2016 Progress Report: Carolina Center for Computational Toxicology: Assays, models and tools for NextGen safety assessments

EPA Grant Number: R835166
Title: Carolina Center for Computational Toxicology: Assays, models and tools for NextGen safety assessments
Investigators: Rusyn, Ivan , Chiu, Weihsueh A , Tropsha, Alex , Wright, Fred A.
Current Investigators: Rusyn, Ivan , Chiu, Weihsueh , Tropsha, Alexander , Wright, Fred A.
Institution: Texas A & M University , North Carolina State University
Current Institution: University of North Carolina at Chapel Hill , North Carolina State University , Texas A&M , University of North Carolina at Chapel Hill
EPA Project Officer: Lasat, Mitch
Project Period: July 1, 2012 through June 30, 2016 (Extended to June 30, 2017)
Project Period Covered by this Report: July 1, 2015 through June 30,2016
Project Amount: $1,200,000
RFA: Developing High-Throughput Assays for Predictive Modeling of Reproductive and Developmental Toxicity Modulated Through the Endocrine System or Pertinent Pathways in Humans and Species Relevant to Ecological Risk Assessment (2011) RFA Text |  Recipients Lists
Research Category: Computational Toxicology , Health , Ecosystems , Safer Chemicals

Objective:

The objective of the Carolina Center for Computational Toxicology is to advance the science and practice of toxicology by (i) filling critical gaps in our knowledge of the toxicity mechanisms, (ii) incorporating the population-based screening methods into the practice of toxicity testing, (iii) developing reliable computational models and tools that address specific existing challenges in hazard identification, and (iv) engaging with the stakeholders to increase the impact of our work.

Progress Summary:

In Specific objective 1 our goal is to generate two major outputs: (i) population-scale qHTS cytotoxicity data in a human in vitro model system on hundreds of environmental chemicals; and (ii) provide molecular-level detail on the pathways perturbed by the select chemicals in a subset of cell lines. We aim to create and implement a novel assay system that probes differential chemical effects across populations.

We have published two studies that address these goals. One study is the “population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study” (Abdo, et al., 2015b) which used 1,086 lymphoblastoid cell lines from the 1000 Genomes Project, representing nine populations from five continents, to assess variation in cytotoxic response to 179 chemicals. This work demonstrated how a population-based human in vitro system can be used for rapid determination of the extent of chemical-specific inter-individual variability in toxicodynamics. Same data can be used to generate testable hypotheses about the mechanisms of toxicity and genetic determinants of inter-individual variability. Second study is evaluation of the potential for hazard, mode of action, and the extent of population variability in responses to chemical mixtures (Abdo, et al., 2015a). We selected 146 lymphoblast cell lines from 4 ancestrally and geographically diverse human populations and exposed them to two pesticide mixtures - an environmental surface water sample comprised primarily of organochlorine pesticides and a laboratory-prepared mixture of 36 currently used pesticides - in concentration response and evaluated for cytotoxicity. This study showed that a combination of in vitro human population-based cytotoxicity screening followed by dosimetric adjustment and comparative population genomics analyses enables quantitative evaluation of human health hazard from complex environmental mixtures. Additionally, such an approach yields testable hypotheses regarding potential toxicity mechanisms.

In collaboration with a team of toxicologists from a number of chemical and petroleum refining companies we are using high-content screening of human induced pluripotent stem cell (iPSC)-derived cells as means of biological read-across of both chemicals (i.e., glycol ethers) and complex substances (i.e., petroleum substances). We are testing the hypothesis that novel cell based and transcriptomic-based assays represent a feasible approach to categorize chemicals based on similarities in their in vitro toxicity profiles. Preliminary data from these studies were presented at 2016 Society of Toxicology Annual meeting (Grimm, et al., 2016; Iwata, et al., 2016). Our results show that in vitro screening approaches can be effectively utilized to categorize both individual chemicals and complex substances/mixtures into categories thereby indicating the potential for using these data in support of read-across in regulatory submissions.

