The term PFAS encompasses diverse per- and polyfluorinated alkyl (and increasingly aromatic) chemicals spanning industrial processes, commercial uses, environmental occurrence, and potential concerns. With increased chemical curation, currently exceeding 14,000 structures in the PFASSTRUCTV5 inventory on EPA's CompTox Chemicals Dashboard, has come increased motivation to profile, categorize, and analyze the PFAS structure space using modern cheminformatics approaches. Making use of the publicly available ToxPrint chemotypes and ChemoTyper application, we have developed a new PFAS-specific fingerprint set consisting of 129 TxP_PFAS chemotypes coded in CSRML, a chemical-based XML-query language. These are split into two groups, the first containing 56 mostly bond-type ToxPrints modified to incorporate attachment to either a CF group or F atom to enforce proximity to the fluorinated portion of the chemical. This focus resulted in a dramatic reduction in TxP_PFAS chemotype counts relative to the corresponding ToxPrint counts (averaging 54%). The remaining TxP_PFAS chemotypes consist of various lengths and types of fluorinated chains, rings, and bonding patterns covering indications of branching, alternate halogenation, and fluorotelomers. Both groups of chemotypes are well represented across the PFASSTRUCT inventory. Using the ChemoTyper application, we show how the TxP_PFAS chemotypes can be visualized, filtered, and used to profile the PFASSTRUCT inventory, as well as to construct chemically intuitive, structure-based PFAS categories. Lastly, we used a selection of expert-based PFAS categories from the OECD Global PFAS list to evaluate a small set of analogous structure-based TxP_PFAS categories. TxP_PFAS chemotypes were able to recapitulate the expert-based PFAS category concepts based on clearly defined structure rules that can be computationally implemented and reproducibly applied to process large PFAS inventories without need to consult an expert. The TxP_PFAS chemotypes have the potential to support computational modeling, harmonize PFAS structure-based categories, facilitate communication, and allow for more efficient and chemically informed exploration of PFAS chemicals moving forward.

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.

Since 2009, the Tox21 project has screened similar to 8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

In support of the Endocrine Disruptor Screening Program (EDSP), the U.S. EPA's Office of Research and Development (ORD) is developing high-throughput screening (HTS) approaches to identify chemicals that alter target sites in the thyroid hormone (TH) pathway. The sodium iodide symporter (NIS) is a transmembrane glycoprotein that mediates iodide uptake into the thyroid as the initial step of TH biosynthesis. Previously, we screened 293 ToxCast chemicals (ph1v2) using a HEK293T cell line expressing human NIS in parallel radioactive iodide uptake (RAIU) and cell viability assays to identify potential environmental NIS inhibitors. Here, we expanded NIS inhibitor screening for a set of 768 ToxCast Phase II (ph2) chemicals, and applied a novel computational toxicology approach based on the ToxPrint chemotype to identify chemical substructures associated with NIS inhibition. Following single-concentration screening (at 1x10(-4) M with a 20% inhibition cutoff), 235 samples (228 chemicals) were further tested in multiple-concentration (1x10(-9) - 1x10(-4) M) format in both RAIU and cell viability assays. The 167 chemicals that exhibited significant RAIU inhibition were then prioritized using combined RAIU and cell viability responses that were normalized relative to the known NIS inhibitor sodium perchlorate. Some of the highest ranked chemicals, such as PFOS, tributyltin chloride, and triclocarban, have been previously reported to be thyroid disruptors. In addition, several novel chemicals were identified as potent NIS inhibitors. The present results were combined with the previous ph1v2 screening results to produce two sets of binary hit-calls for 1028 unique chemicals, consisting of 273 positives exhibiting significant RAIU inhibition, and 63 positives following application of a cell viability filter. A ToxPrint chemotype-enrichment analysis identified > 20 distinct chemical substructural features, represented in > 60% of the active chemicals, as significantly enriched in each NIS inhibition hit-call space. A shared set of 9 chemotypes enriched in both hit-call sets indicates stable chemotype signals (insensitive to cytotoxicity filters) that can help guide structure-activity relationship (SAR) investigations and inform future research.

The present study adapted an existing high content imaging-based high-throughput phenotypic profiling (HTPP) assay known as "Cell Painting" for bioactivity screening of environmental chemicals. This assay uses a combination of fluorescent probes to label a variety of organelles and measures a large number of phenotypic features at the single cell level in order to detect chemical-induced changes in cell morphology. First, a small set of candidate phenotypic reference chemicals (n = 14) known to produce changes in the cellular morphology of U-2 OS cells were identified and screened at multiple time points in concentration-response format. Many of these chemicals produced distinct cellular phenotypes that were qualitatively similar to those previously described in the literature. A novel workflow for phenotypic feature extraction, concentration-response modeling and determination of in vitro thresholds for chemical bioactivity was developed. Subsequently, a set of 462 chemicals from the ToxCast library were screened in concentration-response mode. Bioactivity thresholds were calculated and converted to administered equivalent doses (AEDs) using reverse dosimetry. AEDs were then compared to effect values from mammalian toxicity studies. In many instances (68%), the HTPP-derived AEDs were either more conservative than or comparable to the in vivo effect values. Overall, we conclude that the HTPP assay can be used as an efficient, cost-effective and reproducible screening method for characterizing the biological activity and potency of environmental chemicals for potential use in in vitro-based safety assessments.

The molecular initiating event for many mechanisms of toxicological action comprise the reactive, covalent binding between an exogenous electrophile and an endogenous nucleophile. The target sites for electrophiles are typically peptides, proteins, enzymes or DNA. Of these, the formation of covalent adducts with proteins and DNA are perhaps the most established as they are most closely associated with skin sensitisation and genotoxicity endpoints. As such, being able to identify electrophilic features within a chemical structure provides a starting point to characterise its reactivity profile. There are a number of software tools that have been developed to help identify structural features indicative of electrophilic reactive potential to address various purposes, including: 1) to facilitate category formation for read-across of toxicity effects such as skin sensitisation potential, as well as 2) to profile substances to identify potential confounding factors to rationalise their activity in high-throughput screening (HTS) assays. Here, three such schemes that have been published in the literature as collections of SMARTS patterns and their associated chemical-biological reaction domains have been compared. The goals are 1) to better understand their scope and coverage, and 2) to assess their performance relative to a published skin sensitisation dataset where manual annotations to assign likely mechanistic domains based on expert judgement were already available. The 3 schemes were then applied to the Tox21 library and the consensus outcome was reported to highlight the proportion of chemicals likely to exhibit a reactivity response, specific to a mechanistic reaction domain, but non-specific with respect to target-tissue based activity. ToxPrint fingerprints were computed and activity enrichments computed to compare the structural features identified for the skin sensitisation dataset and Tox21 chemicals for each ‘consensus’ reaction domain. Enriched ToxPrints were also used to identify ToxCast assays potentially informative for reactivity.

In this chapter, we discuss the development and application of molecular modeling methods to analyze and forecast the experimental properties of nanomaterials. We mainly focus on Quantitative Nanostructure—Activity Relationships (QNAR) to evaluate the extent of biological activities potentially induced by various types of nanomaterials. First, we present the basic principles of QNAR modeling that uses machine-learning techniques to establish quantified links between the biological endpoint of interest (e.g., cytotoxicity, cell death, ROS production) and nanomaterials’ characteristics. Second, we briefly review recently published studies reporting on the QNAR modeling of the largest and most significant datasets of nanomaterials available in the public domain. Third, we discuss some perspectives for the use of molecular modeling on nanomaterials. Overall, we show how molecular modeling can represent a key element for enabling the rational design of nanomaterials with the desired activity and safety profile.