ASCCT / ESTIV webinars

European Society of Toxicology In Vitro > Projects / Activities > ASCCT / ESTIV webinars

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

Estiv and ASCCT Webinars are available for all to attend and watch at no cost.

Webinars – 2020

August 20, 2020 – PubMed Abstract Sifter: a literature informatics tool for chemical research

Thursday, August 20th, 2020
10:00-11:00  AM US ET// 16:00-17:00 CET

Presenter: Nancy C. Baker, Leidos, Inc. and US EPA

Assessing and understanding chemical effects requires assembling information from a wide variety of sources, including millions of articles in the biomedical literature. Literature informatics approaches can help researchers make use of this information in more effective ways. We present the PubMed Abstract Sifter, a freely available literature tool from the US Environmental Protection Agency. With the Abstract Sifter, researchers can easily retrieve and triage citations from PubMed and visualize the literature landscape for a set of chemicals. The tool is supplied with template queries that facilitate the exploration of mechanistic information by using the language of Adverse Outcome Pathways (AOPs) and Key Characteristics of Carcinogens. 

This abstract does not necessarily represent US EPA policy.  

Registration is now open:

July 8, 2020 – Human biology-based approaches for COVID 19 therapeutics

Wednesday, July 8, 2020
11:00-12:30 EDT / 17:00-18:30 CET

First speaker: Malcolm Wilkinson, Kirkstall
Title: Building more physiological models of human disease 
Quasi Vivo® Systems are a way to introduce air and media flow over in vitro cell cultures and can be used to create multi-cell or multi-organoid models for the study of disease. Current projects specifically focused on  COVID-19 include: 1) development of a model that mimics infection of by SARS-CoV-2 with natural flow of lymph and blood in the body and assess responses to the entry of SARS-CoV-2 in cells from the respiratory tract and responses on circulating blood cells from the immune system, 2) building a multi-tissue model which replicates human obesity and how different tissues communicate with each other in diseases like SARS-COVID19, and 3) improving our understanding of how cigarette smoke increases susceptibility to SARS-CoV-2 infection in the lungs and help in developing a therapy for prevention and treatment.

Second speaker: Samuel Constant, Epithelix
Title: 3D Human epithelial models to study SARS-CoV-2 pathogenesis
The respiratory system is the main entry portal of SARS-CoV-2 which infects initially and principally the airway epithelia. Epithelix has developed standardized air-liquid interface 3D human airway epithelial cultures from nasal or bronchial (MucilAir™) and small-airway (SmallAir™) origins. These epithelial models closely mimic the morphology and function of the native tissues and have been used for the development of antivirals against influenza, rhinoviruses, respiratory syncytial virus, amongst others. This 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.

Registration link:

June 9, 2020 – The Use of Machine Learning and Artificial Intelligence in Toxicology and Risk Assessment

Tuesday, June 9, 2020
10:00 AM US ET / 16:00 CET

Presenter: Timothy E H Allen, Research Scientist, Willis Group, MRC Tox

Artificial intelligence (AI) and machine learning (ML) algorithms are gaining a lot of attention in toxicology. These algorithms are advantageous as they can identify new patterns in data and make predictions in a way and on a scale that human scientists cannot. What are they and how do they work? How do they learn and help make decisions? And where do they fit into the science of toxicology? Some of these questions and some of the myths surrounding these research areas will be addressed in this talk. This will include introducing some of the ideas in AI and ML and explaining how some algorithms, including random forests and neural networks, work. This will be followed with a focus on the areas of toxicology in which we are trying to apply these ideas – specifically in the area of predictive toxicology. Computational models have been constructed based on structural alerts, random forests and neural networks to predict pharmacologically important human molecular initiating events, the initial interactions molecules make with biomolecules or biosystems that can lead to adverse outcomes. Attempts are also being made to overcome the disadvantages of these algorithms – particularly how they are seen as “black boxes” with little understanding of their internal working – by combining their predictions and comparing how different chemicals are assessed by the algorithms. AI and ML approaches undoubtedly have a major role to play in the future of toxicology – but a greater understanding of the algorithms, how they work and why specific predictions are made are areas that need to be considered to see greater adoption of these valuable tools. Approaches such as those presented here allow us to answer some of these questions and can support the use of their powerful predictivity in safety science.

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May 26, 2020 – International computational collaborations for predictive toxicology

Tuesday, May 26, 2020
11:00 AM US ET / 17:00 CET

Presenter: Kamel Mansouri, Integrated Laboratory Systems, Inc.

