{"id":1557,"date":"2019-07-29T12:06:40","date_gmt":"2019-07-29T10:06:40","guid":{"rendered":"https:\/\/estiv.org\/?page_id=1557"},"modified":"2026-03-11T11:47:20","modified_gmt":"2026-03-11T10:47:20","slug":"ascct-estiv-webinars","status":"publish","type":"page","link":"https:\/\/www.estiv.org\/projects-activities\/ascct-estiv-webinars\/","title":{"rendered":"ASCCT \/ ESTIV webinars"},"content":{"rendered":"\n

ESTIV organizes free webinars on a monthly basis together with the American Society for Cellular and Computational Toxicology (ASCCT)<\/a>. These webinars cover a broad spectrum of topics relevant to the fields of in vitro<\/em> and in silico<\/em> toxicology. ESTIV members are welcome to propose topics and speakers at any time. All webinars are recorded, archived and available for ESTIV members. <\/p>\n\n\n\n

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Webinars – 2026<\/h2>\n\n\n\r\n
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Wednesday, March 18, 2026: ESTIV-ASCCT Award Winners Series: AI & Machine Learning<\/h3>\r\n
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Topic:<\/strong> AI & Machine Learning<\/p>\n

Date:\u00a0<\/strong>Wednesday, March 18, 2025<\/p>\n

Time:\u00a0<\/strong>10:00am ET \/ 14:00 UTC<\/p>\n

FEATURING:<\/strong>
\nAmirreza Daghighi:<\/strong>\u00a0\u201cIntegrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity\u201d
\nASCCT 14th Annual Meeting Travel Award Recipient<\/em><\/p>\n

Ivo Djidrovski, PhD:<\/strong>\u00a0\u201cAugmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents\u201d
\nASCCT 14th Annual Meeting Ray Tice Tox21 Student Award<\/em><\/p>\n

Nyssa Tucker:<\/strong>\u00a0\u201cRADISH: ROBOKOP Assisted Discovery of in silico Hypotheses \u2013 A case study of mechanisms linking nuclear receptors to liver disease\u201d
\nASCCT 14th Annual Meeting Travel Award Recipient<\/em><\/p>\n

A brief Q&A session will follow each presentation.<\/p>\n

REGISTER NOW!<\/a><\/strong><\/p>\n

ABSTRACTS<\/strong><\/p>\n

Integrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity<\/strong>
\nIn recent years, alternative animal testing techniques, including computational and machine learning (ML) methods, have become essential for toxicity testing due to their ability to minimize the use of animals and reduce both costs and time. ML is a powerful tool for in silico property prediction, including toxicity in the drug development process and environmental chemical screening. This research employs a set of linear and non-linear ML techniques to investigate the acute oral toxicity (Lethal Dose 50%, LD\u00ac50) of a series of compounds. We utilized a dataset of 6,639 compounds sourced from the Integrated Chemical Environment (ICE) online databank, which provides high-quality curated data to support computational toxicology projects. Our study presents a comparative analysis of several ML models developed on this dataset, including Graph Convolutional Neural Network (GCNN), Random Forest (RF), Support Vector Regression (SVR), K-nearest Neighbors (KNN), and Linear Regression (LR). Additionally, the study provides interpretations from the GCNN model, offering insights into molecular fragments responsible for changes in the LD\u00ac50 value. The predictive performance of our best-developed model is compared with existing Quantitative Structure-Toxicity Relationship (QSTR) models, demonstrating significant advancements in predictive accuracy and interpretability. The results of these analyses can be extremely helpful to pave the way for fast and accurate toxicity assessment of new compounds, as well as to understand their environmental impact.<\/p>\n

Augmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents<\/strong>
\nThe integration of Large Language Models (LLMs) and AI agents into toxicology offers a transformative opportunity to accelerate and scale hazard assessment. Trained on biomedical and regulatory corpora, LLMs provide context-aware reasoning and act as dynamic interfaces for decision-making in complex data environments. This work presents a prototype multi-agent system that leverages LLMs to support in silico hazard identification, analog selection, and read-across with emphasis on reproducibility, transparency, and regulatory alignment.
\nThe system interfaces with the OECD QSAR Toolbox via an API-enabled framework. Agents autonomously query chemical databases, simulate metabolism, retrieve experimental and predicted data, and apply category formation logic to identify structurally and mechanistically relevant analogs. Through prompt engineering and multi-step reasoning, agents generate narratives that strengthen weight-of-evidence hazard decisions and mechanistic interpretation.
\nCase studies from the VHP4Safety project, including known regulatory substances, were used to benchmark agent performance against expert assessments. Results show AI agents can execute QSAR workflows, fill data gaps, and propose mechanistic hypotheses consistent with existing evidence. The modular architecture further integrates with LLM-native tools (e.g., ChemCrow, ChemThinker) and open-source components, underscoring adaptability across toxicological contexts.
\nA central feature is interoperability: modular design enables scalable, customizable, and trustworthy workflows that bridge regulatory frameworks with AI-native tooling. These agentic systems operationalize New Approach Methodologies (NAMs) and support transparent, collaborative risk assessment. Hybrid intelligence\u2014human oversight augmented by AI-driven insight\u2014emerges as a catalyst for sustainable, data-driven, and probabilistic toxicology.<\/p>\n

RADISH: ROBOKOP Assisted Discovery of in silico Hypotheses \u2013 A case study of mechanisms linking nuclear receptors to liver disease<\/strong>
\nAdverse Outcome Pathways (AOPs) describe the cascade of biological events leading to toxic outcomes. An AOP is built through manual efforts of subject matter experts identifying empirical molecular or biological evidence for each node in the chain. Recent developments in biomedical information retrieval led to the emergence of biomedical knowledge graphs (KG), which integrate multiple elementary semantic triples, i.e., special \u2018subject-predicate-object\u2019 relationships between chemical and biological entities (such as \u2018chemical causes hepatotoxicity\u2019) described in biomedical literature or databases. One KG, ROBOKOP, is a biomedical publicly available graph integrating 50+ databases to include over 9M nodes and 140M edges.
\nROBOKOP Assisted Discovery of in silico Hypotheses (RADisH) is a methodology for automated discovery of AOPs based on graph mining algorithms. We evaluate RADISH using expert-defined AOPs for fatty liver disease. Of the approximately 50 unique events described across the thirteen AOPs in AOPwiki, KG queries considering just one nuclear receptor, AhR, resulted in 34 genes of which 26 were partial or complete matches to AOP events implying a recall of ~70%. The remaining 8 genes not matched to existing AOP events were evaluated for plausibility within the context of fatty liver disease and investigated for potential applicability as hypothetical mechanistic event candidates. The use of these novel hypothetical mechanisms can support future development of quantitative models predicting chemical toxicity. Furthermore, these hypothetical mechanisms can be fed back to biomedical researchers to evaluate the plausibility of these connections and broaden the scope of biomedical knowledge.<\/p>\n

ABOUT THE PRESENTERS:<\/strong><\/p>\n

Amirreza (Amir) Daghighi<\/strong>\u00a0earned his B.S. in Biomedical Engineering (BME) from Tehran Polytechnic, Iran, and joined North Dakota State University in 2021 to pursue graduate studies in BME. He received his M.S. in 2023 and is currently continuing his doctoral studies at NDSU under the supervision of Dr. Bakhtiyor Rasulev. His research focuses on computational toxicology, machine learning, and quantitative structure\u2013toxicity relationship (QSTR) modeling for predicting the toxicity of small molecules<\/p>\n

Ivo Djidrovski, PhD<\/strong>\u00a0\u2013 Bio Coming Soon<\/p>\n

Nyssa Tucker<\/strong>\u00a0is a 5th year Computational Toxicology Ph.D. Candidate at UNC Chapel Hill. Along with the research interests embedded in the associated abstract, Nyssa is also passionate about building strong networks between people towards a resilient community-focused system of collaboration addressing pressing social concerns. Nyssa is a member of SOT, UE150, and UNC\u2019s Molecular Modeling Laboratory.<\/p>\n\n\n\n
Date:\u00a0<\/strong>Wednesday, March 18, 2025<\/p>\n

Time:\u00a0<\/strong>10:00am ET \/ 14:00 UTC<\/p>\n

FEATURING:<\/strong>
\nAmirreza Daghighi:<\/strong>\u00a0\u201cIntegrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity\u201d
\nASCCT 14th Annual Meeting Travel Award Recipient<\/em><\/p>\n

Ivo Djidrovski, PhD:<\/strong>\u00a0\u201cAugmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents\u201d
\nASCCT 14th Annual Meeting Ray Tice Tox21 Student Award<\/em><\/p>\n

Nyssa Tucker:<\/strong>\u00a0\u201cRADISH: ROBOKOP Assisted Discovery of in silico Hypotheses \u2013 A case study of mechanisms linking nuclear receptors to liver disease\u201d
\nASCCT 14th Annual Meeting Travel Award Recipient<\/em><\/p>\n

A brief Q&A session will follow each presentation.<\/p>\n

REGISTER NOW!<\/a><\/strong><\/p>\n

ABSTRACTS<\/strong><\/p>\n

Integrated Chemical Environment: A comparative study of machine learning models to predict acute oral toxicity<\/strong>
\nIn recent years, alternative animal testing techniques, including computational and machine learning (ML) methods, have become essential for toxicity testing due to their ability to minimize the use of animals and reduce both costs and time. ML is a powerful tool for in silico property prediction, including toxicity in the drug development process and environmental chemical screening. This research employs a set of linear and non-linear ML techniques to investigate the acute oral toxicity (Lethal Dose 50%, LD\u00ac50) of a series of compounds. We utilized a dataset of 6,639 compounds sourced from the Integrated Chemical Environment (ICE) online databank, which provides high-quality curated data to support computational toxicology projects. Our study presents a comparative analysis of several ML models developed on this dataset, including Graph Convolutional Neural Network (GCNN), Random Forest (RF), Support Vector Regression (SVR), K-nearest Neighbors (KNN), and Linear Regression (LR). Additionally, the study provides interpretations from the GCNN model, offering insights into molecular fragments responsible for changes in the LD\u00ac50 value. The predictive performance of our best-developed model is compared with existing Quantitative Structure-Toxicity Relationship (QSTR) models, demonstrating significant advancements in predictive accuracy and interpretability. The results of these analyses can be extremely helpful to pave the way for fast and accurate toxicity assessment of new compounds, as well as to understand their environmental impact.<\/p>\n

Augmenting Chemical Hazard and Risk Assessment through Large Language Models and AI Agents<\/strong>
\nThe integration of Large Language Models (LLMs) and AI agents into toxicology offers a transformative opportunity to accelerate and scale hazard assessment. Trained on biomedical and regulatory corpora, LLMs provide context-aware reasoning and act as dynamic interfaces for decision-making in complex data environments. This work presents a prototype multi-agent system that leverages LLMs to support in silico hazard identification, analog selection, and read-across with emphasis on reproducibility, transparency, and regulatory alignment.
\nThe system interfaces with the OECD QSAR Toolbox via an API-enabled framework. Agents autonomously query chemical databases, simulate metabolism, retrieve experimental and predicted data, and apply category formation logic to identify structurally and mechanistically relevant analogs. Through prompt engineering and multi-step reasoning, agents generate narratives that strengthen weight-of-evidence hazard decisions and mechanistic interpretation.
\nCase studies from the VHP4Safety project, including known regulatory substances, were used to benchmark agent performance against expert assessments. Results show AI agents can execute QSAR workflows, fill data gaps, and propose mechanistic hypotheses consistent with existing evidence. The modular architecture further integrates with LLM-native tools (e.g., ChemCrow, ChemThinker) and open-source components, underscoring adaptability across toxicological contexts.
\nA central feature is interoperability: modular design enables scalable, customizable, and trustworthy workflows that bridge regulatory frameworks with AI-native tooling. These agentic systems operationalize New Approach Methodologies (NAMs) and support transparent, collaborative risk assessment. Hybrid intelligence\u2014human oversight augmented by AI-driven insight\u2014emerges as a catalyst for sustainable, data-driven, and probabilistic toxicology.<\/p>\n

RADISH: ROBOKOP Assisted Discovery of in silico Hypotheses \u2013 A case study of mechanisms linking nuclear receptors to liver disease<\/strong>
\nAdverse Outcome Pathways (AOPs) describe the cascade of biological events leading to toxic outcomes. An AOP is built through manual efforts of subject matter experts identifying empirical molecular or biological evidence for each node in the chain. Recent developments in biomedical information retrieval led to the emergence of biomedical knowledge graphs (KG), which integrate multiple elementary semantic triples, i.e., special \u2018subject-predicate-object\u2019 relationships between chemical and biological entities (such as \u2018chemical causes hepatotoxicity\u2019) described in biomedical literature or databases. One KG, ROBOKOP, is a biomedical publicly available graph integrating 50+ databases to include over 9M nodes and 140M edges.
\nROBOKOP Assisted Discovery of in silico Hypotheses (RADisH) is a methodology for automated discovery of AOPs based on graph mining algorithms. We evaluate RADISH using expert-defined AOPs for fatty liver disease. Of the approximately 50 unique events described across the thirteen AOPs in AOPwiki, KG queries considering just one nuclear receptor, AhR, resulted in 34 genes of which 26 were partial or complete matches to AOP events implying a recall of ~70%. The remaining 8 genes not matched to existing AOP events were evaluated for plausibility within the context of fatty liver disease and investigated for potential applicability as hypothetical mechanistic event candidates. The use of these novel hypothetical mechanisms can support future development of quantitative models predicting chemical toxicity. Furthermore, these hypothetical mechanisms can be fed back to biomedical researchers to evaluate the plausibility of these connections and broaden the scope of biomedical knowledge.<\/p>\n

ABOUT THE PRESENTERS:<\/strong><\/p>\n

Amirreza (Amir) Daghighi<\/strong>\u00a0earned his B.S. in Biomedical Engineering (BME) from Tehran Polytechnic, Iran, and joined North Dakota State University in 2021 to pursue graduate studies in BME. He received his M.S. in 2023 and is currently continuing his doctoral studies at NDSU under the supervision of Dr. Bakhtiyor Rasulev. His research focuses on computational toxicology, machine learning, and quantitative structure\u2013toxicity relationship (QSTR) modeling for predicting the toxicity of small molecules<\/p>\n

Ivo Djidrovski, PhD<\/strong>\u00a0\u2013 Bio Coming Soon<\/p>\n

Nyssa Tucker<\/strong>\u00a0is a 5th year Computational Toxicology Ph.D. Candidate at UNC Chapel Hill. Along with the research interests embedded in the associated abstract, Nyssa is also passionate about building strong networks between people towards a resilient community-focused system of collaboration addressing pressing social concerns. Nyssa is a member of SOT, UE150, and UNC\u2019s Molecular Modeling Laboratory.<\/p>\n

Complete details are also available on the\u00a0Event Calendar Listing<\/a><\/strong>.
\nRecordings and other materials from this webinar will be posted to the\u00a0ASCCT Webinar Archive<\/strong><\/a>.
\nInformation on additional upcoming webinars and other events is available on the\u00a0ASCCT Event Calendar<\/a><\/strong>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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Friday, February 27, 2026: ASCCT – Advancing Cannabis Safety Assessments with New Approach Methodologies <\/h3>\r\n
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Due to the U.S. Federal Government shutdown, one session from the ASCCT October 2025 Annual Meeting,\u00a0Advancing Cannabis Safety Assessments with New Approach Methodologies<\/strong>, was cancelled. We are pleased to announce that this FDA scientist-led session will now be presented in full via Zoom. Attendance is free and open to everyone, and we invite all interested participants to join for this rescheduled presentation.<\/p>\n

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\nDate:\u00a0<\/strong>Friday, February 27, 2026
\nTime:\u00a0<\/strong>9:30 am \u2013 12:00 pm ET \/ 14:30 \u2013 17:00 UTC<\/p>\n

FEATURING:<\/strong><\/p>\n

Yitong Liu, PhD, DABT:<\/strong>\u00a0Senior Research Pharmacologist, US FDA
\nMichael Santillo, PhD:<\/strong>\u00a0Senior Research Chemist, US FDA
\nXiugong Gao, PhD:<\/strong>\u00a0Research Biologist, US FDA
\nKirsten Eckstrum, PhD:<\/strong>\u00a0Research Biologist, US FDA
\nPiper Hunt, PhD:<\/strong>\u00a0Research Biologist, US FDA<\/p>\n

There will be ample time throughout the sessions for audience-led Q&A.<\/p>\n

ABSTRACTS:\u00a0<\/strong>
\nOverview of Cannabis Toxicity and Human Use<\/strong>\u00a0(Y. Liu): The use of cannabis-derived products has grown substantially in recent years, driven by an increased public interest in both recreational and health applications. In addition to well-known cannabinoids such as delta-9-tetrahydrocannabinol (THC) and cannabidiol (CBD), a wide array of emerging compounds, including delta-8 THC and cannabigerol (CBG), are now widely available in consumer products. Although often marketed as natural or wellness-enhancing, accumulating evidence suggests that some of these substances may pose risks to human health. Documented adverse effects include the psychoactive and intoxicating effects of THC, as well as hepatotoxicity associated with CBD in both clinical and preclinical studies. The rapid expansion of the market has also led to the proliferation of lesser-known products, such as delta-8 THC, further complicating the evaluation of safety and potential human health impacts. This presentation will begin with an overview of the definitions and major classes of cannabinoids found in cannabis, including major cannabinoids (THC and CBD) and over 100 minor cannabinoids (e.g., THC-type, CBD-type, and CBG-type cannabinoids). It will then review the adverse health effects of cannabis and selected cannabinoids, highlighting key mechanisms of toxicity, including hepatotoxicity and reproductive and developmental toxicity. The presentation will conclude with a discussion of the consumer use of cannabis products, such as edibles, tinctures, capsules, and topicals.<\/p>\n

