1
|
Zhou Y, Wang Z, Huang Z, Li W, Chen Y, Yu X, Tang Y, Liu G. In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches. J Appl Toxicol 2024; 44:892-907. [PMID: 38329145 DOI: 10.1002/jat.4586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/09/2024]
Abstract
The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.
Collapse
Affiliation(s)
- Yiqing Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yuanting Chen
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xinxin Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| |
Collapse
|
2
|
Jia X, Wang T, Zhu H. Advancing Computational Toxicology by Interpretable Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17690-17706. [PMID: 37224004 PMCID: PMC10666545 DOI: 10.1021/acs.est.3c00653] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/05/2023] [Accepted: 05/05/2023] [Indexed: 05/26/2023]
Abstract
Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.
Collapse
Affiliation(s)
- Xuelian Jia
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Tong Wang
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| |
Collapse
|
3
|
Golden E, Ukaegbu DC, Ranslow P, Brown RH, Hartung T, Maertens A. The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data. Chem Res Toxicol 2023; 36:734-746. [PMID: 37126467 DOI: 10.1021/acs.chemrestox.2c00383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.
Collapse
Affiliation(s)
- Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Daniel C Ukaegbu
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Peter Ranslow
- Consortium for Environmental Risk Management (CERM), Hallowell, Maine 04347, United States
| | - Robert H Brown
- School of Medicine, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- CAAT-Europe, University of Konstanz, 78464, Konstanz, Germany
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- Consortium for Environmental Risk Management (CERM), Hallowell, Maine 04347, United States
| |
Collapse
|
4
|
Stucki AO, Barton-Maclaren TS, Bhuller Y, Henriquez JE, Henry TR, Hirn C, Miller-Holt J, Nagy EG, Perron MM, Ratzlaff DE, Stedeford TJ, Clippinger AJ. Use of new approach methodologies (NAMs) to meet regulatory requirements for the assessment of industrial chemicals and pesticides for effects on human health. FRONTIERS IN TOXICOLOGY 2022; 4:964553. [PMID: 36119357 PMCID: PMC9475191 DOI: 10.3389/ftox.2022.964553] [Citation(s) in RCA: 55] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/28/2022] [Indexed: 11/13/2022] Open
Abstract
New approach methodologies (NAMs) are increasingly being used for regulatory decision making by agencies worldwide because of their potential to reliably and efficiently produce information that is fit for purpose while reducing animal use. This article summarizes the ability to use NAMs for the assessment of human health effects of industrial chemicals and pesticides within the United States, Canada, and European Union regulatory frameworks. While all regulations include some flexibility to allow for the use of NAMs, the implementation of this flexibility varies across product type and regulatory scheme. This article provides an overview of various agencies’ guidelines and strategic plans on the use of NAMs, and specific examples of the successful application of NAMs to meet regulatory requirements. It also summarizes intra- and inter-agency collaborations that strengthen scientific, regulatory, and public confidence in NAMs, thereby fostering their global use as reliable and relevant tools for toxicological evaluations. Ultimately, understanding the current regulatory landscape helps inform the scientific community on the steps needed to further advance timely uptake of approaches that best protect human health and the environment.
Collapse
Affiliation(s)
- Andreas O. Stucki
- PETA Science Consortium International e.V., Stuttgart, Germany
- *Correspondence: Andreas O. Stucki,
| | - Tara S. Barton-Maclaren
- Safe Environments Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | - Yadvinder Bhuller
- Pest Management Regulatory Agency, Health Canada, Ottawa, ON, Canada
| | | | - Tala R. Henry
- Office of Pollution Prevention and Toxics, US Environmental Protection Agency, Washington, DC, United States
| | - Carole Hirn
- Scientific and Regulatory Affairs, JT International SA, Geneva, Switzerland
| | | | - Edith G. Nagy
- Bergeson & Campbell PC, Washington, DC, United States
| | - Monique M. Perron
- Office of Pesticide Programs, US Environmental Protection Agency, Washington, DC, United States
| | - Deborah E. Ratzlaff
- Safe Environments Directorate, Healthy Environments and Consumer Safety Branch, Health Canada, Ottawa, ON, Canada
| | | | | |
Collapse
|
5
|
First report of q-RASAR modeling toward an approach of easy interpretability and efficient transferability. Mol Divers 2022; 26:2847-2862. [PMID: 35767129 DOI: 10.1007/s11030-022-10478-6] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/03/2022] [Indexed: 12/21/2022]
Abstract
Quantitative structure-activity relationship (QSAR) and read-across techniques have recently been merged into a new emerging field of read-across structure-activity relationship (RASAR) that uses the chemical similarity concepts of read-across (an unsupervised step) and finally develops a supervised learning model (like QSAR). The RASAR method has so far been used only in case of graded predictions or classification modeling. In this work, we attempt, for the first time, to apply RASAR for quantitative predictions (q-RASAR) using a case study of androgen receptor binding affinity data. We have computed a number of error-based and similarity-based measures such as weighted standard deviation of the predicted values, coefficient of variation of the computed predictions, average similarity level of close training compounds for each query molecule, standard deviation and coefficient of variation of similarity levels, maximum similarity levels to positive and negative close training compounds, a concordance measure indicating similarity to positive, negative or both classes of close training compounds, etc. We have clubbed these additional measures along with the selected chemical descriptors from the previously developed QSAR model and redeveloped new partial least squares models from the training set, and predicted the endpoint using the query data set. Interestingly, these new models outperform the internal and external validation quality of the original QSAR model. In this study, we have also introduced a new similarity-based concordance measure (Banerjee-Roy coefficient) that can significantly contribute to the model quality. A q-RASAR model also has the advantage over read-across predictions in providing easy interpretation and indicating quantitative contributions of important chemical features. The strategy described here should be applicable to other biological/toxicological/property data modeling for enhanced quality of predictions, easy interpretability, and efficient transferability.
