1
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Lee SB, Gupta H, Min BH, Ganesan R, Sharma SP, Won SM, Jeong JJ, Cha MG, Kwon GH, Jeong MK, Hyun JY, Eom JA, Park HJ, Yoon SJ, Lee SY, Choi MR, Kim DJ, Oh KK, Suk KT. A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2024; 52:250-260. [PMID: 38687561 DOI: 10.1080/21691401.2024.2347380] [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: 06/19/2023] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley - signalling pathways - targets - metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.
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Affiliation(s)
- Su-Been Lee
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Haripriya Gupta
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Byeong-Hyun Min
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Raja Ganesan
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Satya Priya Sharma
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sung-Min Won
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Jin-Ju Jeong
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Min-Gi Cha
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Min-Kyo Jeong
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ji-Ye Hyun
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Jung-A Eom
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Hee-Jin Park
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sang-Jun Yoon
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sang Youn Lee
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Mi-Ran Choi
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Dong Joon Kim
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ki-Kwang Oh
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ki-Tae Suk
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
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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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Vij P, Brinkman A, Koch RM, DeGeorge G, Wolter M. Ocular irritation reversibility assessment of a laundry detergent using the Porcine Corneal Opacity Reversibility Assay (PorCORA): a focused study. Cutan Ocul Toxicol 2024; 43:75-86. [PMID: 38099874 DOI: 10.1080/15569527.2023.2284146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/12/2023] [Indexed: 03/09/2024]
Abstract
Consumer product manufacturers utilise a spectrum of alternative ocular irritation assays, as these tests do not require the use of live animals. Despite their usefulness, no regulatory-accepted assay assesses the reversibility of ocular damage, a key criterion of GHS ocular classification, like the rabbit eye test (i.e., Draize Rabbit Eye Test [DRET]) . The Porcine Corneal Opacity Reversibility Assay (PorCORA), an ex vivo intact corneal tissue culture model, predicts the reversibility of damage by ocular irritants. Inclusion of the damage reversibility endpoint in the PorCORA supplements other alternative test methods for ocular irritation, by assessing induced eye damage and the ability of this damage to reverse (heal) without the use of live animals to distinguish between Globally Harmonised System of Classification and Labelling of Chemicals (GHS) ocular classifications. In this focused study, results of a Bovine Corneal Opacity and Permeability (BCOP) test of a laundry detergent, neat and 10% dilution, (product mixture from S.C. Johnson & Son, Inc. [SCJ]) classified the product into GHS Category 1; however, the BCOP test cannot assess the reversibility of ocular damage. The laundry detergent was evaluated using the PorCORA, where ocular damage induced by the detergent was fully reversed within seven days. Evaluation of the reversibility of ocular damage using the PorCORA in this focused study can add strength to the weight-of-evidence (WoE) analysis approach in ocular hazard assessment. This WoE approach strengthens the argument that the PorCORA can be used to supplement BCOP data, and that this laundry detergent is not an irreversible eye irritant.
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Affiliation(s)
- Puneet Vij
- MB Research Laboratories, Spinnerstown, PA, USA
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Toropov AA, Toropova AP, Roncaglioni A, Benfenati E. Semi-Correlations for Building Up a Simulation of Eye Irritation. TOXICS 2023; 11:993. [PMID: 38133394 PMCID: PMC10747944 DOI: 10.3390/toxics11120993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 11/24/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
The OECD recognizes that data on a compound's ability to treat eye irritation are essential for the assessment of new compounds on the market. In silico models are frequently used to provide information when experimental data are lacking. Semi-correlations, as they are called, can be useful to build up categorical models for eye irritation. Semi-correlations are latent regressions that can be used when the endpoint is expressed by two values: 1 for an active molecule and 0 for an inactive molecule. The regression line is based on the descriptor values which serve to distribute the data into four classes: true positive, true negative, false positive, and false negative. These values are applied to calculate the corresponding statistical criterion for assessing the predictive potential of the categorical model. In our model, the descriptor is the sum of what are termed correlation weights. These are defined by optimization using the Monte Carlo method. The target function of the optimization is related to the determination coefficient and the mean absolute error for the training set. Our model gives results that are better than those previously reported for the same endpoint.
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Affiliation(s)
| | - Alla P. Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy; (A.A.T.); (A.R.); (E.B.)
