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Wang H, Huang Z, Lou S, Li W, Liu G, Tang Y. In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory. Chem Res Toxicol 2024; 37:894-909. [PMID: 38753056 DOI: 10.1021/acs.chemrestox.3c00396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.
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Affiliation(s)
- Haoqiang 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, 130 Meilong Road, Shanghai 200237, 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, 130 Meilong Road, Shanghai 200237, China
| | - Shang Lou
- 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, 130 Meilong Road, Shanghai 200237, 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, 130 Meilong Road, Shanghai 200237, 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, 130 Meilong Road, Shanghai 200237, 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, 130 Meilong Road, Shanghai 200237, China
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Böhme A, Ulrich N, Schüürmann G. Amino Chemoassay Profiling of Aromatic Aldehydes-Unraveling Drivers of Their Skin Sensitization Potency. Chem Res Toxicol 2023. [PMID: 37315223 DOI: 10.1021/acs.chemrestox.3c00013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Aromatic aldehydes are ubiquitous in humans' everyday life. As aldehydes, they can form imines (Schiff bases) with amino groups of skin proteins, leading to immune response-triggered allergic contact dermatitis. Many known aromatic aldehydes are considered as weak or nonsensitizers, but others like atranol and chloratranol, two components of the fragrance oak moss absolute, show strong sensitization potency. This large discrepancy in potency and, in particular, the underlying reaction mechanisms are only little understood so far. To reduce this knowledge gap, our chemoassay employing glycine-para-nitroanilide (Gly-pNA) as an amino model nucleophile was applied to 23 aromatic aldehydes. The determined Gly-pNA second-order rate constants for imine formation (k1 ≤ 2.85 L·mol-1·min-1) and the imine stability constant (K ≤ 333 L·mol-1) are on the lower end of the known amino reactivity scale for aldehydes, confirming many aromatic aldehydes as less potent sensitizers in line with animal and human data. The substantially higher sensitization potency of atranol and chloratranol, in turn, is reflected by their unique reaction chemistry profiles, inter alia, identifying them as cross-linkers able to form thermodynamically more stable epitopes with skin proteins (despite low formation kinetics, k1). The discussion further includes a comparison of experimentally determined k1 values with computed reactivity data (Taft σ*), the impact of the substitution pattern of the aryl ring on the reactivity with Gly-pNA, and analytically determined adduct patterns. Overall, this work provides new insights into the reaction of aromatic aldehydes with amino groups under aqueous conditions and fosters a better understanding of the chemistry underlying skin sensitization.
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Affiliation(s)
- Alexander Böhme
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany
| | - Nadin Ulrich
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany
| | - Gerrit Schüürmann
- Institute of Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Straße 29, 09596 Freiberg, Germany
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3
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Wang CC, Wang SS, Liao CL, Tsai WR, Tung CW. Reconfiguring the online tool of SkinSensPred for predicting skin sensitization of pesticides. JOURNAL OF PESTICIDE SCIENCE 2022; 47:184-189. [PMID: 36514692 PMCID: PMC9716044 DOI: 10.1584/jpestics.d22-043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 08/22/2022] [Indexed: 06/17/2023]
Abstract
Adverse outcome pathway (AOP)-based computational models provide state-of-the-art prediction for human skin sensitizers and are promising alternatives to animal testing. However, little is known about their applicability to pesticides due to scarce pesticide data for evaluation. Moreover, pesticides traditionally have been tested on animals without human data, making validation difficult. Direct application of AOP-based models to pesticides may be inappropriate since their original applicability domains were designed to maximize reliability for human response prediction on diverse chemicals but not pesticides. This study proposed to identify a consensus chemical space with concordant human responses predicted by the SkinSensPred online tool and animal testing data to reduce animal testing. The identified consensus chemical space for non-sensitizers achieved high concordance of 85% and 100% for the cross-validation and independent test, respectively. The reconfigured SkinSensPred can be applied as the first-tier tool for identifying non-sensitizers to reduce. animal testing for pesticides by 19.6%.
