The implementation of RAAF in the OECD QSAR Toolbox.

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.

A mechanistic approach to modeling respiratory sensitization.

Chemical respiratory sensitization is an important occupational health problem which may lead to severely incapacitated human health, yet there are currently no validated or widely accepted models for identifying and characterizing the potential of a chemical to induce respiratory sensitization. This is in part due to the ongoing uncertainty about the immunological mechanisms through which respiratory sensitization may be acquired. Despite the lack of test method, regulations such as REACH still require an assessment of respiratory sensitization for risk assessment and/or for the purposes of classification and labeling. The REACH guidance describes an integrated evaluation strategy to characterize what information sources could be available to facilitate such an assessment. The components of this include a consideration of well-established structural alerts and existing data (whether it be derived from read-across, (quantitative) structure-activity relationships ((Q)SAR), in vivo studies etc.). There has been some progress in developing SARs as well as a handful of empirical QSARs. More recently, efforts have been focused on exploring whether the reaction chemistry mechanistic domains first characterized for skin sensitization are relevant for respiratory sensitization and to what extent modifications or refinements are needed to rationalize the differences between the two end points as far as their chemistry is concerned. This study has built upon the adverse outcome pathway (AOP) for skin sensitization that was developed and published by the OECD in 2012. We have structured a workflow to characterize the initiating events that are relevant in driving respiratory sensitization. OASIS pipeline technology was used to encode these events as components in a software platform to enable a prediction of respiratory sensitization potential to be made for new untested chemicals. This prediction platform could be useful in the assessment of respiratory sensitization potential or for grouping chemicals for subsequent read-across.

Towards AOP application--implementation of an integrated approach to testing and assessment (IATA) into a pipeline tool for skin sensitization.

Since the OECD published the Adverse Outcome Pathway (AOP) for skin sensitization, many efforts have focused on how to integrate and interpret nonstandard information generated for key events in a manner that can be practically useful for decision making. These types of frameworks are known as Integrated Approaches to Testing and Assessment (IATA). Here we have outlined an IATA for skin sensitization which focuses on existing information including non testing approaches such as QSAR and read-across. The IATA was implemented into a pipeline tool using OASIS technology to provide a means of systematically collating and compiling relevant information which could be used in an assessment of skin sensitization potential. A test set of 100 substances with available skin sensitization information was profiled using the pipeline IATA. In silico and in chemico profiling information alone was able to correctly predict skin sensitization potential, with a preliminary accuracy of 73.85%. Information from other relevant endpoints (e.g., Ames mutagenicity) was found to improve the accuracy (to 87.6%) when coupled with a reaction chemistry mechanistic understanding. This pipeline platform could be useful in the assessment of skin sensitization potential and marks a step change in how non testing approaches can be practically applied.

Selection of Representative Constituents for Unknown, Variable, Complex, or Biological Origin Substance Assessment Based on Hierarchical Clustering.

Many of the newly produced and registered substances are complex mixtures or substances of unknown or variable composition, complex reaction products, and biological materials (UVCBs). The latter often consist of a large number of constituents, some of them difficult-to-identify constituents, which complicates their (eco)toxicological assessment. In the present study, through a series of examples, different scenarios for selection of representatives via hierarchical clustering of UVCB constituents are exemplified. Hierarchical clustering allows grouping of the individual chemicals into small sets, where the constituents are similar to each other with respect to more than one criterion. To this end, various similarity criteria and approaches for selection of representatives are developed and analyzed. Two types of selection are addressed: (1) selection of the most "conservative" constituents, which could be also used to support prioritization of UVCBs for evaluation, and (2) obtaining of a small set of chemical representatives that covers the structural and metabolic diversity of the whole target UVCBs or a mixture that can then be evaluated for their environmental and (eco)toxicological properties. The first step is to generate all plausible UVCB or mixture constituents. It was found that the appropriate approach for selecting representative constituents depends on the target endpoint and physicochemical parameters affecting the endpoint of interest. Environ Toxicol Chem 2021;40:3205-3218. © 2021 SETAC.

UVCB substances: methodology for structural description and application to fate and hazard assessment.

