Rapid Hazard Characterization of Environmental Chemicals Using a Compendium of Human Cell Lines from Different Organs

The lack of adequate toxicity data for the vast majority of chemicals in the environment has spurred the development of new approach methodologies (NAMs). This study aimed to develop a practical high-throughput in vitro model for rapidly evaluating potential hazards of chemicals using a small number of human cells. Forty-two compounds were tested using human induced pluripotent stem cell (iPSC)-derived cells (hepatocytes, neurons, cardiomyocytes and endothelial cells), and a primary endothelial cell line. Both functional and cytotoxicity endpoints were evaluated using high-content imaging. Concentration-response was used to derive points-of-departure (POD). PODs were integrated with ToxPi and used as surrogate NAM-based PODs for risk characterization. We found chemical class-specific similarity among the chemicals tested; metal salts exhibited the highest overall bioactivity. We also observed cell type-specific patterns among classes of chemicals, indicating the ability of the proposed in vitro model to recognize effects on different cell types. Compared to available NAM datasets, such as ToxCast/Tox21 and chemical structure-based descriptors, we found that the data from the five-cell-type model was as good or even better in assigning compounds to chemical classes. Additionally, the PODs from this model performed well as a conservative surrogate for regulatory in vivo PODs and were less likely to underestimate in vivo potency and potential risk compared to other NAM-based PODs. In summary, we demonstrate the potential of this in vitro screening model to inform rapid risk-based decision-making through ranking, clustering, and assessment of both hazard and risks of diverse environmental chemicals.

[1]  J. Bucher,et al.  New Toxicology Tools and the Emerging Paradigm Shift in Environmental Health Decision-Making , 2019, Environmental health perspectives.

[2]  Division on Earth Toxicity Testing in the 21st Century: A Vision and a Strategy , 2007 .

[3]  G. Miller Making Data Accessible: The Dryad Experience. , 2016, Toxicological sciences : an official journal of the Society of Toxicology.

[4]  Robert J Kavlock,et al.  Incorporating human dosimetry and exposure into high-throughput in vitro toxicity screening. , 2010, Toxicological sciences : an official journal of the Society of Toxicology.

[5]  Nicole Kleinstreuer,et al.  Supporting read-across using biological data. , 2016, ALTEX.

[6]  Lit-Hsin Loo,et al.  Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence , 2018, Archives of Toxicology.

[7]  Sébastien Antoni,et al.  Software Tools to Facilitate Systematic Review Used for Cancer Hazard Identification , 2018, Environmental health perspectives.

[8]  I. Rusyn,et al.  High-content high-throughput assays for characterizing the viability and morphology of human iPSC-derived neuronal cultures. , 2014, Assay and drug development technologies.

[9]  Ivan Rusyn,et al.  High-Content Assay Multiplexing for Toxicity Screening in Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Hepatocytes , 2015, Assay and drug development technologies.

[10]  Markus Schulz,et al.  Fetal Bovine Serum (FBS): Past - Present - Future. , 2018, ALTEX.

[11]  M. Stephens,et al.  Beyond the 3Rs: Expanding the use of human-relevant replacement methods in biomedical research. , 2019, ALTEX.

[12]  Imran Shah,et al.  Toxicokinetic Triage for Environmental Chemicals. , 2015, Toxicological sciences : an official journal of the Society of Toxicology.

[13]  Fred A. Wright,et al.  A chemical–biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6gc01147k Click here for additional data file. , 2016, Green chemistry : an international journal and green chemistry resource : GC.

[14]  C. Mallows,et al.  A Method for Comparing Two Hierarchical Clusterings , 1983 .

[15]  D. Ingber,et al.  Biology-Inspired Microphysiological Systems to Advance Patient Benefit and Animal Welfare in Drug Development , 2020, ALTEX.

[16]  David M. Reif,et al.  Analysis of the Effects of Cell Stress and Cytotoxicity onIn Vitro Assay Activity Across a Diverse Chemical and Assay Space , 2016, Toxicological sciences : an official journal of the Society of Toxicology.

[17]  Ivan Rusyn,et al.  In vitro models for liver toxicity testing. , 2013, Toxicology research.

[18]  Ivan Rusyn,et al.  Assessment of beating parameters in human induced pluripotent stem cells enables quantitative in vitro screening for cardiotoxicity. , 2013, Toxicology and applied pharmacology.

[19]  R. Judson,et al.  Evaluation of androgen assay results using a curated Hershberger database. , 2018, Reproductive toxicology.