Collapse
|
6
|
Borba JV, Alves VM, Braga RC, Korn DR, Overdahl K, Silva AC, Hall SU, Overdahl E, Kleinstreuer N, Strickland J, Allen D, Andrade CH, Muratov EN, Tropsha A. STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:27012. [PMID: 35192406 PMCID: PMC8863177 DOI: 10.1289/ehp9341] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 05/22/2023]
Abstract
BACKGROUND Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.
Collapse
Affiliation(s)
- Joyce V.B. Borba
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Daniel R. Korn
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kirsten Overdahl
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Steven U.S. Hall
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Erik Overdahl
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, Paraiba, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| |
Collapse
|
7
|
Maertens A, Golden E, Luechtefeld TH, Hoffmann S, Tsaioun K, Hartung T. Probabilistic risk assessment - the keystone for the future of toxicology. ALTEX 2022; 39:3-29. [PMID: 35034131 PMCID: PMC8906258 DOI: 10.14573/altex.2201081] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Indexed: 12/12/2022]
Abstract
Safety sciences must cope with uncertainty of models and results as well as information gaps. Acknowledging this uncertainty necessitates embracing probabilities and accepting the remaining risk. Every toxicological tool delivers only probable results. Traditionally, this is taken into account by using uncertainty / assessment factors and worst-case / precautionary approaches and thresholds. Probabilistic methods and Bayesian approaches seek to characterize these uncertainties and promise to support better risk assessment and, thereby, improve risk management decisions. Actual assessments of uncertainty can be more realistic than worst-case scenarios and may allow less conservative safety margins. Most importantly, as soon as we agree on uncertainty, this defines room for improvement and allows a transition from traditional to new approach methods as an engineering exercise. The objective nature of these mathematical tools allows to assign each methodology its fair place in evidence integration, whether in the context of risk assessment, systematic reviews, or in the definition of an integrated testing strategy (ITS) / defined approach (DA) / integrated approach to testing and assessment (IATA). This article gives an overview of methods for probabilistic risk assessment and their application for exposure assessment, physiologically-based kinetic modelling, probability of hazard assessment (based on quantitative and read-across based structure-activity relationships, and mechanistic alerts from in vitro studies), individual susceptibility assessment, and evidence integration. Additional aspects are opportunities for uncertainty analysis of adverse outcome pathways and their relation to thresholds of toxicological concern. In conclusion, probabilistic risk assessment will be key for constructing a new toxicology paradigm – probably!
Collapse
Affiliation(s)
- Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Thomas H Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA.,ToxTrack, Baltimore, MD, USA
| | - Sebastian Hoffmann
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA.,seh consulting + services, Paderborn, Germany
| | - Katya Tsaioun
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA.,CAAT Europe, University of Konstanz, Konstanz, Germany
| |
Collapse
|
8
|
Lebrun S, Nguyen L, Chavez S, Chan R, Le D, Nguyen M, Jester JV. Same-chemical comparison of nonanimal eye irritation test methods: Bovine corneal opacity and permeability, EpiOcular™, isolated chicken eye, ocular Irritection®, OptiSafe™, and short time exposure. Toxicol In Vitro 2021; 72:105070. [PMID: 33352259 PMCID: PMC8544240 DOI: 10.1016/j.tiv.2020.105070] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 11/21/2022]
Abstract
The testing and classification of chemicals to determine adverse ocular effects are routinely conducted to ensure that materials are appropriately classified, labeled, and meet regulatory and safety guidelines. We have performed a same-chemical analysis using publicly available validation study results and compared the performance between tests for the same chemicals. To normalize for chemical selection, we matched chemicals tested by pairs of tests so that each matched set compared performance for the exact same chemicals. Same-chemical accuracy comparisons demonstrate a chemical selection effect that results in a wide range of overlapping false-positive (FP) rates and accuracies for all test methods. In addition, the analysis suggests that a tiered-testing strategy with specific combinations of tests can reduce the FP rate for some combinations. However, reductions in the FP rates were typically accompanied by an increase in the false-negative rates, resulting in minimal advantage in terms of accuracy. In addition, actual improvements in the FP rate after retesting positives with a second test are not as good as the theoretical improvements because some chemicals and functional groups appear to be broadly misclassified by all test methods, which, to the extent the tests make the same-chemical misclassifications, reduces the advantage of using tiered-testing strategies.
