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Niazi SK. The Coming of Age of AI/ML in Drug Discovery, Development, Clinical Testing, and Manufacturing: The FDA Perspectives. Drug Des Devel Ther 2023; 17:2691-2725. [PMID: 37701048 PMCID: PMC10493153 DOI: 10.2147/dddt.s424991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/24/2023] [Indexed: 09/14/2023] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) represent significant advancements in computing, building on technologies that humanity has developed over millions of years-from the abacus to quantum computers. These tools have reached a pivotal moment in their development. In 2021 alone, the U.S. Food and Drug Administration (FDA) received over 100 product registration submissions that heavily relied on AI/ML for applications such as monitoring and improving human performance in compiling dossiers. To ensure the safe and effective use of AI/ML in drug discovery and manufacturing, the FDA and numerous other U.S. federal agencies have issued continuously updated, stringent guidelines. Intriguingly, these guidelines are often generated or updated with the aid of AI/ML tools themselves. The overarching goal is to expedite drug discovery, enhance the safety profiles of existing drugs, introduce novel treatment modalities, and improve manufacturing compliance and robustness. Recent FDA publications offer an encouraging outlook on the potential of these tools, emphasizing the need for their careful deployment. This has expanded market opportunities for retraining personnel handling these technologies and enabled innovative applications in emerging therapies such as gene editing, CRISPR-Cas9, CAR-T cells, mRNA-based treatments, and personalized medicine. In summary, the maturation of AI/ML technologies is a testament to human ingenuity. Far from being autonomous entities, these are tools created by and for humans designed to solve complex problems now and in the future. This paper aims to present the status of these technologies, along with examples of their present and future applications.
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Ford LC, Jang S, Chen Z, Zhou YH, Gallins PJ, Wright FA, Chiu WA, Rusyn I. A Population-Based Human In Vitro Approach to Quantify Inter-Individual Variability in Responses to Chemical Mixtures. TOXICS 2022; 10:toxics10080441. [PMID: 36006120 PMCID: PMC9413237 DOI: 10.3390/toxics10080441] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 07/25/2022] [Accepted: 07/29/2022] [Indexed: 02/01/2023]
Abstract
Human cell-based population-wide in vitro models have been proposed as a strategy to derive chemical-specific estimates of inter-individual variability; however, the utility of this approach has not yet been tested for cumulative exposures in mixtures. This study aimed to test defined mixtures and their individual components and determine whether adverse effects of the mixtures were likely to be more variable in a population than those of the individual chemicals. The in vitro model comprised 146 human lymphoblastoid cell lines from four diverse subpopulations of European and African descent. Cells were exposed, in concentration−response, to 42 chemicals from diverse classes of environmental pollutants; in addition, eight defined mixtures were prepared from these chemicals using several exposure- or hazard-based scenarios. Points of departure for cytotoxicity were derived using Bayesian concentration−response modeling and population variability was quantified in the form of a toxicodynamic variability factor (TDVF). We found that 28 chemicals and all mixtures exhibited concentration−response cytotoxicity, enabling calculation of the TDVF. The median TDVF across test substances, for both individual chemicals or defined mixtures, ranged from a default assumption (101/2) of toxicodynamic variability in human population to >10. The data also provide a proof of principle for single-variant genome-wide association mapping for toxicity of the chemicals and mixtures, although replication would be necessary due to statistical power limitations with the current sample size. This study demonstrates the feasibility of using a set of human lymphoblastoid cell lines as an in vitro model to quantify the extent of inter-individual variability in hazardous properties of both individual chemicals and mixtures. The data show that population variability of the mixtures is unlikely to exceed that of the most variable component, and that similarity in genome-wide associations among components may be used to accrue additional evidence for grouping of constituents in a mixture for cumulative assessments.
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Affiliation(s)
- Lucie C. Ford
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA; (L.C.F.); (S.J.); (Z.C.); (W.A.C.)
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Suji Jang
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA; (L.C.F.); (S.J.); (Z.C.); (W.A.C.)
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Zunwei Chen
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA; (L.C.F.); (S.J.); (Z.C.); (W.A.C.)
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Yi-Hui Zhou
- Departments of Biological Sciences and Statistics, North Carolina State University, Raleigh, NC 27695, USA; (Y.-H.Z.); (F.A.W.)
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA;
| | - Paul J. Gallins
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA;
| | - Fred A. Wright
- Departments of Biological Sciences and Statistics, North Carolina State University, Raleigh, NC 27695, USA; (Y.-H.Z.); (F.A.W.)
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695, USA;
| | - Weihsueh A. Chiu
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA; (L.C.F.); (S.J.); (Z.C.); (W.A.C.)
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA; (L.C.F.); (S.J.); (Z.C.); (W.A.C.)
- Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
- Correspondence: ; Tel.: +979-458-9866
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Model systems and organisms for addressing inter- and intra-species variability in risk assessment. Regul Toxicol Pharmacol 2022; 132:105197. [DOI: 10.1016/j.yrtph.2022.105197] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 12/12/2022]
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Sedykh A. CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints. Methods Mol Biol 2022; 2474:147-154. [PMID: 35294763 DOI: 10.1007/978-1-0716-2213-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample; therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.CurveP is a heuristic, user-tunable curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose-response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as quantitative structure-activity relationship (QSAR ) modeling, computational toxicology, bioinformatics, and cheminformatics.
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A tiered approach to population-based in vitro testing for cardiotoxicity: Balancing estimates of potency and variability. J Pharmacol Toxicol Methods 2022; 114:107154. [PMID: 34999233 PMCID: PMC8930538 DOI: 10.1016/j.vascn.2022.107154] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/11/2021] [Accepted: 01/01/2022] [Indexed: 12/11/2022]
Abstract
Population-wide in vitro studies for characterization of cardiotoxicity hazard, risk, and population variability show that human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) are a powerful and high-throughput testing platform for drugs and environmental chemicals alike. However, studies in multiple donor-derived hiPSC-CMs, across large libraries of chemicals tested in concentration-response are technically complex, and study design optimization is needed to determine sufficient and fit-for-purpose population size considerations. Therefore, we tested a hypothesis that a computational down-sampling analysis based on the data from hiPSC-CM screening of 136 diverse compounds in a population of 43 non-diseased donors, including multiple replicates of the "standard" donor hiPSC-CMs, will inform optimal study designs depending on the decision context (hazard, risk and/or inter-individual variability in cardiotoxicity). Through 50 independent random subsamples of 5, 10, or 20 donors, we estimated accuracy and precision for quantifying potency, inter-individual variability, and QT prolongation risk; the results were compared to the full 43-donor cohort. We found that for potency and clinical risk of QT prolongation, a cohort of 5 randomly-selected unique donors provides accurate and precise estimates. Larger cohort sizes afforded marginal improvements, and 5 replicates of a single donor performed worse. For estimating inter-individual variability, cohorts of at least 20 donors are needed, with smaller populations on average showing bias towards underestimation in population variance. Collectively, this study shows that a variable-size hiPSC-CM-based population-wide in vitro model can be used in a number of decision scenarios for identifying cardiotoxic hazards of drugs and environmental chemicals in the population context.
