Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models

Introduction

The U. S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system-disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.

Methods

Chemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.

Results and Discussion

Performance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.

Conclusion

Our results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.

Optimizing androgen receptor prioritization using high-throughput assay-based activity models

Introduction: Computational models using data from high-throughput screening assays have promise for prioritizing and screening chemicals for testing under the U.S. Environmental Protection Agency's Endocrine Disruptor Screening Program (EDSP). The purpose of this work was to demonstrate a data processing method for the determination of optimal minimal assay batteries from a larger comprehensive model, to provide a uniform method of evaluating the performance of future minimal assay batteries compared with the androgen receptor (AR) pathway model, and to incorporate chemical cluster analysis into this evaluation. Although several of the assays in the AR pathway model are no longer available through the original vendor, this approach could be used for future evaluations of minimal assay models for prioritization and screening. Methods: We compared two previously published models and found that an expanded 14-assay model had higher sensitivity for antagonists, whereas the original 11-assay model had slightly higher sensitivity for agonists. We then investigated subsets of assays in the original AR pathway model to optimize overall testing strategies that minimize cost while maintaining sensitivity across a broad chemical space. Results and Discussion: Evaluation of the critical assays across subset models derived from the 14-assay model identified three critical assays for predicting antagonism and two critical assays for predicting agonism. A minimum of nine assays is required for predicting agonism and antagonism with high sensitivity (95%). However, testing workflows guided by chemical structure-based clusters can reduce the average number of assays needed per chemical by basing the assays selected for testing on the likelihood of a chemical being an AR agonist, according to its structure. Our results show that a multi-stage testing workflow can provide 95% sensitivity while requiring only 48% of the resources required for running all assays from the original full models. The resources can be reduced further by incorporating in silico activity predictions. Conclusion: This work illustrates a data-driven approach that incorporates chemical clustering and simultaneous consideration of antagonism and agonism mechanisms to more efficiently screen chemicals. This case study provides a proof of concept for prioritization and screening strategies that can be utilized in future analyses to minimize the overall number of assays needed for predicting AR activity, which will maximize the number of chemicals that can be tested and allow data-driven prioritization of chemicals for further screening under the EDSP.

Assessment of the applicability of the threshold of toxicological concern for per- and polyfluoroalkyl substances

While toxicity information is available for selected PFAS, little or no information is available for most, thereby necessitating a resource-effective approach to screen and prioritize those needing further safety assessment. The threshold of toxicological concern (TTC) approach proposes a de minimis exposure value based on chemical structure and toxicology of similar substances. The applicability of the TTC approach to PFAS was tested by incorporating a data set of no-observed-adverse-effect level (NOAEL) values for 27 PFAS into the Munro TTC data set. All substances were assigned into Cramer Class III and the cumulative distribution of the NOAELs evaluated. The TTC value for the PFAS-enriched data set was not statistically different compared to the Munro data set. Derived human exposure level for the PFAS-enriched data set was 1.3 μg/kg/day. Structural chemical profiles showed the PFAS-enriched data set had distinct chemotypes with lack of similarity to substances in the Munro data set using Maximum Common Structures. The incorporation of these 27 PFAS did not significantly change TTC Cramer Class III distribution and expanded the chemical space, supporting the potential use of the TTC approach for PFAS chemicals.