In vitro and in silico biological target screening of cannabinoids<\/strong>\u00a0(M. Santillo): Although there has been increasing interest in cannabis derived consumer products, many cannabinoids have unknown effects on human health. Using new approach methods, screening cannabinoids for binding to biological targets (receptors, ion channels, transporters, and enzymes) can serve as an initial step to identify potential human health effects of these compounds. Here, an overview will be presented on over a dozen cannabinoids, their interactions with safety-related targets, and potential health effects. Using in vitro enzyme inhibition and competitive ligand binding assays, important relationships were observed between target binding potency and cannabinoid structure due to carboxylic acid groups, alkyl chain lengths, and ring substructures. These structural features may cause differences in human health effects. Some examples of potent targets for cannabinoids include receptors (cannabinoid, adrenergic, serotonin) and transporters (norepinephrine, serotonin, dopamine). Among these targets, cannabinoids can bind in the high nanomolar range, which may translate to effects in the cardiovascular and nervous systems. Despite target binding being observed in vitro, cannabinoids aggregated into colloids depending on their molecular structure and concentration. Colloidal aggregation is a form of assay interference that may cause false positives in target binding assays, and more thorough work is needed to confirm this phenomenon in the future. In addition to in vitro assays, in silico screening by quantitative structure-activity relationship (QSAR) models identified additional targets for future exploration (e.g., retinoic acid receptors). Overall, in silico and in vitro biological target screening can help identify potential health effects of cannabinoids and prioritize future testing.<\/p>\n

Transcriptomic point-of-departure derivation for hemp extract and four major cannabinoids using a human iPSC-derived hepatotoxicity model<\/strong>\u00a0(X. Gao): The rapid rise of cannabis-derived consumer products in the United States and worldwide has sparked increasing safety concerns. While cannabidiol (CBD) has been linked to hepatotoxicity in clinical and preclinical studies, the safety profiles of other non-intoxicating cannabinoids remain largely uncharacterized. To protect public health, it is essential to establish safe exposure thresholds for these compounds. New approach methodologies (NAMs), particularly those leveraging bioinformatic analyses of high-throughput transcriptomic data, are emerging as powerful tools in chemical risk assessment and regulatory decision-making. Among these, transcriptomic points of departure (tPODs) derived from benchmark concentration (BMC) modeling of gene expression data offer a pragmatic and efficient approach for quantitative risk assessment of data-poor chemicals. In this presentation, we report findings from a concentration-response transcriptomic study of a hemp extract and its four major constituent cannabinoids \u2013 CBD, cannabichromene (CBC), cannabigerol (CBG), and cannabinol (CBN) \u2013 in hepatocytes derived from human induced pluripotent stem cells (iPSCs). Using BMDExpress software, we performed BMC modeling on microarray data to derive tPODs for each compound. The resulting potency rankings were consistent with in vitro cytotoxicity data, and the tPOD for CBD aligned with a previously published value based on apical endpoints from clinical and animal studies. A brief comparative analysis of toxicity mechanisms across the cannabinoids will also be presented. Overall, our findings highlight the value of transcriptomic BMC analysis as a NAM for human health risk assessment and provide tPOD estimates that may support regulatory decision-making on hemp extract and cannabinoids.<\/p>\n

Examining the hepatotoxic potential of cannabidiol, cannabidiol-containing hemp extract, and cannabinol at consumer-relevant exposure concentrations in primary human hepatocytes<\/strong>\u00a0(K. Eckstrum): Hemp extracts and products containing cannabidiol (CBD) and other phytocannabinoids derived from hemp have recently entered the marketplace. Liver injury has been observed in clinical trials of CBD on some occasions in association with Epidiolex, a drug using CBD to treat Dravet syndrome and Lennox-Gastaut syndrome. However, there is limited information on the hepatotoxicity of other phytocannabinoids purified from hemp and hemp extracts. To address this gap, primary human hepatocytes (PHH) were treated with CBD, cannabinol (CBN), or CBD-matched hemp extract for 24 or 48 hours at consumer relevant concentrations, 10 nM to 25 \u03bcM, based on concentrations reported in hemp products. Hepatotoxicity was determined by measuring lactate dehydrogenase, apoptosis, albumin secretion, urea secretion and mitochondrial membrane potential. Cytotoxicity was not significantly altered by CBD, CBN, or hemp extract at any concentration, however, there was a modest but significant loss in hepatocyte function with decreased albumin, urea, and mitochondrial membrane potential when treated with the highest concentration of hemp extract (25 \u03bcM). 25 \u03bcM of CBD alone only decreased albumin compared to control, suggesting that inclusion of other cannabinoids may contribute to either the toxicity of the extract or the solubility and delivery of the CBD to the cells. This study addresses data gaps in understanding cannabinoid hepatotoxicity in vitro, however, further studies are needed to correlate these consumer relevant exposures to biological effects of cannabinoids in the liver.<\/p>\n

Assessment of the effects of cannabidiol in\u00a0Caenorhabditis elegans<\/em><\/strong>\u00a0(P. Hunt): Multiple genetic pathways and molecular signaling cascades involved in organismal development and neuronal function are well conserved across phyla, supporting the use of small alternative animal models such as\u00a0C. elegans<\/em>\u00a0in toxicity assessment. From nematodes to humans, endocannabinoid signaling plays essential roles in organismal development, and exposure to exogenous cannabinoids can alter endocannabinoid signaling resulting in adverse developmental outcomes.\u00a0C. elegans<\/em>\u00a0can consume cannabidiol (CBD) via emulsions mixed with nutrient media, providing a platform for oral toxicity testing in a small model organism. In adult\u00a0C. elegans<\/em>, 25-100\u00b5g\/mL CBD reduced the expression of an oxidative stress response reporter in a dose-response manner. Adult exposures of 400\u00b5g\/mL CBD and above induced acute toxicity and reduced progeny production. Effects on juvenile growth were seen at 200\u00b5g\/mL CBD, consistent with greater CBD toxicity to developing\u00a0C. elegans<\/em>. CBD was associated with hypoactivity in juveniles, but in adults CBD counteracted high-fat diet induced hypoactivity.\u00a0C. elegans<\/em>\u00a0development from hatching to egg-laying adult takes about 3 days and is divided into four post-embryonic developmental stages.\u00a0C. elegans<\/em>\u00a0exposed to CBD for 24h post hatching had longer delays to the third and fourth larval stages than did cohorts continuously exposed to CBD, consistent with a withdrawal-like effect. For all\u00a0C. elegans<\/em>\u00a0endpoints in this study, a CBD-rich hemp extract was slightly less toxic than purified CBD at the same CBD exposures. Dosimetry indicated that all observed\u00a0C. elegans<\/em>\u00a0adverse effects occurred at exposures that exceeded recommended CBD dosing for humans.<\/p>\n

REGISTER NOW!<\/a><\/p>\n

Complete details are also available on the\u00a0Event Calendar Listing<\/a><\/strong>.<\/p>\n

Recordings and other materials from this webinar will be posted to the\u00a0ASCCT Webinar Archive<\/strong><\/a>.<\/p>\n

Information on additional upcoming webinars and other events is available on the\u00a0ASCCT Event Calendar<\/a><\/strong>.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<\/div>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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Wednesday, February 25, 2026: ESTIV-ASCCT: Award Winners Series: Evidence Integration & Predictive Performance<\/h3>\r\n
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Topic:\u00a0<\/strong>Evidence Integration & Predictive Performance<\/p>\n

Date:\u00a0<\/strong>Wednesday, February 25, 2026
\nTime:\u00a0<\/strong>10:00 am ET \/ 15:00 UTC<\/p>\n

FEATURING:<\/strong>
\nPrachi Pradeep, PhD:<\/strong>\u00a0\u201cNAM-based Assessment to Classify Chemicals for Systemic Toxicity Effects\u201d
\nASCCT 14th Annual Meeting Ed Carney Predictive Toxicology Award Recipient<\/em>
\nKenzie Pereira, PhD:<\/strong>\u00a0\u201cPredictivity Assessment of the Human Thyroid Microtissue Assay\u201d
\nASCCT 14th Annual Meeting Suzanne Fitzpatrick Student Travel Award Recipient<\/em>
\nRaymond Hu:<\/strong>\u00a0\u201cDevelopment of a curated database of skin sensitizers released from medical devices\u201d
\nASCCT 14th Annual Meeting Poster Award Recipient<\/em>A brief Q&A session will follow each presentation.<\/p>\n

ABSTRACTS:\u00a0<\/strong>
\nNAM-based Assessment to Classify Chemicals for Systemic Toxicity Effects<\/strong><\/em>
\nAuthors: Prachi Pradeep, Matthias Herzler, Ralph Pirow, Sebastian Schmeisser
\nDepartment of Chemical and Product Safety, German Federal Institute for Risk Assessment (BfR), Berlin, Germany
\nThe EPAA\u2013JRC NAMs Designathon is a joint initiative by the European Partnership for Alternative Approaches to Animal Testing (EPAA) and the EU\u2019s Joint Research Centre (JRC). It explores how New Approach Methodologies (NAMs) can support future systemic toxicity classification. Participants were invited to assign high, medium, or low concern levels to 150 reference chemicals using a transparent, reproducible process.
\nHere, we propose a stepwise screening workflow integrating NAM-based toxicodynamics (TD) and toxicokinetics (TK). Curated in vitro bioactivity data from high-throughput screening (HTS) assays were used to characterize chemical potency for systemic, repeated dose toxicity. In silico QSAR-predicted points of departure (PoDs) provided additional hazard information. Chemicals without sufficient TD data were assessed using read-across. TK modelling was used to estimate systemic availability following oral exposure. Chemicals were categorized into three concern levels for TD and TK separately, then combined using a classification matrix. Finally, a confidence score was assigned based on data type.
\nOf 150 chemicals, 98% were assessed using multiple lines of evidence demonstrating the feasibility of combining diverse NAMs within a single framework. This workflow serves as a proof-of-concept for early chemical hazard prioritization, while also highlighting current limitations, including data gaps, variability across evidence streams, and the need for continued methodological development and collaboration to support future regulatory applications.<\/p>\n

Predictivity Assessment of the Human Thyroid Microtissue Assay<\/em><\/strong>
\nAuthors: Kenzie Pereira, Briana Foley, Scott G. Lynn, Chad Deisenroth
\nThe US EPA aims to validate and adopt new approach methods (NAMs) into hazard screening for endocrine-disrupting chemicals. The human thyroid microtissue (hTMT) assay is a medium throughput in vitro assay that utilizes primary human thyrocytes to evaluate chemical effects on thyroid hormone synthesis. Previous work has provided detailed characterization of the hTMT model, including qualification criteria for human thyrocyte selection, acceptance criteria for assay performance, and benchmark potency ranges of reference chemicals for proficiency testing. While an interlaboratory validation study has assessed the relevance, reliability, and transferability of the assay using a small reference chemical set, additional validation is needed to evaluate the predictive accuracy of the assay using a broader chemical test set. Here, 49 chemicals were tested across 6 concentrations (0.001 \u2013 100 \u00b5M) with the standardized hTMT assay to assess chemical-induced cytotoxicity and disruption of thyroxine (T4) synthesis (n = 4). The chemical test set comprised four previously evaluated reference chemicals, and 45 additional chemicals curated by subject matter experts. These included known inhibitors of molecular initiating events (MIEs) within the thyroid adverse outcome pathway (AOP) network, as well as inactive chemicals. Initial results demonstrate a range of chemical-induced T4 disruption, consistent with previously published data on chemical-specific inhibition of thyroid MIEs. The final assessment of sensitivity and specificity will support EPA initiatives toward the implementation of NAMs to improve chemical hazard assessment strategies while reducing reliance on animal testing.\u00a0This abstract does not reflect US EPA policy.<\/em><\/p>\n

Development of a curated database of skin sensitizers released from medical devices<\/em><\/strong>
\nAuthors: Raymond Hu, Amel Mohamed, Brennan Lawless, Anh Nguyen, Jenna Glockner, Ron Brown
\nThe qualification of New Approach Methods (NAMs) for regulatory use requires the use of methods that are fit-for-purpose and qualified within a context of use that is specific for a narrowly defined group of regulated products (e.g., food additives, agrochemicals, medical devices). Therefore, when used for the safety assessment of medical devices, the NAM should be validated using compounds known to be released from materials used to manufacture medical devices. Doing so requires developing a carefully curated list of device-related skin sensitizers. An initial list of these compounds was compiled during development of the ISO Technical Specification, ISO\/TS 11796:2023 (Biological evaluation of medical devices \u2014 Requirements for inter-laboratory studies to demonstrate the applicability of validated in vitro methods to assess the skin sensitization of medical devices); however, concerns have been expressed by at least one regulatory agency that this list does not fully represent the universe of sensitizers that can be released from devices. To address this concern, an effort was undertaken to develop a comprehensive, curated list of skin sensitizers released from device materials. Following a systematic review of the data, a preliminary list of around 130 compounds that are both known to be sensitizers and also known to be released from device materials was compiled. This list can be used to qualify in vitro and in silico NAMs for the safety assessment of medical devices within a defined context of use.<\/p>\n

ABOUT THE PRESENTERS:<\/strong>
\nDr. Prachi Pradeep<\/strong>\u00a0is a Computational Toxicologist in the Department of Chemicals and Product Safety at the German Federal Institute for Risk Assessment (BfR). With over a decade of experience in cheminformatics and bioinformatics, she brings a strong data science perspective to chemical safety assessment. Her research focuses on the development and validation of predictive models, the regulatory integration of new approach methodologies (NAMs), and the characterization of data variability and uncertainty to support robust, science-based risk assessment. She holds a Master\u2019s degree in Bioinformatics and a PhD in Computational Sciences, completed in collaboration with the U.S. Food and Drug Administration\u2019s Center for Devices and Radiological Health, and undertook postdoctoral training at the U.S. EPA\u2019s Center for Computational Toxicology and Exposure.
\nDr. Kenzie Pereira<\/strong>\u00a0is an ORISE postdoctoral participant within the U.S. EPA Office of Chemical Safety and Pollution Prevention in the Endocrine Disruptor Screening Program. She is a Louisiana native and earned her Ph.D. from Duquesne University. Her background includes integrative research on vertebrate immunity, stress physiology, disease, and environmental drivers of health. Her current work focuses on evaluating the predictive capacity of an in vitro human thyroid microtissue (hTMT) assay as part of new approach method (NAM) prevalidation, supporting human-relevant, mechanistically informed screening for endocrine-disrupting chemicals.
\nRaymond Hu<\/strong>, is a master\u2019s student in Toxicology at Johns Hopkins University, with academic training in occupational health and computational toxicology. His research interests focus on chronic disease-related consumer products, integrating computational approaches with clinical evidence to improve product safety and risk assessment.
\nHe holds a BSN degree and has prior bedside clinical experience, bringing a clinically grounded perspective to toxicological risk assessment and exposure-related health outcomes. He is currently working with Risk Science Consortium, LLC, and JHU-affiliated clinics to develop a curated database of skin sensitizers released from medical devices.<\/p>\n

REGISTER NOW!<\/a><\/p>\n

Complete details are also available on the\u00a0Event Calendar Listing<\/a><\/strong>.
\nRecordings and other materials from this webinar will be posted to the\u00a0ASCCT Webinar Archive<\/strong><\/a>.
\nInformation on additional upcoming webinars and other events is available on the\u00a0ASCCT Event Calendar<\/a><\/strong>.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n <\/div>\r\n<\/div>\n\n\n

Webinars – 2025 <\/h2>\n\n\n\r\n
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Tuesday December 2, 2025: ESTIV\/ASCCT\/JSAAE Webinar Development of in vitro evaluation model for drug-induced proximal tubular injury using human proximal tubular epithelial cell spheroid<\/h3>\r\n
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We are pleased to announce this year’s joint webinar with The Japanese Society for Alternatives to Animal Experiments (JSAAE):<\/p>\n\n\n\n
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Topic:\u00a0<\/strong>Development of in vitro evaluation model for drug-induced proximal tubular injury using human proximal tubular epithelial cell spheroid<\/p>\n

When:<\/strong>Thursday, December 2, 2025
\n8:00am ET \/ 22:00 JST \/ 13:00 UTC<\/p>\n

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Featuring:<\/strong><\/p>\n

Hiroshi Arakawa, PhD,\u00a0<\/strong>Department of Regulatory Science, Graduate School of Pharmaceutical Sciences,\u00a0Nagoya City University<\/p>\n

