted to discern non-bioaccumulative from bioaccumulative substances, permitted to obtain an integrated model with sensitivity equal to 100%.