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Affiliation(s)
- Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University
| | - Shan-Shan Wang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes
| | - Chun-Lin Liao
- Taiwan Agricultural Chemicals and Toxic Substances Research Institute, Council of Agriculture
| | - Wei-Ren Tsai
- Taiwan Agricultural Chemicals and Toxic Substances Research Institute, Council of Agriculture
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes
- Graduate Institute of Data Science, College of Management, Taipei Medical University
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4
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Wang SS, Wang CC, Tung CW. SkinSensPred as a Promising in Silico Tool for Integrated Testing Strategy on Skin Sensitization. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12856. [PMID: 36232156 PMCID: PMC9566590 DOI: 10.3390/ijerph191912856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/29/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Skin sensitization is an important regulatory endpoint associated with allergic contact dermatitis. Recently, several adverse outcome pathway (AOP)-based alternative methods were developed to replace animal testing for evaluating skin sensitizers. The AOP-based assays were further integrated as a two-out-of-three method with good predictivity. However, the acquisition of experimental data is resource-intensive. In contrast, an integrated testing strategy (ITS) capable of maximizing the usage of laboratory data from AOP-based and in silico methods was developed as defined approaches (DAs) to both hazard and potency assessment. There are currently two in silico models, namely Derek Nexus and OECD QSAR Toolbox, evaluated in the OECD Testing Guideline No. 497. Since more advanced machine learning algorithms have been proposed for skin sensitization prediction, it is therefore desirable to evaluate their performance under the ITS framework. This study evaluated the performance of a new ITS DA (ITS-SkinSensPred) adopting a transfer learning-based SkinSensPred model. Results showed that the ITS-SkinSensPred has similar or slightly better performance compared to the other ITS models. SkinSensPred-based ITS is expected to be a promising method for assessing skin sensitization.
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Affiliation(s)
- Shan-Shan Wang
- Ph.D. Program in Environmental and Occupational Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
| | - Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei 10617, Taiwan
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County 35053, Taiwan
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5
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Chayawan, Selvestrel G, Baderna D, Toma C, Caballero Alfonso AY, Gamba A, Benfenati E. Skin sensitization quantitative QSAR models based on mechanistic structural alerts. Toxicology 2022; 468:153111. [DOI: 10.1016/j.tox.2022.153111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/05/2022] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
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Wang CC, Liang YC, Wang SS, Lin P, Tung CW. A machine learning-driven approach for prioritizing food contact chemicals of carcinogenic concern based on complementary in silico methods. Food Chem Toxicol 2022; 160:112802. [PMID: 34979167 DOI: 10.1016/j.fct.2021.112802] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/15/2021] [Accepted: 12/28/2021] [Indexed: 10/19/2022]
Abstract
Carcinogenicity is one of the most critical endpoints for the risk assessment of food contact chemicals (FCCs). However, the carcinogenicity of FCCs remains insufficiently investigated. To fill the data gap, the application of standard experimental methods for identifying chemicals of carcinogenic concerns from a large set of FCCs is impractical due to their resource-intensive nature. In contrast, computational methods provide an efficient way to quickly screen chemicals with carcinogenic potential for subsequent experimental validation. Since every model was developed based on a limited number of training samples, the use of single models for carcinogenicity assessment may not cover the complex mechanisms of carcinogenesis. This study proposed a novel machine learning-based weight-of-evidence (WoE) model for prioritizing chemical carcinogenesis. The WoE model can nonlinearly integrate complementary computational methods of structural alerts, quantitative structure-activity relationship models and in silico toxicogenomics models into a WoE-score. Compared to the best single method, the WoE model gained 8% and 19.7% improvement in the area under the receiver operating characteristic curve (AUC) value and chemical coverage, respectively. The prioritization of 1623 FCCs concludes 44 chemicals of high carcinogenic concern. The machine learning-based WoE approach provides a fast and comprehensive way for prioritizing chemicals of carcinogenic concern.
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Affiliation(s)
- Chia-Chi Wang
- Department and Graduate Institute of Veterinary Medicine, School of Veterinary Medicine, National Taiwan University, Taipei, 10617, Taiwan
| | - Yu-Chih Liang
- School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei, 11031, Taiwan
| | - Shan-Shan Wang
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan
| | - Pinpin Lin
- National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053, Taiwan.
| | - Chun-Wei Tung
- Institute of Biotechnology and Pharmaceutical Research, National Health Research Institutes, Miaoli County, 35053, Taiwan; Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, 106, Taiwan; Doctoral Degree Program in Toxicology, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, 80708, Taiwan.