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Acute kidney injury was caused by exposure to chemicals, including drugs, which significantly affects patient health and prognosis. Drug-induced acute kidney injury is frequently observed in proximal tubule because renal proximal tubular epithelial cells (RPTECs) have abundant expression of drug transporters1<\/sup>. Therefore, reliable risk assessment methodologies for drug-induced proximal tubular injury are essential. Although chemical-induced nephrotoxicity has been evaluated using experimental animals, concerns about animal welfare and limited human relevance highlight the need for useful New Approach Methodologies (NAMs). Our research group found that the expression levels of various drug transporters including OAT1 were increased in spheroidal cultured human RPTECs (3D-RPTECs)2<\/sup>, and that changes in intracellular ATP levels could be used as an indicator to assess drug-induced proximal tubular injury with high predictive performance3<\/sup>. We conducted a domestic pre-validation study of the cytotoxicity test using the intracellular ATP levels to examine the robustness of the evaluation system, and obtained high inter-laboratory reproducibility. Moreover, we have focused on a confocal imaging analysis that can evaluate functional changes in organelles such as mitochondria after drug exposure. In addition, we investigated proximal tubular injury by puberulic acid, a contaminant of red yeast rice supplement4<\/sup>. Collectively, our findings indicate that 3D-RPTEC not only provides mechanistic insights into drug-induced proximal tubular injury but also has potential as a versatile tool for broader chemical safety evaluation.
\nReferences<\/em><\/strong>1) Arakawa et al., Drug Metab Pharmacokint, 61:101056 (2025).
\n2) Ishiguro et al., Drug Metab Dispos, 51:1177\u201387 (2023).
\n3) Arakawa et al., J Pharm Sci, 113:3255\u201364 (2024).
\n4) Peng et al., Biol Pharm Bull, 48:928\u201331 (2025).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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About Dr. Arakawa<\/strong>
\nDr. Hiroshi Arakawa is a professor in the Department of Regulatory Science at Nagoya City University Graduate School of Pharmaceutical Sciences.\u00a0 He holds a PhD, MS, and BS all in pharmacokinetics.\u00a0 His research and ongoing work include construction and development of biomimetic models for drug safety assessment; drug side effects and drug-drug interactions via drug transporters; elucidation of mechanisms involved in pharmacokinetics and safety using clinical data; and elucidation of dynamics and physiological effects of uremic toxins.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
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Thursday July 24, 2025: ESTIV\/ASCCT Webinar on Next generation risk assessment for occupational chemical safety: A real world example with sodium-2-hydroxyethane sulfonate <\/h3>\r\n
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When:\u00a0Thursday, July 24, 2025
\n11:00 am \u2013 12:00 pm ET\u00a0 \/\u00a0 15:00 \u2013 16:00 UTC<\/p>\n

Featuring:
\nAdam Wood, MRes, Risk Assessor at Unilever
\nSteve Gutsell, PhD, Head of Regulatory Science for Chemical Safety at Unilever.<\/p>\n

A brief Q&A session will follow each presentation.<\/p>\n

Abstracts:
\nNext Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU\u00a0REACH<\/a>.
\nContrastingly, a large number of\u00a0 worker\/occupational safety assessments are underpinned by toxicological studies using experimental animals, hence occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Adapting previous NGRA workflows, we developed an occupational specific NGRA workflow, before testing the feasibility of this to make real-world safety decisions with a case study chemical; sodium 2-hydroxyethane\u00a0sulphonate\u00a0<\/a>(sodium\u00a0isethionate<\/a>\u00a0or SI).
\nExposures were first estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8\u202f\u03bcM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and\u00a0developmental toxicity<\/a>; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044\u202f\u00b5M. Cmax\u00a0values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management.
\nIn summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that\u00a0animal testing<\/a>\u00a0is only conducted as a \u201clast resort\u201d e.g. under EU REACH.<\/p>\n

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Thursday, April 24, 2025: ESTIV-ASCCT Award Winners Series: Gabriela De Oliveira Prado Correa, Sezin Aday Aydin, and Amber Daniel<\/h3>\r\n
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\u00a0When:\u00a0Thursday, April 24, 2025
\n10:00 am ET \/ 14:00 UTC<\/p>\n

Featuring:
\nGabriela De Oliveira Prado Correa:\u00a0\u201cAssessing Computational Approaches for Predicting Estrogen Receptor Binding\u201d
\nASCCT 13th Annual Meeting Travel Award Recipient<\/em><\/p>\n

Sezin Aday Aydin:\u00a0\u201cLung-on-a-chip Model for the Discovery of Chlorine Exposure Biomarkers\u201d
\nASCCT 13th Annual Meeting Poster Award Recipient<\/em><\/p>\n

Amber Daniel:\u00a0\u201cDefined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals\u201d
\nASCCT 13th Annual Meeting Poster Award Recipient<\/em>\u00a0A brief Q&A session will follow each presentation.<\/p>\n

Abstracts:
\nAssessing Computational Approaches for Predicting Estrogen Receptor Binding:\u00a0Endocrine-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 \u2265 -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,\u00a0in silico<\/em>\u00a0approaches 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.<\/p>\n

Lung-on-a-chip Model for the Discovery of Chlorine Exposure Biomarkers:\u00a0Adverse 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.\u00a0The 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.<\/em><\/p>\n

Defined Approaches for Predicting GHS and EPA Eye Irritation Classification of Agrochemicals:\u00a0Certain 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 (\u201cDA-BCOP+\u201d), or combined with EpiOcular\u2122, SkinEthic time-to-toxicity for liquids, or EyeIRR-IS (\u201cDA-EO+\u201d, \u201cDA-TTL+\u201d, and \u201cDA-EyeIRR-IS+\u201d, 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.\u00a0This project was funded with federal funds from the NIEHS, NIH under Contract No. HHSN273201500010C.<\/em><\/p>\n

About the Presenters:
\nGabriela De Oliveira Prado Correa, PhD\u00a0is a researcher in the field of human and environmental toxicology, with a particular focus on\u00a0in silico<\/em>\u00a0methods. 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.<\/p>\n

Sezin Aday Aydin, PhD\u00a0is a research associate at the University of Pennsylvania and program manager in the BARDA-funded \u201cInhalation Toxicology of Chlorine Gas-on-a-chip\u201d project. She develops organ-on-a-chip models\u00a0to reproduce living tissues and their native microenvironments, to understand cell-cell interactions, and to find biomarkers for different injuries.<\/p>\n

Amber Daniel\u00a0is 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.
\nRecordings and other materials from this webinar will be posted on the ASCCT webinar archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a>.<\/p>\n

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Tuesday, March 11, 2025: ESTIV – ASCCT Award Winners Series: Madison Feshuk, Bhaja Padhi, and Rachel Broughton<\/h3>\r\n
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When:<\/strong>Tuesday, March 11, 2025
\n10:00am ET \/ 15:00 UTC
\nFeaturing:<\/strong><\/p>\n

Madison Feshuk, MPH&TM:<\/strong>\u00a0\u201cGenerating GD211-Aligned Documentation for ToxCast to Support Assay Interpretation and Data Use\u201d
\nASCCT 13th Annual Meeting Poster Award Recipient<\/em><\/p>\n

Bhaja K. Padhi, PhD, DABT:<\/strong>\u00a0\u201cFacilitating the regulatory uptake of transcriptional biomarkers for chemical hazard screening: From gene sequence to biology\u201d
\nPhD Posters Award Recipient at the ASCCT 13th Annual Meeting<\/em><\/p>\n

Rachel Broughton, PhD:<\/strong>\u00a0\u201cDevelopment of Mathematical New Approach Methods to Assess Chemical Mixtures\u201d
\nThe Ed Carney Predictive Toxicology Award Recipient at the ASCCT 13th Annual Meeting<\/em><\/p>\n

A brief Q&A session will follow each presentation<\/p>\n

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Generating GD211-Aligned Documentation for ToxCast to Support Assay Interpretation and Data Use:<\/strong>\u00a0The U.S. EPA\u2019s\u00a0Toxicity Forecaster (ToxCast)<\/a>\u00a0program provides a large and impactful public repository of targeted new approach methods (NAMs) for toxicology. Assays employ a variety of technologies to evaluate the effects of chemical exposure on diverse biological targets for almost 10,000 substances. The\u00a0ToxCast\u00a0pipeline R software package, tcpl, enables flexible data processing and analysis to process, model, and visualize concentration-response screening data as well as populate a linked MySQL database,\u00a0invitrodb. Invitrodb contains manually curated and expert-reviewed assay annotation information, describing experimental and biological details that are essential for proper interpretation of screening results. Complementary to any data generation and processing effort, assay documentation using internationally harmonized standards ensures data are transparent, accessible, and interoperable, thereby increasing confidence for the adoption of assay data in next generation chemical assessment. Organisation for Economic Co-operation and Development (OECD) guidance document (GD) 211 suggests components of comprehensive assay documentation describing non-guideline\u00a0in<\/em>\u00a0vitro<\/em>\u00a0test methods and their interpretation. The intent of GD 211 is to harmonize non-guideline,\u00a0in vitro<\/em>\u00a0method descriptions to allow assessment of the relevance of the test method for biological response and data quality. Legacy GD 211-based documentation was manually maintained and covered less than 100 assay endpoints. Given major ToxCast software and database enhancements plus a desire to expand assay coverage and automate report generation, a complete overhaul to existing documentation process was undertaken. A compiled report and assay description documents for 809 out of 1507 endpoints in the database were released in September 2024, accompanying the invitrodb v4.2 database release.\u00a0 For this iteration, assay description documents cover 18 assay sources and a diverse biological space, including, but not limited to, the priority areas of androgen and estrogen receptor (AR\/ER), steroidogenesis, thyroid bioactivity, and the developmental neurotoxicity\u00a0in<\/em>\u00a0vitro<\/em>\u00a0battery (DNT-IVB). These reports are a work in progress and will be iteratively updated as descriptions improve from user feedback and more information becomes available. This documentation effort represents a novel, semi-automated and large-scale application of the GD211 template with all underlying information also databased. Along with the standardized and documented data processing procedures within tcpl, ToxCast continues to serve as an important publicly available data resource that follows and supports the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles needed for data interoperability in workflows that utilize bioactivity data.\u00a0This abstract does not necessarily reflect U.S. EPA policy.<\/em><\/p>\n

Facilitating the regulatory uptake of transcriptional biomarkers for chemical hazard screening: From gene sequence to biology:\u00a0<\/strong>Regulatory authorities worldwide are pushing for the use of gene expression data to assess chemical toxicity. However, issues with gene expression data reproducibility and interpretation still need to be addressed to take full advantage of this approach. A panel of transcriptional (mRNA) biomarkers can offer valuable insights into molecular response to chemical exposure. We will present a framework and workflow that combines bioinformatics and experimental analyses to characterize gene expression biomarkers by RT-qPCR, an affordable and widely available method. The framework includes the identification of biomarker genes based on conserved evolutionary (orthology) and biological process (ontology). Additionally, the incomplete annotation of the gene transcript diversity resulting from alternative splicing can interfere with proper measurement of transcript levels. As a mitigation strategy, we leveraged human, mouse and rat genomic databases and developed a strategic amplicon sequencing workflow to validate transcript target sequences in orthologous coding exons of biomarker genes from these species. This approach can help to avoid gene regions affected by alternative splicing, and also facilitate the comparison of gene expression data across rodents and humans. Preliminary proof-of-concept work assessing rat primary neuronal cells differentiation and synaptogenesis identified potential biomarkers of developmental neurotoxicity, designed robust gene expression measurement protocols, and generated promising results. Future studies will test additional neurotoxic and non-neurotoxic chemicals to evaluate the assay\u2019s biological relevance and predictivity. Perturbations of these evolutionarily conserved transcriptional biomarkers may be used to monitor Key Events in an Adverse Outcome Pathway approach.<\/p>\n

Development of Mathematical New Approach Methods to Assess Chemical Mixtures:<\/strong>\u00a0The U.S. EPA\u2019s Toxicity Forecaster (ToxCast) program contains targeted bioactivity screening data for thousands of single chemicals to inform prioritization and hazard prediction. However, environmental contaminants in practice are often encountered as coexposures; thus, it is of high importance to study chemical mixtures. The aim of this work is to leverage the readily available single chemical screening assay data in ToxCast to make predictions of the bioactivity of binary chemical mixtures. Mathematical relationships between mixtures and their single chemical constituents are applied with two different models, concentration addition and independent action. To evaluate the performance of these mixture models, an empirical dataset was collected for 21 chemical mixtures and their single constituents screened in concentration-response using a multidimensional\u00a0in vitro\u00a0<\/em>platform for transcription factor activity. Point of departure estimates were compared among the mixture model predictions and bootstrap resampling was performed to obtain confidence intervals on the models. Out of 1701 mixture-assay endpoint responses, 214 demonstrated a positive response with at least one active single constituent; in these cases, about 90% of the predicted mixture activity concentrations at the cutoff level fell within 0.5 log10<\/sub>-\u00b5M of the observed cutoff concentrations and about 80% of the predicted mixture bootstrapped intervals exhibited significant overlap with the observed mixture intervals. As it is resource-prohibitive to screen all combinations of chemicals, the determination of conservative mixture predictions and uncertainties is critical for operationalizing existing ToxCast data for the forecasting of simulated mixtures from real-life coexposures.\u00a0This abstract does not necessarily reflect U.S. EPA policy.<\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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About the Presenters:\u00a0<\/strong>
\nMadison Feshuk<\/a><\/strong>\u00a0is a biologist at the US Environmental Protection Agency\u2019s Center for Computational Toxicology and Exposure (CCTE). She supports efforts\u00a0crucial to promoting the adoption of new approach methods (NAMs) and increasing data interoperability to ultimately achieve reductions in animal testing and improve environmental risk assessment and regulatory decision-making.<\/p>\n

Bhaja K. Padhi, PhD, DABT<\/strong>, is a Research Biologist at the Environmental Health Science and Research Bureau of Health Canada. He completed his postdoctoral training at the Ottawa Health Research Institute and earned a Certificate in Bioinformatics accredited by the University of British Columbia, Canada. Additionally, he has served as a Visiting Scientist at Johns Hopkins University in Baltimore, Maryland, USA. Padhi is a Diplomate of the American Board of Toxicology and is a committee member of the Natural Sciences and Engineering Research Council of Canada (NSERC) on the \u201cGenes, Cells, and Molecules\u201d team. To date, he has published more than 40 research papers and authored a book.<\/p>\n

Rachel Broughton<\/strong>\u00a0is an ORISE postdoctoral fellow at the U.S. Environmental Protection Agency within the Center for Computational Toxicology and Exposure. Her research focuses on developing computational techniques to predict the bioactivity behavior of chemical mixtures that are found in common co-exposures.<\/p>\n

Register now!<\/a><\/p>\n

Recordings and other materials from this webinar will be posted on the ASCCT webinar archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a>.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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Thursday, February 20, 2025: ASCCT-ESTIV Award Winners Series: Hung-Lin Kan and Ricardo Scheufen Tieghi <\/h3>\r\n
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Thursday, February 20, 2025<\/span><\/strong><\/p>\n

10:00am ET \/ 15:00 UTC <\/span><\/strong><\/p>\n

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Featuring:<\/span><\/p>\n

Hung-Lin Kan, PhD:<\/span><\/strong>\u00a0“Inferring potential effects of PFASs via a novel chemical-phenotype inference system ZFinfer”<\/span> <\/span><\/p>\n

ASCCT 13th Annual Meeting Travel Award Recipient<\/span><\/em> <\/span><\/p>\n

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Ricardo Scheufen Tieghi:<\/span><\/strong>\u00a0\u201cDeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential\u201d<\/span>
\n<\/span>The Suzanne Fitzpatrick Student Travel Award Recipient at the ASCCT 13th Annual Meeting<\/span><\/em><\/p>\n

A brief Q&A session will follow each presentation.<\/span><\/p>\n

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ABSTRACTS<\/span><\/strong><\/p>\n

Inferring potential effects of PFASs via a novel chemical-phenotype inference system ZFinfer: <\/span><\/strong>Zebrafish is a useful model organism for toxicological research due to their small size, fast reproduction, and genetic similarity to humans. However, as environmental pollutants increase, it becomes difficult to identify all hazards using zebrafish models alone. In-silico models can help prioritize chemicals for further experimental evaluation and provide insight into underlying mechanisms. Chemical-phenotype inference system for zebrafish (ZFinfer) is a tool that uses chemical-protein interaction data from the STITCH database and gene-phenotype annotation data from the Zebrafish Information Network (ZFIN) to derive potentially affected phenotypes for quick data gap filling. ZFinfer currently includes 419,328 chemicals, 23,180 zebrafish proteins, and 3,104 zebrafish phenotypes. The system was validated using 777 ToxCast chemicals and 51 priority pollutants from the USEPA. The inference results demonstrated a sensitivity of 0.72 in critical morphological endpoints and a 93% rediscovery rate in the effect groups of known toxicity records in the ECOTOX knowledgebase. ZFinfer can help fill data gaps in environmental contaminants research, such as per- and poly-fluoroalkyl substances (PFAS), which are of concern due to their persistence and harmful effects on humans and the environment. For 164 PFAS chemicals, ZFinfer identified 5,188 potentially affected phenotypes. Additionally, ZFinfer can predict the affected phenotypes of unknown compounds based on chemical similarity. Using a Tanimoto similarity threshold of 0.65, ZFinfer predicted 1,917,718 chemical-protein interactions and identified 302,575 potentially affected phenotypes for 5,032 PFAS chemicals. ZFinfer could be used to prioritize chemicals for further evaluation, and it can be employed in drug discovery and environmental chemical hazard regulations.\u00a0<\/span><\/p>\n