Collapse
Affiliation(s)
| | | | | | | | - Debby Le
- Lebrun Labs LLC, Anaheim, CA, USA
| | | | - James V Jester
- Department of Ophthalmology and Biomedical Engineering, University of California Irvine, Irvine, CA, USA
| |
Collapse
|
9
|
Golden E, Maertens M, Hartung T, Maertens A. Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools? Chem Res Toxicol 2020; 34:473-482. [PMID: 33320000 DOI: 10.1021/acs.chemrestox.0c00320] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Chemical respiratory sensitization is an immunological process that manifests clinically mostly as occupational asthma and is responsible for 1 in 6 cases of adult asthma, although this may be an underestimate of the prevalence, as it is under-diagnosed. Occupational asthma results in unemployment for roughly one-third of those affected due to severe health issues. Despite its high prevalence, chemical respiratory sensitization is difficult to predict, as there are currently no validated models and the mechanisms are not entirely understood, creating a significant challenge for regulatory bodies and industry alike. The Adverse Outcome Pathway (AOP) for respiratory sensitization is currently incomplete. However, some key events have been identified, and there is overlap with the comparatively well-characterized AOP for dermal sensitization. Because of this, and the fact that dermal sensitization is often assessed by in vivo, in chemico, or in silico methods, regulatory bodies are defaulting to the dermal sensitization status of chemicals as a proxy for respiratory sensitization status when evaluating chemical safety. We identified a data set of known human respiratory sensitizers, which we used to investigate the accuracy of a structural alert model, Toxtree, designed for skin sensitization and the Centre for Occupational and Environmental Health (COEH)'s model, a model developed specifically for occupational asthma. While both models had a reasonable level of accuracy, the COEH model achieved the highest balanced accuracy at 76%; when the models agreed, the overall accuracy was 87%. There were important differences between the models: Toxtree had superior performance for some structural alerts and some categories of well-characterized skin sensitizers, while the COEH model had high accuracy in identifying sensitizers that lacked identified skin sensitization reactivity domains. Overall, both models achieved respectable accuracy. However, neither model addresses potency, which, along with data quality, remains a hurdle, and the field must prioritize these issues to move forward.
Collapse
Affiliation(s)
- Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Mikhail Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States.,CAAT-Europe, University of Konstanz, 78464 Konstanz, Germany
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| |
Collapse
|
10
|
Human-Derived In Vitro Models Used for Skin Toxicity Testing Under REACh. Handb Exp Pharmacol 2020. [PMID: 33136188 DOI: 10.1007/164_2020_368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
In regulatory toxicology, in vivo studies are still prevailing, and human-derived in vitro models are mostly used in testing for local toxicity to the skin and the eyes. A single in vitro model may be limited to address one or few molecular or cellular events leading to adverse outcomes. Hence, in many instances their regulatory use involves the combination of several in vitro models to assess the hazard potential of test substance. A so-called defined approach combines different testing methods and a 'data interpretation procedure' to obtain a comprehensive overall assessment which is used for the regulatory hazard classification of the test substance.Validation is a prerequisite of regulatory acceptance of new testing methods: This chapter provides an overview of the method development from an experimental method to a test guideline via application of GIVIMP (good in vitro method practice), standardization, validation to the regulatory adoption as an OECD test guidelines. Quandaries associated with the validation towards reference data from in vivo animal studies with limited accuracy and limited human relevance are discussed, as well as uncertainty and limitations arising from restricted applicability and technical and biological variance of the in vitro methods.This chapter provides an overview of human-derived in vitro models currently adopted as OECD test guidelines: From the first skin corrosion tests utilizing reconstructed human epidermis models (RhE), to models to test for skin irritation, phototoxicity, eye irritation, and skin sensitization. The latter is using a battery of different methods and defined approaches which are still under discussion for their regulatory adoption. They will be a vanguard of future applications of human-derived models in regulatory toxicology. RhEs for testing of genotoxicity and of dermal penetration and absorption, have been developed, underwent validation studies and may soon be adopted for regulatory use; these are included in this chapter.
