Comparison of Cramer classification between Toxtree, the OECD QSAR Toolbox and expert judgment

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals

BACKGROUND

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data.

OBJECTIVES

As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models.

METHODS

We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays.

RESULTS

QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q² of 0.25-0.45, mean model errors of 0.70-1.11 log₁₀ units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org.

CONCLUSIONS

An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

Derivation of an inhalation TTC for the workplace based on DNEL values reported under REACH

The Threshold of Toxicological Concern (TTC) concept defines a generic tolerable exposure for chemicals of unknown toxicity below which the risk of adverse health effects is considered very small. The original concept was refined and extended over the years, based either on differentiated structural classes or on additional information on certain toxicological endpoints. Initially, the focus of the TTC application was only on systemic toxic effects after repeated oral intake and consisted of one value. However, under well-defined boundary conditions, a long-term systemic inhalation TTC could also serve as a cut-off criterion for occupational exposure in those cases where workers are exposed to very low levels of chemicals by inhalation contact and could therefore reduce the need to perform animal tests. Within the scope of the European REACH legislation, several thousand systemic long-term inhalation Derived No Effect Levels (DNELs) for workers have been published. By statistical evaluation of the DNEL distribution of 1876 chemicals and the resulting 99th percentiles, we propose an inhalation workplace TTC for systemic effects in the region of 50 μg/m³ (7 μg/kg body weight/day). Specific exclusion criteria apply for the discussed concept.

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Toxicity assessment of the extractables from multi-layer coextrusion poly ethylene bags exposed to pH=5 solution containing 4% benzyl alcohol and 0.1 M sodium acetate

A non-target analysis was developed for the analysis of extractables from multi-layer coextrusion bags exposed to 4% benzyl alcohol solution and 0.1 M sodium acetate at pH = 5 for defined periods (15 day, 45 day and 90 day) according to manufacturer instructions based on the ultra-performance liquid chromatography (UPLC) quadrupole-time of flight mass spectrometry (Q-TOF MS). In order to confirm the extractables, principal component analysis (PCA) was used to indicate the differences among samples of different periods. Then, the extractables were identified based on searching the self-built library or online searching. The total content of extractables of 90 day samples was 589.78 μg/L, and the content was in the range of acceptable levels for pharmaceutical manufacturers. The risk assessment of the extractables were evaluated by Toxtree and T.E.S.T. software to avoid the animals bioexperiment.

Predictive Systems Toxicology

In this review we address to what extent computational techniques can augment our ability to predict toxicity. The first section provides a brief history of empirical observations on toxicity dating back to the dawn of Sumerian civilization. Interestingly, the concept of dose emerged very early on, leading up to the modern emphasis on kinetic properties, which in turn encodes the insight that toxicity is not solely a property of a compound but instead depends on the interaction with the host organism. The next logical step is the current conception of evaluating drugs from a personalized medicine point of view. We review recent work on integrating what could be referred to as classical pharmacokinetic analysis with emerging systems biology approaches incorporating multiple omics data. These systems approaches employ advanced statistical analytical data processing complemented with machine learning techniques and use both pharmacokinetic and omics data. We find that such integrated approaches not only provide improved predictions of toxicity but also enable mechanistic interpretations of the molecular mechanisms underpinning toxicity and drug resistance. We conclude the chapter by discussing some of the main challenges, such as how to balance the inherent tension between the predicitive capacity of models, which in practice amounts to constraining the number of features in the models versus allowing for rich mechanistic interpretability, i.e., equipping models with numerous molecular features. This challenge also requires patient-specific predictions on toxicity, which in turn requires proper stratification of patients as regards how they respond, with or without adverse toxic effects. In summary, the transformation of the ancient concept of dose is currently successfully operationalized using rich integrative data encoded in patient-specific models.

Mode of Action (MOA) Assignment Classifications for Ecotoxicology: An Evaluation of Approaches

The mode of toxic action (MOA) is recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, MOA classification has never been standardized in ecotoxicology, and a comprehensive comparison of classification tools and approaches has never been reported. Here we critically evaluate three MOA classification methodologies using an aquatic toxicity data set of 3448 chemicals, compare the approaches, and assess utility and limitations in screening and early tier assessments. The comparisons focused on three commonly used tools: Verhaar prediction of toxicity MOA, the U.S. Environmental Protection Agency (EPA) ASsessment Tool for Evaluating Risk (ASTER) QSAR (quantitative structure activity relationship) application, and the EPA Mode of Action and Toxicity (MOAtox) database. Of the 3448 MOAs predicted using the Verhaar scheme, 1165 were classified by ASTER, and 802 were available in MOAtox. Of the subset of 432 chemicals with MOA assignments for each of the three schemes, 42% had complete concordance in MOA classification, and there was no agreement for 7% of the chemicals. The research shows the potential for large differences in MOA classification between the five broad groups of the Verhaar scheme and the more mechanism-based assignments of ASTER and MOAtox. Harmonization of classification schemes is needed to use MOA classification in chemical hazard and risk assessment more broadly.