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ASCCT-ESTIV Award Winners Series: Gabriela De Oliveira Prado Correa, Sezin Aday Aydin, and Amber Daniel
Thursday, April 24, 2025, 10:00 AM - 11:30 AM EST
Category: ASCCT Webinar

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Featuring:

Gabriela De Oliveira Prado Correa: "Assessing Computational Approaches for Predicting Estrogen Receptor Binding"

ASCCT 13th Annual Meeting Travel Award Recipient

Sezin Aday Aydin: "Lung-on-a-chip Model for the Discovery of Chlorine Exposure Biomarkers"

ASCCT 13th Annual Meeting Poster Award Recipient

Amber Daniel: "Defined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals"
ASCCT 13th Annual Meeting Poster Award Recipient

A brief Q&A session will follow each presentation.

ABSTRACTS

Assessing Computational Approaches for Predicting Estrogen Receptor Binding: Endocrine-disrupting chemicals are considered a serious health threat, contributing to major diseases. Interaction with estrogen receptors (ER) can affect the transcription of estrogen-controlled genes, leading to the induction or inhibition of cellular processes. This study aimed to evaluate the sensitivity and specificity of predicting the interaction of chemical substances with ER for proficiency substances listed in OECD guidelines 455 and 493. Three platforms were selected: Endocrine Disruptome (ED), Vega QSAR (VQ), and Danish QSAR (DQ). Additionally, molecular docking (MD) analysis was performed using AutoDock 4.2 software. The results were assessed based on sensitivity (ability to predict true positives/active substances) and specificity (ability to predict true negatives/inactive substances). In MD, substances with a binding affinity ≥ -7.5 were considered positive, while those with a value < -7.5 were considered negative. Among the predictions made, only MD evaluated all the substances. VQ and DQ did not produce false-positive or false-negative predictions, achieving sensitivities of 100% and 92.86%, respectively, and both obtained a specificity of 83.33%. MD achieved a sensitivity of 78.57% and specificity of 83.33%, while ED achieved a sensitivity of 64.28% and specificity of 66.37%. According to OECD 150, in silico approaches fall within the initial evaluation level for endocrine disruptors and should be used in a weight-of-evidence context before advancing to higher assessment levels. Computational methods are increasingly vital in toxicology, offering extensive applications from preliminary molecule screening to regulatory uses, particularly where in vitro methods are insufficient.

Lung-on-a-chip Model for the Discovery of Chlorine Exposure Biomarkers: Adverse effects of inhaled toxicants remain a significant public health concern; thus, understanding the biological processes associated with these exposures is crucial for developing better biomarkers and countermeasures. One of these toxicants, chlorine, is a poisonous gas causing acute damage to the airway. Although 2D monocultures have been used in chlorine toxicity testing due to their simplicity and convenience, they often fail to capture complex tissue-specific microenvironments that are essential to respiratory responses. To address the limitations of these models, we established a microfluidic design with three channels separated from an open well by a semipermeable membrane. In our design, upper airway epithelial cells are cultured in the open well and exposed to air to mimic their native environment. Fibroblasts and endothelial cells are embedded in the underlying hydrogel scaffold to form perfusable vascular networks mimicking pulmonary vasculature. We developed computational models to predict physiologically relevant chlorine concentrations at the target and performed studies by mimicking different environmental exposure scenarios. Our MS-based -omics analyses identified chlorotyrosine, proline, lysine, and histidine as novel biomarkers for chlorine exposure. We also discovered over 100 lipids secreted by the lung cells after chlorine exposure. Although the current work focuses on chlorine, our lung-on-a-chip platform can also provide human-relevant data for a wide array of Chemical, Biological, Radiological, and Nuclear (CBRN) threats. The study described in this presentation was funded with federal funds from the HHS; ASPR; BARDA, under contract number 75A50120C00134. The contract and federal funding are not an endorsement of the study results.

Defined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals: Certain regulatory frameworks require in vivo testing to determine hazard labeling for agrochemical products. We conducted prospective in vitro testing to develop defined approaches (DAs) to predict GHS and EPA eye irritation classifications without animal testing. We developed four DAs, comprising bovine corneal opacity and permeability with histopathology alone (“DA-BCOP+”), or combined with EpiOcular™, SkinEthic time-to-toxicity for liquids, or EyeIRR-IS (“DA-EO+”, “DA-TTL+”, and “DA-EyeIRR-IS+”, respectively). We used prospective data to apply these four DAs. For both GHS and EPA, we orthogonally analyzed concordance of classification and labeling predictions across the four DAs and historical rabbit data. For both classification systems, majority orthogonal concordance was achieved for 97% (28/29) of formulations, and all four DAs were equally or more protective of human health than the rabbit test. These DAs therefore have high utility for predicting GHS and EPA classifications of agrochemical formulations. This project was funded with federal funds from the NIEHS, NIH under Contract No. HHSN273201500010C.


About the Presenters

Gabriela De Oliveira Prado Correa, PhD is a researcher in the field of human and environmental toxicology, with a particular focus on in silico methods. One of her life goals is to contribute to research aimed at replacing the use of animals in chemical safety testing. In recent years, she has concentrated on this mission and has actively shared her knowledge at various scientific events, making it accessible to all interested parties.

Sezin Aday Aydin, PhD is a research associate at the University of Pennsylvania and program manager in the BARDA-funded "Inhalation Toxicology of Chlorine Gas-on-a-chip" project. She develops organ-on-a-chip models to reproduce living tissues and their native microenvironments, to understand cell-cell interactions, and to find biomarkers for different injuries.

Amber Daniel is a Senior Toxicologist within the Predictive Toxicology and Information Sciences group at Inotiv. She holds a Bachelor of Science in animal science and a Master of Toxicology degree, both from North Carolina State University. As a contractor supporting the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), she has worked on a variety of projects to promote the development, use, and regulatory acceptance of alternatives to animal use for chemical safety testing, including validation studies of in vitro test methods and defined approaches to detect potential chemical safety hazards.


Recordings and other materials from this webinar will be posted on the ASCCT webinar archive: https://www.ascctox.org/webinar-archive


Contact: [email protected]