Estimating Hepatotoxic Doses Using High-Content Imaging in Primary Hepatocytes

Using in vitro data to estimate point of departure (POD) values is an essential component of new approach methodologies (NAM)-based chemical risk assessments. In this case study, we evaluated a NAM for hepatotoxicity based on rat primary hepatocytes, high-content imaging (HCI), and toxicokinetic modeling. First, we treated rat primary hepatocytes with 10 concentrations (0.2 to 100 µM) of 51 chemicals that produced hepatotoxicity in repeat-dose subchronic and chronic exposures. Second, we used HCI to measure endoplasmic reticulum stress, mitochondrial function, lysosomal mass, steatosis, apoptosis, DNA texture, nuclear size, and cell number at 24, 48, and 72 h and calculated concentrations at 50% maximal activity (AC50). Third, we estimated administered equivalent doses (AEDs) from AC50 values using toxicokinetic modeling. AEDs using physiologically-based toxicokinetic models were 4.1-fold (SD 6.3) and 8.1-fold (SD 15.5) lower than subchronic and chronic lowest observed adverse effect levels (LOAELs), respectively. In contrast, AEDs from ToxCast and Tox21 assays were 89.8-fold (SD 149.5) and 168-fold (SD 323.7) lower than subchronic and chronic LOAELs. Individual HCI end-points also estimated AEDs for specific hepatic lesions that were lower than in vivo PODs. Lastly, AEDs were similar for different in vitro exposure durations, but steady-state toxicokinetic models produced 7.6-fold lower estimates than dynamic physiologically-based ones. Our findings suggest that NAMs from diverse cell types provide conservative estimates of PODs. In contrast, NAMs based on the same species and cell type as the adverse outcome may produce estimates closer to the traditional in vivo PODs.

Assessing children's exposure to manganese in drinking water using a PBPK model

A previously published human PBPK model for manganese (Mn) in infants and children has been updated with Mn in drinking water as an additional exposure source. Built upon the ability to capture differences in Mn source-specific regulation of intestinal uptake in nursing infants who are breast-fed and formula-fed, the updated model now describes the bioavailability of Mn from drinking water in children of ages 0-18. The age-related features, including the recommended age-specific Mn dietary intake, age-specific water consumption rates, and age-specific homeostasis of Mn, are based on the available human data and knowledge of the biology of essential-metal homeostasis. Model simulations suggest that the impact of adding drinking-water exposure to daily Mn exposure via dietary intake and ambient air inhalation in children is not greater than the impacts in adults, even at a drinking-water concentration that is 2 times higher than the USEPA's lifetime health advisory value. This conclusion was also valid for formula-fed infants who are considered at the highest potential exposure to Mn from drinking water compared to all other age groups. Our multi-route, multi-source Mn PBPK model for infants and children provides insights about the potential for Mn-related health effects on growing children and will thereby improve the level of confidence in properly interpreting Mn exposure-health effects relationships in children in human epidemiological studies.

Ordering and stability in lipid droplets with applications to low-density lipoproteins

In this article, we present a framework for investigating the order-disorder transition in lipid droplets using the standard Ising model. While a single lipid droplet is itself a complex system whose constituent cholesteryl esters each possesses many degrees of freedom, we present justification for using this effective approach to isolate the underlying physics. It is argued that the behavior of the esters confined within lipid droplets is significantly different from that of a bulk system of similar esters, which is adequately described by continuum mean-field theory in the thermodynamic limit. When the droplet's shell is modeled as an elastic membrane, a simple picture emerges for a transition between two ordered phases within the core which is tuned by the strength of interactions between the esters. Triglyceride concentration is proposed as a variable which strongly influences the strength of interactions between cholesteryl esters within droplets. The possible relevance of this mechanism to the well known atherogenic nature of small low-density lipoprotein particles is discussed in detail.

Modeling order-disorder transition in Low-Density Lipoprotein

Low Density Lipoproteins (LDL) undergo a reversible order-disorder thermal transition close to biological temperature due to cooperative melting of the cholesteryl esters (CE) in the core of the LDL particle. We have noticed that chain-chain interactions between CE molecules are responsible for the stability of the ordered smectic phase; thus, we formulated a simple "coarse-grained" two-state model to describe the melting process. In this model only nearest neighbor interactions are allowed. On the basis of these assumptions we performed Metropolis Monte Carlo (MC) simulation in order to obtain the heat capacity curve. The resulting profile reveals well-known features of the systems with a finite size.