Humans are exposed to an ever-increasing number of chemicals, but only a fraction of these have been evaluated for potential risks to human health and the environment. Thus, both regulators and manufacturers need rapid and efficient approaches to evaluate the potential toxicity of thousands of chemicals already in commerce and others in development. Advances in information technology and machine learning have fostered the development of in silico approaches that leverage the relationships between chemical structures and their biological activities. However, individual predictive computational tools are associated with certain limitations, and they are only as good as the input data upon which they are built. To address these challenges, international consortia involving over 100 scientists from governmental agencies, academia, and industry were formed to collaboratively develop in silico tools for predicting chemical toxicity. These consortia have successfully concluded three projects: the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP), the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA), and the Collaborative Acute Toxicity Modeling Suite (CATMoS). These projects used data from both the published literature and the ToxCast/Tox21 programs curated to meet defined quality specifications. Limitations of individual modeling approaches were overcome by establishing consensus models that leveraged each model’s strengths. The resulting consensus models have been used to screen hundreds of thousands of chemicals from the U.S. Environmental Protection Agency’s (EPA’s) DSSTox database. These models are freely available for further use through the OPEn structure-activity/property Relationship Application (OPERA), as an open-source standalone application and by querying the EPA’s CompTox chemistry Dashboard ( and NTP’s Integrated Chemical Environment (

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[1] Mansouri, K. et al. EHP:

[2] Mansouri K. et al. J Cheminform:

[3] Kleinstruer N.C. et al. Comput Toxicol:

[4] Mansouri, K. et al. EHP:

April 30, 2020 – Use of New Approach Methodologies for cosmetic safety assessment without animal testing

Thursday, Apr 30, 2020
11:00 AM US ET / 17:00 CET

Recent advances in chemical safety assessment now allow the complete evaluation of personal care products without the use of animals. Exposure based approaches and hypothesis-driven data generation form the basis of non-animal cosmetic safety assessments.  The safety assessment approach begins with problem formulation and evaluation of existing information before considering the development of targeted testing approaches. Assessment tools, often described as New Approach Methodologies (NAMs), can include computational models and human-relevant in vitro assays which are applied in combination to provide information on ingredient hazard and risk assessment. Finally, these disparate types of information are integrated and weighted in a transparent assessment that captures uncertainty. Each presentation in this webinar will cover different aspects of this decision process.

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11:00 Introduction: Hypothesis led safety assessment of cosmetics,
Catherine Willett, Humane Society International

11:10  Exposure based safety assessment of cosmetics,
Corie Ellison, Procter and Gamble

11:25  The application of in silico models to support decision making in toxicology: QSAR, informatics, pathway modelling, uncertainty,
Chris Barber, Lhasa, LTD

11:40 In vitro approaches to cosmetic safety assessment
Paul Walker, Cyprotex Discovery Ltd

11:55 Integration of new approach methodologies for cosmetic safety decision making
Matt Dent, Unilever

11:10 Q & A
For more information about the project, see

March 11th, 2020 – ASCCT Award Winners Webinar – Toxicological mechanistic inference: Generating mechanistic explanations of adverse outcomes

Wednesday, March 11th, 2020
11:00 AM US ET / 17:00 CET

Presenter: Ignacio J. Tripodi, PhD Candidate in Computer Science / Interdisciplinary Quantitative Biology, University of Colorado, Boulder
Registration Link:

Government regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of a mechanism of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. We thus present a mechanistic inference framework, which can generate hypotheses of the most likely mechanisms of toxicity for a specific chemical and cell type, using gene expression time series on human tissue and a semantically-interconnected knowledge graph. We seek enrichment in our manually-curated list of high-level mechanisms of toxicity (e.g. “Triggering of caspase-mediated apoptosis via release of cytochrome C”, or “Mitochondria-mediated toxicity by inhibition of electron transport chain”), represented as causally-linked Gene Ontology concepts.