[20]  Steven K. Gibb Toxicity testing in the 21st century: a vision and a strategy. , 2008, Reproductive toxicology.

[21]  Barbara Zdrazil,et al.  Towards grouping concepts based on new approach methodologies in chemical hazard assessment: the read-across approach of the EU-ToxRisk project , 2019, Archives of Toxicology.

[22]  Andrew Worth,et al.  Chemical Safety Assessment Using Read-Across: Assessing the Use of Novel Testing Methods to Strengthen the Evidence Base for Decision Making , 2015, Environmental health perspectives.

[23]  Alicia Paini,et al.  In vitro to in vivo extrapolation for high throughput prioritization and decision making. , 2018, Toxicology in vitro : an international journal published in association with BIBRA.

[24]  Thomas Knudsen,et al.  Predictive models and computational toxicology. , 2013, Methods in molecular biology.

[25]  M. LeBaron,et al.  Use of connectivity mapping to support read across: A deeper dive using data from 186 chemicals, 19 cell lines and 2 case studies. , 2019, Toxicology.

[26]  Skylar W. Marvel,et al.  ToxPi Graphical User Interface 2.0: Dynamic exploration, visualization, and sharing of integrated data models , 2018, BMC Bioinformatics.

[27]  I. Rusyn,et al.  Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. , 2014, Current topics in medicinal chemistry.

[28]  Bernhard Ø. Palsson,et al.  Systems Biology: METABOLISM , 2011 .

[29]  Robert J Kavlock,et al.  Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms , 2014, Nature Biotechnology.

[30]  Jessica A. Wignall,et al.  In vitro cardiotoxicity assessment of environmental chemicals using an organotypic human induced pluripotent stem cell‐derived model , 2017, Toxicology and applied pharmacology.

[31]  Robert G. Pearce,et al.  httk: R Package for High-Throughput Toxicokinetics. , 2017, Journal of statistical software.

[32]  Lan Zhou,et al.  Grouping of complex substances using analytical chemistry data: A framework for quantitative evaluation and visualization , 2019, PloS one.

[33]  Robert J Kavlock,et al.  Accelerating the Pace of Chemical Risk Assessment. , 2018, Chemical research in toxicology.

[34]  K. Taylor,et al.  Ten Years of REACH — An Animal Protection Perspective , 2018, Alternatives to laboratory animals : ATLA.

[35]  Alexander Tropsha,et al.  Curation of chemogenomics data. , 2015, Nature chemical biology.

[36]  Ivan Rusyn,et al.  Use of high-throughput in vitro toxicity screening data in cancer hazard evaluations by IARC Monograph Working Groups. , 2018, ALTEX.

[37]  Ellen Mantus,et al.  Using 21st Century Science to Improve Risk-Related Evaluations , 2017 .

[38]  Fred A. Wright,et al.  Standardizing Benchmark Dose Calculations to Improve Science-Based Decisions in Human Health Assessments , 2014, Environmental health perspectives.

[39]  Fabian A. Grimm,et al.  A human population-based organotypic in vitro model for cardiotoxicity screening , 2018, ALTEX.

[40]  C. Borgert,et al.  Data Disclosure for Chemical Evaluations , 2012, Environmental health perspectives.

[41]  Catherine Mahony,et al.  SEURAT: Safety Evaluation Ultimately Replacing Animal Testing—Recommendations for future research in the field of predictive toxicology , 2014, Archives of Toxicology.

[42]  Ran Su,et al.  High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures , 2015, Archives of Toxicology.

[43]  Ruili Huang,et al.  Analysis of eight oil spill dispersants using rapid, in vitro tests for endocrine and other biological activity. , 2010, Environmental science & technology.

[44]  Anna Forsby,et al.  In vitro acute and developmental neurotoxicity screening: an overview of cellular platforms and high-throughput technical possibilities , 2016, Archives of Toxicology.

[45]  Anton Simeonov,et al.  The US Federal Tox21 Program: A strategic and operational plan for continued leadership. , 2018, ALTEX.

[46]  I. Rusyn,et al.  High-Content Assay Multiplexing for Muscle Toxicity Screening in Human-Induced Pluripotent Stem Cell-Derived Skeletal Myoblasts. , 2018, Assay and drug development technologies.

[47]  Kwanjeera Wanichthanarak,et al.  Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine , 2018, Omics : a journal of integrative biology.