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Böhme A, Moldrickx J, Schüürmann G. Amino Reactivity of Glutardialdehyde and Monoaldehydes─Chemoassay Profile vs Skin Sensitization Potency. Chem Res Toxicol 2021; 34:2353-2365. [PMID: 34726385 DOI: 10.1021/acs.chemrestox.1c00266] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Chemoassay profiling of organic electrophiles through the direct peptide reactivity assay has become an OECD-accepted nonanimal component in the REACH evaluation of potential skin sensitizers. For aldehydes forming imines (Schiff bases), however, existing chemoassays yielded inconclusive results, indicating issues with their NH2 sensitivity and the reversibility of the reaction. In the present study, a new kinetic chemoassay employing the N terminus of glycine-para-nitroanilide, Gly-pNA, as a model nucleophile for protein NH2 groups is introduced and applied to nine aliphatic monoaldehydes and glutardialdehyde (1,5-pentanedial) that have log Kow (octanol/water partition coefficient) values from 0.63 to 3.99. The Gly-pNA second-order rate constants k1 range from 8.56 to 150 L·mol-1·min-1 for the monoaldehydes. Interestingly, glutardialdehyde with a k1 of 17 731 L·mol-1·min-1 is 170-fold more reactive than its monoaldehyde counterpart pentanal. This can be rationalized by hydration or tautomerization of the dialdehyde to monoaldehydic forms, now facilitating Schiff base formation through an intramolecular H bond. Comparison with murine local lymph node assay data from the literature reveals that adduct stability in terms of reaction thermodynamics (K = k1/k-1pseudo) rather than formation kinetics (k1) governs the skin sensitization potency of Schiff-base-forming aldehydes. The discussion includes analytically determined adduct patterns, and the impact of α- and β-carbon substitution as well as hydrophobicity on aldehyde reactivity.
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Affiliation(s)
- Alexander Böhme
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany
| | - Johannes Moldrickx
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany.,Institute of Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Straße 29, 09596 Freiberg, Germany
| | - Gerrit Schüürmann
- UFZ Department of Ecological Chemistry, Helmholtz Centre for Environmental Research, Permoserstraße 15, 04318 Leipzig, Germany.,Institute of Organic Chemistry, Technical University Bergakademie Freiberg, Leipziger Straße 29, 09596 Freiberg, Germany
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Ta GH, Weng CF, Leong MK. In silico Prediction of Skin Sensitization: Quo vadis? Front Pharmacol 2021; 12:655771. [PMID: 34017255 PMCID: PMC8129647 DOI: 10.3389/fphar.2021.655771] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/20/2021] [Indexed: 01/10/2023] Open
Abstract
Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.
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Affiliation(s)
- Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Taiwan
| | - Ching-Feng Weng
- Department of Basic Medical Science, Institute of Respiratory Disease, Xiamen Medical College, Xiamen, China
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Taiwan
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Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 2020; 25:1624-1638. [PMID: 32663517 PMCID: PMC7572559 DOI: 10.1016/j.drudis.2020.07.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/26/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023]
Abstract
Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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10
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Skin sensitization in silico protocol. Regul Toxicol Pharmacol 2020; 116:104688. [PMID: 32621976 DOI: 10.1016/j.yrtph.2020.104688] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/18/2020] [Accepted: 05/21/2020] [Indexed: 01/03/2023]
Abstract
The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework. Based on the relevance of the mechanisms and effects as well as the strengths and limitations of the experimental systems used to identify them, rules and principles are defined for deriving skin sensitization in silico assessments. Further, the assignments of reliability and confidence scores that reflect the overall strength of the assessment are discussed. This skin sensitization protocol supports the implementation and acceptance of in silico approaches for the prediction of skin sensitization.