Substances of unknown or variable composition, complex reaction products, or biological materials (UVCBs) have been conventionally described in generic terms. Commonly used substance identifiers are generic names of chemical classes, generic structural formulas, reaction steps, physical-chemical properties, or spectral data. Lack of well-defined structural information has significantly restricted in silico fate and hazard assessment of UVCB substances. A methodology for the structural description of UVCB substances has been developed that allows use of known identifiers for coding, generation, and selection of representative constituents. The developed formats, Generic Simplified Molecular-Input Line-Entry System (G SMILES) and Generic Graph (G Graph), address the need to code, generate, and select representative UVCB constituents; G SMILES is a SMILES-based single line notation coding fixed and variable structural features of UVCBs, whereas G Graph is based on a workflow paradigm that allows generation of constituents coded in G SMILES and end point-specific or nonspecific selection of representative constituents. Structural description of UVCB substances as afforded by the developed methodology is essential for in silico fate and hazard assessment. Data gap filling approaches such as read-across, trend analysis, or quantitative structure-activity relationship modeling can be applied to the generated constituents, and the results can be used to assess the substance as a whole. The methodology also advances the application of category-based data gap filling approaches to UVCB substances.

A systematic approach to simulating metabolism in computational toxicology. I. The TIMES heuristic modelling framework.

Designing biologically active chemicals and managing their risks requires a holistic perspective on the chemical-biological interactions that form the basis of selective toxicity. The balance of therapeutic and adverse outcomes for new drugs and pesticides is managed by shaping the probabilities for transport, metabolism, and molecular initiating events. For chemicals activated as well as detoxified by metabolism, selective toxicity may be considered in terms of relative probabilities, which shift dramatically across species as well as within a population, depending on many factors. The complexity in toxicology that results from metabolism has been troublesome in QSAR research because the parent structure is less relevant to predicting ultimate effects and finding reference species/conditions for metabolic rates seems hopeless. Even the complexity of comparative xenobiotic metabolism itself seems paradoxical in light of the evidence of highly conserved catabolic processes across species. Clearly, predicting the role of metabolism in selective toxicity and adverse health outcomes requires a probabilistic framework for deterministic models as well as the many factors shaping the metabolic probability distributions under specific conditions. This paper presents a tissue metabolism simulator (TIMES), which uses a heuristic algorithm to generate plausible metabolic maps from a comprehensive library of biotransformations and abiotic reactions and estimates for system-specific transformation probabilities. The transformation probabilities can be calibrated to specific reference conditions using transformation rate information from systematic testing. In the absence of rate data, a combinatorial algorithm is used to translate known metabolic maps taken from reference systems into best-fit transformation probabilities. Finally, toxicity test data itself can be used to shape the transformation probabilities for toxicity pathways in which the metabolic activation is the rate-limiting process leading to a toxic effect. The conceptual approach for metabolic simulation will be presented along with practical uses in forecasting plausible activated metabolites.

Accessing and using chemical databases.

Computer-based representation of chemicals makes it possible to organize data in chemical databases-collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered-identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.

Conformational coverage by a genetic algorithm: saturation of conformational space.

The molecular modeling is traditionally based on analysis of minimum energy conformers. Such simplifying assumptions could doom to failure the modeling studies given the significant variation of the geometric and electronic characteristics across the multitude of energetically reasonable conformers representing the molecules. Moreover, it has been found that the lowest energy conformers of chemicals are not necessarily the active ones with respect to various endpoints. Hence, the selection of active conformers appears to be as important as the selection of molecular descriptors in the modeling process. In this respect, we have developed effective tools for conformational analysis based on a genetic algorithm (GA), published in J. Chem. Inf. Comput. Sci. (1994, 34, 234; 1999, 39 (6), 997) and J. Chem. Inf. Model. (2005, 45 (2), 283). This paper presents a further improvement of the evolutionary algorithm for conformer generation minimizing the sensitivity of conformer distributions from the effect of smoothing parameter and improving the reproducibility of conformer distributions given the nondeterministic character of the genetic algorithm (GA). The ultimate goal of the saturation is to represent the conformational space of chemicals with an optimal number of conformers providing a stable conformational distribution which cannot be further perturbed by the addition of new conformers. The generation of stable conformational distributions of chemicals by a limited number of conformers will improve the adequacy of the subsequent molecular modeling analysis. The impact of the saturation procedure on conformer distributions in a specific structural space is illustrated by selected examples. The effect of the procedure on similarity assessment between chemicals is discussed.

Representation of chemical information in OASIS centralized 3D database for existing chemicals.

