Carcinogenesis Specialty Section (CSS) and Computational Toxicology Specialty Section (CTSS) Joint Webinar
Integrating Computational Tools into Carcinogenicity Assessments
The webinar will explore computational tools and predictive models for assessing carcinogenicity as well as address opportunities to improve on current methods and increase their implementation in safety evaluations for drugs and potentially hazardous chemicals.
This session will introduce foundational aspects of computational toxicology and detail how computational methods can be used to improve cancer safety assessments. The primary concepts covered in this webinar will include 1) an overview of in silico methods, including artificial intelligence and machine learning, and 2) potential application of in silico methods in regulatory science. The webinar will be of broad interest to SOT members, especially graduate students, and postdoctoral fellows who may not directly work in the field of carcinogenesis or computational toxicology but wish to expand their knowledgebase.
The webinar will consist of two 20-minute presentations followed by a 20-minute moderated question/answer session with the speakers.
In Silico Methods for the Prediction of Carcinogenicity
Dr. Mark Cronin, PhD
School of Pharmacy and Biomolecular Science, Liverpool John Moores University, Liverpool, UK
An overview of in silico methods to predict carcinogenicity will be provided. The complexity of this endpoint means it is one of the most challenging adverse outcomes to predict, although much progress has been made, especially for DNA reactive carcinogens. The methods currently available and routinely used include read-across, structural alerts and quantitative structure-activity relationships (QSARs). Potential future applications of machine learning and artificial intelligence will be considered.
The EURL ECVAM Curated Database of Genotoxicity & Carcinogenicity Results: Opportunities for Application in Regulatory Science.
Dr. Federica Madia, PhD
European Commission, Joint Research Centre (JRC) Ispra, Italy
Collection of data, consolidation of databases, criteria for the selection and expert review of data results are all prerequisites for the design and development of new testing tools. The EURL ECVAM Consolidated Genotoxicity and Carcinogenicity Database as a reference for a number of scientific endeavors in the area of genotoxicity and carcinogenicity testing across different product-type sectors is presented as well the potential application within regulatory science.
Tuesday, December 14, 2021
10:00 AM ET
Moving from One-Size-Fits-All to Fit-for Purpose TTC Values
Ron Brown, PhD, Risk Sciences Consortium
Grace Patlewicz, PhD, US EPA
Threshold of Toxicological Concern (TTC) values have historically been derived from comprehensive datasets that include structurally diverse compounds that span a wide range of toxicological or carcinogenic potency. The goal of this "one-size-fits-all" approach is to derive a TTC value that is adequately protective for any data-poor compound that a person may be exposed to, with the exception of very highly potent Cohort of Concern compounds. However, there is increasing interest in developing fit-for-purpose TTC values for compounds in specific use categories (e.g., cosmetics, medical devices, food, consumer) or structural classes. This webinar will explore recent efforts to develop fit-for-purpose TTC values and describe the factors that should be considered (e.g., chemical space) when deriving TTC values for compounds in specific use or structural categories.
Thursday, August 5, 2021
11:00 AM ET
Application of In Vitro and In Silico Data in Predictive Modeling of Human Organ Toxicity
Ruili Huang, PhD, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH)
Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with increasing numbers of environmental chemicals that need toxicological evaluation. This presentation will describe machine learning models for the prediction of various human organ-level toxicity endpoints, e.g., carcinogenicity, cardio, developmental, hepato, nephro, neuro, reproductive, and skin toxicities, using chemical structure and Tox21 in vitro quantitative high-throughput screening (qHTS) assay data. The assay targets contributing the most to model performance could be used to provide insight into the mechanisms and pathways related to different toxicity endpoints. A systematic approach for toxicity pathway identification will also be discussed.