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11
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In-silico prediction of role of chitosan, chondroitin sulphate and agar in process of wound healing towards scaffold development. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100406] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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12
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Tung CW, Lin YH, Wang SS. Transfer learning for predicting human skin sensitizers. Arch Toxicol 2019; 93:931-940. [PMID: 30806762 DOI: 10.1007/s00204-019-02420-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 02/21/2019] [Indexed: 12/20/2022]
Abstract
Computational prioritization of chemicals for potential skin sensitization risks plays essential roles in the risk assessment of environmental chemicals and drug development. Given the huge number of chemicals for testing, computational methods enable the fast identification of high-risk chemicals for experimental validation and design of safer alternatives. However, the development of robust prediction model requires a large dataset of tested chemicals that is usually not available for most toxicological endpoints, especially for human data. A small training dataset makes the development of effective models difficult with insufficient coverage and accuracy. In this study, an ensemble tree-based multitask learning method was developed incorporating three relevant tasks in the well-defined adverse outcome pathway (AOP) of skin sensitization to transfer shared knowledge to the major task of human sensitizers. The results show both largely improved coverage and accuracy compared with three state-of-the-art methods. A user-friendly prediction server was available at https://cwtung.kmu.edu.tw/skinsensdb/predict . As AOPs for various toxicity endpoints are being actively developed, the proposed method can be applied to develop prediction models for other endpoints.
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Affiliation(s)
- Chun-Wei Tung
- Graduate Institute of Data Science, College of Management, Taipei Medical University, 172-1, Sec. 2, Keelung Rd., Taipei, 10675, Taiwan.
- National Institute of Environmental Health Sciences, National Health Research Institutes, 35 Keyan Rd., Zhunan, Miaoli County, 35053, Taiwan.
| | - Yi-Hui Lin
- School of Pharmacy, Kaohsiung Medical University, 100 Shihchuan 1st Rd., Kaohsiung, 80708, Taiwan
| | - Shan-Shan Wang
- School of Pharmacy, Kaohsiung Medical University, 100 Shihchuan 1st Rd., Kaohsiung, 80708, Taiwan
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13
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Wilm A, Kühnl J, Kirchmair J. Computational approaches for skin sensitization prediction. Crit Rev Toxicol 2018; 48:738-760. [DOI: 10.1080/10408444.2018.1528207] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Anke Wilm
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- HITeC e.V, Hamburg, Germany
| | - Jochen Kühnl
- Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- Department of Chemistry, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), University of Bergen, Bergen, Norway
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14
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Mechanism-informed read-across assessment of skin sensitizers based on SkinSensDB. Regul Toxicol Pharmacol 2018; 94:276-282. [DOI: 10.1016/j.yrtph.2018.02.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2017] [Revised: 02/14/2018] [Accepted: 02/22/2018] [Indexed: 11/21/2022]
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15
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Vinken M, Knapen D, Vergauwen L, Hengstler JG, Angrish M, Whelan M. Adverse outcome pathways: a concise introduction for toxicologists. Arch Toxicol 2017; 91:3697-3707. [PMID: 28660287 DOI: 10.1007/s00204-017-2020-z] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Accepted: 06/22/2017] [Indexed: 12/14/2022]
Abstract
Adverse outcome pathways (AOPs) are designed to provide a clear-cut mechanistic representation of critical toxicological effects that propagate over different layers of biological organization from the initial interaction of a chemical with a molecular target to an adverse outcome at the individual or population level. Adverse outcome pathways are currently gaining momentum, especially in view of their many potential applications as pragmatic tools in the fields of human toxicology, ecotoxicology, and risk assessment. A number of guidance documents, issued by the Organization for Economic Cooperation and Development, as well as landmark papers, outlining best practices to develop, assess and use AOPs, have been published in the last few years. The present paper provides a synopsis of the main principles related to the AOP framework for the toxicologist less familiar with this area, followed by two case studies relevant for human toxicology and ecotoxicology.
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Affiliation(s)
- Mathieu Vinken
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Dries Knapen
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | - Lucia Vergauwen
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.,Systemic Physiological and Ecotoxicological Research (SPHERE), Department of Biology, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Jan G Hengstler
- Leibniz Research Centre for Working Environment and Human Factors (IfADo), Technical University of Dortmund, 44139, Dortmund, Germany
| | - Michelle Angrish
- National Center for Environmental Assessment, Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, 27709, USA
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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