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Chatterjee N, Zhang X. CRISPR approach in environmental chemical screening focusing on population variability. J Toxicol Sci 2021; 46:499-507. [PMID: 34719552 DOI: 10.2131/jts.46.499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
A significant barrier to include population variability in risk assessment is our incomplete understanding of inter-individual variability and the differential susceptibility to environmental exposures induced adverse outcomes. By combining genome editing tools with the population diversity model, this article intended to highlight a potential strategy to identify and characterize the inter-individual variability factors, the determinant gene anchoring to a particular phenotype. The goal could be achieved by integrating the perturbed CRISPR-based unbiased functional genomics screening, genome-wide or a focused subset of genes, in a population-based in vitro model system (such as the lymphoblastoid cell lines, LCL, available from HapMap and 1000 Genomes project). Then data can be translated to genetic variability and individual (or subpopulation) susceptibility by incorporating ethnicity and corresponding genome-wide association studies (GWAS) with functional genomics screening results. This approach can provide complementary data for next-generation risk assessment, in particular, for environmental stressors. The current paper outlined the previous work conducted with a population-based in vitro model system, perturbed CRISPR-based functional toxicogenomic screening of environmental chemicals, and finally, the potential strategies to combine these two platforms with their opportunities and challenges to achieve a mechanistic understanding of population variability.
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Affiliation(s)
- Nivedita Chatterjee
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, China.,INL-International Iberian Nanotechnology Laboratory, Portugal
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, China
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Axelrad DA, Setzer RW, Bateson TF, DeVito M, Dzubow RC, Fitzpatrick JW, Frame AM, Hogan KA, Houck K, Stewart M. Methods for evaluating variability in human health dose-response characterization. HUMAN AND ECOLOGICAL RISK ASSESSMENT : HERA 2019; 25:1-24. [PMID: 31404325 PMCID: PMC6688638 DOI: 10.1080/10807039.2019.1615828] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 05/03/2019] [Indexed: 05/21/2023]
Abstract
The Reference Dose (RfD) and Reference Concentration (RfC) are human health reference values (RfVs) representing exposure concentrations at or below which there is presumed to be little risk of adverse effects in the general human population. The 2009 National Research Council report Science and Decisions recommended redefining RfVs as "a risk-specific dose (for example, the dose associated with a 1 in 100,000 risk of a particular end point)." Distributions representing variability in human response to environmental contaminant exposures are critical for deriving risk-specific doses. Existing distributions estimating the extent of human toxicokinetic and toxicodynamic variability are based largely on controlled human exposure studies of pharmaceuticals. New data and methods have been developed that are designed to improve estimation of the quantitative variability in human response to environmental chemical exposures. Categories of research with potential to provide new database useful for developing updated human variability distributions include controlled human experiments, human epidemiology, animal models of genetic variability, in vitro estimates of toxicodynamic variability, and in vitro-based models of toxicokinetic variability. In vitro approaches, with further development including studies of different cell types and endpoints, and approaches to incorporate non-genetic sources of variability, appear to provide the greatest opportunity for substantial near-term advances.
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Affiliation(s)
- Daniel A. Axelrad
- Office of Policy, U.S. Environmental Protection Agency, Washington, DC, USA
| | - R. Woodrow Setzer
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Thomas F. Bateson
- Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Michael DeVito
- National Institute of Environmental Health Sciences, National Toxicology Program, Research Triangle Park, NC, USA
| | - Rebecca C. Dzubow
- Office of Children’s Health Protection, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Julie W. Fitzpatrick
- Office of the Science Advisor, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Alicia M. Frame
- Office of Land and Emergency Management, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Karen A. Hogan
- Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
| | - Keith Houck
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Michael Stewart
- Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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Rusyn I, Greene N. The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives. Toxicol Sci 2019; 161:276-284. [PMID: 29378069 DOI: 10.1093/toxsci/kfx196] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.
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Affiliation(s)
- Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843
| | - Nigel Greene
- Predictive Compound Safety and ADME, AstraZeneca Pharmaceuticals LP, Waltham, Massachusetts 02451
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Filonov D, Tice R, Luo R, Grotegut C, Van Kanegan MJ, Ludlow JW, Il'yasova D, Kinev A. Initial Assessment of Variability of Responses to Toxicants in Donor-Specific Endothelial Colony Forming Cells. Front Public Health 2018; 6:369. [PMID: 30622937 PMCID: PMC6308159 DOI: 10.3389/fpubh.2018.00369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 12/03/2018] [Indexed: 12/14/2022] Open
Abstract
There is increased interest in using high throughput in vitro assays to characterize human population variability in response to toxicants and drugs. Utilizing primary human endothelial colony-forming cells (ECFCs) isolated from blood would be highly useful for this purpose because these cells are involved in neonatal and adult vasculogenesis. We characterized the cytotoxicity of four known toxic chemicals (NaAsO2, CdCl2, tributyltin [TBT], and menadione) and their four relatively nontoxic counterparts (Na2HAsO4, ZnCl2, SnCl2, and phytonadione, respectively) in eight ECFC clones representing four neonatal donors (2 male and 2 female donors, 2 clones per donor). ECFCs were exposed to 9 concentrations of each chemical in duplicate; cell viability was evaluated 48 h later using the fluorescent vital dye fluorescent dye 5-Carboxyfluorescein Diacetate (CFDA), yielding concentration-effect curves from each experiment. Technical (day-to-day) variability of the assay, assessed from three independent experiments, was low: p-values for the differences of results were 0.74 and 0.64 for the comparison of day 2 vs. day 1 and day 3 vs. day 1, respectively. The statistical analysis used to compare the entire concentration-effect curves has revealed significant differences in levels of cytotoxicity induced by the toxic and relatively nontoxic chemical counterparts, demonstrating that donor-specific ECFCs can clearly differentiate between these two groups of chemicals. Partitioning of the total variance in the nested design assessed the contributions of between-clone and between-donor variability for different levels of cytotoxicity. Individual ECFC clones demonstrated highly reproducible responses to the chemicals. The most toxic chemical was TBT, followed by NaAsO2, CdCl2, and Menadione. Nontoxic counterparts exhibited low cytotoxicity at the higher end of concentration ranges tested. Low variability was observed between ECFC clones obtained from the same donor or different donors for CdCl2, NaAsO2, and TBT, but for menadione, the between-donor variability was much greater than the between-clone variability. The low between-clone variability indicates that an ECFC clone may represent an individual donor in cell-based assays, although this finding must be confirmed using a larger number of donors. Such confirmation would demonstrate that an in vitro ECFC-based testing platform can be used to characterize the inter-individual variability of neonatal ECFCs exposed to drugs and/or environmental toxicants.
