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Niżnik Ł, Jabłońska K, Orczyk M, Orzechowska M, Toporowska-Kaźmierak J, Sowińska M, Jasińska J, Jurowski K. Toxicity of New Psychoactive Substance (NPS): Threo-4-methylmethylphenidate (4-Mmph) - Prediction of toxicity using in silico methods. Toxicol In Vitro 2024; 99:105891. [PMID: 38972515 DOI: 10.1016/j.tiv.2024.105891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 06/28/2024] [Accepted: 07/03/2024] [Indexed: 07/09/2024]
Abstract
This study represents the first application of in silico methods to evaluate the toxicity of 4-methylphenidate (4-Mmph), a new psychoactive substance (NPS). Using advanced in silico toxicology tools, it was feasible to anticipate key aspects of 4-Mmph's toxicological profile, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and possible endocrine disruption. The findings indicate significant acute toxicity with variability among species, a high potential for adverse effects in the gastrointestinal system and lungs, a low genotoxic potential, a significant likelihood of skin irritation, and a notable cardiotoxicity risk associated with hERG channel inhibition. Evaluation of endocrine disruption revealed a low likelihood that 4-Mmph interacts with the estrogen receptor alpha (ER-α), indicating minimal estrogenic activity. These insights, derived from in silico studies, play a crucial role in improving the comprehension of 4-Mmph in forensic and clinical toxicology. These initial toxicological inquiries establish the foundation for future investigations and help formulate risk assessment and management strategies regarding the use and abuse of NPS. This article is part of a larger project funded by the Polish Ministry of Education and Science, titled "Toxicovigilance, Poisoning Prevention, and First Aid in Poisoning with Xenobiotics of Current Clinical Importance in Poland" (Grant Number SKN/SP/570184/2023).
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Affiliation(s)
- Łukasz Niżnik
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertises in Łódź, ul. Aleksandrowska 67/93, 91-205 Łódź, Poland
| | - Karolina Jabłońska
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Michał Orczyk
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Martyna Orzechowska
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Joanna Toporowska-Kaźmierak
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Marta Sowińska
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Judyta Jasińska
- Toxicological Science Club 'Paracelsus', Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland
| | - Kamil Jurowski
- Department of Regulatory and Forensic Toxicology, Institute of Medical Expertises in Łódź, ul. Aleksandrowska 67/93, 91-205 Łódź, Poland; Laboratory of Innovative Toxicological Research and Analyses, Institute of Medical Sciences, Medical College, Rzeszów University, Al. mjr. W. Kopisto 2a, 35-959 Rzeszów, Poland.
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2
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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.
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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
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3
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Andersson M, Norinder U, Chavan S, Cotgreave I. In Silico Prediction of Eye Irritation Using Hansen Solubility Parameters and Predicted pKa Values. Altern Lab Anim 2023:2611929231175676. [PMID: 37184299 DOI: 10.1177/02611929231175676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
An in silico method has been developed that permits the binary differentiation between pure liquids causing serious eye damage or eye irritation, and pure liquids with no need for such classification, according to the UN GHS system. The method is based on the finding that the Hansen Solubility Parameters (HSP) of a liquid are collectively important predictors for eye irritation. Thus, by applying a two-tier approach in which in silico predicted pKa values (firstly) and a trained model based solely on in silico-predicted HSP data (secondly) were used, we have developed, and validated, a fully in silico approach for predicting the outcome of a Draize test (in terms of UN GHS Cat. 1/Cat. 2A/Cat. 2B or UN GHS No Cat.) with high validation set performance (sensitivity = 0.846, specificity = 0.818, balanced accuracy = 0.832) using SMILES only. The method is applicable to pure non-ionic liquids with molecular weight below 500 g/mol, fewer than six hydrogen bond donors (e.g. nitrogen-hydrogen or oxygen-hydrogen bonds) and fewer than eleven hydrogen bond acceptors (e.g. nitrogen or oxygen atoms). Due to its fully in silico characteristics, this method can be applied to pure liquids that are still at the desktop design stage and not yet in production.
