19th International Workshop on (Quantitative) Structure-Activity Relationships in Environmental and Health Sciences June 7th – June 9th, 2021 Workshop Program QSAR 2021 Workshop Program Table of Contents th June 7 , 2021 ............................................................................................................. 3 Development, Evaluation and Application of QSARs to Fill Data Gaps ............................................ 3 Cheminformatic Approaches to 'Big Data' and Biological Activity Profiling ..................................... 6 Keynote – Professor Mark Cronin ................................................................................................... 9 Poster Sessions Track 1 .................................................................................................................. 9 Poster Sessions Track 2 ................................................................................................................ 18 Poster Sessions Track 3 ................................................................................................................ 27 th June 8 , 2021 ........................................................................................................... 36 Development, Evaluation and Application of QSARs and Thresholds of Toxicological Concern (TTC) ............................................................................................................................................. 36 Emerging Issues ............................................................................................................................ 39 Keynote – Dr. Russell Thomas ...................................................................................................... 42 Poster Sessions Track 4 ................................................................................................................ 42 Poster Sessions Track 5 ................................................................................................................ 51 Poster Sessions Track 6 ................................................................................................................ 60 th June 9 , 2021 ........................................................................................................... 70 Application of Tools ....................................................................................................................... 70 Non Targeted Screening, Characterising Uncertainty and Informatics ......................................... 72 Keynote – Dr. Elizabeth Mannshardt ............................................................................................. 75 Poster Sessions Track 7 ................................................................................................................ 75 Poster Sessions Track 8 ................................................................................................................ 83 Poster Sessions Track 9 ................................................................................................................ 91 Sponsors ................................................................................................................ 100 2 QSAR 2021 Workshop Program June 7th, 2021 Development, Evaluation and Application of QSARs to Fill Data Gaps 286 Using physiologically-based kinetic models to inform read-across Judith Madden1, Courtney Thompson1, Steven Webb2, David Ebbrell1, Peter Penson1, Yu-Mei Tan3, Alicia Paini4 1Liverpool John Moores University, Liverpool, United Kingdom. 2Syngenta, Bracknell, United Kingdom. 3US Environmental Protection Agency, Research Triangle Park, USA. 4European Commission Joint Research Centre, Ispra, Italy Abstract Physiologically-based kinetic (PBK) models can be used to describe organ level concentration-time profiles of xenobiotics. This is key to accurate prediction of the potential effects of xenobiotics as these are determined by both inherent activity and internal exposure. Read-across, an increasingly important tool in many sectors, is reliant on data being available for other “similar” chemicals; similar in terms of activity and internal exposure profile. PBK models are data-hungry and time- consuming to develop de novo. However, it has been shown that using data from an existing model can be used to inform the development of a model for a similar chemical (Lu et al 2016). In order to ascertain for which chemicals PBK models are currently available, we have undertaken a systematic review of literature, to generate a readily-searchable source of existing PBK models. Selecting the most appropriate model to use as a template and combining this with appropriately adjusted input parameters is key to generating a reliable model for the target chemical. Here we discuss the results of the systematic review and demonstrate how an existing PBK model for one chemical was used to develop an acceptable PBK model for an analogue. Lu et al (2016) PLoS Comput Biol 12(2): e1004495. Disclaimer: The views expressed in this abstract are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency The funding of the European Partnership for Alternative Approaches to Animal Testing (EPAA) is gratefully acknowledged. 333 Analog Approach for PBPK Modeling of Synthetic Food Dyes James Rathman1,2, Vinnie Ribeiro1, Judith Madden3, Aleksandra Mostrag1, Bryan Hobocienski1, Mark Cronin3, Chihae Yang1,4,2 1MN-AM, Columbus, USA. 2Ohio State University, Columbus, USA. 3Liverpool John Moores University, Liverpool, United Kingdom. 4MN-AM, Nuremberg, Germany Abstract To improve our understanding of the fate of synthetic food dyes in vivo, physiologically based pharmacokinetic (PBPK) modeling was used to explore exposure scenarios for several FDA-approved synthetic dyes. Given a target structure for which the available data are insufficient for building a PBPK model, we describe a read-across inspired approach in which data and PBPK models for one or more data-rich analogs were used to develop a PBPK model for the target. A large database covering a diverse chemical space (e.g., pharmaceuticals, agrochemicals, cosmetics, food ingredients) was leveraged to find analog structures and identify relevant studies. Oral absorption (HIA), plasma protein binding (PPB) and blood-brain barrier (BBB) permeability were necessary inputs for the PBPK models in this study. QSAR models were developed for these properties to enable their estimation when experimental values were not available. As an example, relatively few data were available for Sunset Yellow or its metabolites, so analog candidates were identified from the PK 3 QSAR 2021 Workshop Program knowledgebase. Amaranth and Tartrazine were identified as the closest analogs. PBPK simulations for Tartrazine, a data- rich food dye, and its metabolites were performed to explore the effects of varying dose, routes of administration (oral- gavage, oral-dietary), and species (mouse, rat). This Tartrazine model was then used to construct a PBPK model for Sunset Yellow. Accurately estimating the additional uncertainty introduced by using analog data, rather than data for the target itself, depends on quantitatively capturing the target-analog similarity in terms of the relevant chemical and biological mechanisms. 210 Simulating the kinetic of metabolism for explaining differences between in vitro vs in vivo mutagenicity Petko Petkov1, Masamitsu Honma2, Takashi Yamada2, Takeshi Morita2, Ayako Furuhama2, Hristiana Ivanova1, Stefan Kotov1, Elena Kaloyanova1, Ovanes Mekenyan1 1Laboratory of Mathematical Chemistry (LMC), As. Zlatarov University, Bourgas, Bulgaria. 2Center for Biological Safety Research, National Institute of Health Sciences, Kawasaki, Japan Abstract The traditional (Q)SAR models predict mutagenicity as a result of identification of alerts for the interaction of chemicals with macromolecules. However, the in vivo mutagenicity tests have longer duration as compared with the in vitro tests which is one of the factors to make them incomparable. In the current existing (Q)SARs different duration of mutagenicity tests are not taken into account. Conceptually new SAR approach is introduced accounting for the duration of the test and relating the potency to the amount of formed DNA/protein adducts, which, in turn, depends on the kinetic of metabolism and adducts formation. The differences between in vitro and in vivo metabolic systems are investigated for chemicals having in vitro negative and in vivo positive data in mutagenicity tests with similar capacity (interacting by same macromolecules), such as the pairs in vitro Ames vs. in vivo TGR and in vitro CA vs. in vivo MN tests. Kinetic models have been derived for these mutagenicity effects. Two major factors are found to affect the conflicting mutagenicity data: 1) the generation of in vivo-specific metabolites driven by different enzyme expression and 2) duration of the in vitro and in vivo mutagenicity tests. Addressing these two factors requires explicit introduction of metabolic transformations simulating the formation of DNA/protein adducts. Empirically-defined thresholds for the adducts are introduced to each mutagenicity alert to distinguish mutagens from non-mutagens. Developing of the new kinetic models allows to explain the differences between in vitro and in vivo mutagenicity. 317 Predicting in vitro intrinsic hepatic clearance in mammals Ester Papa1, Linda Bertato1, Ilaria Casartelli1, Nicola Chirico1, Alessandro