The present inventory of existing chemicals in regulatory agencies in North America and Europe, encompassing the chemicals of the European Chemicals Bureau (EINECS, with 61 573 discrete chemicals); the Danish EPA (159 448 chemicals); the U.S. EPA (TSCA, 56 882 chemicals; HPVC, 10 546 chemicals) and pesticides' active and inactive ingredients of the U.S. EPA (1379 chemicals); the Organization for Economic Cooperation and Development (HPVC, 4750 chemicals); Environment Canada (DSL, 10851 chemicals); and the Japanese Ministry of Economy, Trade, and Industry (16811), was combined in a centralized 3D database for existing chemicals. The total number of unique chemicals from all of these databases exceeded 185 500. Defined and undefined chemical mixtures and polymers are handled, along with discrete (hydrolyzing and nonhydrolyzing) chemicals. The database manager provides the storage and retrieval of chemical structures with 2D and 3D data, accounting for molecular flexibility by using representative sets of conformers for each chemical. The electronic and geometric structures of all conformers are quantum-chemically optimized and evaluated. Hence, the database contains over 3.7 million 3D records with hundreds of millions of descriptor data items at the levels of structures, conformers, or atoms. The platform contains a highly developed search subsystem--a search is possible on Chemical Abstracts Service numbers; names; 2D and 3D fragment searches; structural, conformational, or atomic properties; affiliation in other chemical databases; structure similarity; logical combinations; saved queries; and search result exports. Models (collections of logically related descriptors) are supported, including information on a model's author, date, bioassay, organs/tissues, conditions, administration, and so forth. Fragments can be interactively constructed using a visual structure editor. A configurable database browser is designed for the inspection and editing of all types of data items. Database statistics are maintained on the number and quality of structures, conformers, and descriptors. Reports can be generated presenting any chosen subset of structures and descriptors into different formats suitable for inclusion into documents. In addition to fixed report formats, there is a powerful report template designer module with a visual report template editor to produce a customized page layout. The database is compatible at the import/export level with SDF, MOL, SMILES, and other known formats. The precalculated centralized 3D database could be useful for quantitative structure-activity relationship developers avoiding the time-consuming and cumbersome 3D calculation phase of model development.

A stepwise approach for defining the applicability domain of SAR and QSAR models.

A stepwise approach for determining the model applicability domain is proposed. Four stages are applied to account for the diversity and complexity of the current SAR/QSAR models, reflecting their mechanistic rationality (including metabolic activation of chemicals) and transparency. General parametric requirements are imposed in the first stage, specifying in the domain only those chemicals that fall in the range of variation of the physicochemical properties of the chemicals in the training set. The second stage defines the structural similarity between chemicals that are correctly predicted by the model. The structural neighborhood of atom-centered fragments is used to determine this similarity. The third stage in defining the domain is based on a mechanistic understanding of the modeled phenomenon. Here, the model domain combines the reliability of specific reactive groups hypothesized to cause the effect and the domain of explanatory variables determining the parametric requirements in order for functional groups to elicit their reactivity. Finally, the reliability of simulated metabolism (metabolites, pathways, and maps) is taken into account in assessing the reliability of predictions, if metabolic activation of chemicals is a part of the (Q)SAR model. Some of the stages of the proposed approach for defining the model domain can be eliminated depending on the availability and quality of the experimental data used to derive the model, the specificity of (Q)SARs, and the goals of their ultimate application. The performance of the proposed definition of the model domain is tested using several examples of (Q)SARs that have been externally validated, including models for predicting acute toxicity, skin sensitization, and biodegradation. The results clearly showed that credibility in predictions of QSAR models for chemicals belonging to their domain is much higher than for chemicals outside this domain.

2D-3D migration of large chemical inventories with conformational multiplication. Application of the genetic algorithm.

Mathematical chemistry has afforded a variety of research areas with important tools to understand and predict the behavior of chemicals without having to consider the complexities of three-dimensional conformations of molecules. Predictive toxicology, an area of increasing importance to toxicity assessments critical to molecular design and risk management, must be based on more explicit descriptions of structure, however. Minimum energy conformations are often used for convenience due, in part, to the difficulty of computing a representative population of conformers in all but rigid structures. Such simplifying assumptions fail to reveal the variance of the stereoelectronic nature of molecules as well as the misclassification of chemicals which initiate receptor-based toxicity pathways. Because these errors impact both the success in discovering new lead and the identification of possible hazards, it is important that mathematical chemistry develop additional tools for conformational analysis. This paper presents a new system for automated 2D-3D migration of chemicals in large databases with conformer multiplication. The main advantages of this system are its straightforward performance, reasonable execution time, simplicity and applicability to building large 3D chemical inventories. The module for conformer multiplication within the 2D-3D migration system is based on a new formulation of the genetic algorithm for computing populations of possible conformers. The performance of the automated 2D-3D migration system in building a centralized 3D database for all chemicals in commerce worldwide is discussed. The applicability of the 3D database in assessing the impact of molecular flexibility on identifying active conformers in QSAR analysis and assessing similarity between chemicals is illustrated.