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Affiliation(s)
- Martin Andersson
- Division Bioeconomy and Health, RISE Research Institutes of Sweden AB, Stockholm, Sweden
| | - Ulf Norinder
- Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden
| | - Swapnil Chavan
- Unit of Chemical and Pharmaceutical Toxicology, RISE Research Institutes of Sweden AB, Södertälje, Sweden
| | - Ian Cotgreave
- Unit of Chemical and Pharmaceutical Toxicology, RISE Research Institutes of Sweden AB, Södertälje, Sweden
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4
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Di P, Zheng M, Yang T, Chen G, Ren J, Li X, Jiang H. Prediction of serious eye damage or eye irritation potential of compounds via consensus labelling models and active learning models based on uncertainty strategies. Food Chem Toxicol 2022; 169:113420. [PMID: 36108981 DOI: 10.1016/j.fct.2022.113420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/24/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022]
Abstract
Serious eye damage and eye irritation have been authenticated to be significant human health issues in various fields such as ophthalmic pharmaceuticals. Due to the shortcomings of traditional animal testing methods, in silico methods have advanced to study eye toxicity. The models for predicting serious eye damage and eye irritation potential of compounds were developed using 2299 and 5214 compounds, respectively. The 40 global single models and 40 local models were developed by combining 5 molecular description methods and 4 machine learning methods. The 40 active learning models were developed by adopting uncertainty-based active learning strategies and taking local models as initial models. The 110 global consensus models based on 40 global single models were developed using a consensus strategy. Active learning models and global consensus models performed high prediction accuracy. The test accuracy of the best serious eye damage model and eye irritation model reached 0.972 and 0.959, respectively. The applicability domains for all models were calculated to verify the rationality of prediction effect. In addition, 8 structural alerts probably causing serious eye damage or eye irritation were sought out. The prediction models and structural alerts contributed to providing hazard identification and assessing chemical safety.
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Affiliation(s)
- Peiwen Di
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Mingyue Zheng
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Tianbiao Yang
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Geng Chen
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Jianan Ren
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
| | - Hualiang Jiang
- School of Pharmacology Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
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5
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Kang Y, Jeong B, Lim DH, Lee D, Lim KM. In silico prediction of the full United Nations Globally Harmonized System eye irritation categories of liquid chemicals by IATA-like bottom-up approach of random forest method. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2021; 84:960-972. [PMID: 34328061 DOI: 10.1080/15287394.2021.1956661] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
As an alternative to in vivo Draize rabbit eye irritation test, this study aimed to construct an in silico model to predict the complete United Nations (UN) Globally Harmonized System (GHS) for classification and labeling of chemicals for eye irritation category [eye damage (Category 1), irritating to eye (Category 2) and nonirritating (No category)] of liquid chemicals with Integrated approaches to testing and assessment (IATA)-like two-stage random forest approach. Liquid chemicals (n = 219) with 34 physicochemical descriptors and quality in vivo data were collected with no missing values. Seven machine learning algorithms (Naive Bayes, Logistic Regression, First Large Margin, Neural Net, Random Forest (RF), Gradient Boosted Tree, and Support Vector Machine) were examined for the ternary categorization of eye irritation potential at a single run through 10-fold cross-validation. RF, which performed best, was further improved by applying the 'Bottom-up approach' concept of IATA, namely, separating No category first, and discriminating Category 1 from 2, thereafter. The best performing training dataset achieved an overall accuracy of 73% and the correct prediction for Category 1, 2, and No category was 80%, 50%, and 77%, respectively for the test dataset. This prediction model was further validated with an external dataset of 28 chemicals, for which an overall accuracy of 71% was achieved.