In Specific objective 2 our goal is to provide the computational toxicology solutions for risk characterization in NexGen assessments with a focus on point-of-departure and population variability. In this Objective sought to answer the questions “what tools are needed/lacking to facilitate understanding and quantitative assessment of population variability in toxicity from qHTS data?” and “what statistical/modeling tools are appropriate for the available in vitro datasets?” To this effect, we have continued our work in several areas. First, a critical test for what value our efforts in collecting in vitro data in a population-based model as detailed in Objective 1 was the use of these data for a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. Data (Abdo, et al., 2015b) was used to crowdsource algorithms to predict inter-individual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds (Eduati, et al., 2015). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal. Second, we are combining QSAR and regression modeling to develop a web-based predictor for a variety of regulatory-use toxicity values (reference dose and concentration, cancer slope factors, points of departure, etc.). Third, we actively continued developing HAWC (Health Assessment Workspace Collaborative, https://hawcproject.org/), a modular, cloud-ready, informatics-based system to synthesize multiple data and information. This web tool seamlessly integrates and documents the overall workflow from literature search and review, data extraction and evidence synthesis, dose-response analysis and uncertainty characterization, to creation of customized reports. In the past year, HAWC has been adopted as the primary tool for systematic literature reviews and other parts of hazard and risk assessments by a number of major stakeholders such as the International Agency for Research on Cancer, California EPA, ICF International, National Toxicology Program, etc.

In Specific objective 3 we develop cheminformatics-based, as well as enhanced chemical-biological, models of in vivo reproductive and developmental toxicity that rely on concomitant exploration of chemical descriptors and population-based screening data. In the past year, Dr. Tropsha’s group completed their work and co-authored two publications. First, they were actively participating in the EPA/NCCT-driven effort to build a computational predictor of ER activity through EPA/NCCT-led CERAPP-Collaborative Estrogen Receptor Activity Prediction Project (Mansouri, et al., 2016). CERAPP demonstrated that predictive computational models and HTS data can be combined to prioritize a large chemical universe for one specific molecular target – the estrogen receptor. Second, Dr. Tropsha’s group also collaborated with EPA/NCCT on the high-throughput toxicokinetics (HTTK) project aimed at predicting toxicokinetics from rapid in vitro measurements and chemical structure-based properties (Wambaugh, et al., 2015). Based on literature in vivo data for 87 chemicals, the investigators identified specific properties (e.g., in vitro HTTK data, physico-chemical descriptors, and predicted transporter affinities) that correlate with poor HTTK predictive ability. The project proposed a 4-element framework for chemical TK triage that can group chemicals into 7 different categories associated with varying levels of confidence in HTTK predictions, including the data on physico-chemical properties.

Future Activities:

In Specific Objective 1, we will further explore the utility of iPSC models for population-based high-content/high-throughput screening by developing toxicity screening methods and protocols for additional cell types: macrophages, endothelial cells, myoblasts, etc. We will also use a collection of fibroblasts from ~100 animal species (mammals and birds) and sub-species that represent a more than 100,000-fold difference in body size to probe inter-species toxicodynamic variability with in vitro models. We will test the hypotheses that (i) interspecies TD variability contributes to overall allometric scaling; and (ii) in vitro TD screening can identify chemical- and/or species-specific interspecies “outliers.”

In Specific Objective 2, we will finish development of chemical structure- and biological data-based conditional toxicity value predictor; continue development of HAWC in collaboration with end users; and develop novel analysis pipeline for the analysis of high-throughput gene expression data from concentration-response experiments in vitro using the novel TempO-seq platform. In Specific Objective 3, no activities are planned.

References:

Abdo N, Wetmore BA, Chappell GA, Shea D, Wright FA, Rusyn I. 2015a. In vitro screening for population variability in toxicity of pesticide-containing mixtures. Environ Int 85:147-155.

Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA. 2015b. Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study. Environ Health Perspect 123:458-466.