Our knowledge representation is an extension of the PheKnowLator knowledge graph. It consists of an integration of concepts from multiple ontologies (GO, PRO, HPO, ChEBI, PATO, DOID, CL), as well as relevant concepts from Reactome, Uniprot, the cellular toxicogenomics database (CTD), and the AOP Wiki. The expression assays were obtained from the Open TG-Gates, CarcinoGenomics, and other projects, consisting of human liver, kidney, and lung, bronchial, buccal, and nasal epithelial cells exposed to a sizeable number of chemicals that elicit different mechanisms of toxicity. Both our knowledge graph and experimental transcriptomics data are human-centric. Besides predicting the most likely mechanisms of toxicity from the transcriptomics assays, we generate putative explanations based on the most significant genes at each time point with known links to their corresponding mechanism steps. This provides a transparent, possible explanation for the mechanisms of toxicity, that would help a researcher’s decision-making process and aid further experimental design. Furthermore, we were able to experimentally validate our mechanistic predictions for some chemicals without an established mechanism of toxicity.

Co-expression Network Analysis to Identify Heterogeneity Between the Breast Cancer Cell Line MCF-7 and Human Breast Cancer Tissues

February 25th, 2020 – Moving away from animal testing for acute inhalation toxicity testing

Tuesday, February 25th, 2020
10:00 AM US ET / 17:00 CET

Presented by Emilie Da Silva, PhD student, Technical University of Denmark/National Research Center for the Working Environment, Denmark
Registration Link:

ABSTRACT: Testing for acute inhalation toxicity is required for chemicals manufactured or imported at tonnages ≥ 10 tons per year (Commission Regulation (EC) No 440/2008) and for biocides (Regulation (EU) No 528/2012) and plant protection products (Regulation (EU) No 283/2013) before they are allowed on the market. Currently, three OECD Test Guidelines are available (TG 403, TG 436, and TG 433), all based on the exposure of rodents to the substance of interest for up to 4 hours. The use of animals for toxicological evaluation does not come without challenges. Animal experiments are costly and time consuming. It is estimated that the turnaround time for carrying out an acute inhalation test is 3 to 4 months. Besides, using rodents in order to predict the toxicity of a compound in humans is arguable given the differences in the features of the respiratory tract. The development and the validation of alternative methods in chemico, in vitro and/or in silico are needed. To do so, the mechanistic understanding of the toxicity of inhaled chemicals is key.

An innovative in vitro method for acute inhalation toxicity will be presented. This cell-free method is based on the monitoring of lung surfactant function. Indeed, the lung surfactant layer in the alveoli is the first barrier that inhaled compounds will encounter when they reach the respiratory region in the lungs. The correlation between the inhibition of the lung surfactant function and the decrease in tidal volume in mice was shown with a variety of compounds including excipients for drug formulation, and impregnation spray products. During this webinar, the method will be introduced and explained, and case studies using different chemical groups will be presented.

January 21, 2020 – ESTIV Award Winners Webinar – Role of In Vitro Mechanistic Data for the Assessment of Endocrine Disruptors in the Regulatory Context

Presented by Dr. Laura Escrivá, Assistant Professor, University of Valencia, Spain.
Tuesday, January 21st, 2020 at 4:00 pm CET

Available online at:

ABSTRACT: Scientific EU criteria and guidance from ECHA and EFSA to identify substances with Endocrine Disruptor (ED) properties have recently been implemented for plant protection products and biocidal products. The ECHA/EFSA guidance, mainly addressing EATS (estrogen, androgen, thyroid, steridogenesis) modalities, provides a protocol for ED identification, which includes the evaluation of both the adverse effects and the endocrine activity, by applying weight of evidence analysis.

The assessment of the endocrine activity requires in vitro mechanistic data providing information on the mechanism through which a substance could be considered endocrine active (e.g. by binding to and activating a receptor or interfering with hormone production). Moreover, when potentially endocrine-related adverse effects and endocrine activity are identified, the biological plausibility of the link between endocrine activity and the endocrine mediated adversity should be established by a mode of action analysis. The webinar will focus on the different steps for ED identification according to the ECHA/EFSA guidance illustrating how essential it is the in vitro data for ED assessment under EU regulatory context.