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Affiliation(s)
| | - Raymond Tice
- Creative Scientist, Inc.Durham, NC, United States
| | - Ruiyan Luo
- School of Public Health, Georgia State University, Atlanta, GA, United States
| | - Chad Grotegut
- Duke University Medical Center, Durham, NC, United States
| | | | | | - Dora Il'yasova
- School of Public Health, Georgia State University, Atlanta, GA, United States
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10
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O'Connell MJ, Lock EF. Linked matrix factorization. Biometrics 2018; 75:582-592. [PMID: 30516272 DOI: 10.1111/biom.13010] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 08/16/2018] [Indexed: 11/26/2022]
Abstract
Several recent methods address the dimension reduction and decomposition of linked high-content data matrices. Typically, these methods consider one dimension, rows or columns, that is shared among the matrices. This shared dimension may represent common features measured for different sample sets (horizontal integration) or a common sample set with features from different platforms (vertical integration). We introduce an approach for simultaneous horizontal and vertical integration, Linked Matrix Factorization (LMF), for the general case where some matrices share rows (e.g., features) and some share columns (e.g., samples). Our motivating application is a cytotoxicity study with accompanying genomic and molecular chemical attribute data. The toxicity matrix (cell lines × chemicals) shares samples with a genotype matrix (cell lines × SNPs) and shares features with a molecular attribute matrix (chemicals × attributes). LMF gives a unified low-rank factorization of these three matrices, which allows for the decomposition of systematic variation that is shared and systematic variation that is specific to each matrix. This allows for efficient dimension reduction, exploratory visualization, and the imputation of missing data even when entire rows or columns are missing. We present theoretical results concerning the uniqueness, identifiability, and minimal parametrization of LMF, and evaluate it with extensive simulation studies.
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Affiliation(s)
| | - Eric F Lock
- Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota 55455
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Zhang Q, Li J, Middleton A, Bhattacharya S, Conolly RB. Bridging the Data Gap From in vitro Toxicity Testing to Chemical Safety Assessment Through Computational Modeling. Front Public Health 2018; 6:261. [PMID: 30255008 PMCID: PMC6141783 DOI: 10.3389/fpubh.2018.00261] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Accepted: 08/21/2018] [Indexed: 12/18/2022] Open
Abstract
Chemical toxicity testing is moving steadily toward a human cell and organoid-based in vitro approach for reasons including scientific relevancy, efficiency, cost, and ethical rightfulness. Inferring human health risk from chemical exposure based on in vitro testing data is a challenging task, facing various data gaps along the way. This review identifies these gaps and makes a case for the in silico approach of computational dose-response and extrapolation modeling to address many of the challenges. Mathematical models that can mechanistically describe chemical toxicokinetics (TK) and toxicodynamics (TD), for both in vitro and in vivo conditions, are the founding pieces in this regard. Identifying toxicity pathways and in vitro point of departure (PoD) associated with adverse health outcomes requires an understanding of the molecular key events in the interacting transcriptome, proteome, and metabolome. Such an understanding will in turn help determine the sets of sensitive biomarkers to be measured in vitro and the scope of toxicity pathways to be modeled in silico. In vitro data reporting both pathway perturbation and chemical biokinetics in the culture medium serve to calibrate the toxicity pathway and virtual tissue models, which can then help predict PoDs in response to chemical dosimetry experienced by cells in vivo. Two types of in vitro to in vivo extrapolation (IVIVE) are needed. (1) For toxic effects involving systemic regulations, such as endocrine disruption, organism-level adverse outcome pathway (AOP) models are needed to extrapolate in vitro toxicity pathway perturbation to in vivo PoD. (2) Physiologically-based toxicokinetic (PBTK) modeling is needed to extrapolate in vitro PoD dose metrics into external doses for expected exposure scenarios. Linked PBTK and TD models can explore the parameter space to recapitulate human population variability in response to chemical insults. While challenges remain for applying these modeling tools to support in vitro toxicity testing, they open the door toward population-stratified and personalized risk assessment.
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Affiliation(s)
- Qiang Zhang
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Jin Li
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, United Kingdom
| | - Alistair Middleton
- Unilever, Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, United Kingdom
| | - Sudin Bhattacharya
- Biomedical Engineering, Michigan State University, East Lansing, MI, United States
| | - Rory B Conolly
- Integrated Systems Toxicology Division, National Health and Environmental Effects Research Laboratory, United States Environmental Protection Agency, Durham, NC, United States
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12
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The use of high-throughput screening techniques to evaluate mitochondrial toxicity. Toxicology 2017; 391:34-41. [PMID: 28789971 DOI: 10.1016/j.tox.2017.07.020] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 07/28/2017] [Accepted: 07/31/2017] [Indexed: 01/30/2023]
Abstract
Toxicologists and chemical regulators depend on accurate and effective methods to evaluate and predict the toxicity of thousands of current and future compounds. Robust high-throughput screening (HTS) experiments have the potential to efficiently test large numbers of chemical compounds for effects on biological pathways. HTS assays can be utilized to examine chemical toxicity across multiple mechanisms of action, experimental models, concentrations, and lengths of exposure. Many agricultural, industrial, and pharmaceutical chemicals classified as harmful to human and environmental health exert their effects through the mechanism of mitochondrial toxicity. Mitochondrial toxicants are compounds that cause a decrease in the number of mitochondria within a cell, and/or decrease the ability of mitochondria to perform normal functions including producing adenosine triphosphate (ATP) and maintaining cellular homeostasis. Mitochondrial dysfunction can lead to apoptosis, necrosis, altered metabolism, muscle weakness, neurodegeneration, decreased organ function, and eventually disease or death of the whole organism. The development of HTS techniques to identify mitochondrial toxicants will provide extensive databases with essential connections between mechanistic mitochondrial toxicity and chemical structure. Computational and bioinformatics approaches can be used to evaluate compound databases for specific chemical structures associated with toxicity, with the goal of developing quantitative structure-activity relationship (QSAR) models and mitochondrial toxicophores. Ultimately these predictive models will facilitate the identification of mitochondrial liabilities in consumer products, industrial compounds, pharmaceuticals and environmental hazards.