Collapse
|
11
|
García-García LA, Rodríguez-Salvador M. Disclosing Main authors and Organisations collaborations in bioprinting through network maps analysis. J Biomed Semantics 2020; 11:3. [PMID: 32357922 PMCID: PMC7195781 DOI: 10.1186/s13326-020-0219-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 01/30/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Scientific activity for 3D bioprinting has increased over the past years focusing mainly on fully functional biological constructs to overcome issues related to organ transplants. This research performs a scientometric analysis on bioprinting based on a competitive technology intelligence (CTI) cycle, which assesses scientific documents to establish the publication rate of science and technology in terms of institutions, patents or journals. Although analyses of publications can be observed in the literature, the identification of the most influential authors and affiliations has not been addressed. This study involves the analysis of authors and affiliations, and their interactions in a global framework. We use network collaboration maps and Betweenness Centrality (BC) to identify of the most prominent actors in bioprinting, enhancing the CTI analysis. RESULTS 2088 documents were retrieved from Scopus database from 2007 to 2017, disclosing an exponential growth with an average publication increase of 17.5% per year. A threshold of five articles with ten or more cites was established for authors, while the same number of articles but cited five or more times was set for affiliations. The author with more publications was Atala A. (36 papers and a BC = 370.9), followed by Khademhosseini A. (30 documents and a BC = 2104.7), and Mironov (30 documents and BC = 2754.9). In addition, a small correlation was observed between the number of collaborations and the number of publications. Furthermore, 1760 institutions with a median of 10 publications were found, but only 20 within the established threshold. 30% of the 20 institutions had an external collaboration, and institutions located in and close to the life science cluster in Massachusetts showed a strong cooperation. The institution with more publications was the Harvard Medical School, 61 publications, followed by the Brigham and Women's hospital, 46 papers, and the Massachusetts Institute of Technology with 37 documents. CONCLUSIONS Network map analysis and BC allowed the identification of the most influential authors working on bioprinting and the collaboration between institutions was found limited. This analysis of authors and affiliations and their collaborations offer valuable information for the identification of potential associations for bioprinting researches and stakeholders.
Collapse
|
12
|
Leung MCK, Meyer JN. Mitochondria as a target of organophosphate and carbamate pesticides: Revisiting common mechanisms of action with new approach methodologies. Reprod Toxicol 2019; 89:83-92. [PMID: 31315019 PMCID: PMC6766410 DOI: 10.1016/j.reprotox.2019.07.007] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 06/19/2019] [Accepted: 07/09/2019] [Indexed: 01/01/2023]
Abstract
Mitochondrial toxicity has been proposed as a potential cause of developmental defects in humans. We evaluated 51 organophosphate and carbamate pesticides using the U.S. EPA ToxCast and Tox21 databases. Only a small number of them bind directly to cholinesterases in the parent form. The hydrophobicity of organophosphate pesticides is correlated significantly to TSPO binding affinity, mitochondrial membrane potential reduction in HepG2 cells, and developmental toxicity in Caenorhabditis elegans and Danio rerio (p < 0.05). Structural analysis suggests that in some cases the Krebs cycle is a potential target of organophosphate and carbamate exposure at early life stages. The results support the hypothesis that mitochondrial effects of some organophosphate pesticides-particularly those that require enzymatic activation to the oxon form-may augment the documented effects of disruption of acetylcholine signaling. This study provides a proof of concept for applying new approach methodologies to interrogate mechanisms of action for cumulative risk assessment.
Collapse
Affiliation(s)
- Maxwell C K Leung
- Department of Environmental Toxicology, University of California, Davis, CA, United States; Nicholas School of the Environment, Duke University, Durham, NC, United States.
| | - Joel N Meyer
- Nicholas School of the Environment, Duke University, Durham, NC, United States
| |
Collapse
|
13
|
Abstract
We created earlier a large machine-readable database of 10,000 chemicals and 800,000 associated studies by natural language processing of the public parts of Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) registrations until December 2014. This database was used to assess the reproducibility of the six most frequently used Organisation for Economic Co-operation and Development (OECD) guideline tests. These tests consume 55% of all animals in safety testing in Europe, i.e. about 600,000 animals. With 350-750 chemicals with multiple results per test, reproducibility (balanced accuracy) was 81% and 69% of toxic substances were found again in a repeat experiment (sensitivity 69%). Inspired by the increasingly used read-across approach, we created a new type of QSAR, which is based on similarity of chemicals and not on chemical descriptors. A landscape of the chemical universe using 10 million structures was calculated, when based on Tanimoto indices similar chemicals are close and dissimilar chemicals far from each other. This allows placing any chemical of interest into the map and evaluating the information available for surrounding chemicals. In a data fusion approach, in which 74 different properties were taken into consideration, machine learning (random forest) allowed a fivefold cross-validation for 190,000 (non-) hazard labels of chemicals for which nine hazards were predicted. The balanced accuracy of this approach was 87% with a sensitivity of 89%. Each prediction comes with a certainty measure based on the homogeneity of data and distance of neighbours. Ongoing developments and future opportunities are discussed.