Wednesday, June 23, 2021
11:00 AM ET
MDCPSS and CTSS Webinar: Practical Application of Computational Models to Predict Release Kinetics and Toxicity of Compounds Released from Medical Devices
Ron Brown, Risk Science Consortium, LLC
David Saylor, US Food and Drug Administration
Use of Computational Models to Predict the Toxicity of Compounds Released from Medical Device Materials, Ron Brown, Risk Science Consortium, LLC
Toxicity data are not available for many compounds released from medical device materials. However, computational models can be used to help predict the toxicity of data-poor compounds. This webinar will show how computational toxicology models can be used for the biological safety assessment of medical devices and will provide some tips on the practical application of the models and interpretation of model-derived predictions. Some of the more recently developed tools to help with compound grouping and Read Across will be featured and promising models to provide quantitative toxicity threshold values (e.g. NOAEL, LOAEL) will be explored, along with models to predict biocompatibility endpoints such as skin sensitization.
Physics-based models to predict patient exposure to medical device leachables, David Saylor, US Food and Drug Administration
Medical device materials contain chemicals that may pose toxicological concern(s) if released in sufficient quantities. Toxicological risk assessment approaches are increasingly being used in lieu of animal testing to address these concerns. Currently, these approaches rely primarily on in vitro extraction testing to estimate the potential for patients to be exposed to chemicals that may possibly leach out of device materials, but the clinical relevance of the test results are often ambiguous. Recent developments suggest physics-based models can be used to provide more clinically relevant exposure estimates. However, the lack of data available to parameterize and validate these models presents a barrier to routine use. This presentation will provide an overview of these approaches, including considerations in developing and parameterizing exposure models, strategies that can be used to address the challenges associated with limited data, and potential future directions and improvements.
Wednesday, June 2, 2021
CTSS and IVAMSS Webinar
State of the Science: QSAR Modeling of Skin Sensitization
Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers
Vinicius Alves, PhD, Researcher Assistant Professor, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina at Chapel Hill
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure–activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
Evaluation of the Global Performance of Eight In Silico Skin Sensitization Models Using Human Data
Emily Golden, MS Graduate Student, Center for Alternatives to Animal Testing, Johns Hopkins University
Allergic contact dermatitis, or the clinical manifestation of skin sensitization, is a leading occupational hazard. Several testing approaches exist to assess skin sensitization, but in silico models are perhaps the most advantageous due to their high speed and low-cost results. Many in silico skin sensitization models exist, though many have only been tested against results from animal studies (e.g., LLNA); this creates uncertainty in human skin sensitization assessments in both a screening and regulatory context. This project’s aim was to evaluate the accuracy of eight in silico skin sensitization models against two human data sets: one highly curated (Basketter et al., 2014) and one screening level (HSDB). The binary skin sensitization status of each chemical in each of’s QSAR Toolbox, UL’s REACHAcross™, Danish QSAR Database, TIMES-SS, and Lhasa Limited’s Derek Nexus). Models were assessed for coverage, accuracy, sensitivity, and specificity, as well as optimization features (e.g., probability of accuracy, applicability domain, etc.), if available. While there was a wide range of sensitivity and specificity, the models generally performed comparably to the LLNA in predicting human skin sensitization status (i.e., approximately 70–80% accuracy). Additionally, the models did not mispredict the same compounds, suggesting there might be an advantage in combining models. In silico skin sensitization models offer accurate and useful insights in a screening context; however, further improvements are necessary so these models may be considered fully reliable for regulatory applications.
Skin Sensitization In Silico Protocol
Glenn Myatt, PhD, CEO, Leadscope, Inc.
In silico toxicology protocol for skin sensitization computational frameworks that incorporate in silico best practices will have a critical role in expanding the application of computational toxicology. The in silico toxicology protocol consortium is one example which is developing a series of protocols (equivalent to experimental test guidelines) to support the acceptance and adoption of such methods. A general framework for organizing such protocols has been published with endpoint-specific protocols completed for genetic toxicology and skin sensitization. A series of additional protocols are in development. The skin sensitization protocol incorporates recent advances in the understanding of key events along the adverse outcome pathway. These events, along with other supporting information, are incorporated into the in silico toxicology hazard assessment framework. Information from both experimental data and/or in silico model results for individual effects or mechanisms are incorporated within this framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization assessments based on the weight of the information. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This presentation will provide an overview of the in silico toxicology protocol framework, describe the skin sensitization hazard assessment framework and present a worked example.