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13
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Waldmann T, Grinberg M, König A, Rempel E, Schildknecht S, Henry M, Holzer AK, Dreser N, Shinde V, Sachinidis A, Rahnenführer J, Hengstler JG, Leist M. Stem Cell Transcriptome Responses and Corresponding Biomarkers That Indicate the Transition from Adaptive Responses to Cytotoxicity. Chem Res Toxicol 2016; 30:905-922. [PMID: 28001369 DOI: 10.1021/acs.chemrestox.6b00259] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Analysis of transcriptome changes has become an established method to characterize the reaction of cells to toxicants. Such experiments are mostly performed at compound concentrations close to the cytotoxicity threshold. At present, little information is available on concentration-dependent features of transcriptome changes, in particular, at the transition from noncytotoxic concentrations to conditions that are associated with cell death. Thus, it is unclear in how far cell death confounds the results of transcriptome studies. To explore this gap of knowledge, we treated pluripotent stem cells differentiating to human neuroepithelial cells (UKN1 assay) for short periods (48 h) with increasing concentrations of valproic acid (VPA) and methyl mercury (MeHg), two compounds with vastly different modes of action. We developed various visualization tools to describe cellular responses, and the overall response was classified as "tolerance" (minor transcriptome changes), "functional adaptation" (moderate/strong transcriptome responses, but no cytotoxicity), and "degeneration". The latter two conditions were compared, using various statistical approaches. We identified (i) genes regulated at cytotoxic, but not at noncytotoxic, concentrations and (ii) KEGG pathways, gene ontology term groups, and superordinate biological processes that were only regulated at cytotoxic concentrations. The consensus markers and processes found after 48 h treatment were then overlaid with those found after prolonged (6 days) treatment. The study highlights the importance of careful concentration selection and of controlling viability for transcriptome studies. Moreover, it allowed identification of 39 candidate "biomarkers of cytotoxicity". These could serve to provide alerts that data sets of interest may have been affected by cell death in the model system studied.
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Affiliation(s)
- Tanja Waldmann
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Chair Foundation, University of Konstanz , 78457 Konstanz, Germany
| | - Marianna Grinberg
- Department of Statistics, Technical University of Dortmund , D-44221 Dortmund, Germany
| | - André König
- Department of Statistics, Technical University of Dortmund , D-44221 Dortmund, Germany
| | - Eugen Rempel
- Department of Statistics, Technical University of Dortmund , D-44221 Dortmund, Germany
| | - Stefan Schildknecht
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Chair Foundation, University of Konstanz , 78457 Konstanz, Germany
| | - Margit Henry
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK) , D-50931 Cologne, Germany
| | - Anna-Katharina Holzer
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Chair Foundation, University of Konstanz , 78457 Konstanz, Germany
| | - Nadine Dreser
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Chair Foundation, University of Konstanz , 78457 Konstanz, Germany
| | - Vaibhav Shinde
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK) , D-50931 Cologne, Germany
| | - Agapios Sachinidis
- Center of Physiology and Pathophysiology, Institute of Neurophysiology, University of Cologne (UKK) , D-50931 Cologne, Germany
| | - Jörg Rahnenführer
- Department of Statistics, Technical University of Dortmund , D-44221 Dortmund, Germany
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund , D-44139 Dortmund, Germany
| | - Marcel Leist
- In Vitro Toxicology and Biomedicine, Department inaugurated by the Doerenkamp-Zbinden Chair Foundation, University of Konstanz , 78457 Konstanz, Germany
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Chiu WA, Wright FA, Rusyn I. A tiered, Bayesian approach to estimating of population variability for regulatory decision-making. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2016; 34:377-388. [PMID: 27960008 DOI: 10.14573/altex.1608251] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 12/09/2016] [Indexed: 11/23/2022]
Abstract
Characterizing human variability in susceptibility to chemical toxicity is a critical issue in regulatory decision-making, but is usually addressed by a default 10-fold safety/uncertainty factor. Feasibility of population-based in vitro experimental approaches to more accurately estimate human variability was demonstrated recently using a large (~1000) panel of lymphoblastoid cell lines. However, routine use of such a large population-based model poses cost and logistical challenges. We hypothesize that a Bayesian approach embedded in a tiered workflow provides efficient estimation of variability and enables a tailored and sensible approach to selection of appropriate sample size. We used the previously collected lymphoblastoid cell line in vitro toxicity data to develop a data-derived prior distribution for the uncertainty in the degree of population variability. The resulting prior for the toxicodynamic variability factor (the ratio between the median and 1% most sensitive individuals) has a median (90% CI) of 2.5 (1.4-9.6). We then performed computational experiments using a hierarchical Bayesian population model with lognormal population variability with samples sizes of n = 5 to 100 to determine the change in precision and accuracy with increasing sample size. We propose a tiered Bayesian strategy for fit-for-purpose population variability estimates: (1) a default using the data-derived prior distribution; (2) a pilot experiment using samples sizes of ~20 individuals that reduces prior uncertainty by > 50% with > 80% balanced accuracy for classification; and (3) a high confidence experiment using sample sizes of ~50-100. This approach efficiently uses in vitro data on population variability to inform decision-making.
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Affiliation(s)
- Weihsueh A Chiu
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Fred A Wright
- Bioinformatics Research Center and Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, North Carolina, USA
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
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15
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Systems pharmacology exploration of botanic drug pairs reveals the mechanism for treating different diseases. Sci Rep 2016; 6:36985. [PMID: 27841365 PMCID: PMC5107896 DOI: 10.1038/srep36985] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Accepted: 10/24/2016] [Indexed: 11/30/2022] Open
Abstract
Multi-herb therapy has been widely used in Traditional Chinese medicine and tailored to meet the specific needs of each individual. However, the potential molecular or systems mechanisms of them to treat various diseases have not been fully elucidated. To address this question, a systems pharmacology approach, integrating pharmacokinetics, pharmacology and systems biology, is used to comprehensively identify the drug-target and drug-disease networks, exemplified by three representative Radix Salviae Miltiorrhizae herb pairs for treating various diseases (coronary heart disease, dysmenorrheal and nephrotic syndrome). First, the compounds evaluation and the multiple targeting technology screen the active ingredients and identify the specific targets for each herb of three pairs. Second, the herb feature mapping reveals the differences in chemistry and pharmacological synergy between pairs. Third, the constructed compound-target-disease network explains the mechanisms of treatment for various diseases from a systematic level. Finally, experimental verification is taken to confirm our strategy. Our work provides an integrated strategy for revealing the mechanism of synergistic herb pairs, and also a rational way for developing novel drug combinations for treatments of complex diseases.