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Affiliation(s)
- Yeonsoo Kang
- College of Pharmacy, Ewha Womans University, Seoul, Republic of Korea
| | - Boram Jeong
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | | | - Donghwan Lee
- Department of Statistics, Ewha Womans University, Seoul, Republic of Korea
| | - Kyung-Min Lim
- College of Pharmacy, Ewha Womans University, Seoul, Republic of Korea
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Silva AC, Borba JV, Alves VM, Hall SU, Furnham N, Kleinstreuer N, Muratov E, Tropsha A, Andrade CH. Novel computational models offer alternatives to animal testing for assessing eye irritation and corrosion potential of chemicals. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2021; 1. [PMID: 35935266 PMCID: PMC9355119 DOI: 10.1016/j.ailsci.2021.100028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Eye irritation and corrosion are fundamental considerations in developing chemicals to be used in or near the eye, from cleaning products to ophthalmic solutions. Unfortunately, animal testing is currently the standard method to identify compounds that cause eye irritation or corrosion. Yet, there is growing pressure on the part of regulatory agencies both in the USA and abroad to develop New Approach Methodologies (NAMs) that help reduce the need for animal testing and address unmet need to modernize safety evaluation of chemical hazards. In furthering the development and applications of computational NAMs in chemical safety assessment, in this study we have collected the largest expertly curated dataset of compounds tested for eye irritation and corrosion, and employed this data to build and validate binary and multi-classification Quantitative Structure-Activity Relationships (QSAR) models that can reliably assess eye irritation/corrosion potential of novel untested compounds. QSAR models were generated with Random Forest (RF) and Multi-Descriptor Read Across (MuDRA) machine learning (ML) methods, and validated using a 5-fold external cross-validation protocol. These models demonstrated high balanced accuracy (CCR of 0.68–0.88), sensitivity (SE of 0.61–0.84), positive predictive value (PPV of 0.65–0.90), specificity (SP of 0.56–0.91), and negative predictive value (NPV of 0.68–0.85). Overall, MuDRA models outperformed RF models and were applied to predict compounds’ irritation/corrosion potential from the Inactive Ingredient Database, which contains components present in FDA-approved drug products, and from the Cosmetic Ingredient Database, the European Commission source of information on cosmetic substances. All models built and validated in this study are publicly available at the STopTox web portal (https://stoptox.mml.unc.edu/). These models can be employed as reliable tools for identifying potential eye irritant/corrosive compounds
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Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
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Rácz A, Bajusz D, Miranda-Quintana RA, Héberger K. Machine learning models for classification tasks related to drug safety. Mol Divers 2021; 25:1409-1424. [PMID: 34110577 PMCID: PMC8342376 DOI: 10.1007/s11030-021-10239-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022]
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | | | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
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Ecotoxicological QSARs of Personal Care Products and Biocides. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2020. [DOI: 10.1007/978-1-0716-0150-1_16] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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10
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A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8071121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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11
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Yang H, Sun L, Li W, Liu G, Tang Y. In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts. Front Chem 2018. [PMID: 29515993 PMCID: PMC5826228 DOI: 10.3389/fchem.2018.00030] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
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Affiliation(s)
- Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Lixia Sun
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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12
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Wang Q, Li X, Yang H, Cai Y, Wang Y, Wang Z, Li W, Tang Y, Liu G. In silico prediction of serious eye irritation or corrosion potential of chemicals. RSC Adv 2017. [DOI: 10.1039/c6ra25267b] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Chemical fingerprints combined with machine learning methods were used to build binary classification models for predicting the potential EC/EI of compounds.
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Affiliation(s)
- Qin Wang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Xiao Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Hongbin Yang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yingchun Cai
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yinyin Wang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Zhuang Wang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design
- School of Pharmacy
- East China University of Science and Technology
- Shanghai 200237
- China
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13
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Luechtefeld T, Maertens A, Russo DP, Rovida C, Zhu H, Hartung T. Analysis of Draize eye irritation testing and its prediction by mining publicly available 2008-2014 REACH data. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2016; 33:123-34. [PMID: 26863293 PMCID: PMC5461467 DOI: 10.14573/altex.1510053] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Accepted: 02/05/2016] [Indexed: 12/22/2022]
Abstract
Public data from ECHA online dossiers on 9,801 substances encompassing 326,749 experimental key studies and additional information on classification and labeling were made computable. Eye irritation hazard, for which the rabbit Draize eye test still represents the reference method, was analyzed. Dossiers contained 9,782 Draize eye studies on 3,420 unique substances, indicating frequent retesting of substances. This allowed assessment of the test's reproducibility test based on all substances tested more than once. There was a 10% chance of a non-irritant evaluation given after a prior severe-irritant result as given by UN GHS classification criteria. The most reproducible outcomes were the results negative (94% reproducible) and severe eye irritant (73% reproducible). To evaluate whether other GHS categorizations predict eye irritation we built a dataset of 5,629 substances (1,931 'irritant' and 3,698 'non-irritant'). The two best decision trees with up to three other GHS classifications resulted in balanced accuracies of 68% and 73%, i.e., in the rank order of the Draize rabbit eye test itself, but both use inhalation toxicity data ("May cause respiratory irritation"), which is not typically available. Next, a dataset of 929 substances with at least one Draize study was mapped to PubChem to compute chemical similarity using 2D conformational fingerprints and Tanimoto similarity. Using a minimum similarity of 0.7 and simple classification by the closest chemical neighbor resulted in balanced accuracy from 73% over 737 substances to 100% at a threshold of 0.975 over 41 substances. This represents a strong support of read-across and (Q)SAR approaches in this area.