DeTox: an In-Silico Alternative to Animal Testing for Predicting Developmental Toxicity Potential:<\/span><\/strong>\u00a0<\/span> <\/span><\/p>\n

Background<\/span><\/i><\/strong>:<\/span><\/em> Understanding potential developmental toxicity hazards associated with pharmaceutical and personal care products is vital for healthy pregnancies. These hazards can be predicted from chemical structures using Quantitative Structure-Activity Relationship (QSAR) models; however, developing reliable models is challenging due to the complexity of this endpoint.<\/span> <\/span><\/p>\n

Objectives<\/span><\/i><\/strong>:<\/span><\/em> This study aims to collect and curate a database of compounds, classify them according to developmental toxicity potential, develop and validate QSAR models for predicting prenatal developmental toxicity, and implement these models via an online platform to support improved regulatory assessments.<\/span> <\/span><\/p>\n

Methods<\/span><\/i><\/strong>:<\/span><\/em> By aggregating and curating data from the Food and Drug Administration (FDA), Teratogen Information System (TERIS), and select independent studies, we created the largest publicly available dataset of compounds annotated as developmental toxicants or non-toxicants.<\/span> <\/span><\/p>\n

Results<\/span><\/i><\/strong>:<\/span><\/em> We built binary classification QSAR models exhibiting a correct classification rate of 62-72%, a sensitivity of 66-75%, a specificity of 59-82%, and high coverage of 70-90%, assessed using five-fold external validation protocols. We developed a publicly accessible web portal (https:\/\/detox.mml.unc.edu\/<\/span><\/a>) for predicting developmental toxicity, including trimester-specific toxicity predictions.
\nConclusions<\/span><\/i><\/strong>:<\/span><\/em> Due to the high accuracy, coverage, and public accessibility of the web portal, our models can support screening and regulatory assessments of pharmaceutical and cosmetic products, aligning with the 3Rs of animal testing. This in silico model has the potential to support regulatory practices toward safer drug development for pregnant women and better environmental chemical toxicological assessments. The curated dataset and developed models are available as a user-friendly web tool, DeTox, at\u00a0https:\/\/detox.mml.unc.edu\/<\/span><\/a>.<\/span> <\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

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About the Presenters<\/span><\/strong><\/p>\n

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Dr. Hung-Lin Kan<\/span><\/strong>\u00a0is a Postdoctoral Research Fellow at the National Health Research Institutes in Taiwan. His research focuses on developing alternative methods to animal testing, particularly through in silico models designed to predict toxicity endpoints. During his PhD, he created a predictive model for neurotoxicity related to Parkinsonian motor deficits, based on the Adverse Outcome Pathway (AOP) concept. Currently, he is working on advancing a virtual zebrafish system to assess environmental hazards and minimize the use of animals in research.<\/span><\/p>\n

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Ricardo Scheufen Tieghi<\/span><\/strong>\u00a0is a Carolina Research Scholar at the University of North Carolina at Chapel Hill and a computational\u00a0toxicology\u00a0intern at the NIEHS working with Dr. Nicole Kleinstreuer. His projects focus on replacing animal testing by leveraging artificial intelligence and machine learning.\u00a0<\/span><\/p>\n

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Register now!<\/a><\/p>\n

Recordings and other materials from this\u00a0webinar\u00a0will be posted on the\u00a0ASCCT\u00a0webinar\u00a0archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a>.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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January 10, 2025: ASCCT-ESTIV Award Winners Series: Giulia De Negri Atanasio & Kim To<\/h3>\r\n
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Friday, January 10, 2025<\/strong><\/p>\n

10:00am ET \/ 15:00 UTC<\/strong><\/p>\n

Giulia De Negri Atanasio:\u00a0<\/strong>\u201cEvaluation of Physiological Repeated Exposure of Aluminium in a 3D Intestinal Tissue Model.\u201d\u00a0ESTIV Best Oral Presentation Award at ESTIV 2024<\/em><\/p>\n

Kim To:<\/strong>\u00a0“DASS App v2.0: Implementing OECD Guideline No. 497 Updates”\u00a0PhD Posters Award at the ASCCT 13th Annual Meeting<\/em><\/p>\n

A brief Q&A session will follow each presentation.<\/p>\n

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ABSTRACTS<\/strong><\/p>\n

Evaluation of Physiological Repeated Exposure of Aluminium in a 3D Intestinal Tissue Model<\/strong><\/p>\n

Background and Objectives:\u00a0<\/em>One of the most prevalent elements in the crust of the Earth is aluminium (Al3+). The free cation of Al3+ is very physiologically reactive and might disrupt metabolism, perhaps causing gastrointestinal problems and neurotoxicity, which would raise the prevalence of Alzheimer’s disease.<\/p>\n

Since soil contains a significant quantity of Al3+, the amount that plants absorb depends on how much of it is present there. Humans, indeed, consume Al3+ every day through their food and drink. The total weekly exposure to Al3+ was calculated to be 1 mg\/kg body weight.<\/p>\n

Material and methods:\u00a0<\/em>This study aimed to assess the impact of repeated exposure to varying concentrations of Al3+ on a 3D intestinal tissue model. Over a 12-day period, different Al3+ concentrations (5, 20, and 50 ppm) were applied on epiIntestinal 3D models (MatTek). The study employed multiple methods to evaluate the effects, including TEER measurements to assess tissue viability, ICP-AES analysis to quantify Al3+ trespass, histological assessment for tissue architecture, evaluation of tissue barrier integrity, transmission electron microscopy (TEM) analysis of microvilli structure, and mRNA expression analysis (CLDN-5, and OCLN,) to investigate oxidative processes at the end of the 12-day exposure period.
\nResults:\u00a0<\/em>Toxicity was not observed across all tested Al3+ concentrations, as indicated by TEER measurements that showed no significant differences between the treatments and the negative control. These findings were further corroborated through histological examination of the tissues.
\nDiscussion and Conclusion:\u00a0<\/em>Overall, the results of this study suggest that the repeated exposure to Al3+ in the tested concentrations did not lead to significant detrimental effects on the 3D intestinal tissue model. These findings contribute to our understanding of the safety of Al3+ exposure within the context of the gastrointestinal system. Further research may be needed to explore any potential long-term effects and to understand the possible implications for human health.<\/p>\n

DASS App v2.0: Implementing OECD Guideline No. 497 Updates<\/strong><\/p>\n

The National Toxicology Program\u2019s DASS App (https:\/\/ntp.niehs.nih.gov\/go\/40498<\/a><\/span>)\u00a0is an open-source web application developed to streamline the implementation of defined approaches (DA) to predict skin sensitization hazard and potency. Here we describe release updates for DASS App v2.0, which coincide with pending updates to OECD Guideline No. 497: Defined Approaches on Skin Sensitisation. DAs are mechanistically driven non-animal test strategies that outline rules for interpreting data from multiple in vitro or in chemico tests or in silico models to derive high-confidence toxicity predictions. The DASS App features three DAs that users can apply to their uploaded data: the Two-out-of-Three (2o3), Integrated Testing Strategy (ITS), and Key Event 3\/1 Sequential Testing Strategy. The pending OECD Guideline update defines additional assays and model options suitable for the 2o3 and ITS DAs. With the score-based ITS DA, users can choose from various assay-specific scoring schemes (previously limited to the direct peptide reactivity assay and human cell line activation test). For the consensus-based 2o3 DA, the app can now process data from individual assay runs to flag borderline results before applying the 2o3 data interpretation procedure. Additionally, the app now enables evaluating DA results against reference data from the Integrated Chemical Environment (https:\/\/ice.ntp.niehs.nih.gov\/<\/a><\/span>)\u00a0and visualizing DA performance based on chemical-specific parameters. The new capabilities within the DASS App support consistent application and increased adoption of guideline DAs and provide new performance evaluation options for building confidence in non-animal methods. This project was funded by the NIEHS under Contract No. HHSN273201500010C.<\/p>\n

About the Presenters<\/strong><\/p>\n

Giulia De Negri Atanasio<\/strong>\u00a0is currently a postdoctoral at the Department of Earth, Environment, and Life Science (DISTAV), University of Genoa, Italy in the group coordinated by Prof. Elena Grasselli.\u00a0Now she is involved in the Horizon 2020 \u2018EcoeFISHent\u2019 and working on the biocompatibility tests.\u00a0Her work is focused on the study of skin and intestine physiology related to ingredients or formulated products in the cosmetics and nutraceutical field.<\/p>\n

Kim To<\/strong>\u00a0is an environmental statistician at ICF supporting toxicological and environmental health data analysis. Prior to ICF, Dr. To was a principal biostatistician at Inotiv where she supported the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods through computational tool development and leadership of the Integrated Chemical Environment.<\/p>\n

Register now.<\/a><\/p>\n

Please see the\u00a0Event Calendar Listing<\/span><\/strong><\/a> for complete details.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n <\/div>\r\n<\/div>\n\n\n

Webinars – 2024<\/h2>\n\n\n\r\n
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December 04, 2024 Webinar: JSAAE & ASCCT-ESTIV Joint Webinar: Prediction of skin sensitization using machine learning<\/h3>\r\n
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Wednesday, December 04, 2024 8:00 AM – 9:00 AM EST<\/p>\n

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Presenter:<\/strong> Dr. Kaori Ambe<\/div>\n
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ABSTRACT<\/strong><\/p>\n

Animal testing for the safety evaluations of cosmetic ingredients has been banned in Europe since 2013, the development of alternatives to animal testing in evaluating skin sensitization has been promoted. Some alternative methods using\u00a0in vitro<\/em>\u00a0assay addressing different key events of skin sensitization adverse outcome pathway (AOP) have been officialized. Because skin sensitization is a complex immune reaction, it is difficult to evaluate using a single alternative method. Therefore, Integrated Approaches to Testing and Assessment (IATA) that integrate results from multiple approaches such as\u00a0in vitro\u00a0<\/em>or\u00a0in silico<\/em>\u00a0are required.<\/p>\n

In this presentation, I will introduce our machine learning-based prediction method for skin sensitization evaluation. The estimated concentrations for a stimulation index of 3 (EC3) in murine local lymph node assay (LLNA) is an important quantitative value for determining the strength of skin sensitization to chemicals. We developed an LLNA EC3 regression model using CatBoost, a new gradient boosting decision tree, based on the reliable Cosmetics Europe database. The regression model combining\u00a0in chemico<\/em>\/in vitro<\/em>\u00a0tests, physical properties, and chemical information related to the key event of AOP on skin sensitization showed excellent performance with a coefficient of determination (R2) of 0.75. Furthermore, Next Generation Risk Assessment (NGRA) case study of skin sensitization in hair dye was conducted using this regression model. Our LLNA EC3 regression model is expected to serve as a useful approach for the quantitative assessment of skin sensitization.<\/p>\n

About Dr. Ambe<\/strong><\/p>\n

Dr. Ambe is an Associate Professor at the Department of Data Science, Nagoya City University in Japan, and a researcher in computational toxicology and regulatory science. She works on developing and applying in silico approaches using machine learning for predictive toxicology.<\/p>\n<\/div>\n

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Recordings and other materials from this webinar will be posted on the ASCCT webinar archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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November 22, 2024 Webinar: ASCCT-ESTIV Award Winners Series: Dr. Eliska Kuchovska & Tiago Marques Pedro<\/h3>\r\n
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ASCCT-ESTIV webinar will be featuring two excellent early career scientists and award recipients:<\/p>\n

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Dr. Eliska Kuchovska, PhD<\/strong>: \u201cOntology-Based AI-driven Innovative Approach Using DNT NAMs for NGRA. JSAAE Best Oral Presentation Award at ESTIV 2024<\/strong>
\nTiago Marques Pedro<\/strong>: \u201cUsing a Machine Learning Framework to Improve the Efficiency of Mitochondrial Toxicity Screening by Guiding Compound Selection.\u201dESTIV Best Oral Presentation award at EUROTOX 2024<\/strong><\/p>\n\n\n\n
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Friday, November 22, 2024 at 10:00 AM – 11:00 <\/span>AM<\/span> EST<\/span><\/p>\n<\/div>\n<\/div>\n

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ABSTRACTS<\/strong><\/p>\n

Ontology-Based AI-driven Innovative Approach Using DNT NAMs for NGRA:<\/strong>\u00a0The current regulatory guidelines for assessing developmental neurotoxicity (DNT) are inadequate for hazard assessment of the vast array of chemicals in our environment. Additionally, our understanding of human brain development often relies on animal-derived data, which may not accurately represent the human context. Therefore, there is a critical need for more reliable and efficient human-centered new approach methodologies (NAMs), ideally combining\u00a0in vitro<\/em>\u00a0and\u00a0in silico<\/em>\u00a0methods: a strategy applied in the European H2020 ONTOX project [1].<\/p>\n<\/div>\n

To construct the DNT ontology, a framework for data integration, we first shaped its pillars. The first pillar consists of a comprehensive physiological map of the developing human brain, serving as a foundational knowledge base that provides insights into the biological underpinnings and physiological mechanisms crucial for healthy brain development. The second pillar is a meticulously curated adverse outcome pathway (AOP) network which describes events leading to the adverse outcome of decreased cognitive function and impaired learning and memory. Subsequently, a tailored human\u00a0in vitro<\/em>\u00a0battery was selected and characterized regarding its biological applicability domain to determine its relevance to human physiology and neurodevelopmental disorders.<\/p>\n

The DNT\u00a0in vitro<\/em>\u00a0NAMs, as well as the physiological map, also serve to derive new AOPs to complete the current AOP network. Furthermore, the DNT ontology and assay characterization contribute to bolstering the regulatory acceptance of\u00a0in vitro<\/em>\u00a0NAMs by reducing their uncertainty. Ultimately, the final iteration of this ontology-based approach, which combines\u00a0in silico<\/em>\u00a0and\u00a0in vitro\u00a0<\/em>NAMs with exposure assessment, will predict the effects of chemicals on the developing human brain without the need for animal testing and advance human risk assessment in line with the principles of next generation risk assessment (NGRA).<\/p>\n

[1]\u00a0Vinken, M., Benfenati, E., Busquet, F., Castell, J., Clevert, D.-A., de Kok, T. M., Dirven, H., Fritsche, E., Geris, L., Gozalbes, R., Hartung, T., Jennen, D., Jover, R., Kandarova, H., Kramer, N., Krul, C., Luechtefeld, T., Masereeuw, R., Roggen, E., \u2026 Piersma, A. H. 2021, \u2018Safer chemicals using less animals: kick-off of the European ONTOX project\u2018,\u00a0Toxicology<\/em>, 458, 152846<\/p>\n

Using a Machine Learning Framework to Improve the Efficiency of Mitochondrial Toxicity Screening by Guiding Compound Selection:<\/strong>\u00a0Mitochondrial dysfunction plays a major role in the onset of off-target drug effects, including hepatotoxicity and cardiotoxicity. Early identification of potential mitochondrial toxicants during drug development is essential to prevent these off-target toxicities. Despite the utilisation of established assays for mitochondrial function measurement, these methods usually lack efficiency and intent, reflected in their indiscriminate approach to compound screening selection. Therefore, harnessing machine learning (ML) workflows may provide avenues to improve the screening proficiency of mitochondrial toxicants.<\/p>\n

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Active learning (AL), a ML framework, was employed to guide compound selection and improve screening time to demonstrate the strengths of this hybrid screening approach. An initial screen, conducted on 1520 compounds from the Prestwick Chemical Library using an ATP cell viability assay in metabolically-switched HepG2 cells, revealed over 100 mitochondrial toxins. These results were then used to iteratively train the AL model, demonstrating a 2-fold improvement in identifying true positives compared to random selection when only half of the library was used in the training set. Further investigation is required to determine the features driving uncertainty, with structural similarity across mitotox class, physicochemical properties and mechanism of action theorised as potential key players. This study demonstrates integrating AL-enhanced mitochondrial toxicity screening may reduce screening time whilst preserving true positive identification for drug safety assessment.<\/p>\n

About the Presenters<\/strong><\/p>\n

Eli\u0161ka Kuchovsk\u00e1<\/a>\u00a0is a postdoctoral researcher at the Leibniz Research Institute for Environmental Medicine in Germany, specializing in the development of human-relevant, animal-free New Approach Methodologies (NAMs) to evaluate the effects of chemicals on brain development. Driven by her fascination with the complexity of the brain, Eli\u0161ka combines rigorous research with an enthusiasm for science communication. She actively promotes career development for early-career researchers, serving as chair of the\u00a0ASPIS Academy<\/a>\u00a0and the\u00a0EUROTOX Early Career Forum<\/a>.<\/p>\n

Tiago Marques Pedro earned his BSc in Biomedical Science at the University of Surrey, UK, which included a research placement year in toxicology at Weill Cornell Medicine in New York, USA. He went on to pursue a Master’s in Toxicology at Karolinska Institute in Stockholm, Sweden, where he explored the effects of environmental pollution mixtures on genotoxicity, and the use of DNA repair inhibitors to combat resistance to\u00a0cisplatin-based chemotherapies, across various research projects. Currently a\u00a03rd<\/sup>\u00a0year PhD student at the University of Cambridge, Tiago’s project focuses on using machine learning to predict mitochondrial toxicity.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

A brief Q&A session will follow each presentation. More details will be provided soon.<\/p>\n

Registration:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_ZdJ5Lj5DSiKmYO2y_B1FNQ<\/a><\/strong><\/p>\n