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16
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Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:1671-1682. [PMID: 27091369 PMCID: PMC5089888 DOI: 10.1289/ehp233] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 10/30/2015] [Accepted: 03/29/2016] [Indexed: 05/19/2023]
Abstract
BACKGROUND The Next Generation (NexGen) of Risk Assessment effort is a multi-year collaboration among several organizations evaluating new, potentially more efficient molecular, computational, and systems biology approaches to risk assessment. This article summarizes our findings, suggests applications to risk assessment, and identifies strategic research directions. OBJECTIVE Our specific objectives were to test whether advanced biological data and methods could better inform our understanding of public health risks posed by environmental exposures. METHODS New data and methods were applied and evaluated for use in hazard identification and dose-response assessment. Biomarkers of exposure and effect, and risk characterization were also examined. Consideration was given to various decision contexts with increasing regulatory and public health impacts. Data types included transcriptomics, genomics, and proteomics. Methods included molecular epidemiology and clinical studies, bioinformatic knowledge mining, pathway and network analyses, short-duration in vivo and in vitro bioassays, and quantitative structure activity relationship modeling. DISCUSSION NexGen has advanced our ability to apply new science by more rapidly identifying chemicals and exposures of potential concern, helping characterize mechanisms of action that influence conclusions about causality, exposure-response relationships, susceptibility and cumulative risk, and by elucidating new biomarkers of exposure and effects. Additionally, NexGen has fostered extensive discussion among risk scientists and managers and improved confidence in interpreting and applying new data streams. CONCLUSIONS While considerable uncertainties remain, thoughtful application of new knowledge to risk assessment appears reasonable for augmenting major scope assessments, forming the basis for or augmenting limited scope assessments, and for prioritization and screening of very data limited chemicals. Citation: Cote I, Andersen ME, Ankley GT, Barone S, Birnbaum LS, Boekelheide K, Bois FY, Burgoon LD, Chiu WA, Crawford-Brown D, Crofton KM, DeVito M, Devlin RB, Edwards SW, Guyton KZ, Hattis D, Judson RS, Knight D, Krewski D, Lambert J, Maull EA, Mendrick D, Paoli GM, Patel CJ, Perkins EJ, Poje G, Portier CJ, Rusyn I, Schulte PA, Simeonov A, Smith MT, Thayer KA, Thomas RS, Thomas R, Tice RR, Vandenberg JJ, Villeneuve DL, Wesselkamper S, Whelan M, Whittaker C, White R, Xia M, Yauk C, Zeise L, Zhao J, DeWoskin RS. 2016. The Next Generation of Risk Assessment multiyear study-highlights of findings, applications to risk assessment, and future directions. Environ Health Perspect 124:1671-1682; http://dx.doi.org/10.1289/EHP233.
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Affiliation(s)
- Ila Cote
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
- Address correspondence to I. Cote, U.S. Environmental Protection Agency, Region 8, Room 8152, 1595 Wynkoop St., Denver, CO 80202-1129 USA. Telephone: (202) 288-9539. E-mail:
| | | | - Gerald T. Ankley
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Duluth, Minnesota, USA
| | - Stanley Barone
- Office of Chemical Safety and Pollution Prevention, U.S. EPA, Washington, District of Columbia, USA
| | - Linda S. Birnbaum
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Kim Boekelheide
- Department of Pathology and Laboratory Medicine, Brown University, Providence, Rhode Island, USA
| | - Frederic Y. Bois
- Unité Modèles pour l’Écotoxicologie et la Toxicologie, Institut National de l’Environnement Industriel et des Risques, Verneuil en Halatte, France
| | - Lyle D. Burgoon
- U.S. Army Engineer Research and Development Center, Research Triangle Park, North Carolina, USA
| | - Weihsueh A. Chiu
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | | | | | - Michael DeVito
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Robert B. Devlin
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, North Carolina, USA
| | - Stephen W. Edwards
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Research Triangle Park, North Carolina, USA
| | | | - Dale Hattis
- George Perkins Marsh Institute, Clark University, Worcester, Massachusetts, USA
| | | | - Derek Knight
- European Chemicals Agency, Annankatu, Helsinki, Finland
| | - Daniel Krewski
- McLaughlin Centre for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada
| | - Jason Lambert
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Elizabeth Anne Maull
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - Donna Mendrick
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Chirag Jagdish Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Edward J. Perkins
- U.S. Army Engineer Research and Development Center, Vicksburg, Mississippi, USA
| | - Gerald Poje
- Grant Consulting Group, Washington, District of Columbia, USA
| | | | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA
| | - Paul A. Schulte
- Education and Information Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA
| | - Anton Simeonov
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Martyn T. Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | - Kristina A. Thayer
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | | | - Reuben Thomas
- Gladstone Institutes, University of California, San Francisco, San Francisco, California, USA
| | - Raymond R. Tice
- National Institute of Environmental Health Sciences, and
- National Toxicology Program, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Research Triangle Park, North Carolina, USA
| | - John J. Vandenberg
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
| | - Daniel L. Villeneuve
- National Health and Environmental Effects Research Laboratory, U.S. EPA, Duluth, Minnesota, USA
| | - Scott Wesselkamper
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Maurice Whelan
- Systems Toxicology Unit, European Commission Joint Research Centre, Ispra, Italy
| | - Christine Whittaker
- Education and Information Division, National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, Ohio, USA
| | - Ronald White
- Center for Effective Government, Washington, District of Columbia, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Carole Yauk
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada
| | - Lauren Zeise
- Office of Environmental Health Hazard Assessment, California EPA, Oakland, California, USA
| | - Jay Zhao
- National Center for Environmental Assessment, U.S. EPA, Cincinnati, Ohio, USA
| | - Robert S. DeWoskin
- National Center for Environmental Assessment, U.S. Environmental Protection Agency (EPA), Washington, District of Columbia, USA
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Sedykh A. CurveP Method for Rendering High-Throughput Screening Dose-Response Data into Digital Fingerprints. Methods Mol Biol 2016; 1473:135-41. [PMID: 27518631 DOI: 10.1007/978-1-4939-6346-1_14] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The nature of high-throughput screening (HTS) puts certain limits on optimal test conditions for each particular sample, therefore, on top of usual data normalization, additional parsing is often needed to account for incomplete read outs or various artifacts that arise from signal interferences.CurveP is a heuristic, user-tunable, curve-cleaning algorithm that attempts to find a minimum set of corrections, which would give a monotonic dose-response curve. After applying the corrections, the algorithm proceeds to calculate a set of numeric features, which can be used as a fingerprint characterizing the sample, or as a vector of independent variables (e.g., molecular descriptors in case of chemical substances testing). The resulting output can be a part of HTS data analysis or can be used as input for a broad spectrum of computational applications, such as Quantitative Structure-Activity Relationship (QSAR) modeling, computational toxicology, bio- and cheminformatics.
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Affiliation(s)
- Alexander Sedykh
- Multicase Inc., 23811 Chagrin Blvd., Ste 305,, Beachwood, OH, 44122, USA.