Collapse
Affiliation(s)
- Thomas Hartung
- Johns Hopkins University Center for Alternatives to Animal Testing (CAAT) Baltimore MD USA
- University of Konstanz CAAT-Europe Konstanz Germany
| |
Collapse
|
14
|
Ciallella HL, Zhu H. Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity. Chem Res Toxicol 2019; 32:536-547. [PMID: 30907586 DOI: 10.1021/acs.chemrestox.8b00393] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.
Collapse
|
15
|
Abstract
Quantitative in vitro to in vivo extrapolation (QIVIVE) is broadly considered a prerequisite bridge from in vitro findings to a dose paradigm. Quality and relevance of cell systems are the first prerequisite for QIVIVE. Information-rich and mechanistic endpoints (biomarkers) improve extrapolations, but a sophisticated endpoint does not make a bad cell model a good one. The next need is reverse toxicokinetics (TK), which estimates the dose necessary to reach a tissue concentration that is active in vitro. The Johns Hopkins Center for Alternatives to Animal Testing (CAAT) has created a roadmap for animal-free systemic toxicity testing, in which the needs and opportunities for TK are elaborated, in the context of different systemic toxicities. The report was discussed at two stakeholder forums in Brussels in 2012 and in Washington in 2013; the key recommendations are summarized herein. Contrary to common belief and the Paracelsus paradigm of everything is toxic, the majority of industrial chemicals do not exhibit toxicity. Strengthening the credibility of negative results of alternative approaches for hazard identification, therefore, avoids the need for QIVIVE. Here, especially the combination of methods in integrated testing strategies is most promising. Two further but very different approaches aim to overcome the problem of modeling in vivo complexity: The human-on-a-chip movement aims to reproduce large parts of living organism's complexity via microphysiological systems, that is, organ equivalents combined by microfluidics. At the same time, the Toxicity Testing in the 21st Century (Tox-21c) movement aims for mechanistic approaches (adverse outcome pathways as promoted by Organisation for Economic Co-operation and Development (OECD) or pathways of toxicity in the Human Toxome Project) for high-throughput screening, biological phenotyping, and ultimately a systems toxicology approach through integration with computer modeling. These 21st century approaches also require 21st century validation, for example, by evidence-based toxicology. Ultimately, QIVIVE is a prerequisite for extrapolating Tox-21c such approaches to human risk assessment.
Collapse
Affiliation(s)
- Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.,University of Konstanz, Konstanz, Germany
| |
Collapse
|
16
|
Luechtefeld T, Rowlands C, Hartung T. Big-data and machine learning to revamp computational toxicology and its use in risk assessment. Toxicol Res (Camb) 2018; 7:732-744. [PMID: 30310652 PMCID: PMC6116175 DOI: 10.1039/c8tx00051d] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 04/20/2018] [Indexed: 01/08/2023] Open
Abstract
The creation of large toxicological databases and advances in machine-learning techniques have empowered computational approaches in toxicology. Work with these large databases based on regulatory data has allowed reproducibility assessment of animal models, which highlight weaknesses in traditional in vivo methods. This should lower the bars for the introduction of new approaches and represents a benchmark that is achievable for any alternative method validated against these methods. Quantitative Structure Activity Relationships (QSAR) models for skin sensitization, eye irritation, and other human health hazards based on these big databases, however, also have made apparent some of the challenges facing computational modeling, including validation challenges, model interpretation issues, and model selection issues. A first implementation of machine learning-based predictions termed REACHacross achieved unprecedented sensitivities of >80% with specificities >70% in predicting the six most common acute and topical hazards covering about two thirds of the chemical universe. While this is awaiting formal validation, it demonstrates the new quality introduced by big data and modern data-mining technologies. The rapid increase in the diversity and number of computational models, as well as the data they are based on, create challenges and opportunities for the use of computational methods.
Collapse
Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing at Johns Hopkins Bloomberg School of Public Health , 615 N. Wolfe Street , Baltimore , MD 21205 , USA .
| | - Craig Rowlands
- Underwriters Laboratories (UL) , UL Product Supply Chain Intelligence , 333 Pfingsten Road , Northbrook , IL 60062 , USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing at Johns Hopkins Bloomberg School of Public Health , 615 N. Wolfe Street , Baltimore , MD 21205 , USA .
| |
Collapse
|
17
|
Luechtefeld T, Marsh D, Rowlands C, Hartung T. Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility. Toxicol Sci 2018; 165:198-212. [PMID: 30007363 PMCID: PMC6135638 DOI: 10.1093/toxsci/kfy152] [Citation(s) in RCA: 144] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350-700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%-96% (sensitivity 50%-87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs-"Simple" and "Data Fusion". The "Simple" RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The "Data Fusion" RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%-80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%-95% range across 9 health hazards with no constraints on tested compounds.