Eduati F, Mangravite LM, Wang T, Tang H, Bare JC, Huang R, Norman T, Kellen M, Menden MP, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, Collaboration N-N-UDT, Friend S, Dearry A, Simeonov A, Tice RR, Rusyn I, Wright FA, Stolovitzky G, Xie Y, Saez-Rodriguez J. 2015. Prediction of human population responses to toxic compounds by a collaborative competition. Nat Biotechnol 33:933-940. 

Grimm FA, Iwata Y, Sirenko O, Russell WK, Luo Y, Crittenden C, Wright FA, Reif DM, Yeakley J, Seligmann B, Shepard P, Roy T, Boogaard PJ, Ketelslegers H, Rohde AM, Rusyn I. 2016. Categorization Of UVCBs Using Chemical-Biological Read Across. In: Annual Meeting of the Society of Toxicology. New Orleans, LA. 

Iwata Y, Grimm F, Wilson M, Bittner M, Sirenko O, Rowlands JC, Ball N, Rusyn I. 2016. Toxicological categorization of P- and E-series glycol ethers using high-content screening of human induced pluripotent stem cell (iPSC)-derived cells. In: Soeciety of Toxicology Annual Meeting. New Orleans, LA. 

Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect. 

Shapiro AJ, Cook N, Ross PK, Fox J, Cogliano V, Chiu WA, Wang N, Zeise L, Guyton KZ, Rusyn I. 2013. Web-based benchmark dose modeling module as a prototype component of an informatics-based system for human health assessments of chemicals. In: Society of Toxicology Annual Meeting. San Antonio, TX. 

Wambaugh JF, Wetmore BA, Pearce R, Strope C, Goldsmith R, Sluka JP, Sedykh A, Tropsha A, Bosgra S, Shah I, Judson R, Thomas RS, Woodrow Setzer R. 2015. Toxicokinetic Triage for Environmental Chemicals. Toxicol Sci 147:55-67. 

Wignall JA, Muratov E, Fourches D, Tropsha A, Woodruff T, Zeise L, Wang N, Reif DM, Cogliano V, Chiu WA, Guyton KZ, Rusyn I. 2013. Conditional Toxicity Value (CTV) predictor for generating toxicity values for data-sparse chemicals. In: Society of Toxicology Annual Meeting. San Antonio, TX.