[48]  Imran Shah,et al.  Considerations for Strategic Use of High-Throughput Transcriptomics Chemical Screening Data in Regulatory Decisions. , 2019, Current opinion in toxicology.

[49]  Eric H. Nguyen,et al.  Versatile synthetic alternatives to Matrigel for vascular toxicity screening and stem cell expansion , 2017, Nature Biomedical Engineering.

[50]  Barbara A Wetmore,et al.  Quantitative in vitro-to-in vivo extrapolation in a high-throughput environment. , 2015, Toxicology.

[51]  Ivan Rusyn,et al.  Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. , 2011, Chemical research in toxicology.

[52]  I. Rusyn,et al.  High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells. , 2014, Assay and drug development technologies.

[53]  Antony J. Williams,et al.  The CompTox Chemistry Dashboard: a community data resource for environmental chemistry , 2017, Journal of Cheminformatics.

[54]  David Andrew,et al.  Experiences of the REACH testing proposals system to reduce animal testing. , 2014, ALTEX.

[55]  Patience Browne,et al.  Screening Chemicals for Estrogen Receptor Bioactivity Using a Computational Model. , 2015, Environmental science & technology.

[56]  Alexander Golbraikh,et al.  Integrative chemical-biological read-across approach for chemical hazard classification. , 2013, Chemical research in toxicology.

[57]  Jens Timmer,et al.  Profile likelihood in systems biology , 2013, The FEBS journal.

[58]  David M. Reif,et al.  In Vitro Screening of Environmental Chemicals for Targeted Testing Prioritization: The ToxCast Project , 2009, Environmental health perspectives.

[59]  David M. Reif,et al.  Endocrine Profiling and Prioritization of Environmental Chemicals Using ToxCast Data , 2010, Environmental health perspectives.

[60]  Ann M Richard,et al.  Utility of In Vitro Bioactivity as a Lower Bound Estimate of In Vivo Adverse Effect Levels and in Risk-Based Prioritization. , 2019, Toxicological sciences : an official journal of the Society of Toxicology.

[61]  Menghang Xia,et al.  Review of high-content screening applications in toxicology , 2019, Archives of Toxicology.

[62]  Bertrand Desprez,et al.  A strategy for systemic toxicity assessment based on non-animal approaches: The Cosmetics Europe Long Range Science Strategy programme. , 2018, Toxicology in vitro : an international journal published in association with BIBRA.

[63]  J. Yun,et al.  Use of stem cells as alternative methods to animal experimentation in predictive toxicology. , 2019, Regulatory toxicology and pharmacology : RTP.

[64]  Fabian A. Grimm,et al.  Thorough QT/QTc in a Dish: An In Vitro Human Model That Accurately Predicts Clinical Concentration‐QTc Relationships , 2018, Clinical pharmacology and therapeutics.

[65]  Catherine Mahony,et al.  The SEURAT-1 approach towards animal free human safety assessment. , 2015, ALTEX.

[66]  K L Kolaja,et al.  Opportunities for Use of Human iPS Cells in Predictive Toxicology , 2011, Clinical pharmacology and therapeutics.

[67]  Weihsueh A. Chiu,et al.  Addressing Human Variability in Next-Generation Human Health Risk Assessments of Environmental Chemicals , 2012, Environmental health perspectives.

[68]  Ivan Rusyn,et al.  Application of the key characteristics of carcinogens in cancer hazard identification , 2018, Carcinogenesis.

[69]  D. B. Myers,et al.  A replacement-first approach to toxicity testing is necessary to successfully reauthorize TSCA. , 2011, ALTEX.

[70]  Thomas C. Wiegers,et al.  The Comparative Toxicogenomics Database: update 2019 , 2018, Nucleic Acids Res..

[71]  I. Rusyn,et al.  Advancing chemical risk assessment decision-making with population variability data: challenges and opportunities , 2018, Mammalian Genome.

[72]  Robert G. Pearce,et al.  An Intuitive Approach for Predicting Potential Human Health Risk with the Tox21 10k Library. , 2017, Environmental science & technology.

[73]  Fabian A. Grimm,et al.  High-Content Assay Multiplexing for Vascular Toxicity Screening in Induced Pluripotent Stem Cell-Derived Endothelial Cells and Human Umbilical Vein Endothelial Cells , 2017 .

[74]  David M. Reif,et al.  Population-based toxicity screening in human induced pluripotent stem cell-derived cardiomyocytes. , 2019, Toxicology and applied pharmacology.