Collapse
Affiliation(s)
- Thomas Luechtefeld
- Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, Maryland
- ToxTrack, Baltimore, Maryland
| | | | - Craig Rowlands
- UL Product Supply Chain Intelligence, Underwriters Laboratories (UL), Northbrook, Illinois
| | - Thomas Hartung
- Johns Hopkins University, Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, Maryland
- University of Konstanz, CAAT-Europe, Konstanz, Germany
| |
Collapse
|
18
|
Luechtefeld T, Hartung T. Computational approaches to chemical hazard assessment. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2018; 34:459-478. [PMID: 29101769 PMCID: PMC5848496 DOI: 10.14573/altex.1710141] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Indexed: 01/10/2023]
Abstract
Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.
Collapse
Affiliation(s)
- Thomas Luechtefeld
- Johns Hopkins Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Thomas Hartung
- Johns Hopkins Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.,CAAT-Europe, University of Konstanz, Konstanz, Germany
| |
Collapse
|
19
|
The Threshold of Toxicological Concern for prenatal developmental toxicity in rats and rabbits. Regul Toxicol Pharmacol 2017. [PMID: 28645885 DOI: 10.1016/j.yrtph.2017.06.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The Threshold Toxicological Concern (TTC) is based on the concept that in absence of experimental data reasonable assurance of safety can be given if exposure is sufficiently low. Using the REACH database the low 5th percentile of the NO(A)EL distribution, for prenatal developmental toxicity (OECD guideline 414) was determined. For rats, (434 NO(A)ELs values) for maternal toxicity, this value was 10 mg/kg-bw/day. For developmental toxicity (469 NO(A)ELs): 13 mg/kg-bw/day. For rabbits, (100 NO(A)ELs), the value for maternal toxicity was 4 mg/kg-bw/day, for developmental toxicity, (112 NO(A)EL values): 10 mg/kg-bw/day. The maternal organism may thus be slightly more sensitive than the fetus. Combining REACH- (industrial chemicals) and published BASF-data (mostly agrochemicals), 537 unique compounds with NO(A)EL values for developmental toxicity in rats and 150 in rabbits were evaluated. The low 5th percentile NO(A)EL for developmental toxicity in rats was 10 mg/kg-bw/day and 9.5 mg/kg-bw/day for rabbits. Using an assessment factor of 100, a TTC value for developmental toxicity of 100 μg/kg-bw/day for rats and 95 μg/kg-bw/day for rabbits is calculated. These values could serve as guidance whether or not to perform an animal experiment, if exposure is sufficiently low. In emergency situations this value may be useful for a first tier risk assessment.
Collapse
|
20
|
Crawford SE, Hartung T, Hollert H, Mathes B, van Ravenzwaay B, Steger-Hartmann T, Studer C, Krug HF. Green Toxicology: a strategy for sustainable chemical and material development. ENVIRONMENTAL SCIENCES EUROPE 2017; 29:16. [PMID: 28435767 PMCID: PMC5380705 DOI: 10.1186/s12302-017-0115-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 03/24/2017] [Indexed: 05/04/2023]
Abstract
Green Toxicology refers to the application of predictive toxicology in the sustainable development and production of new less harmful materials and chemicals, subsequently reducing waste and exposure. Built upon the foundation of "Green Chemistry" and "Green Engineering", "Green Toxicology" aims to shape future manufacturing processes and safe synthesis of chemicals in terms of environmental and human health impacts. Being an integral part of Green Chemistry, the principles of Green Toxicology amplify the role of health-related aspects for the benefit of consumers and the environment, in addition to being economical for manufacturing companies. Due to the costly development and preparation of new materials and chemicals for market entry, it is no longer practical to ignore the safety and environmental status of new products during product development stages. However, this is only possible if toxicologists and chemists work together early on in the development of materials and chemicals to utilize safe design strategies and innovative in vitro and in silico tools. This paper discusses some of the most relevant aspects, advances and limitations of the emergence of Green Toxicology from the perspective of different industry and research groups. The integration of new testing methods and strategies in product development, testing and regulation stages are presented with examples of the application of in silico, omics and in vitro methods. Other tools for Green Toxicology, including the reduction of animal testing, alternative test methods, and read-across approaches are also discussed.