January 21, 2020 – ESTIV Award Winners Webinar – A battery of animal-free in vitro assays for evaluating prenatal developmental toxicity potency of highly complex petroleum substances

Presented by Dr. Lenny Kamelia, Division of Toxicology, Wageningen University and Research, The Netherlands.
Tuesday, January 21st, 2020 at 4:00 pm CET

Available online at

ABSTRACT: REACH legislation requires prenatal developmental toxicity (PDT) testing for substances registered at a volume of >100 tonnes/year, which also applies to petroleum substances (PS). Given that i) PDT testing is one of the most complex, and animal- and resource-intensive regulatory requirements for substances produced at >100 tonnes/year, and ii) PS are highly complex materials (UVCBs), the development of alternative non-animal based testing strategies for PS poses huge challenges. Some PS contain high concentrations of polycyclic aromatic hydrocarbons (PAHs) and we hypothesize that PDT as observed for some PS is caused by certain types of PAH present in these products. To this purpose, DMSO-extracts of 9 PS (varying in PAH content; from 5 different product categories), 1 highly refined base oil (HRBO) (containing no PAHs), and 2 gas-to-liquid (GTL) products (devoid of PAHs) were tested in a battery of in vitro assays, including the embryonic stem cell test (EST), the zebrafish embryotoxicity test (ZET), and the aryl hydrocarbon (AhR) CALUX assay. All DMSO-extracts of the PS, but not of the HRBO and GTL, induced concentration-dependent PDT as quantified in the EST and ZET, with their potency being proportional to their 3- to 7-ring PAH content. Moreover, all PS extracts also showed AhR-mediated activity in the AhR CALUX assay, suggesting a role of the AhR in mediating the observed PDT by these substances. Combining the results of the EST, ZET, AhR CALUX assay, and the PAH content, ranked and clustered the test compounds in line with their in vivo PDT potencies. In conclusion, our battery of in vitro assays, consisting of the EST, ZET and AhR CALUX assay, is able to evaluate and differentiate the PDT potency of highly complex PS, within and among categories. The results are also in concordance with our hypothesis on the role of specific groups of PAHs present in some PS for the observed PDT induced by these substances.

Archived Webinars – 2019

Archived videos of past webinars are available to ASCCT membersContact the ASCCT Secretary for more information or to suggest a topic for a future webinar.

Deep Learning in Toxicity Prediction: Reasons for Use and Possible Applications

Presented by Dr. Suman Chakravarti, Chief Scientific Officer, MultiCASE
December 4, 2019 at 4:00 pm CET

ABSTRACT: Use of deep learning is increasing in solving different problems in computational toxicology and QSAR methodology in general. However, it is important to recognize the strengths of deep learning techniques in order to get the best out of them.
This webinar will focus on different aspects of computational toxicity assessments that can be improved using deep learning techniques beyond just increasing the accuracy of the predictions. For example, building scalable QSARs using big data, eliminating the use of precomputed descriptors from QSARs, expanding domain of applicability of QSARs and identification of toxicity alerts using deep learning techniques. In addition, available free tools and different types of deep learning architectures that are relevant to toxicity assessments will be discussed with the aid of case studies.

December 4th Webinar, Deep Learning in Toxicity Prediction: Reasons for Use and Possible Applications, presented by Suman Chakravarti, Chief Scientific Officer, MultiCASE, has been posted online and is now available for viewing at your convenience.

The pros and cons of 3D in vitro cultures of liver fibrosis

Presented by Leo van Grunsven, Vrije Universiteit Brussel.
November 4, 2019 at 4:00 pm CET

ABSTRACT: Chronic liver disease is the major cause of progressive liver fibrosis which, in turn, leads to cirrhosis of the liver. One major obstacle in the development of efficient therapies is the lack of robust and representative in vitro models of human liver fibrosis to aid in understanding the basic mechanisms of the disease and in the development phase of pharmaceuticals. The aim of this presentation is to give some background on the mechanisms involved in liver fibrosis development and why our work is based on the central hypothesis that liver fibrosis in vitro cannot be studied using only hepatic stellate cells (HSCs)–the main producer of scar tissue during fibrosis. I will present and discuss some results and problems we faced while developing an in vitro liver fibrosis model. We established a model in which HepaRG cells (Biopredic) were co-cultured with either in-house isolated HSCs or in-house differentiated induced pluripotent stem cell derived HSCs. In both cases, exposure to hepatotoxins resulted in hepatocytic damage and consequent HSC activation. We now work on a model using only primary mouse HSCs and hepatocytes. I will also discuss some of our work aiming at developing more complex primary mouse co-cultures for liver disease modeling and briefly address the work of other groups that reported on such complex in vitro culture systems.

The November 4th Webinar, The pros and cons of 3D in vitro culture models of liver fibrosis, presented Prof. Leo A. van Grunsven, Vrije Universiteit Brussel, has been posted online and is now available for viewing at your convenience.

