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.