Collapse
Affiliation(s)
- Sarah E. Crawford
- Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Thomas Hartung
- John Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205 USA
- CAAT-Europe, University of Konstanz, Universitaetsstrasse 10, 78467 Constance, Germany
| | - Henner Hollert
- Institute for Environmental Research, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
| | - Björn Mathes
- DECHEMA e.V., Theodor-Heuss-Allee 25, 60486 Frankfurt, Germany
| | | | | | - Christoph Studer
- Federal Office of Public Health, Schwarzenburgstraße 157, 3003 Bern, Switzerland
| | - Harald F. Krug
- Empa, Materials Science and Technology, Lerchenfeld-straße 5, 9014 St. Gallen, Switzerland
| |
Collapse
|
21
|
The ascendance of microphysiological systems to solve the drug testing dilemma. Future Sci OA 2017; 3:FSO185. [PMID: 28670475 PMCID: PMC5481853 DOI: 10.4155/fsoa-2017-0002] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 02/14/2017] [Indexed: 01/15/2023] Open
Abstract
The development of drugs is a process obstructed with manifold security and efficacy concerns. Although animal models are still widely used to meet the diligence required, they are regarded as outdated tools with limited predictability. Novel microphysiological systems intend to create systemic models of human biology. Their ability to host 3D organoid constructs in a controlled microenvironment with mechanical and electrophysiological stimuli enables them to create and maintain homeostasis. These platforms are, thus, envisioned to be superior tools for testing and developing substances such as drugs, cosmetics and chemicals. We will present reasons why microphysiological systems are required for the emerging demands, highlight current technological and regulatory obstacles, and depict possible solutions from state-of-the-art platforms from major contributors. Microphysiological systems are devices constructed for the cocultivation of miniaturized human organ equivalents. They are commonly placed into a continuous stream of nutrient solution. The microphysiological tools aim to reshape current development, toxicity testing and efficacy assessment of therapeutic agents, food additives, chemicals and environmental pollutants. We are on the verge of initiating a paradigm shift away from established, but often misleading animal and single tissue culture techniques toward the generation of predictive data for a compound's safety and efficacy prior to its exposure to humans.
Collapse
|
22
|
Ball N, Cronin MTD, Shen J, Blackburn K, Booth ED, Bouhifd M, Donley E, Egnash L, Hastings C, Juberg DR, Kleensang A, Kleinstreuer N, Kroese ED, Lee AC, Luechtefeld T, Maertens A, Marty S, Naciff JM, Palmer J, Pamies D, Penman M, Richarz AN, Russo DP, Stuard SB, Patlewicz G, van Ravenzwaay B, Wu S, Zhu H, Hartung T. Toward Good Read-Across Practice (GRAP) guidance. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2016; 33:149-66. [PMID: 26863606 PMCID: PMC5581000 DOI: 10.14573/altex.1601251] [Citation(s) in RCA: 104] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Accepted: 02/11/2016] [Indexed: 12/04/2022]
Abstract
Grouping of substances and utilizing read-across of data within those groups represents an important data gap filling technique for chemical safety assessments. Categories/analogue groups are typically developed based on structural similarity and, increasingly often, also on mechanistic (biological) similarity. While read-across can play a key role in complying with legislation such as the European REACH regulation, the lack of consensus regarding the extent and type of evidence necessary to support it often hampers its successful application and acceptance by regulatory authorities. Despite a potentially broad user community, expertise is still concentrated across a handful of organizations and individuals. In order to facilitate the effective use of read-across, this document presents the state of the art, summarizes insights learned from reviewing ECHA published decisions regarding the relative successes/pitfalls surrounding read-across under REACH, and compiles the relevant activities and guidance documents. Special emphasis is given to the available existing tools and approaches, an analysis of ECHA's published final decisions associated with all levels of compliance checks and testing proposals, the consideration and expression of uncertainty, the use of biological support data, and the impact of the ECHA Read-Across Assessment Framework (RAAF) published in 2015.