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Recordings and other materials from this webinar will be posted on the ASCCT webinar archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a><\/p>\n

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July 16, 2024 Webinar: Progress in in vitro Thyroid Disruption Approaches<\/h3>\r\n
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Tuesday, July 16, 2024 at 10:00 AM EDT<\/p>\n

Registration is open!<\/a><\/h3>\n

Featuring:\u00a0Chad Deisenroth, PhD<\/strong>\u00a0and\u00a0Kostja Renko, Dr. rer. Nat.<\/strong><\/p>\n

Chad Deisenroth, PhD,<\/strong>\u00a0will present “Advancing Translational Application and Acceptance of The 3D Human Thyroid Microtissue Assay”<\/p>\n

Dr. Deisenroth is a cell biologist in the Center for Computational Toxicology and Exposure at the U.S. EPA with 14 years of experience in the development and application of in vitro new approach methods for predictive toxicology.<\/p>\n

Abstract:<\/em>\u00a0The human thyroid microtissue (hTMT) assay utilizes primary human thyrocytes to reproduce structural and functional features of the thyroid gland to enable testing for potential thyroid-disrupting chemicals. As a variable-donor assay platform, conventional principles for assay performance standardization need to be balanced with the ability to predict a range of human responses. This presentation will highlight current efforts to evaluate biological and toxicological variability in the model to establish criteria for donor thyrocyte qualification and performance guidelines for method validation.<\/p>\n

Kostja Renko, Dr. rer. Nat.,<\/strong>\u00a0will present “Identification of potential thyroid hormone disruptors using the Sandell-Kolthoff reaction as a technology platform.”<\/p>\n

Dr. Renko is a scientist at the German Federal Institute for Risk Assessment in the unit “Strategies for Toxicological Assessments.” As a biotechnologist, he works on in vitro methods for the identification and characterization of potential thyroid disrupting chemicals.<\/p>\n

Abstract:\u00a0<\/em>The Sandell-Kolthoff (SK) reaction, as a “classical” analytical tool for the detection and quantification of minute amounts of iodine, has recently gained interest through its increasing use as a readout for in vitro methods in the thyroid field. As many processes and key functions of the thyroid axis involve the transport or metabolism of iodine-containing molecules, the SK reaction has emerged as a useful and easy-to-use readout for several assays that reflect crucial key functions\/molecular initiating events of the hormonal axis, including TH metabolising deiodinases, TH transport via transmembrane transporters (e.g. MCT8) or activity of the sodium\/iodide symporter. The webinar will give an overview of the applications and the potential of using a unified SK setup as a central platform to perform in vitro tests and ex vivo analysis.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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May 14, 2024 Webinar: From physiological maps to ontology maps using a systems biology approach<\/h3>\r\n
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Tuesday, May 14, 2024 at 16:00 CET<\/p>\n

Registration is open!<\/a><\/h3>\n

Presenter: Luiz Ladeira, PhD\u00a0\u2013 Postdoctoral researcher at the Biomechanics Research Unit, GIGA Molecular & Computational Biology, at the University of Li\u00e8ge (Belgium).<\/p>\n

Physiological Maps (PM) provide comprehensive graphic representations of biological processes and molecular interactions. They facilitate the description of physiological processes in a mechanistic and modularized fashion. This approach enables the depiction of intricate interactions and the incorporation and organization of data from various sources, establishing the groundwork for ontology maps. Defined as mode-of-action frameworks, ontology maps integrate and structure qualitatively and quantitatively relevant biological, toxicological, chemical, and kinetic data from various sources and are ultimately designed to support chemical risk assessment. Building upon the work of the Disease Maps community, we have created five organ-specific PMs as a part of the H2020 ONTOX project (https:\/\/ontox-project.eu\/<\/a>): bile secretion & lipid metabolism (liver), nephron physiology (kidney), and neural tube closure & cognitive function development (developing brain). They adhere to standardized guidelines for thorough annotation and documentation, ensuring alignment with the FAIR principles, which emphasize interoperability with various modeling tools and resources. Their evolution is anticipated to accelerate the creation of new approach methodologies for next-generation risk assessment, which can immensely benefit from the collaboration of the toxicology and systems biology communities.<\/p>\n

Recordings and other materials from this webinar will be posted on the ASCCT webinar archive:\u00a0https:\/\/www.ascctox.org\/webinar-archive<\/a><\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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April 11, 2024: 2023 ASCCT and ESTIV Award Winners, Part 3<\/h3>\r\n
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When: Thursday, April 11, 2024 10:00 AM – 11:30 AM EST \/\u00a016:00 – 17:00 CET<\/p>\n

Register here:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_X_HUOpHHQDyHK6sf-Wqq4w#\/registration<\/a><\/p>\n

Join us for a webinar featuring presentations from several 2023 ASCCT and ESTIV Award Winners, including:<\/p>\n

Damaris Cristine Marios Ferreira Pinto,<\/strong>\u00a0winner of the ASCCT International Travel Award
\nGrupo Botic\u00e1rio in Brazil
\nResearch Topic: “Assessing Sunscreen Product Toxicity on Coral Fragments: A New Approach methodology for Enhancing Reliability and Addressing Environmental Concerns”
\nBrief Abstract:\u00a0A New Approach Methodology (NAM) for assessing sunscreen toxicity on coral fragments by evaluating the entire product composition rather than individual ingredients. The study assessed seven sunscreens and found that two had harmful effects while five did not. This approach could help establish criteria for “reef-safe” sunscreens.<\/p>\n

Jerry Achar,<\/strong>\u00a0winner of the Suzanne Fitzpatrick Student Travel Award (ASCCT)
\nUniversity of British Columbia
\nResearch Topic: “Facilitating analysis of implicit uncertainties in QSAR prediction of chemical toxicity: a case study of neurotoxicity”<\/p>\n

Kirsten Veltman,<\/strong>\u00a0winner of the ESTIV Best Poster Presentation at the EUROTOX2023 Congress in Ljubljana, Slovenia
\nCentre for Health Protection (GZB), Innovating Testing Strategies (VTS), Dutch National Institute for Public Health and the Environment (RIVM)
\nResearch Topic: “Application of Weighted Gene Co-expression Network Analysis as Mode of Action Screening Method for Suspected Non-Genotoxic Carcinogens Inducing Oxidative Stress”
\nThese promising early career scientists will share brief discussions of their work followed by a Q&A.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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April 3, 2024: ASCCT-ESTIV AWARD Winners Webinar, Part 2<\/h3>\r\n
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When: Wednesday, April 3, 2024 10:00 AM – 11:00 AM EDT \/ 15:00-16:00 CET<\/p>\n

Register here:https:\/\/us06web.zoom.us\/webinar\/register\/WN_4vKcWkfNQIGIkkSc8rGJIA#\/registration\u00a0<\/a><\/p>\n

Join us for a webinar featuring presentations from several 2023 ASCCT Award Winners, including:<\/p>\n

Alexandre (Alex) Borrel,<\/strong>\u00a0winner of the Edward Carney Predictive Toxicology Award (ASCCT)
\nInotiv, Senior Computational Chemist
\nResearch Topic: “Deep Learning Profile QSAR Modeling to Impute In Vitro Assay Results and Predict Chemical Carcinogenesis Mechanisms”<\/p>\n

Agnes Karmaus,<\/strong>\u00a0winner of an ASCCT 12th Annual Meeting Poster Award
\nSyngenta, Senior Human Safety Project Expert
\nResearch Topic: “Incorporating new approach methodologies into a tiered assessment framework for agrochemical metabolite human safety assessment”<\/p>\n

These promising early career scientists will share brief discussions of their work followed by a Q&A.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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February 27, 2024: ASCCT-ESTIV AWARD Winners Webinar, Part 1<\/h3>\r\n
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When: Tuesday, February 27, 2024 10:00 AM – 11:00 AM EST\/ 16:00-17:00 CET<\/p>\n<\/div>\n

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Register here:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_az3SaqJGRJeDGM4mYaQIoQ#\/registration\u00a0<\/a><\/p>\n<\/div>\n

Join us for our first webinar of 2024, featuring:<\/strong><\/p>\n

Victoria Hull,<\/strong>\u00a0ASCCT 2023 Annual Meeting Poster Award Winner
\nSenior Bioinformatician, Inotiv-RTP
\nResearch Topic: “Curating chemical use categories and exposure predictions to inform chemical assessment”<\/p>\n

Elena Kromidas,\u00a0<\/strong>ESTIV Best Oral Presentation Award Recipient at the 2023 World Congress (WC12)
\nDepartment for Microphysiological Systems, Institute of Biomedical Engineering, Faculty of Medicine,\u00a0Eberhard Karls University Tuebingen, Germany
\nResearch Topic: \u201cDevelopment of a\u00a0Healthy Cervix-, a Pre-Cancerous CIN- and an Immunocompetent Cervical Cancer-on-Chip.\u201d<\/p>\n

Worldwide, women suffer from diseases affecting the cervix, including infections and malignant transformations. Leveraging microfabrication techniques and tissue engineering, three tissue models of the cervix were developed in an Organ-on-Chip platform. Long-term cultivation and perfusion allowed for treatment at patient-relevant doses and schedules or neutrophil integration, emphasizing the applicability of the models for basic mechanistic research and drug development.<\/p>\n

These promising early career scientists will share brief discussions of their work followed by a Q&A.<\/p>\n

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Webinars – 2023<\/h2>\n\n\n\r\n
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November 27, 2023 (Reschedulled from September): Application of Cell Painting for chemical hazard evaluation in support of screening-level assessments<\/h3>\r\n
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November 27, 2023\u00a010:00 AM\u00a0–\u00a011:00 AM EDT \/ 16:00 – 17:00 CET<\/p>\n

Register here:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_HKJ5RrWLS4atk72Xwv5nxg#\/registration<\/a><\/p>\n

Presenter: Jo Nyffeler, PhD, Helmholtz Centre for Environmental Research (UFZ)<\/p>\n

\u201cCell Painting\u201d is an imaging-based, high-throughput method to obtain phenotypic information of individual cells. Initially used mainly in pharma industry for hit discovery, Cell Painting can be applied to other research questions, such as safety assessment.
\nThe Center for Computational Toxicology and Exposure at the U.S. Environmental Protection Agency has applied Cell Painting to environmental chemicals and explored its use for screening-level chemical hazard evaluation of environmental chemicals.<\/p>\n

In this webinar, I will walk you through the findings of our recent publication (Nyffeler et al. 2023, PMID: 37044265, DOI: 10.1016\/j.taap.2023.116513). Using multiple examples, I will outline the applicability of Cell Painting. Cell Painting can be used to obtain in vitro potency estimates for chemicals that can be compared to exposure information. Moreover, as others have already shown, Cell Painting can be used to group chemicals with similar mode-of-actions. We showed that this also works for environmental chemicals with less defined mechanisms-of-action. Cell Painting is able to also highlight critical differences that would not have been apparent when only looking at structural information. Thus, this webinar will give you an overview of possible application of Cell Painting in the context of safety assessment.<\/p>\n

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November 14, 2023: Effect-based trigger values in water quality assessment<\/h3>\r\n
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Tuesday, November 14, 2023 10:00 AM – 11:00 AM EDT\u00a0\/\u00a016:00 – 17:00 CET<\/p>\n

Register here:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_ikYuOLk4QAqFCcJnR0_kbg#\/registration<\/a><\/p>\n

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Presenter: Beate\u00a0Escher, PhD<\/p>\n<\/div>\n

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Bioanalytical tools have been applied for monitoring of water quality worldwide for several decades. Most research has focused on surface waters and domestic and industrial wastewaters. In recent years, screening of wastewater and advanced water treatment processes, disinfected drinking water and recreational waters have\u00a0emerged. Cell-based assays can either assess general cytotoxicity or target a specific mode of toxic action or a defined step of the toxicity pathway. Comprehensive risk assessment thus requires a battery of bioassays to cover a range of MOAs and\/or toxicity pathways relevant for the water sample to be tested.\u00a0<\/span>\u00a0<\/span><\/p>\n<\/div>\n

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Cell-based bioassays have been used for benchmarking drinking and surface water quality as well as assessing treatment efficacy of wastewater and advanced water treatment.\u00a0<\/span>\u00a0<\/span><\/p>\n<\/div>\n

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Many in vitro\u00a0bioassays are\u00a0highly sensitive, so an effect in a bioassay does not necessarily\u00a0indicate\u00a0poor chemical water quality. Consequently, effect-based trigger values (EBTs) have been introduced to differentiate between acceptable and unacceptable chemical water quality and are\u00a0required\u00a0for the wider acceptance of EBM by the water sector and regulatory bodies. EBTs have been derived for both drinking water and surface water to protect human- and ecological health, respectively, and are available for assays indicative of specific receptor-mediated effects, as well as assays indicative of adaptive stress responses, apical effects and receptor-mediated effects triggered by many chemicals.<\/span>\u00a0<\/span><\/p>\n<\/div>\n

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A lack of trigger values and interpretation guidelines have hampered greater adoption of bioanalytical tools for (regulatory) monitoring applications. In\u00a0this\u00a0webinar\u00a0I plan to give\u00a0an\u00a0overview on the derivation of effect-based trigger (EBT) values for human health (drinking water) and the environment (surface water). These EBT are designed to be protective for mixture effects stemming from chemical cocktails in water.<\/span><\/p>\n<\/div>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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October 30, 2023 ASCCT-ESTIV: New OECD (Q)SAR Assessment Framework: Details and Examples <\/h3>\r\n
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Monday, October 30, 2023 9:00 AM – 11:00 AM EDT\u00a0 \/ 14:00 -16:00 CET<\/p>\n

Register here:\u00a0https:\/\/us06web.zoom.us\/webinar\/register\/WN_9nSZrHAwS-uSE954Mni-7A#\/registration<\/a><\/p>\n

Presenters:Olga Tcheremenskaia, PhD, Istituto Superiore di Sanit\u00e0 (ISS)
\nAndrea Gissi, PhD, European Chemicals Agency (ECHA)
\nPatience Browne, PhD, Organisation for Economic Co-operation and Development (OECD)<\/p>\n

This webinar will provide an overview new OECD (Quantitative) Structure-Activity Relationship ((Q)SAR) Assessment Framework (QAF) which is a systematic and harmonised framework for the regulatory assessment of (Q)SAR models, predictions, and results based on multiple predictions. The framework includes\u00a0new principles for the assessment of (Q)SAR predictions and results,\u00a0a checklist of criteria, along with guidance for assessing each criterion, to help establish confidence in the use of (Q)SARs in evaluating chemical safety.\u00a0 The QAF is meant to be applicable irrespective of the modelling technique used to build the model, the predicted endpoint, and the intended regulatory purpose. The primary audience of this document is regulatory authorities and their stakeholders. In addition, any other (Q)SAR user is encouraged to refer to the QAF when using (Q)SARs for regulatory purposes.<\/p>\n

The webinar will start with an overview of the project. Then, two talks will cover the main aspects of the framework, namely the assessment of models and of results based on individual or multiple predictions. The presentations will also include example of assessment according to the checklists part of the OECD document.<\/p>\n

Recordings and other materials from this webinar will be posted on the\u00a0ASCCT webinar archive<\/a> shortly after the webinar’s close.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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June 23, 2023: PARC Consortium and Human Biomonitoring<\/h3>\r\n
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The recording is available here: https:\/\/www.youtube.com\/watch?v=wRX-nU3oacg<\/a> <\/p>\n

Human\u00a0biomonitoring to\u00a0support risk assessment and chemical policies in Europe: results from HBM4EU<\/b>\u00a0<\/b><\/span><\/p>\n

Presented by Greta Schoeters, PhD<\/span><\/p>\n

Human biomonitoring\u00a0(HBM)\u00a0measures chemicals or their metabolites directly in human tissues or fluids and hence\u00a0aggregates chemical exposure from\u00a0different sources\u00a0and intake routes.\u00a0As such it fits with the\u00a0one chemical-\u00a0one assessment concept\u00a0which is part of\u00a0Europe\u2019s strategy for sustainable use of chemicals.\u00a0Human biomonitoring\u00a0provides\u00a0a picture of\u00a0reallife\u00a0exposure to\u00a0chemicals which is\u00a0essential information for\u00a0hazard characterisation and\u00a0chemical risk assessment.\u00a0A\u00a0coherent human biomonitoring framework is\u00a0still\u00a0lacking in Europe.\u00a0However, it is needed to track the progress towards\u00a0EU\u2019s Zero Pollution Action\u00a0which aims to reduce pollution levels to be no\u00a0more\u00a0harmful for health by 2050.\u00a0The European project\u00a0HBM4EU\u00a0(2017-2022)\u00a0operated\u00a0at the science policy interface and\u00a0has\u00a0established\u00a0a European Union-wide HBM programme to generate knowledge on human internal exposure to chemical pollutants and their potential health impacts in Europe.\u202f<\/span>\u00a0Priority substance groups were\u00a0identified\u00a0to meet\u00a0the most important needs of both European and national policy makers and risk assessors.\u00a0\u00a0Building\u00a0on existing capacities,\u00a0HBM4EU\u00a0brought\u00a0together scientists\u00a0from 30 countries.\u00a0As one of\u00a0the\u00a0core activities,\u00a0harmonized human biomonitoring data\u00a0from different\u00a0European\u00a0regions and age groups\u00a0were collected.\u00a0The HBM4EU Aligned Studies generated new HBM data of 10,795 participants.\u00a0HBM4EU has\u00a0set up\u00a0a\u00a0laboratory\u00a0network\u00a0to produce\u00a0comparable high quality analytical data,\u00a0provided\u00a0wide access to data and\u00a0facilitated\u00a0interpretation of results by developing indicators and\u00a0human biomonitoring guidance values.\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Results displayed the heterogeneity of internal chemical exposure of European residents: geographically, according to the age group, according to the sampling period, according to the educational level of study participants.\u00a0<\/span>The levels of some substances in the human body of the European population are still so high that adverse health effects cannot be excluded according to current knowledge.<\/span>\u00a0<\/span><\/p>\n