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18
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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; 85:147-55. [PMID: 26386728 PMCID: PMC4773193 DOI: 10.1016/j.envint.2015.09.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Revised: 09/07/2015] [Accepted: 09/08/2015] [Indexed: 05/07/2023]
Abstract
Population-based human in vitro models offer exceptional opportunities for evaluating the potential hazard and mode of action of chemicals, as well as variability in responses to toxic insults among individuals. This study was designed to test the hypothesis that comparative population genomics with efficient in vitro experimental design can be used for evaluation of the potential for hazard, mode of action, and the extent of population variability in responses to chemical mixtures. We selected 146 lymphoblast cell lines from 4 ancestrally and geographically diverse human populations based on the availability of genome sequence and basal RNA-seq data. Cells were exposed 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. On average, the two mixtures exhibited a similar range of in vitro cytotoxicity and showed considerable inter-individual variability across screened cell lines. However, when in vitro-to-in vivo extrapolation (IVIVE) coupled with reverse dosimetry was employed to convert the in vitro cytotoxic concentrations to oral equivalent doses and compared to the upper bound of predicted human exposure, we found that a nominally more cytotoxic chlorinated pesticide mixture is expected to have greater margin of safety (more than 5 orders of magnitude) as compared to the current use pesticide mixture (less than 2 orders of magnitude) due primarily to differences in exposure predictions. Multivariate genome-wide association mapping revealed an association between the toxicity of current use pesticide mixture and a polymorphism in rs1947825 in C17orf54. We conclude 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.
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Affiliation(s)
- Nour Abdo
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Public Health, Jordan University of Science and Technology, Ibrid, Jordan
| | - Barbara A Wetmore
- The Hamner Institutes for Health Sciences, Research Triangle Park, NC, USA
| | - Grace A Chappell
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA
| | - Damian Shea
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
| | - Fred A Wright
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA; Department of Statistics and the Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA
| | - Ivan Rusyn
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX, USA.
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19
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Barrett JR. Profiles in cytotoxicity: a first step toward chemical-specific adjustment factors. ENVIRONMENTAL HEALTH PERSPECTIVES 2015; 123:A136. [PMID: 25932681 PMCID: PMC4421758 DOI: 10.1289/ehp.123-a136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
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20
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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; 123:458-66. [PMID: 25622337 PMCID: PMC4421772 DOI: 10.1289/ehp.1408775] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 01/12/2015] [Indexed: 05/20/2023]
Abstract
BACKGROUND Understanding of human variation in toxicity to environmental chemicals remains limited, so human health risk assessments still largely rely on a generic 10-fold factor (10½ each for toxicokinetics and toxicodynamics) to account for sensitive individuals or subpopulations. OBJECTIVES We tested a hypothesis that population-wide in vitro cytotoxicity screening can rapidly inform both the magnitude of and molecular causes for interindividual toxicodynamic variability. METHODS We 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. Analysis included assessments of population variation and heritability, and genome-wide association mapping, with attention to phenotypic relevance to human exposures. RESULTS For about half the tested compounds, cytotoxic response in the 1% most "sensitive" individual occurred at concentrations within a factor of 10½ (i.e., approximately 3) of that in the median individual; however, for some compounds, this factor was > 10. Genetic mapping suggested important roles for variation in membrane and transmembrane genes, with a number of chemicals showing association with SNP rs13120371 in the solute carrier SLC7A11, previously implicated in chemoresistance. CONCLUSIONS This experimental approach fills critical gaps unaddressed by recent large-scale toxicity testing programs, providing quantitative, experimentally based estimates of human toxicodynamic variability, and also testable hypotheses about mechanisms contributing to interindividual variation.
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Affiliation(s)
- Nour Abdo
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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21
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Regulatory toxicology in the twenty-first century: challenges, perspectives and possible solutions. Arch Toxicol 2015; 89:823-50. [DOI: 10.1007/s00204-015-1510-0] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 03/17/2015] [Indexed: 10/23/2022]
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22
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Characterization of human lymphoblastoid cell lines as a novel in vitro test system to predict the immunotoxicity of xenobiotics. Toxicol Lett 2015; 233:8-15. [DOI: 10.1016/j.toxlet.2014.12.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2014] [Revised: 12/19/2014] [Accepted: 12/19/2014] [Indexed: 12/19/2022]
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23
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Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 2014; 3. [PMID: 25415239 PMCID: PMC4238051 DOI: 10.7554/elife.03641] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 10/28/2014] [Indexed: 12/11/2022] Open
Abstract
Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.
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Affiliation(s)
- Keren Yizhak
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Edoardo Gaude
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Sylvia Le Dévédec
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Yedael Y Waldman
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Gideon Y Stein
- Department of Internal Medicine 'B', Beilinson Hospital, Rabin Medical Center, Petah-Tikva, Israel
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Eytan Ruppin
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
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24
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Suzuki OT, Frick A, Parks BB, Trask OJ, Butz N, Steffy B, Chan E, Scoville DK, Healy E, Benton C, McQuaid PE, Thomas RS, Wiltshire T. A cellular genetics approach identifies gene-drug interactions and pinpoints drug toxicity pathway nodes. Front Genet 2014; 5:272. [PMID: 25221565 PMCID: PMC4148776 DOI: 10.3389/fgene.2014.00272] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2014] [Accepted: 07/24/2014] [Indexed: 12/03/2022] Open
Abstract
New approaches to toxicity testing have incorporated high-throughput screening across a broad-range of in vitro assays to identify potential key events in response to chemical or drug treatment. To date, these approaches have primarily utilized repurposed drug discovery assays. In this study, we describe an approach that combines in vitro screening with genetic approaches for the experimental identification of genes and pathways involved in chemical or drug toxicity. Primary embryonic fibroblasts isolated from 32 genetically-characterized inbred mouse strains were treated in concentration-response format with 65 compounds, including pharmaceutical drugs, environmental chemicals, and compounds with known modes-of-action. Integrated cellular responses were measured at 24 and 72 h using high-content imaging and included cell loss, membrane permeability, mitochondrial function, and apoptosis. Genetic association analysis of cross-strain differences in the cellular responses resulted in a collection of candidate loci potentially underlying the variable strain response to each chemical. As a demonstration of the approach, one candidate gene involved in rotenone sensitivity, Cybb, was experimentally validated in vitro and in vivo. Pathway analysis on the combined list of candidate loci across all chemicals identified a number of over-connected nodes that may serve as core regulatory points in toxicity pathways.