Wednesday, May 19, 2021
11:00 AM ET
Advanced tissue imaging and AI Data Analysis: The Opportunities and Challenge for Application in Supporting Drug Discovery
Richard Goodwin, PhD, Head of Imaging & AI Imaging & Data Analytics, Clinical Pharmacology & Safety Sciences, AstraZeneca R&D
Matthew Jacobsen, PhD, DipACVP, Director of Pathology Regulatory Safety Centre of Excellence, Clinical Pharmacology & Safety Sciences, AstraZeneca R&D
Novel and integrated molecular imaging technologies are offering a new view on the molecular and cellular landscape across the tissue microenvironment. They are able to map in unprecedented detail the impact of new therapies and so provide new ways to study disease, the patient population and the efficacy and safety of drugs. This is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. Today cutting-edge imaging technologies are increasingly used to support studies in to the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis that integrate spatial relationships in unprecedented detail.
In parallel to the development and deployment of novel molecular imaging technologies, a revolution in toxicological digital pathology is also occurring. We are moving to a digital future where our pathologists are assessing and reviewing safety studies digitally. This requires a significant change and acceptance by regulators. During this presentation we will also hear how we are transforming toxicologic pathology peer review with the first GLP validation of the digital pathology and how this is a critical step and delivering a digital future for drug discovery and development.
Wednesday, April 7, 2021
Computational Toxicology Specialty Section Virtual Meeting
This virtual annual business meeting included a brief welcome from the CTSS President, highlights announcing 2020–2021 events and accomplishments, recognition to sponsors and supporters, and an award ceremony followed by awardee presentations.
Tuesday, March 16, 2021
CTSS Award Winners: Government and Academic initiatives with AOPs, Endocrine Predictions, and Deep Neural Network Modeling
Predictive Approaches for Assessing Cross-Species Conservation of Endocrine Targets
Sara Vliet, PhD, ORISE Postdoctoral Fellow, United States Environmental Protection Agency (EPA), Great Lakes Toxicology
Developed computational predictions for cross-species susceptibility to chemical exposures of agency concern. This session will highlight current advances in computational tools and, through an Androgen Receptor case study, demonstrate current applications. This presentation will cover efforts to corroborate computational predictions through systematic review of existing data.
Deep Neural Network Modeling Identifies NSAIDs as Highly Bioactivated Hepatotoxic Metabolites
Mary Schleiff (formerly Davis), Doctor of Philosophy Candidate, University of Arkansas for Medical Sciences, Recipient of the 2019–2020 CTSS Pfizer Student Travel Award
Computational approaches and tools are used to identify a subset of structurally similar drugs with variable hepatotoxic risk on the US market: diphenylamine non-steroidal anti-inflammatory drugs (NSAID). A Xenosite metabolic model was used to identify potential mechanisms and sites of metabolic bioactivation. All seven diphenylamine NSAIDs are predicted to form quinone-species reactive metabolites but the sites and likelihood of bioactivation varied depending upon their minor structural differences (methylation, halogenation, etc.). This work can guide pharmaceutical development and identify at-risk patients to promote more personalized patient care.
Adverse Outcome Pathway (AOP) Network Guided High-Dimensional Modeling for Drug Induced Liver Injury
Dong Wang, Senior Statistician, US Food and Drug Administration (FDA), National Center for Toxicological Research, Recipient of the 2019–2020 CTSS Pfizer Paper of the Year
Adverse outcome pathway (AOP) networks were used to filter high dimensional in vitro assay data for constructing predictive models of drug induced liver injury (DILI). The findings were also corroborated with data from post market surveillance.
Wednesday, December 2, 2020
11:00 AM to 12:00 Noon EST
CTSS and DTTSS Jointly Sponsored Webinar
Artificial Intelligence in the Design of Safer Medicines—Science or Science Fiction?
Nigel Greene, AstraZeneca
There are strong economic drivers to reduce the costs associated with the discovery of new medicines. However, drug discovery and development are multiparameter optimization problems that require a novel medicine to have a fine balance between its efficacy, ADME properties and safety. Although the number of clinical failures from safety has been reduced in recent years, there are still improvements that could be made. Data science and artificial intelligence is a potential method to both improve the safety profile of new drugs as well as reduce the costs and time to bring these to the clinic. This talk will highlight some of the current investments in computational methods and highlight some of the key gaps in realizing these benefits.