Collapse
Affiliation(s)
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc. Woodcliff Lake, NJ, USA
| | | | - Ewan D Booth
- Syngenta Ltd, Jealott's Hill International Research Centre, Bracknell, Berkshire, UK
| | - Mounir Bouhifd
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Laura Egnash
- Stemina Biomarker Discovery Inc., Madison, WI, USA
| | - Charles Hastings
- BASF SE, Ludwigshafen am Rhein, Germany, and Research Triangle Park, NC, USA
| | | | - Andre Kleensang
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - E Dinant Kroese
- Risk Analysis for Products in Development, TNO Zeist, The Netherlands
| | - Adam C Lee
- DuPont Haskell Global Centers for Health and Environmental Sciences, Newark, DE, USA
| | - Thomas Luechtefeld
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Alexandra Maertens
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Sue Marty
- The Dow Chemical Company, Midland, MI, USA
| | | | | | - David Pamies
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Andrea-Nicole Richarz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Daniel P Russo
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | | | - Grace Patlewicz
- US EPA/ORD, National Center for Computational Toxicology, Research Triangle Park, NC, USA
| | | | - Shengde Wu
- The Procter and Gamble Co., Cincinatti, OH, USA
| | - Hao Zhu
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA.,University of Konstanz, CAAT-Europe, Konstanz, Germany
| |
Collapse
|
23
|
Zhu H, Bouhifd M, Donley E, Egnash L, Kleinstreuer N, Kroese ED, Liu Z, Luechtefeld T, Palmer J, Pamies D, Shen J, Strauss V, Wu S, Hartung T. Supporting read-across using biological data. ALTEX 2016; 33:167-82. [PMID: 26863516 PMCID: PMC4834201 DOI: 10.14573/altex.1601252] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2016] [Accepted: 02/09/2016] [Indexed: 01/08/2023]
Abstract
Read-across, i.e. filling toxicological data gaps by relating to similar chemicals, for which test data are available, is usually done based on chemical similarity. Besides structure and physico-chemical properties, however, biological similarity based on biological data adds extra strength to this process. In the context of developing Good Read-Across Practice guidance, a number of case studies were evaluated to demonstrate the use of biological data to enrich read-across. In the simplest case, chemically similar substances also show similar test results in relevant in vitro assays. This is a well-established method for the read-across of e.g. genotoxicity assays. Larger datasets of biological and toxicological properties of hundreds and thousands of substances become increasingly available enabling big data approaches in read-across studies. Several case studies using various big data sources are described in this paper. An example is given for the US EPA's ToxCast dataset allowing read-across for high quality uterotrophic assays for estrogenic endocrine disruption. Similarly, an example for REACH registration data enhancing read-across for acute toxicity studies is given. A different approach is taken using omics data to establish biological similarity: Examples are given for stem cell models in vitro and short-term repeated dose studies in rats in vivo to support read-across and category formation. These preliminary biological data-driven read-across studies highlight the road to the new generation of read-across approaches that can be applied in chemical safety assessment.
Collapse
Affiliation(s)
- Hao Zhu
- Department of Chemistry and Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, USA
| | - Mounir Bouhifd
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - Laura Egnash
- Stemina Biomarker Discovery Inc., Madison, WI, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - E Dinant Kroese
- Risk Analysis for Products in Development, TNO Zeist, The Netherlands
| | | | - Thomas Luechtefeld
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | | | - David Pamies
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc. Woodcliff Lake, New Jersey, USA
| | - Volker Strauss
- BASF Aktiengesellschaft, Experimental Toxicology and Ecology, Ludwigshafen, Germany
| | | | - Thomas Hartung
- Johns Hopkins Bloomberg School of Public Health, Center for Alternatives to Animal Testing (CAAT), Baltimore, MD, USA
- University of Konstanz, CAAT-Europe, Konstanz, Germany
| |
Collapse
|
24
|
Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Global analysis of publicly available safety data for 9,801 substances registered under REACH from 2008-2014. ALTEX 2016; 33:95-109. [PMID: 26863090 PMCID: PMC5408747 DOI: 10.14573/altex.1510052] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 01/29/2016] [Indexed: 12/21/2022]
Abstract
The European Chemicals Agency (ECHA) warehouses the largest public dataset of in vivo and in vitro toxicity tests. In December 2014 this data was converted into a structured, machine readable and searchable database using linguistic search engines. It contains data for 9,801 unique substances, 3,609 unique study descriptions and 816,048 study documents.This allows exploring toxicological data on a scale far larger than previously available. Substance similarity analysis was used to determine clustering of substances for hazards by mapping to PubChem. Similarity was measured using PubChem 2D conformational substructure fingerprints, which were compared via the Tanimoto metric. Following K-Core filtration, the Blondel et al.(2008) module recognition algorithm was used to identify chemical modules showing clusters of substances in use within the chemical universe. Global Harmonized System of Classification and Labelling provides a valuable information source for hazard analysis. The most prevalent hazards are H317 "May cause an allergic skin reaction" with 20% and H318 "Causes serious eye damage" with 17% positive substances. Such prevalences obtained for all hazards here are key for the design of integrated testing strategies. The data allowed estimation of animal use. ECHA cover about 20% of substances in the high-throughput biological assay database Tox21 (1,737 substances) and have a 917 substance overlap with the Comparative Toxicogenomics Database (~7% of CTD). The biological data available in these datasets combined with ECHA in vivo endpoints have enormous modeling potential. A case is made that REACH should systematically open regulatory data for research purposes.
Collapse
Affiliation(s)
- Thomas Luechtefeld
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
| | - Daniel P. Russo
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
| | | | - Hao Zhu
- The Rutgers Center for Computational & Integrative Biology, Rutgers University at Camden, NJ, USA
- Department of Chemistry, Rutgers University at Camden, NJ, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Environmental Health Sciences, Baltimore, MD, USA
- CAAT-Europe, University of Konstanz, Konstanz, Germany
| |
Collapse
|