HBM4EU produced data that can serve as a baseline to evaluate the success of the measures taken to operationalise the Chemicals Strategy for Sustainability and the Zero Pollution Agenda in the frame of the European Green Deal, it\u00a0signaled\u00a0upcoming\u00a0substances\u00a0that\u00a0are\u00a0present in the body.\u00a0HBM4EU demonstrated\u00a0that not only regulations\u00a0but clear strategies are needed to prevent\u00a0further\u00a0pollution on the human body.<\/span>\u00a0<\/span><\/p>\n

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Human biomonitoring\u00a0for the identification of real-life chemical mixtures<\/b><\/span>\u00a0<\/b><\/span><\/p>\n

Presented by Mirjam Luijten, PhD<\/span>\u00a0<\/span><\/p>\n

Human biomonitoring (HBM) approaches\u00a0are valuable\u00a0for chemical risk\u00a0assessment, since\u00a0they allow for\u00a0measurements of\u00a0chemicals or their metabolites directly in human tissues or fluids and hence aggregates chemical exposure from\u00a0different sources\u00a0and intake routes.\u00a0As such, HBM data are also highly valuable for mixture risk assessment.\u00a0Network analysis applied to\u00a0HBM\u00a0data may\u00a0provide\u00a0insight into real-life mixtures by visualizing chemical exposure patterns. The identification of groups of more densely correlated biomarkers, so-called “communities”, within these networks highlights which combination of substances should be considered in terms of real-life mixtures to which a population is exposed. We applied network analyses to HBM datasets from\u00a0four different countries.\u00a0Our approach\u00a0demonstrates\u00a0that network analysis applied to HBM data of highly varying origin provides useful information with regards to the existence of groups of biomarkers that are densely correlated.\u00a0The next\u00a0questions are\u00a0whether the combined body burden of multiple chemicals is of potential health concern. If so,\u00a0subsequent\u00a0questions are which chemicals and which co-occurrence patterns are driving the potential health risks. To address this,\u00a0we developed\u00a0a biomonitoring hazard index.\u00a0Our analysis revealed that this biological index method can put forward communities of co-occurrence patterns of chemicals on a population level that need further assessment in toxicology or health effects studies.\u00a0<\/span>\u00a0<\/span><\/p>\n

Another valuable approach for\u00a0mixture risk assessment\u00a0is suspect screening. Within HBM4EU\u00a0a\u00a0\u00a0harmonized\u00a0suspect screening\u00a0approach was developed, aimed at the identification of new\u00a0exposure\u00a0biomarkers\u00a0for\u00a0pesticides. This approach was used in a\u00a0suspect-screening based study to describe the probability of (concomitant) exposure to a set of pesticide profiles in five European countries We explored whether living in an agricultural area (compared to living in a peri-urban area), being a\u00a0a\u00a0child (compared to being an adult), and the season in which the urine sample was collected had an impact on the probability of detection of pesticides.\u00a0Forty\u00a0pesticide biomarkers relating to 29 pesticides were\u00a0identified\u00a0at\u00a0high levels\u00a0of confidence in samples across all study sites. We\u00a0observed\u00a0differences in the probability of detection of a pesticide (metabolite) among children compared to adults,\u00a0but not for\u00a0area or season. This survey demonstrates the feasibility of conducting a harmonized pan-European sample collection\u00a0combined with suspect screening to provide insight\u00a0into\u00a0the presence of exposure to pesticide mixtures in the European population.\u00a0<\/span>\u00a0<\/span><\/p>\n

Taken together, our results from HBM4EU demonstrate the usefulness of HBM data for human health risk assessment of chemical mixtures.\u00a0<\/span>\u00a0<\/span><\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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May 24, 2023: The Latest in Liver Toxicity Assessment In Vitro<\/h3>\r\n
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Wednesday, May 24, 2023 10:30 AM – 12:00 PM EDT\/ 16:30 – 18:00 CET<\/p>\n

Liver spheroids as an in vivo like human in vitro system for prediction of NASH, drug metabolism and drug-induced hepatotoxicity<\/strong><\/p>\n

Presented by Magnus Ingelman-Sundberg, PhD, BSc.Med<\/p>\n

Drug development is a challenging pursuit, with over 85% of drugs failing during clinical development due to inadequate preclinical systems for compound selection. Liver toxicity is a significant contributor to these safety failures, and despite numerous attempts to develop pharmacological therapies for chronic liver diseases, including NASH and fibrosis, success has remained elusive. Given the costs and patient burden associated with clinical trials, novel strategies are urgently needed to improve success rates. One promising approach involves using human liver spheroids, which can maintain patient-specific phenotypes and functions for several weeks in culture. In this lecture I explore recent applications of liver spheroids for analyzing drug-induced liver toxicity, evaluating the disposition and metabolism of low clearance drugs, and developing treatments for metabolic and infectious liver diseases. With increased use, liver spheroids have the potential to become the new gold standard in translational pharmacology and toxicology.<\/p>\n

In vitro prediction of liver toxicity using adverse outcome pathways<\/strong><\/p>\n

Presented by Mathieu Vinken, PhD, PharmD, ERT<\/p>\n

Adverse outcome pathways (AOP) and their networks are tools to capture and visualize mechanisms of toxicity. A multitude of AOPs and AOP networks related to liver toxicity has been introduced over the past decade. This is not a surprise, as the liver is the poison control center of the body due to its unique location and function. The present webinar will focus on the development and practical validation of an AOP network mechanistically describing the accumulation of noxious bile acids in the liver or so-called cholestasis induced by chemical compounds, including pharmaceutical drugs, food additives and cosmetic ingredients. It will be demonstrated how this AOP network can serve as the conceptual basis for reliable in vitro detection and prediction of chemical-induced cholestatic liver injury<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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April 28, 2023: Modeling of Adverse Outcomes – Insights from the ONTOX Project <\/h3>\r\n
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Friday, April 28, 10:00 – 11:00 ET\/ 16-17:00 CET
\nThe recording is available here: https:\/\/www.youtube.com\/watch?v=yYcgSB53t38<\/a><\/p>\n

Development of an adverse outcome pathway for kidney tubular necrosis<\/strong><\/p>\n

Presented by Devon Barnes, Utrecht Institute for Pharmaceutical Sciences<\/p>\n

Adverse outcome pathway (AOP) networks combine AOPs that share one or more key events (KEs). Our aim is to develop an AOP network determining KEs and the relationships that drive chemical-induced kidney tubular necrosis (TN). To weigh the evidence between KEs, the network will be assessed in accordance with guidelines from the Organization for Economic Co-operation and Development (OECD). The objective is to develop an ontological knowledge framework that integrates biological, toxicological, and chemical data toward predicting systemic repeated dose toxicity effects of nephrotoxic chemicals associated with kidney TN.<\/p>\n

Embase was used to search for literature on chemical-induced kidney TN using key search terms relevant to clinical presentations, biochemistry, histology, and chemically applicable, data-rich nephrotoxic compounds. Initial title\/abstract screening of papers employed SysRev, a computational tool for systematic reviewing and data extraction, using labeling strategies for inclusion\/exclusion criteria. Tailored Bradford-Hill criteria described in OECD guidelines will assess confidence levels and weight of evidence for KEs within the AOP network. Kidney physiological maps were designed to establish mechanisms contributing to TN, with systemic mapping of currently reported AOPs involving nephrotoxicity identifying relevant MIEs and KEs.<\/p>\n

The Embase search retrieved 2735 papers to upload to SysRev. The title\/abstract screening would further identify papers eligible for data extraction in the full-text screening process. A total of 19 existing AOPs related to kidney dysfunction were identified and analyzed to support the implementation of additional in vitro endpoints for TN.<\/p>\n

Data extracted will assess confidence levels in previously described KEs and KERs and identify potential new KEs. The AOP network will form the conceptual basis for establishing a test battery of in vitro assays to characterize nephrotoxic\u00a0chemicals by measuring individual KEs for the generation and evaluation of AOPs of TN-related kidney failure.<\/p>\n

Computational modelling of neural tube closure defects<\/strong><\/p>\n

Presented by Job Berkhout, RIVM<\/p>\n

Closure of the caudal neural tube is a critical event that occurs early in development, around day 27 of human gestation. Failure of neural tube closure results in severe birth defects, such as spina bifida. These neural tube defects (NTD) are among the most prevalent human congenital malformations, which warrants specific attention in chemical safety assessment. Computational models of biological processes are likely to revolutionize chemical safety assessment in the near future. Such models can be used to predict the effect of chemical-induced gene expression changes and provide a template for establishing quantitative adverse outcome pathway networks. This study aims to develop an in silico model of the human neural tube closure, which will be applied to predict chemical-induced NTDs.<\/p>\n

By extensively mining the developmental biology and toxicology literature, we first created a physiological map of human neural tube closure. Based on the physiological map, we built a multicellular agent-based model using CompuCell3D.\u00a0The constructed physiological map depicts the all-trans-retinoic acid (ATRA) related molecular pathways linked to the various cell types in which they occur, and their morphogenetic consequences, that lead to closure of the neural tube. The morphogenetic events driven by gene expression changes are visualized by the computational model.<\/p>\n

We simulated in silico the complex biological process of neural tube closure, in order to demonstrate the feasibility of this approach. At a later stage in the project, the computational model will be applied to predict chemical-induced changes in gene expression and cell characteristics. The predictions of the model will be validated using a set of dedicated in vitro assays in conjunction with existing knowledge on in vivo developmental neurotoxicity. Such computational models may ultimately provide an alternative in silico approach for chemical safety assessment without the use of animals.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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March 24, 2023: Award winners webinar: New approach methodologies for evaluating cardio- and developmental toxicity<\/h3>\r\n
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Friday, March 24, 2023, 10:00 – 11:00 a.m. ET \/ 16:00 -17:00 CET<\/p>\n

The recording is not available<\/p>\n

A multiorgan-on-chip platform for the in vitro investigation of off-target cardiotoxicity of liver-metabolized anticancer drugs<\/strong><\/p>\n

Presented by Erika Ferrari, PhD<\/p>\n

Off-target cardiotoxicity is one of the main causes of drug withdrawal from the market. Multiorgan-on-chip (MOoC) platforms represent a disruptive solution to predict liver metabolism on off-target organs to ultimately improve drug safety testing during drug development. Here we integrate liver and cardiac models in a compartmentalized valve-based MOoC and we show its application in studying the effects of liver-metabolized Terfenadine on cardiac microtissues. The two-compartment MOoC integrates a liver micropatterned co-culture (MPCC) of HepG2 and fibroblasts with a 3D mechanically stimulated cardiac microtissue generated from neonatal rat cardiomyocytes. Communication between compartments is achieved through a system of normally-closed valves, whose aperture is controlled via an overlaying vacuum-activated control layer. Numerical and experimental simulations were conducted to evaluate the dynamics of drug diffusion across the compartments. Medium supplemented with 10\u03bcM Terfenadine was administered to the liver; once metabolized (after 24h), cardiac microtissue viability and functionality were assessed by means of an integrated electrical recording system upon valve aperture. The platform was validated for pharmacokinetic-based drug screenings by measuring the effect of the anticancer drug Terfenadine after metabolism by MPCCs in the liver compartment. While non-metabolized Terfenadine caused a significant decrease in cardiac cell viability and an increase in field potential duration (FDP), its non-toxic metabolite Fexofenadine did not cause any significant alteration in cardiac microtissues. We developed a valve-based MOoC for liver-heart compartmentalized cultures. The system allows for controlled diffusion of liver-metabolized drugs (e.g., Terfenadine) to a cardiac compartment while excluding cell-cell interactions and eliminating convective transport, demonstrating the potential for studying drugs off-target cardiotoxicity upon liver metabolism.<\/p>\n

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Predicting developmental toxicity of pyrethroid insecticides in vitro using human-induced pluripotent stem cells\u00a0<\/strong><\/p>\n

Presented by Yanying Ma<\/p>\n

Metabolites of pyrethroid insecticides are detected in most urine samples from the general population. Pyrethroids act primarily by interfering with voltage-gated sodium channels, which are essential for nerve cell function and cardiac muscle contraction. Therefore, pyrethroids are suspected to be cardiotoxic. Furthermore, pyrethroids are suggested to interfere with thyroid hormones that are essential for foetal brain development. Thus, exposure to pyrethroids during vulnerable windows in pregnancy may adversely affect child neurodevelopment and cardiovascular health. The adverse effects of three pyrethroid insecticides (deltamethrin, \u03b1-cypermethrin and etofenprox) and the common metabolite 3-phenoxybenzoic acid (3-PBA) on cardiomyocyte differentiation were assessed in an in vitro 3D model of human induced pluripotent stem cell-derived embryoid bodies (EBs) that mimic the very early stage of the human embryo \u2013 the blastocyst. A cardiac-specific reporter gene assay – the PluriLum, was used as a cardio-developmental toxicity endpoint. The binding ability of pyrethroids to transthyretin (TTR) was assessed using the cell-free ANSA-TTR binding assay. The EBs were differentiated into cardiomyocytes and the effects of the pyrethroids on this process was measured. Deltamethrin, \u03b1-cypermethrin and etofenprox had a significant negative impact on differentiation (LOECs: 13, 6.3 and 1.6 \u03bcM, respectively). 3-PBA showed no effect at any tested concentrations (up to 100 \u03bcM). However, it significantly displaced ANSA from TTR (LOEC = 1.6 \u03bcM), whereas the parent pyrethroids showed no TTR-binding potential at the range of tested concentrations (up to 200 \u03bcM). Our results indicate that pyrethroid parent compounds have the potential to adversely affect cardiac differentiation. On the other hand, only the metabolite, 3-PBA, could bind to TTR and potentially disturb thyroid hormone transportation. These findings highlight the risks posed by insecticides on human foetal development, supporting the need to restrict their use in order to reduce human exposure.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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February 22, 2023: Gaining Confidence in Advanced Methodologies for Studying Challenging Chemicals,<\/h3>\r\n
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Wednesday, February 22 at 11:00am – 12:30pm ET \/\u00a0\u00a016:00 -17:30 CET
\nThe recording is available here:https:\/\/www.youtube.com\/watch?v=FTpgjMcCc1k&t=2s<\/a><\/p>\n

Integrating High Throughput Transcriptomics into a Tiered Framework to Prioritize Chemicals for Toxicity Testing\u00a0<\/strong><\/p>\n

Presented by Jesse Rogers, PhD<\/p>\n

US EPA is developing a tiered assessment strategy for chemical toxicity testing by integrating multiple data streams. Pairing high content assays such as high-throughput transcriptomics (HTTr) with high-throughput screening (HTS) of specific molecular targets may improve confidence when assessing key hazards. Here, we used HTTr screening data to generate new signatures representing known molecular targets, and signature-level potency estimates were integrated with orthogonal HTS assays as a proof-of-concept framework for chemical prioritization.<\/p>\n

Transcriptomic profiles generated via the TempO-Seq platform in both HepaRG and U-2 OS cell lines were used to develop signatures comprised of genes selectively responsive to reference chemicals for one of 13 distinct molecular targets. Of 1,218 chemicals screened in HTTr to date, 232 chemicals demonstrated selective potency in at least one reference signature versus non-selective potency estimates. In examining these chemicals using available orthogonal HTS assays from US EPA\u2019s ToxCast program, 74 chemicals were confirmed as selective AHR, GR, or RAR\/RXR nuclear receptor agonists.<\/p>\n

Our work demonstrates that HTTr data can inform putative molecular targets and identify chemicals for further screening in a framework to support chemical risk assessment.<\/p>\n

The views expressed in this abstract are those of the authors and do not necessarily reflect the views or policies of the US EPA.<\/p>\n

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Evaluation of Per- and Poly fluoroalkyl Substances (PFAS) <\/strong>in vitro<\/b><\/em> toxicity testing for developmental neurotoxicity<\/strong><\/p>\n

Presented by Kelly Carstens, PhD<\/p>\n

Per- and poly fluoroalkyl substances (PFAS) are a diverse set of commercial chemicals widely detected in humans and the environment. However, only a limited number of PFAS are associated with epidemiological or experimental data for hazard identification. To provide developmental neurotoxicity (DNT) hazard information, the work herein employed DNT new approach methods (NAMs) to generate in vitro <\/em>screening data for a set of 160 PFAS. The DNT NAMs battery was comprised of the microelectrode array neuronal network formation assay (NFA) and high-content imaging (HCI) assays to evaluate proliferation, apoptosis, and neurite outgrowth. The majority of PFAS (118\/160) were inactive or equivocal in the DNT NAMs, leaving 42 active PFAS that decreased measures of neural network connectivity and neurite length. Analytical quality control indicated 43\/118 inactive PFAS samples and 10\/42 active PFAS samples were degraded; as such, careful interpretation is required as some negatives may have been due to loss of the parent PFAS, and some actives may have resulted from a mixture of parent and\/or degradants of PFAS. PFAS containing a perfluorinated carbon (C) chain length \u22658, a high C:fluorine ratio, or a carboxylic acid moiety were more likely to be bioactive in the DNT NAMs. Of the PFAS positives in DNT NAMs, 85% were also active in other EPA ToxCast assays, whereas 79% of PFAS inactives in the DNT NAMs were active in other assays. These data demonstrate that a subset of PFAS perturb neurodevelopmental processes in vitro <\/em>and suggest focusing future studies of DNT on PFAS with certain structural feature descriptors. (This abstract does not reflect U.S. EPA Policy).<\/p>\n