Thursday, April 16, 2020
11:00 AM to 12:30 PM (EDT)
CTSS and RASS Jointly Sponsored Webinar
Computational Methods in Next-Generation Risk Assessment of Consumer Products
Steve Gutsell, PhD, Unilever Safety and Environmental Assurance Centre
Colworth Science Park, Sharnbrook, London, United Kingdom
The use of computational models is common-place across the consumer product development pipeline. The mantra of “in silico first” applies to almost all areas of efficacy and safety. Safety assessments of novel chemicals (or novel uses of existing chemicals) is required to cover human exposure and release to the environment. A key principle is that modern risk assessments should be exposure led and quantitative, so that predictions relating to dose may be interpretable in the context of exposure. Interpreting outputs from in vitro assays requires an understanding of both cellular exposure and how these relate to in vivo internal concentrations. The concept of exposure-led assessment allows methods to identify and characterize hazard and risk to be applied in a tiered manner starting with exposure-based waiving e.g.TTC, DST, ecoTTC) and building through in silico, in chemico and in vitro assays combined in weight of evidence assessments. As with any safety assessment the need for robustness, reliability and traceability of decisions made using computational approaches is paramount to their acceptance and success. The concept and benefits of pathways-based approaches to risk assessment will also be introduced in the context of both environmental and human health risk assessment.
Thursday, April 16, 2020
11:00 AM to 12:30 PM (EDT)
US FDA Experience in the Regulatory Application of (Q)SAR Modeling
Naomi Kruhlak, BS, PhD
US Food & Drug Administration, Center for Drug Evaluation & Research
The US Food and Drug Administration (FDA) actively promotes and supports research into state-of-the-art computational toxicology approaches including (quantitative) structure-activity relationship, also known as(Q)SAR, modeling for regulatory decision-making. Within FDA’s Center for Drug Evaluation and Research, a significant focus of this research is on the safety assessment of drug impurities under the ICH M7 guideline, where a (Q)SAR prediction can be used as an alternative to empirical testing to assess mutagenic potential. The successful implementation of the ICH M7 guideline has led to the development of best practices for the conduct of (Q)SAR analyses, evaluation of model outputs, and submission of (Q)SAR data to regulators. Building on this success, new opportunities for the regulatory application of (Q)SARs are being explored for other endpoints including carcinogenicity and endocrine disruption, as well as for other components of FDA-regulated products beyond impurities.
This presentation will provide an in-depth look at current regulatory practice and expectations for the use of (Q)SAR models under ICH M7, as well as highlight emerging applications of the methodology to chemical entities and endpoints of regulatory importance to FDA.
February 5, 2020
11:00 AM EDT
Computational Toxicology Specialty Section Luncheon Annual Meeting
This annual luncheon/business meeting included a brief welcome from the CTSS President, highlights announcing 2019–2020 events and accomplishments, recognition to sponsors and supporters, and an award ceremony followed by awardee presentations.
An Introduction to In Silico Toxicology
In silico toxicology has a number of unique benefits compared to in vivo or in vitro methods: it is fast to run, it does not require any test material, and often provides an understanding of the structural (and mechanistic) basis for any toxicity prediction. In addition, it has been validated as fit-for-purpose for specific toxicology endpoints and there exist protocols and other documentation to support its adoption. As such, it is being successfully used in a variety of applications across toxicology.
This webinar will outline the process of developing and using in silico methods to predict toxicity. The two commonly used in silico methodologies, expert rule-based (or structural alerts) and statistical-based (or QSAR models), will be described. In silico models are built from existing knowledge or automatically derived from training sets of historical toxicity data. The construction of these models will be described as well as how these models could be used to make a prediction and support an expert review. A series of case studies will be used to illustrate this process using first principles and commercial software.
- Introduction to different methodologies
- Expert rule-based methodologies
- Statistical-based methodologies
- Predicting toxicity
- Expert review
September 20, 2019
11:00 AM EDT