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Affiliation(s)
- Oscar T Suzuki
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Amber Frick
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Bethany B Parks
- The Hamner Institutes for Health Sciences, Research Triangle Park NC, USA
| | - O Joseph Trask
- The Hamner Institutes for Health Sciences, Research Triangle Park NC, USA
| | - Natasha Butz
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Brian Steffy
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Emmanuel Chan
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - David K Scoville
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | - Eric Healy
- The Hamner Institutes for Health Sciences, Research Triangle Park NC, USA
| | - Cristina Benton
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
| | | | - Russell S Thomas
- The Hamner Institutes for Health Sciences, Research Triangle Park NC, USA
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy at the University of North Carolina at Chapel Hill Chapel Hill, NC, USA
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25
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Wetmore BA, Allen B, Clewell HJ, Parker T, Wambaugh JF, Almond LM, Sochaski MA, Thomas RS. Incorporating population variability and susceptible subpopulations into dosimetry for high-throughput toxicity testing. Toxicol Sci 2014; 142:210-24. [PMID: 25145659 DOI: 10.1093/toxsci/kfu169] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Momentum is growing worldwide to use in vitro high-throughput screening (HTS) to evaluate human health effects of chemicals. However, the integration of dosimetry into HTS assays and incorporation of population variability will be essential before its application in a risk assessment context. Previously, we employed in vitro hepatic metabolic clearance and plasma protein binding data with in vitro in vivo extrapolation (IVIVE) modeling to estimate oral equivalent doses, or daily oral chemical doses required to achieve steady-state blood concentrations (Css) equivalent to media concentrations having a defined effect in an in vitro HTS assay. In this study, hepatic clearance rates of selected ToxCast chemicals were measured in vitro for 13 cytochrome P450 and five uridine 5'-diphospho-glucuronysyltransferase isozymes using recombinantly expressed enzymes. The isozyme-specific clearance rates were then incorporated into an IVIVE model that captures known differences in isozyme expression across several life stages and ethnic populations. Comparison of the median Css for a healthy population against the median or the upper 95th percentile for more sensitive populations revealed differences of 1.3- to 4.3-fold or 3.1- to 13.1-fold, respectively. Such values may be used to derive chemical-specific human toxicokinetic adjustment factors. The IVIVE model was also used to estimate subpopulation-specific oral equivalent doses that were directly compared with subpopulation-specific exposure estimates. This study successfully combines isozyme and physiologic differences to quantitate subpopulation pharmacokinetic variability. Incorporation of these values with dosimetry and in vitro bioactivities provides a viable approach that could be employed within a high-throughput risk assessment framework.
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Affiliation(s)
- Barbara A Wetmore
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Brittany Allen
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Harvey J Clewell
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Timothy Parker
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
| | - John F Wambaugh
- United States Environmental Protection Agency, Office of Research and Development, National Center for Computational Toxicology, Research Triangle Park, North Carolina 27711
| | - Lisa M Almond
- Simcyp Limited (a Certara company), Blades Enterprise Centre, John Street, Sheffield S2 4SU, UK
| | - Mark A Sochaski
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
| | - Russell S Thomas
- The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina 27709-2137
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Chiu WA, Campbell JL, Clewell HJ, Zhou YH, Wright FA, Guyton KZ, Rusyn I. Physiologically based pharmacokinetic (PBPK) modeling of interstrain variability in trichloroethylene metabolism in the mouse. ENVIRONMENTAL HEALTH PERSPECTIVES 2014; 122:456-63. [PMID: 24518055 PMCID: PMC4014769 DOI: 10.1289/ehp.1307623] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2013] [Accepted: 02/10/2014] [Indexed: 05/22/2023]
Abstract
BACKGROUND Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, interindividual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data. OBJECTIVES We evaluated the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study. METHODS We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and 1 hybrid mouse strains to calibrate and extend existing physiologically based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). We used a Bayesian population analysis of interstrain variability to quantify variability in TCE metabolism. RESULTS Concentration-time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation were less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production were less variable (2-fold range) than DCA production (5-fold range), although the uncertainty bounds for DCA exceeded the predicted variability. CONCLUSIONS Population PBPK modeling of genetically diverse mouse strains can provide useful quantitative estimates of toxicokinetic population variability. When extrapolated to lower doses more relevant to environmental exposures, mouse population-derived variability estimates for TCE metabolism closely matched population variability estimates previously derived from human toxicokinetic studies with TCE, highlighting the utility of mouse interstrain metabolism studies for addressing toxicokinetic variability.
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Affiliation(s)
- Weihsueh A Chiu
- National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA
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Ciencewicki JM, Wang X, Marzec J, Serra ME, Bell DA, Polack FP, Kleeberger SR. A genetic model of differential susceptibility to human respiratory syncytial virus (RSV) infection. FASEB J 2014; 28:1947-56. [PMID: 24421397 DOI: 10.1096/fj.13-239855] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Respiratory syncytial virus (RSV) is the primary cause of lower respiratory tract infection during childhood and causes severe symptoms in some patients, which may cause hospitalization and death. Mechanisms for differential responses to RSV are unknown. Our objective was to develop an in vitro model of RSV infection to evaluate interindividual variation in response to RSV and identify susceptibility genes. Populations of human-derived HapMap lymphoblastoid cell lines (LCLs) were infected with RSV. Compared with controls, RSV-G mRNA expression varied from ~1- to 400-fold between LCLs. Basal expression of a number of gene transcripts, including myxovirus (influenza virus) resistance 1 (MX1), significantly correlated with RSV-G expression in HapMap LCLs. Individuals in a case-control population of RSV-infected children who were homozygous (n=94) or heterozygous (n=172) for the predicted deleterious A allele in a missense G/A SNP in MX1 had significantly greater risk for developing severe RSV disease relative to those with the major allele (n=108) (χ(2)=5.305, P=0.021; OR: 1.750, 95% CI: 1.110, 2.758, P=0.021). We conclude that genetically diverse human LCLs enable identification of susceptibility genes (e.g., MX1) for RSV disease severity in children, providing insight for disease risk.
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Affiliation(s)
- Jonathan M Ciencewicki
- 1Laboratory of Respiratory Biology National Institute of Environmental Health Sciences, 111 T. W. Alexander Dr., Bldg. 101, MD D-201, Research Triangle Park, NC 27709, USA.
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28
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Rowlands JC, Sander M, Bus JS. FutureTox: building the road for 21st century toxicology and risk assessment practices. Toxicol Sci 2013; 137:269-77. [PMID: 24204016 DOI: 10.1093/toxsci/kft252] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
This article reports on the outcome of FutureTox, a Society of Toxicology (SOT) Contemporary Concepts in Toxicology (CCT) workshop, whose goal was to address the challenges and opportunities associated with implementing 21st century technologies for toxicity testing, hazard identification, and risk assessment. One goal of the workshop was to facilitate an interactive multisector and discipline dialog. To this end, workshop invitees and participants included stakeholders from governmental and regulatory agencies, research institutes, academia, and the chemical and pharmaceutical industry in Europe and the United States. The workshop agenda was constructed to collectively review and discuss the state-of-the-science in these fields, better define the problems and challenges, outline their collective goals for the future, and identify areas of common agreement key to advancing these technologies into practice.