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A Modern Framework to Establish Scientific Confidence in New Methods<\/strong><\/p>\n

Presented by Anna van der Zalm, MChem<\/p>\n

Current processes to validate new non-animal methods are costly, time-consuming, and do not necessarily produce methods that are fit for regulatory purposes. This talk will build on previous efforts from the Organisation for Economic Co-operation and Development and the International Cooperation on Alternative Test Methods to propose a modern, flexible framework comprising five essential elements to establish scientific confidence in non-animal methods for regulatory use: fitness for purpose, human biological relevance, technical characterization, data integrity and transparency, and independent review.<\/p>\n

Updates to the current process are based on the recognition that (a) the relevance of the results of the new method need not be determined through direct alignment with the results of the traditional animal test method, and instead may be determined through alignment with, or fidelity to, human biological understanding; (b) the new method should not be required to replace the traditional animal test method one to one, nor produce the same information generated by the traditional animal test method; (c) the currently accepted levels of reproducibility in traditional animal test methods can be used to inform performance benchmarks for new methods; (d) ring trials may not be necessary for the assessment of the reproducibility of a new method; and (e) preferably before a method is developed, its purpose should be clearly defined and discussed amongst the method developer, regulators, and the regulated industry to ensure the production of new non-animal methods that are fit for purpose.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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January 19, 2023: ASCCT Award Winners Webinar <\/h3>\r\n
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Thursday, January 19 at 10:00 – 11:00 a.m. ET
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Facilitating Global Connections through the Microphysiological Systems for COVID Research (MPSCoRe) Working Group<\/strong><\/p>\n

Presented by\u00a0Amber Daniel, M.S.<\/p>\n

The emergence and global spread of the coronavirus disease 2019 (COVID-19) emphasizes the need for effective approaches to prevent, control, and treat infectious diseases. Although animal models have historically been used to address such challenges, human cell-based in vitro platforms known as microphysiological systems (MPS) have the potential to more efficiently and effectively model the human lung and other organ systems affected by COVID-19. However, absent a venue for coordination, widespread application of MPS to COVID-19 research presents a risk of overlapping investigations and duplication of efforts among researchers. The MPS for COVID Research (MPSCoRe) working group was organized to reduce this risk by globally connecting key MPS stakeholders from the research, method development, drug and vaccine manufacturing, and regulatory sectors. The working group facilitates open communication among stakeholders to maximize the impact of MPS technologies in understanding disease mechanisms and treatments, and reducing animal use while improving human health. In this way, the group aims to promote adoption of MPS for studying COVID-19 and future emerging infectious diseases. Activities currently supported by MPSCoRe include a proof-of-concept study to evaluate MPS models for testing the safety and efficacy of novel COVID-19 therapeutics. Additionally, MPSCoRe is supporting development of a web-based repository for sharing COVID-19 experimental data and other MPS resources. These efforts will accelerate the development and adoption of MPS in infectious disease research, thereby reducing the reliance on animal models in this space. This project was funded with federal funds from NIEHS, NIH under Contract No. HHSN273201500010C.<\/p>\n

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Applying Deep Learning Toxicity Models Across the Chemical Universe<\/strong><\/p>\n

Sunggun Lee<\/p>\n

Chemicals are ubiquitously found in many exposure routes in day-to-day life. It is important that toxicity testing guides maximally informed decisions on chemical use and regulation to best protect human health. Traditional toxicity testing generally relies on in vivo or in vitro methods which are time-consuming, resource-intensive, and frequently results in flawed generalizations to humans. Collaborators at the FDA\/NCTR developed two novel deep learning models called DeepCarc and DeepDILI to predict endpoints for carcinogenicity and drug-induced liver injury, respectively, outperforming state-of-the-art methods. These deep learning models incorporate an ensemble approach of five conventional machine learning algorithms integrated into a neural network to generate probabilistic predictions for carcinogenicity and liver injury. Here, DeepCarc and DeepDILI were reproduced and refined, and the externally validated models were applied to screen the carcinogenicity and liver injury potential of 7176 compounds in the Tox21 database. The majority of the Tox21 compounds were predicted to have low carcinogenicity concern with only 88 of the compounds possessing a carcinogenicity probability of greater than 0.9. DeepDILI predictions suggested that the compounds in Tox21 generally have a higher risk for liver injury with many compounds predicted to have a liver injury probability of greater than 0.9. Compounds with high toxicity probability predictions were run through Integrated Chemical Environment Characterization workflows to investigate the distribution of physicochemical properties, presence in consumer products, and bioactivity profiles from high-throughput screening assays. Such computational models can be used to rapidly screen large chemical libraries to prioritize potentially hazardous substances for further examination.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n <\/div>\r\n<\/div>\n\n\n

Webinars – 2022 <\/h2>\n\n\n\n

ESTIV and ASCCT Webinars are available for all to attend and watch at no cost. <\/p>\n\n\n\r\n

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December 1, 2022: Tools & Recommendations for Improving Peer Review in the Scientific Literature<\/h3>\r\n
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December 1 at 9:00 – 10:00 a.m. EST \/ 15:00 – 16:00 CET<\/p>\n

PRIVAT: A tool for improving the peer-review of in vitro studies<\/strong>
\nPaul Whaley, PhD
\nAchieving consistent, thorough peer-review of in vitro studies in a way that provides transparent, actionable information for editorial decision-making is very challenging. To help with this, a new tool (PRIVAT) is being developed by the Evidence-Based Toxicology Collaboration. This presentation will discuss the unusually rigorous methods behind ensuring the tool is a genuine help to reviewers and editors, rather than just another questionnaire that complicates the peer-review process without obviously improving it. These methods include a systematic review of existing tools (yielding over 700 unique quality criteria) and a Delphi process that whittled the large number of criteria down into a meaningfully applicable set of questions and quality judgements. Prospects for uptake of the tool and the general challenges of improving publishing standards in toxicology, including ensuring data availability, will also be discussed.<\/p>\n

Increasing reliability of science with open and dynamic methods in academic publications
\nSofia Batista Leite, PhD
\nClear reporting of scientific methods and protocols in academic publications is the basis for reproducible, reliable and progressive science. Open data has recently received a great deal of attention, but in reality, the usability of data is only ensured when the methods for generating it are clearly described.\u202f Making progress\u202f towards open, detailed and dynamic protocols in peer reviewed publications requires\u202f huge investment from all of the different agents in the publication system, from researchers to publishers, the institutions in which they work and the funders that support them. A concerted community effort by all of these different agents is needed in order to create a culture that embraces, supports and rewards good method reporting. With prior experience in the acceptance of methods and data for regulatory purposes, the European Commission Joint Research Centre convened a group of scientists and publishers to undertake the translation of discussion about making methods more clearly reported in life science publications into clear action points. In this presentation, I will talk about the main actions to be undertaken by the key actors in research institutions, funding bodies and publishers that can trigger and support the needed culture change. The work will soon be open for consultation. With this work, we expect to support more transparent, reliable and robust science, of a higher quality and more impact.\u202f\u202f\u202f
\nOn the top of increasing the quality and impact of scientific research we believe that this can facilitate the acceptances of new methods in the regulatory field. This activity promotes clarity to build trust in non-guideline methods in support of the use of academic data for regulatory purposes. <\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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September 30, 2022: Using NAMs in Risk Assessment<\/h3>\r\n
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September 30 at 10:00-11:30am EDT\/14:00-15:00 CET<\/p>\n

Expectations for new approach methods performance in predicting effects in repeat dose animal studies<\/strong>
\nKatie Paul Friedman, PhD, Center for Computational Toxicology and Exposure, Office of Research and Development, US EPA
\nBuilding scientific confidence in new approach methodologies (NAMs) for regulatory toxicology may include a comparison to in vivo results. This presentation overviews work to characterize benchmark expectations for NAM performance in predicting systemic and organ-level effects in repeat dose studies of adult animals using data curated into the Toxicity Reference Database. The reproducibility, variability, and differences in systemic and organ-level findings (liver, kidney, stomach, spleen, thyroid, and adrenal) in repeat dose studies, using lowest effect level (LELs) for body and\/or organ weight, as well as gross and\/or histopathological changes, were investigated.
\nQuantitative variability in LELs was examined with multilinear regression modeling, using study descriptors as covariates to estimate total variance, mean square error (MSE), and root residual mean square error (RMSE) in systemic and organ LELs. The MSE values suggest study descriptors accounted up to 70% of total variance in organ and systemic LELs, and that RMSE ranged 0.4 \u2013 0.6 log10-mg\/kg\/day.
\nA quantitative structure activity relationship model for repeat dose point-of-departure (POD) estimates was constructed that used this RMSE estimate of variability to construct a POD distribution and 95% confidence interval for each POD prediction.
\nQualitative variability was further examined. The concordance of organ-level findings in replicate studies, defined by chemical only, chemical and species, and chemical and study type, ranged 39 – 88%. Odds ratios indicated it is unlikely to observe organ effects in a chronic study if the chemical was negative in a subchronic study.
\nOverall, quantitative and qualitative variability in repeat dose LEL values suggests that a good NAM might predict systemic or organ LELs within \u00b1 1 log10-mg\/kg\/day with accuracy approaching 70%. Organ level findings are most reproducible within-species, and a subchronic study is likely to identify effects in organs that demonstrate effects in the chronic study. This abstract does not necessarily reflect U.S. EPA policy.
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\nEFSA’s roadmap on NAMs and related case studies<\/strong>
\nGeorge E. N. Kass, PhD, European Food Safety Authority (EFSA)
\nThe European Food Safety Authority (EFSA) carries out risk assessments in relation to food and feed safety, and in the case of chemicals, provides health-based guidance values. These are mostly derived from in vivo animal toxicity data, using traditional endpoint measurements. However, EFSA is following closely and investing in the development and regulatory application of new methodologies and tools, the so-called New Approach Methodologies or NAM as part of its scientific strategy. The use of NAMs in regulatory chemical risk assessment has so far been very limited in EFSA. To overcome these challenges and generate opportunities for NAMs in regulatory chemical risk assessment, EFSA launched a contract to develop a roadmap for action on NAMs to reduce animal testing. The roadmap aims to define EFSA\u2019s priorities for the incorporation of NAMs as well as to inform a multiannual strategy for increasing the use of NAMs in human health risk assessment to minimise the need for animal-based verification studies. Ultimately, it envisions that by 2027 the majority of EFSA’s requests for additional data will be based on NAMs. The presentation will provide an overview of the roadmap on NAMs and some of the activities currently ongoing in the context of its implementation.\u2002\u2002<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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August 30, 2022: Modeling tools for better prediction of the kinetics and dynamics of xenobiotics, <\/h3>\r\n
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August 30, 2022 at 10:00-11:00am EDT \/ 14:00-15:00 CET<\/p>\n

3D Molecular modelling meets toxicology: a useful tool to investigate the TD and TK of food related toxicants<\/strong>
\nLuca Dellafiora, PhD
\nThe term \u201c3D molecular modelling\u201d refers to a group of computational methodologies relying on the 3D structures of biological (macro)molecules that study inter-molecular interactions from an atomistic and mechanistic standpoint. Such techniques have been widely used in medicinal chemistry and have succeeded to precisely study the molecular basis of low-molecular weight compounds\u2019 activity, substantially supporting drug design during the last decades.
\nMore recently, 3D molecular modelling techniques have been also successfully applied to investigate the toxicodynamics of food related toxicants like mycotoxins and other contaminants allowing either the precise understanding of their mechanisms of action or the prioritisation of molecules for more detailed experimental analysis. Moreover, those techniques have been usefully applied also to study the toxicokinetics of small molecules. In this context, we have applied 3D molecular modelling to study specific kinetic aspects of food-relevant chemicals ochratoxin A (OTA), alkenylbenzenes and perfluoralkyl substances (PFASs).
\nOur work focused on the transporter kinetics involved in the urinary excretion and reabsorption of members of the PFAS family and OTA by Organic Anion Transporter 4 (OAT4) and OAT1, respectively, and on the cytochrome P450 mediated biotransformation of OTA and alkenylbenzenes, with a focus on inter- and intra-species differences. Overall, our outcomes provided sound mechanistic basis supporting reported in vitro and in vivo data on toxicokinetics and may therefore also prove useful in the prediction of toxicokinetics for chemicals for which these data are lacking. <\/p>\n

Measuring and modeling the distribution of test chemicals in in vitro toxicity assays <\/strong>
\nNynke L. Kramer, PhD
\nIn vitro toxicity assays lie at the heart of next generation risk assessment. A battery of in vitro assays provide concentration response relationships to extrapolate to bioequivalent doses in humans, a process referred to as quantitative in vitro-in vivo extrapolation (QIVIVE). The importance of in vivo toxicokinetic processes (absorption, distribution, metabolism and excretion, ADME) in driving toxicity is well acknowledged. That these processes also drive toxicity in in vitro assays and contribute to the variability and uncertainty of in vitro readouts is less well recognised. Generally, the nominal effect concentration, i.e., the concentration added to the exposure medium at the start of the exposure, is used to compare toxic potencies of test chemicals and assay sensitivity. However, chemicals differentially bind to serum constituents and well plate plastic, evaporate and accumulate in cells. Due to these kinetic processes, the concentration at the molecular target, which is ultimately responsible for the effect, is a fraction of the nominal concentration and differs per chemical and between assays. In this presentation, tools to measure and model this fraction are explained and the consequences of accounting for in vitro disposition kinetics for QIVIVE are discussed. <\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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July 28, 2022: Guidance and standards for increasing regulatory confidence in in vitro and PBK models<\/h3>\r\n
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July 28, 2022 at 1:00-2:00pm EDT \/ 19:00-20:00 CET<\/p>\n

Guidance for increasing confidence in Physiologically Based Kinetic models \u2026 are we ready for a regulatory change? <\/strong>
\nAlicia Paini, PhD, ERT, esqLABS GmbH
\nPhysiologically based kinetic (PBK) models are routinely applied in several sectors, including academia and industry, as a means of integrating data on absorption, distribution, metabolism, and excretion (ADME) in humans and animals to predict whole-body biokinetic behavior. Use of these models has increased, especially in conjunction with emerging alternative methods to animal testing, now called new approach methodologies (NAMs). Experimental information derived from NAMs can be used as input for model parameterization and allows for increased confidence in models for chemicals that are lacking in vivo data for model calibration and evaluation. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. To fill this gap the Organisation for Economic Cooperation and Development (OECD) published a guidance document (GD) describing a scientific workflow for characterising and validating PBK models developed using NAMs data and with a lack of in vivo data for model evaluation. The GD provides an assessment framework for evaluating these models, with emphasis on the major uncertainties underlying model inputs and outputs. To help end-users develop and evaluate a PBK model for regulatory purposes, the document includes a template for documenting the model characteristics, and a checklist for evaluating the quality of a model. It was designed to facilitate the dialogue between model developers and risk assessors. The following presentation will highlight the principles, criteria and tools laid out in the OECD PBK model GD, with insight into the journey that lead to this document. <\/p>\n

Implementing Good In Vitro Method Practices (GIVIMP) as a Quality Standard in a Laboratory<\/strong>
\nAmanda Ulrey, Institute for In Vitro Sciences, Inc. (IIVS)
\nOver the past several years science has put renewed energy into improving the reproducibility of data. There are many articles, strategies, and tools to help with this that are specific to the field of in vitro toxicology, such as systematic reviews of published work (and tools to help educate on how to perform them), publishing Good Cell Culture Practices (and GCCP 2.0), improvements made to the method validation process itself, and increased acceptance and use of in vitro methods within the Good Laboratory Practices (GLP) quality system. Another tool for improving the reproducibility of in vitro work is the implementation of the practices described in the OECD\u2019s guidance document Good In Vitro Method Practices, or GIVIMP. The GIVIMP guideline is applicable to academic laboratories developing new alternative methodologies, established laboratories participating in validations and performing routine in vitro studies, and industry laboratories intending to submit in vitro data to regulatory agencies. These diverse types of institutions have different capabilities for developing and instituting quality systems that adhere to published recommendations while also suiting the unique operational environment of their facilities. This webinar will provide examples of and share a practical strategy for using the GIVIMP document to assess current laboratory processes and increase the reliability and robustness of work performed within a facility. GIVIMP has been used to prioritize areas of focus for building new and expanding existing good quality practices within the laboratory.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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June 27, 2022: Special webinar for Ukraine: Science for Policy, Policy for Science <\/h3>\r\n
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Wednesday, June 27, 10:00 AM – 11:00 AM EDT\/16:00-17:00 CET<\/p>\n

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Registration is open!<\/a><\/p>\n