Journal Articles on this Report : 16 Displayed | Download in RIS Format

Other project views: All 53 publications 39 publications in selected types All 39 journal articles
Type Citation Project Document Sources
Journal Article Abdo N, Wetmore BA, Chappell GA, Shea D, Wright FA, Rusyn I. In vitro screening for population variability in toxicity of pesticide-containing mixtures. Environment International 2015;85:147-155. R835166 (2016)
R835166 (Final)
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  • Journal Article Abdo N, Xia M, Brown CC, Kosyk O, Huang R, Sakamuru S, Zhou YH, Jack JR, Gallins P, Xia K, Li Y, Chiu WA, Motsinger-Reif AA, Austin CP, Tice RR, Rusyn I, Wright FA. Population-based in vitro hazard and concentration-response assessment of chemicals: the 1000 genomes high-throughput screening study. Environmental Health Perspectives 2015;123(5):458-466. R835166 (2014)
    R835166 (2016)
    R835166 (Final)
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  • Journal Article Berggren E, Amcoff P, Benigni R, Blackburn K, Carney E, Cronin M, Deluyker H, Gautier F, Judson RS, Kass GE, Keller D, Knight D, Lilienblum W, Mahony C, Rusyn I, Schultz T, Schwarz M, Schuurmann G, White A, Burton J, Lostia AM, Munn S, Worth A. Chemical safety assessment using read-across: assessing the use of novel testing methods to strengthen the evidence base for decision making. Environmental Health Perspectives 2015;123(12):1232-1240. R835166 (2016)
    R835166 (Final)
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  • Journal Article Chiu WA, Wright FA, Rusyn I. A tiered, Bayesian approach to estimating population variability for regulatory decision-making. ALTEX 2017;34(3):377-388. R835166 (2016)
    R835166 (Final)
    R835802 (2016)
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    R835802 (2018)
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  • Journal Article Eduati F, Mangravite LM, Wang T, Tang H, Bare JC, Huang R, Norman T, Kellen M, Menden MP, Yang J, Zhan X, Zhong R, Xiao G, Xia M, Abdo N, Kosyk O, NIEHS-NCATS-UNC DREAM Toxicogenetics Collaboration, Friend S, Dearry A, Simeonov A, Tice RR, Rusyn I, Wright FA, Stolovitzky G, Xie Y, Saez-Rodriguez J. Prediction of human population responses to toxic compounds by a collaborative competition. Nature Biotechnology 2015;33(9):933-940. R835166 (2016)
    R835166 (Final)
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  • Journal Article Low YS, Sedykh AY, Rusyn I, Tropsha A. Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Current Topics in Medicinal Chemistry 2014;14(11):1356-1364. R835166 (2014)
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  • Journal Article Low Y, Sedykh A, Fourches D, Golbraikh A, Whelan M, Rusyn I, Tropsha A. Integrative chemical-biological read-across approach for chemical hazard classification. Chemical Research in Toxicology 2013;26(8):1199-1208. R835166 (2013)
    R835166 (2016)
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  • Journal Article Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environmental Health Perspectives 2016;124(7):1023-1033. R835166 (2016)
    R835166 (Final)
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  • Journal Article Politi R, Rusyn I, Tropsha A. Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicology and Applied Pharmacology 2014;280(1):177-189. R835166 (2014)
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  • Journal Article Rusyn I, Lemon SM. Mechanisms of HCV-induced liver cancer: what did we learn from in vitro and animal studies? Cancer Letters 2014;345(2):210-215. R835166 (2014)
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  • Journal Article Sirenko O, Cromwell EF, Crittenden C, Wignall JA, Wright FA, Rusyn I. Assessment of beating parameters in human induced pluripotent stem cells enables quantitative in vitro screening for cardiotoxicity. Toxicology and Applied Pharmacology 2013;273(3):500-507. R835166 (2013)
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  • Journal Article Sirenko O, Crittenden C, Callamaras N, Hesley J, Chen YW, Funes C, Rusyn I, Anson B, Cromwell EF. Multiparameter in vitro assessment of compound effects on cardiomyocyte physiology using iPSC cells. Journal of Biomolecular Screening 2013;18(1):39-53. R835166 (2013)
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  • Journal Article Sirenko O, Hesley J, Rusyn I, Cromwell EF. High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells. Assay and Drug Development Technologies 2014;12(1):43-54. R835166 (2013)
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  • Journal Article Sirenko O, Hesley J, Rusyn I, Cromwell E. High-content high-throughput assays for characterizing the viability and morphology of human iPSC-derived neuronal cultures. Assay and Drug Development Technologies 2014;12(9-10):536-547. R835166 (2014)
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  • Journal Article Wambaugh JF, Wetmore BA, Pearce R, Strope C, Goldsmith R, Sluka JP, Sedykh A, Tropsha A, Bosgra S, Shah I, Judson R, Thomas RS, Setzer RW. Toxicokinetic triage for environmental chemicals. Toxicological Sciences 2015;147(1):55-67. R835166 (2016)
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  • Journal Article Wignall JA, Shapiro AJ, Wright FA, Woodruff TJ, Chiu WA, Guyton KZ, Rusyn I. Standardizing benchmark dose calculations to improve science-based decisions in human health assessments. Environmental Health Perspectives 2014;122(5):499-505. R835166 (2013)
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  • Supplemental Keywords:

    Bioinformatics, biostatistics, computational toxicology, QSAR, ToxCast, high throughput screening;

    Progress and Final Reports:

    Original Abstract
  • 2013 Progress Report
  • 2014 Progress Report
  • 2015
  • Final Report