Translational challenges in in vitro nephrotoxicity model

Presented by Roos Masereeuw, Div. Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht, The Netherlands.
October 22, 2019 at 4:00 pm CET

ABSTRACT: Adverse effects caused by exposure to foreign compounds, including drugs, often involve the kidney. In fact, 14 to 26% of the acute kidney injury events reported can be related to drug-induced kidney injury. Although acute damage to the kidney may be reversible, depending on severity, 30% of the patients develop structural renal dysfunction leading to chronic kidney disease eventually progressing into end-stage kidney disease. Early prediction of adverse effects such as drug interactions and renal toxicity is therefore imperative for clinical practice and for the development of new and safe drugs. Current in vitro assays do not accurately allow such prediction, predominantly due to inadequate preservation of the organs’ microenvironment. The kidney epithelium is highly polarized, and the maintenance of this polarity is critical for optimal functioning and responsiveness to environmental signals influencing cell proliferation, migration and differentiation. This presentation will provide an overview of advances in 3D cultures of human renal cells and organoids in microfluidics, and in particular kidney tubules, thereby improving physiological performance of the tissue. These microphysiological systems have great potential for drug screenings and provide novel alternative strategies for the prediction of renal drug disposition and safety assessment in a human-specific context. However, knowledge gaps in quantitative translation of renal drug disposition from microphysiological systems still exist and will be discussed.

Developmental neurotoxicity evaluation: on the road to regulatory acceptance

Presented by Prof. Ellen Fritsche, Leibniz Research Institute for Environmental Medicine-Germany

August 28, 2019 at 4:00 PM CET

Nerve-on-a-chip platform for assessing chemotherapy-induced peripheral neuropathy

Presented by Dr. Lowry Curley, AxoSim Inc.
September 17, 2019 at 5:00 PM CET:

Using Quantitative Systems Toxicology (QST): Improving the safety of drugs while reducing animal testing (July 2019) Presenter: Paul B. Watkins, M.D., University of North Carolina – Chapel Hill

GARD™air – An in vitro assay to test for respiratory sensitizers using genomic biomarkers and machine learning (June 2019) Presenter: Joshua Schmidt, SenzaGen Inc., USA Co-author: Andy Forreryd, SenzaGen AB, Lund, Sweden

EPA computational tools (May 2019)
The CompTox Chemicals Dashboard and Generalized Read Across
Presenters: Antony Williams and Grace Patlewicz, EPA

Automated and Integrated Analysis Workflow for Adverse Outcome Pathway Identification, Hypothesis Generation and Risk Assessment (April 2019) Presenters:
Noffisat O. Oki (1) and Tatyana Doktorova (2)
1Edelweiss Connect Inc, Durham, NC, USA
2Edelweiss Connect Gmbh, Basel, Switzerland

Advancing tools for predictive toxicology (March 2019) Title: Establishment of bile duct tubular structure mimicking the intrahepatic bile duct morphogenesis for an in vitro bile recovery
Presenters: Astia Rizki-­‐Safitri, Marie Shinohara, Minoru Tanaka, and Yasuyuki Sakai

Title: Generation of recombinant human anti-diphtheria toxin neutralizing antibody to replace equine sera
Presenters: Esther Wenzel, Paul Stickings, Jeffrey Brown, Thea Sesardic, Androulla Efstratiou, Michael Hust

Title: Development and Use of Adverse Outcome Pathway (AOP) Networks to Support Assessment of Organ Level Effects
Presenters: Nicoleta Spinu, Mark TD Cronin, Steven J. Enoch, and Judith C Madden

Combining biological and computational approaches (February 2019) Presenter 1: Daniel Russo, Rutgers University
Title: Developing Mechanism-Based Animal Toxicity Models: A Chemocentric Approach Using Big Data

Presenter 2: Sudin Bhattacharya, Michigan State University
Title: Integrating Genomics and Epigenomics into Predictive Toxicology of the AH Receptor

Nanoceramic pulmonary toxicity & reproducible cell line technology (January 2019)

Award Winners Series: Nanoceramic pulmonary toxicity & reproducible cell line technology
Jan 23, 2019 10:00 AM EST

Presenter 1: Maria João Bessa, PhD candidate, Portuguese National Institute of Health
Title: Pairwise toxicity evaluation of ceramic nanoparticles exposure in human alveolar epithelial A549 cells at submerged and air-liquid cultures

Presenter 2: Tom Wahlicht, PhD, InSCREENeX GmbH
Title: Reproducible in vitro toxicology testing using functional immortalized cells