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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
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14
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Zhang D, Wang HJ, Cui XM, Wang CX. Evaluations of imidazolium ionic liquids as novel skin permeation enhancers for drug transdermal delivery. Pharm Dev Technol 2016; 22:511-520. [DOI: 10.3109/10837450.2015.1131718] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Ding Zhang
- School of Life Science and Technology, Kunming University of Science and Technology, Kunming, China
| | - Huai-Ji Wang
- School of Life Science and Technology, Kunming University of Science and Technology, Kunming, China
| | - Xiu-Ming Cui
- School of Life Science and Technology, Kunming University of Science and Technology, Kunming, China
| | - Cheng-Xiao Wang
- School of Life Science and Technology, Kunming University of Science and Technology, Kunming, China
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Verma RP, Matthews EJ. An in silico expert system for the identification of eye irritants. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:383-395. [PMID: 25967253 DOI: 10.1080/1062936x.2015.1039578] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
This report describes development of an in silico, expert rule-based method for the classification of chemicals into irritants or non-irritants to eye, as defined by the Draize test. This method was developed to screen data-poor cosmetic ingredient chemicals for eye irritancy potential, which is based upon exclusion rules of five physicochemical properties - molecular weight (MW), hydrophobicity (log P), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA) and polarizability (Pol). These rules were developed using the ADMET Predictor software and a dataset of 917 eye irritant chemicals. The dataset was divided into 826 (90%) chemicals used for training set and 91 (10%) chemicals used for external validation set (every 10th chemical sorted by molecular weight). The sensitivity of these rules for the training and validation sets was 72.3% and 71.4%, respectively. These rules were also validated for their specificity using an external validation set of 2011 non-irritant chemicals to the eye. The specificity for this validation set was revealed as 77.3%. This method facilitates rapid screening and prioritization of data poor chemicals that are unlikely to be tested for eye irritancy in the Draize test.
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Affiliation(s)
- R P Verma
- a Office of Cosmetics and Colors, Center for Food Safety and Applied Nutrition , US Food and Drug Administration , 5100 Paint Branch Parkway, College Park, MD 20740 , USA
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Verma RP, Matthews EJ. Estimation of the chemical-induced eye injury using a Weight-of-Evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part II: corrosion potential. Regul Toxicol Pharmacol 2014; 71:331-6. [PMID: 25510831 DOI: 10.1016/j.yrtph.2014.12.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Revised: 11/26/2014] [Accepted: 12/07/2014] [Indexed: 10/24/2022]
Abstract
This is part II of an in silico investigation of chemical-induced eye injury that was conducted at FDA's CFSAN. Serious eye damage caused by chemical (eye corrosion) is assessed using the rabbit Draize test, and this endpoint is an essential part of hazard identification and labeling of industrial and consumer products to ensure occupational and consumer safety. There is an urgent need to develop an alternative to the Draize test because EU's 7th amendment to the Cosmetic Directive (EC, 2003; 76/768/EEC) and recast Regulation now bans animal testing on all cosmetic product ingredients and EU's REACH Program limits animal testing for chemicals in commerce. Although in silico methods have been reported for eye irritation (reversible damage), QSARs specific for eye corrosion (irreversible damage) have not been published. This report describes the development of 21 ANN c-QSAR models (QSAR-21) for assessing eye corrosion potential of chemicals using a large and diverse CFSAN data set of 504 chemicals, ADMET Predictor's three sensitivity analyses and ANNE classification functionalities with 20% test set selection from seven different methods. QSAR-21 models were internally and externally validated and exhibited high predictive performance: average statistics for the training, verification, and external test sets of these models were 96/96/94% sensitivity and 91/91/90% specificity.
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Affiliation(s)
- Rajeshwar P Verma
- Office of Cosmetics and Colors, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States; Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States.
| | - Edwin J Matthews
- Office of Food Additive Safety, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, 5100 Paint Branch Parkway, College Park, MD 20740, United States
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