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This webinar discusses the impact of science on policymakers and provides advice on how to bring science to their attention. The webinar is dedicated to researchers and research organizations aiming to achieve policy impact. Attendees will hear from two EU and US speakers on the skills and best practices for researchers and the elements of institutional change necessary to bring scientists and policymakers into effective communication. Our third guest, Dr. Nataliia Bubalo of Ukraine, will then speak to her experience working to bridge the science\/policy gap and promote the implementation of NAMs in Ukraine. Through this broad examination of experts’ experiences around the world, this webinar seeks to highlight knowledge areas and identify processes in which to invest.<\/p>\n

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The collected registration fees will be used for direct support of Ukraine via the organization\u00a0People in Need<\/a>.<\/p>\n

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A $10 USD registration fee is required to attend. If you would like to donate to People in Need beyond this amount, please\u00a0follow this link<\/a>.<\/p>\n

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Thank you for your support!<\/p>\n

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Moderators of the session: Helena Kandarova, ESTIV \/ Erin Hill, ASCCT<\/p>\n

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Speakers:
\nKristie Sullivan, MPH, Physicians Committee for Responsible Medicine
\nFrancois Busquet, PhD, Altertox
\nNataliia Bubalo, PhD, National University of Food Technologies, Ukraine<\/p>\n

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May 16, 2022: Advancing In Vitro Models for Genotoxicity and Carcinogenicity<\/h3>\r\n
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Monday, May 16, 2022<\/p>\n

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11:00 AM -12:00 PM EDT \/ 17:00-18:00 CEST<\/p>\n

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Registration is open!<\/a><\/p>\n

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A novel prediction model to evaluate genotoxicity based on biomarker genes in human HepaRGTM cells<\/strong>
\nPresented by Anouck Thienpont, PhD student, Laboratory of In Vitro Toxicology and Dermato-Cosmetology
\nTranscriptomics-based biomarker discovery is a promising new approach methodology (NAM) to identify molecular events underlying the genotoxic mode of action of chemicals. Previously, we developed the GENOMARK biomarker, consisting of 84 genes selected based on whole genomics DNA microarray profiles of 24 (non-)genotoxic reference chemicals covering different modes of action in metabolically competent human HepaRG\u2122 cells. In the present study, new prediction models for genotoxicity were developed based on an extended reference dataset of 38 chemicals including existing as well as newly generated gene expression data. Both unsupervised and supervised machine learning algorithms were applied to the extended dataset, but as unsupervised machine learning was not able to clearly distinguish both groups, the performance of two supervised machine learning algorithms, i.e. support vector machine (SVM) and random forest (RF), was evaluated. More specifically, the predictive accuracy was compared, the sensitivity to outliers for one or more biomarker genes was assessed and the prediction performance for 10 misleading positive chemicals exposed at their IC10 concentration was determined. Furthermore, the applicability of both prediction models on a public gene expression dataset, generated with RNA-sequencing, was investigated. Overall, the RF and SVM models showed to be complementary in their classification of chemicals for genotoxicity. To facilitate data analysis, an online application was developed, combining the outcomes of both prediction models. Furthermore, this research demonstrates that the combination of gene expression data with supervised machine learning algorithms can contribute to the ongoing paradigm shift towards a more human-relevant in vitro genotoxicity testing strategy.<\/p>\n

A novel fluidic microphysiological system for 3D cancer tissue culture towards a better understanding of human cancer progression<\/strong>
\nPresented by Silvia Scaglione, React4life, ESTIV corporate member
\nThe medical research focused to better understand human cancer disease and the preclinical assessment of novel drug therapies are currently carried out through traditional too simplistic 2D cell culture, and in vivo animal models, whose cost, time, and poor predictivity remain important challenges.
\nA novel Multi-In Vitro Organ (MIVO) organ on a chip platform has been recently developed to culture human tumor models under physiological culture conditions. Biologically relevant cancer samples were cultured within the MIVO chamber under blood capillary fluid flows that (i) feed the 3D tumor tissue, (ii) resemble cancer cells migration and intravasation through a permeable barrier resembling the vascular barrier, and (iii) supporting their migration towards metastatic target tissues. The same experimental approach was adopted to test the efficacy of an anticancer drug that was placed in the fluidic circuit, mimicking the systemic drug route. The regression of the ovarian tumor was observed and measured within the MIVO device: results were in line with the in vivo data, with a significant time reduction (75%). Similarly, a human 3D neuroblastoma model with proper immunophenotype was optimized to develop a complex tumor\/immune cell coculture within the MIVO as paradigm of an immune-oncology screening platform. Immune cells have been introduced within the capillary fluid flow circulation of the MIVO and their migration and infiltration towards the 3D tumor model was analyzed. Flow cytometry analysis highlighted a tumor mediated immune cells extravasation and infiltration, with an initial reduced tumor cell viability.
\nIn conclusion, we generated a functional and relevant human cancer model, through the adoption of MIVO device, that can be efficiently employed as an drugs screening platform, both for pharmacological treatments and for cell-based therapies, and for basic research purposes.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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April 14, 2022: Development and evaluation of NAMs for new endpoints: Developmental neurotoxicity and respiratory sensitization<\/h3>\r\n
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Evaluation of in vitro New Approach Methodologies for Developmental Neurotoxicity<\/strong>
\nPresented by Kelly Carstens, PhD, US EPA ORISE
\nCurrent developmental neurotoxicity (DNT) hazard assessment relies on in vivo testing that is resource intensive and lacks information on key cellular processes. To address these limitations, DNT new approach methodologies (NAMs) are being evaluated for their utility to inform DNT hazard, including: functional microelectrode array network formation assay (NFA) to evaluate neuronal network formation; and high-content imaging to evaluate proliferation, apoptosis, neurite outgrowth, and synaptogenesis. This work applies computational approaches to address three related hypotheses: (1) a broad screening battery will provide a sensitive marker of potential DNT bioactivity; (2) evaluating selective bioactivity may provide a more specific indicator of the functional processes underlying DNT; (3) some subset of the DNT NAM endpoints may optimally classify DNT in vivo reference chemicals. The dataset contained 57 assay endpoints, with a union set of 92 screened chemicals, including 53 in vivo DNT reference positives and 13 putative negatives. K-means clustering of selectivity revealed five chemical clusters with distinct DNT-relevant activity and identified DNT reference compounds with 66% sensitivity and 92% specificity, capturing 18 false negatives. We used a supervised machine learning approach to identify the most informative endpoints in classifying a DNT reference chemical. The five most important features included three cytotoxicity endpoints, network formation, and neurite outgrowth endpoints. This work highlights current obstacles in DNT NAM data interpretation, including a limited set of reference negatives and the question of whether cytotoxicity should be considered off-target or is relevant to deriving key DNT-related points-of-departure. Together, these data emphasize the importance of an integrated analysis that combines computational approaches, a broad chemical set, and a diverse suite of assays to demonstrate the fit-for-purpose utility of DNT NAMs for identification, hazard characterization, and prioritization of chemicals. This abstract does not reflect EPA policy.<\/p>\n

A 3D alveolar in vitro model for the prediction of chemical respiratory sensitizers and irritants<\/strong>
\nPresented by Sabina Burla, Luxembourg Institute of Science and Technology (LIST)
\nThe increasing prevalence of chemical respiratory allergic diseases resulting in high morbidity and mortality deems necessary a precise classification and labelling of chemicals inducing respiratory sensitization, to ensure the hazard is communicated and allow the safe handling and use. The immunological mechanisms underlying the development of respiratory sensitization are not fully understood, and the available new approach methodologies (NAMs) for identification of skin sensitizers fail to categorize chemicals as respiratory or skin sensitizers. Therefore, physiologically relevant test systems for the respiratory tract are essential to anticipate the sensitizing potential of chemicals.
\nThe aim of the present study was to evaluate the performance of a 3D alveolar-capillary barrier designed for the prediction of respiratory sensitizing chemicals. The model is built using human cell lines: alveolar type II epithelial (A549), endothelial (EA.hy926) and monocyte (THP-1). The cellular arrangement within the test system favors the development of a tissue-like microenvironment by cell-to-cell direct communication and indirectly through the secreted messenger molecules. Additionally, it facilitates the exposure of respiratory sensitizers in a more realistic approach, at the air-liquid-interface (ALI).
\nA panel of test items including known respiratory sensitizing chemicals and irritants was used for exposures to assess the capacity of the test system. Cell viability was measured in the resazurin assay 24h post-exposure at ALI, and the dose-response curves were modelled for the apical, basolateral compartments, and the complete test system. The test system was subsequently exposed to a test item dose leading to a cell viability of at least 75% and the expression of CD54, CD86 and TSLPr cell surface markers was measured by flow cytometry.
\nResults show that the test system has the potential to distinguish respiratory sensitizers from irritants. In addition, tested pro-haptens were correctly identified as respiratory sensitizers, chemicals reported as false negatives by other NAMs for respiratory sensitization evaluation. <\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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March 17, 2022: Maturing three-dimensional toxicology models of the barrier organs: Examples from lung and skin, featuring <\/h3>\r\n
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Thursday, March 17 2022, 9:00-10:30 am EST \/ 15:00-16:30 CET<\/p>\n

Full-thickness skin-on-a-chip model for in vitro drug testing and disease modelling<\/strong>
\nPresented by Patr\u00edcia Zoio, PhD candidate, Institute of Chemical and Biological Technology, NOVA University of Lisbon<\/p>\n

Reconstructed human skin models are a valuable tool for drug discovery, disease modeling, and basic research. However, currently available skin models do not fully reproduce the structure and function of the native human skin, mainly due to their use of animal-derived collagen and their lack of a dynamic flow system. Here, we present a full-thickness skin-on-a-chip (SoC) system that reproduces key aspects of the in vivo cellular microenvironment. This approach combines the production of a fibroblast-derived matrix (FDM) with the use of an inert porous scaffold for the long-term, stable cultivation of a human skin model. The use of a scaffold resulted in a structure with mechanical stability due to its noncontracting nature. The coculture of primary human keratinocytes resulted in a terminally differentiated skin equivalent that could maintain its architecture and homeostasis for up to 50 days. The developed SoC resulted in a differentiated epidermal compartment with increased thickness and barrier function than tissues cultured under static conditions. Integrated electrodes were used to measure transepithelial electrical resistance (TEER) during tissue culture, quantitatively evaluating tissue barrier function. After 14 days of culture, the barrier tissue was challenged with a benchmark irritant and its impact was evaluated on-chip through TEER measurements. In the future, this innovative low-cost SoC with integrated electrodes could provide a new in vitro tissue system compatible with long-term studies to study skin diseases and evaluate the safety and efficacy of novel drugs.<\/p>\n

Developing and investigating a new in vitrohepato-pulmonary coculture model for the toxicological study of inhaled xenobiotics<\/strong><\/p>\n

Presented by Sabrina Madiedo-Podvr\u0161an, PhD candidate, Universit\u00e9 de Technologie de Compi\u00e8gne (UTC)<\/p>\n

The respiratory system is exposed daily to a wide range of airborne xenobiotics. Associated toxicity mechanisms are complex and involve local and systemic responses which aggravate, stabilize, or protect our body from adverse effects. Because of this complexity, animal models are considered prime study models. However, in the European context of animal experimentation reduction, we developed a new alternative method to consider dynamic interactions between the lungs and a detoxicating organ such as the liver. According to literature, both organs reportedly interact under stress, therefore our goal is to consider any eventual toxicity modulating crosstalk behaviors
\nfollowing exposure. Two kinds of Lung\/Liver (LuLi) models have been developed: a developmental model which allowed for the technical setup of the coculture platform and a physiological-like model which better approximates a human vivo situation. Coculture models were characterized using 72-hour acetaminophen (APAP) xenobiotic exposures. Morphology, viability, and functionality properties of both compartments were monitored following exposure giving us an insight into the passage and circulation of the model substance throughout the device. Results show that while APAP has adverse effects on both compartments, toxicity is decreased in a coculture setting. Thus, the developed LuLi model displays a relevantly active and functional inter-organ communication between both compartments. Further characterization of the developed lung-liver-on-a-chip is
\nongoing but our results are promising and could open the way to a new physiologically relevant way of studying inhalation toxicology.<\/p>\n

3D Human airway epithelial models to study SARS-CoV-2 pathogenesis and to discover antivirals
\nPresented by Samuel Constant, PhD, CEO, Epithelix
\nThe recent outbreak of SARS-CoV-2 (COVID-19) is a major threat to human beings. The respiratory system is the main entry portal of SARS-CoV-2 which infects initially and principally the airway epithelia; then it gradually propagate to other human organs, causing symptoms such as fever, dry cough, fatigue, diarrhoea, conjunctivitis, pneumonia, respiratory failure, loss of taste, etc… To fight against SARS-CoV-2, confinement is necessary but not sufficient. Vaccination is certainly a priority, but new anti-viral drugs are also indispensable. Since the first step of SARS-CoV-2 infection is taking place in airway epithelial cells, it is logic to use 3D airway epithelial model as drug testing platform. Epithelix has developed and is offering standardized air-liquid interface 3D human airway epithelial cultures from nasal or bronchial (MucilAir\u2122) and small-airway (SmallAir\u2122) origins. These epithelial models closely mimic the morphology and function of the native tissues: such as cilia formation and beating, mucus production and secretion, mucociliary clearance, and secretion of antiviral molecules. These models have been successfully used for the development of antivirals against influenza, rhinoviruses, respiratory syncytial virus, amongst others.
\nThis talk will highlight how these reconstituted human airway epithelial models can be used to characterize viral infection kinetics, tissue-level tropism and transcriptional immune signatures induced by SARS-CoV-2. Relevance of these models for the preclinical evaluation of antiviral candidates will also be addressed in the context of repositioning of marketed drugs or evaluation of novel therapies and combinations delivered systematically or through aerosol therapy.<\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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February 24, 2022: Serum-Free Cultures: Why and How? <\/h3>\r\n
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Thursday, February 24 2022
\n9:30-10:30 AM EST \/ 15:30 -16:30 CET<\/p>\n

Presenter 1: Barbara Jozef, PhD, EAWAG<\/p>\n

Title: Nutritional requirements of fish cell lines: development of a serum-free culture medium for routine culturing of the RTgill-W1 cell line <\/strong><\/p>\n

Abstract:<\/p>\n

Efforts to replace fish for regulatory testing have resulted in the development of strategies and assay procedures based on permanent fish cell lines, especially of rainbow trout (Oncorhynchus mykiss). The rainbow trout gill cell line, RTgill-W1, is at the core of an acute cytotoxicity assay to predict fish acute toxicity to chemical exposure. It is the first of its kind to be approved by international standardization organizations, namely the Organisation for Economic Co-operation and Development (OECD) and the International Organization for Standardization (ISO). However, though the assay as such is performed in a simple exposure medium without the addition of animal components, RTgill-W1 cells are grown routinely in Leibovitz\u2019s L-15 medium (L-15) supplemented with 5\u201310% fetal bovine serum (FBS). FBS is a very complex, ill-defined mixture, which is additionally derived by a highly unethical method. We therefore have set out to develop a serum-free formulation capable of supporting RTgill-W1 cell proliferation through a systematic, bottom-up medium design. <\/p>\n

The novel medium formulation enables RTgill-W1 cells to proliferate in the absence of serum. Adaptation of RTgill-W1 clones to this new serum-free medium, including attachment, passaging, and freeze\/thawing, is well underway \u2013 we may not be far from having the first fully defined culture medium for at least one type of fish cell. <\/p>\n

Presenter 2: Aline Chary, PhD, LIST<\/p>\n

Title: Using FBS-free media in in vitro cell cultures: A case study in transitioning and characterizing A549 cells<\/strong><\/p>\n

Abstract: <\/p>\n

Replacing the use of fetal bovine serum (FBS) in cell culture media bolsters the reproducibility of in vitro research and overcomes the ethical and legal challenges associated with its use. Increasingly, scientists are focusing on replacing the use of FBS as a supplement in cell culture media with animal-component-free media. <\/p>\n

Using A549 cells\u2014an immortalized human epithelial alveolar-like cell line commonly used in respiratory research\u2014as a case study, we demonstrate the process of transitioning cells cultured in medium containing FBS to commercially-available media without FBS. To determine whether the transition was successful, cellular morphology and functionality were assessed by imaging (scanning electron microscopy); calculating cell doubling time, cytokine release (Bio-Plex), and cell viability (Alamar blue assay); monitoring the expression of relevant genes; and determining surfactant production (surfactant droplet test). Our results show that, while success varies based on the transition process and type of media, animal-derived components can be replaced in the culture of A549 cells. Because FBS-free media can replicate the phenotype of A549 cells similar to that observed in FBS-supplemented medium or a phenotype more similar to normal alveolar epithelial cells, the medium should be chosen based on the objective of the study. This case study can be used as a guide to transition other cell types to FBS-free media. <\/p>\n\r\n\r\n <\/div>\r\n <\/div>\r\n <\/div>\r\n\r\n

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January 21, 2022: Advances in Genotoxicity: Reconstructed Skin Comet and Micronucleus Assays<\/h3>\r\n
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Friday, January 21, 2022
\n10:00-11:30\u00a0EST\u00a0\/ 4:00-5:30 CET<\/p>\n

Recording is available at<\/strong>: https:\/\/youtu.be\/PPzRdm4I0gU<\/a><\/p>\n