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Low Y, Sedykh A, Fourches D, Golbraikh A, Whelan M, Rusyn I, Tropsha A. Integrative chemical-biological read-across approach for chemical hazard classification. Chem Res Toxicol 2013; 26:1199-208. [PMID: 23848138 DOI: 10.1021/tx400110f] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Traditional read-across approaches typically rely on the chemical similarity principle to predict chemical toxicity; however, the accuracy of such predictions is often inadequate due to the underlying complex mechanisms of toxicity. Here, we report on the development of a hazard classification and visualization method that draws upon both chemical structural similarity and comparisons of biological responses to chemicals measured in multiple short-term assays ("biological" similarity). The Chemical-Biological Read-Across (CBRA) approach infers each compound's toxicity from both chemical and biological analogues whose similarities are determined by the Tanimoto coefficient. Classification accuracy of CBRA was compared to that of classical RA and other methods using chemical descriptors alone or in combination with biological data. Different types of adverse effects (hepatotoxicity, hepatocarcinogenicity, mutagenicity, and acute lethality) were classified using several biological data types (gene expression profiling and cytotoxicity screening). CBRA-based hazard classification exhibited consistently high external classification accuracy and applicability to diverse chemicals. Transparency of the CBRA approach is aided by the use of radial plots that show the relative contribution of analogous chemical and biological neighbors. Identification of both chemical and biological features that give rise to the high accuracy of CBRA-based toxicity prediction facilitates mechanistic interpretation of the models.
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Affiliation(s)
- Yen Low
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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Tice RR, Austin CP, Kavlock RJ, Bucher JR. Improving the human hazard characterization of chemicals: a Tox21 update. ENVIRONMENTAL HEALTH PERSPECTIVES 2013; 121:756-65. [PMID: 23603828 PMCID: PMC3701992 DOI: 10.1289/ehp.1205784] [Citation(s) in RCA: 403] [Impact Index Per Article: 36.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2012] [Accepted: 04/18/2013] [Indexed: 05/17/2023]
Abstract
BACKGROUND In 2008, the National Institute of Environmental Health Sciences/National Toxicology Program, the U.S. Environmental Protection Agency's National Center for Computational Toxicology, and the National Human Genome Research Institute/National Institutes of Health Chemical Genomics Center entered into an agreement on "high throughput screening, toxicity pathway profiling, and biological interpretation of findings." In 2010, the U.S. Food and Drug Administration (FDA) joined the collaboration, known informally as Tox21. OBJECTIVES The Tox21 partners agreed to develop a vision and devise an implementation strategy to shift the assessment of chemical hazards away from traditional experimental animal toxicology studies to one based on target-specific, mechanism-based, biological observations largely obtained using in vitro assays. DISCUSSION Here we outline the efforts of the Tox21 partners up to the time the FDA joined the collaboration, describe the approaches taken to develop the science and technologies that are currently being used, assess the current status, and identify problems that could impede further progress as well as suggest approaches to address those problems. CONCLUSION Tox21 faces some very difficult issues. However, we are making progress in integrating data from diverse technologies and end points into what is effectively a systems-biology approach to toxicology. This can be accomplished only when comprehensive knowledge is obtained with broad coverage of chemical and biological/toxicological space. The efforts thus far reflect the initial stage of an exceedingly complicated program, one that will likely take decades to fully achieve its goals. However, even at this stage, the information obtained has attracted the attention of the international scientific community, and we believe these efforts foretell the future of toxicology.
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Affiliation(s)
- Raymond R Tice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA
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Zeise L, Bois FY, Chiu WA, Hattis D, Rusyn I, Guyton KZ. Addressing human variability in next-generation human health risk assessments of environmental chemicals. ENVIRONMENTAL HEALTH PERSPECTIVES 2013; 121:23-31. [PMID: 23086705 PMCID: PMC3553440 DOI: 10.1289/ehp.1205687] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 10/19/2012] [Indexed: 05/19/2023]
Abstract
BACKGROUND Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. OBJECTIVE Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum. METHODS This review was informed by a National Research Council workshop titled "Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making." We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals. DISCUSSION One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification-and extent of characterization-of interindividual variability in the human population. CONCLUSIONS Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.
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Affiliation(s)
- Lauren Zeise
- Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, Oakland, California 94612, USA.
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Mesli F, Medjahed K, Ghalem S. Prediction of structural and thermodynamic properties of three products: 1-bromobenzene, tetrachlorethylene and 4-hydroxy-chromen-2-one using numerical methods. RESEARCH ON CHEMICAL INTERMEDIATES 2013. [DOI: 10.1007/s11164-012-0722-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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A systems biology approach to uncovering pharmacological synergy in herbal medicines with applications to cardiovascular disease. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2012; 2012:519031. [PMID: 23243453 PMCID: PMC3518963 DOI: 10.1155/2012/519031] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2012] [Accepted: 10/10/2012] [Indexed: 12/14/2022]
Abstract
Background. Clinical trials reveal that multiherb prescriptions of herbal medicine often exhibit pharmacological and therapeutic superiority in comparison to isolated single constituents. However, the synergistic mechanisms underlying this remain elusive. To address this question, a novel systems biology model integrating oral bioavailability and drug-likeness screening, target identification, and network pharmacology method has been constructed and applied to four clinically widely used herbs Radix Astragali Mongolici, Radix Puerariae Lobatae, Radix Ophiopogonis Japonici, and Radix Salviae Miltiorrhiza which exert synergistic effects of combined treatment of cardiovascular disease (CVD). Results. The results show that the structural properties of molecules in four herbs have substantial differences, and each herb can interact with significant target proteins related to CVD. Moreover, the bioactive ingredients from different herbs potentially act on the same molecular target (multiple-drug-one-target) and/or the functionally diverse targets but with potentially clinically relevant associations (multiple-drug-multiple-target-one-disease). From a molecular/systematic level, this explains why the herbs within a concoction could mutually enhance pharmacological synergy on a disease. Conclusions. The present work provides a new strategy not only for the understanding of pharmacological synergy in herbal medicine, but also for the rational discovery of potent drug/herb combinations that are individually subtherapeutic.
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Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A. Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data. Toxicol Sci 2012; 127:1-9. [PMID: 22387746 DOI: 10.1093/toxsci/kfs095] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR-like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage.
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Affiliation(s)
- Ivan Rusyn
- Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, North Carolina 27599, USA.
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