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Oh KK, Gupta H, Ganesan R, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. The seamless integration of dietary plant-derived natural flavonoids and gut microbiota may ameliorate non-alcoholic fatty liver disease: a network pharmacology analysis. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:217-232. [PMID: 37129458 DOI: 10.1080/21691401.2023.2203734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
We comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.
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Affiliation(s)
- Ki-Kwang Oh
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Haripriya Gupta
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Raja Ganesan
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Satya Priya Sharma
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Sung-Min Won
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Jin-Ju Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Su-Been Lee
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Min-Gi Cha
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Min-Kyo Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Byeong-Hyun Min
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Ji-Ye Hyun
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Jung-A Eom
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Hee-Jin Park
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Sang-Jun Yoon
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Mi-Ran Choi
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Dong Joon Kim
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Ki-Tae Suk
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
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Mittal RK, Purohit P, Sankaranarayanan M, Muzaffar-Ur-Rehman M, Taramelli D, Signorini L, Dolci M, Basilico N. In-vitro antiviral activity and in-silico targeted study of quinoline-3-carboxylate derivatives against SARS-Cov-2 isolate. Mol Divers 2023:10.1007/s11030-023-10703-w. [PMID: 37480422 DOI: 10.1007/s11030-023-10703-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/16/2023] [Indexed: 07/24/2023]
Abstract
In recent years, the viral outbreak named COVID-19 showed that infectious diseases have a huge impact on both global health and the financial and economic sectors. The lack of efficacious antiviral drugs worsened the health problem. Based on our previous experience, we investigated in vitro and in silico a series of quinoline-3-carboxylate derivatives against a SARS-CoV-2 isolate. In the present study, the in-vitro antiviral activity of a series of quinoline-3-carboxylate compounds and the in silico target-based molecular dynamics (MD) and metabolic studies are reported. The compounds' activity against SARS-CoV-2 was evaluated using plaque assay and RT-qPCR. Moreover, from the docking scores, it appears that the most active compounds (1j and 1o) exhibit stronger binding affinity to the primary viral protease (NSP5) and the exoribonuclease domain of non structural protein 14 (NSP14). Additionally, the in-silico metabolic analysis of 1j and 1o defines CYP2C9 and CYP3A4 as the major P450 enzymes involved in their metabolism.
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Affiliation(s)
- Ravi Kumar Mittal
- National Institute of Pharmaceutical Education and Research, S A S Nagar Mohali, Punjab, 160062, India
- Galgotias College of Pharmacy, Greater Noida, UttarPradesh, India
| | - Priyank Purohit
- School of Pharmacy, Graphic Era Hill University, Dehradun, Uttarakhand, 248002, India.
| | - Murugesan Sankaranarayanan
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India
| | - Mohammed Muzaffar-Ur-Rehman
- Medicinal Chemistry Research Laboratory, Department of Pharmacy, BITS Pilani, Pilani Campus, Pilani, Rajasthan, 333031, India
| | - Donatella Taramelli
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Lucia Signorini
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Maria Dolci
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
| | - Nicoletta Basilico
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Pascal Street 36, 20133, Milan, Italy
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7
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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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8
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Pereira M, Macmillan DS, Willett C, Seidle T. REACHing for solutions: Essential revisions to the EU chemicals regulation to modernise safety assessment. Regul Toxicol Pharmacol 2022; 136:105278. [DOI: 10.1016/j.yrtph.2022.105278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/05/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022]
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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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10
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Hua Y, Cui X, Liu B, Shi Y, Guo H, Zhang R, Li X. SApredictor: An Expert System for Screening Chemicals Against Structural Alerts. Front Chem 2022; 10:916614. [PMID: 35910729 PMCID: PMC9326022 DOI: 10.3389/fchem.2022.916614] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
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Affiliation(s)
- Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Bo Liu
- Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
- Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- *Correspondence: Xiao Li, , , orcid.org/0000-0002-1148-9898
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11
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Xu M, Yang H, Liu G, Tang Y, Li W. In Silico Prediction of Chemical Aquatic Toxicity by Multiple Machine Learning and Deep Learning Approaches. J Appl Toxicol 2022; 42:1766-1776. [PMID: 35653511 DOI: 10.1002/jat.4354] [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: 04/12/2022] [Revised: 05/16/2022] [Accepted: 05/31/2022] [Indexed: 11/08/2022]
Abstract
Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, i.e. toxic (LC50 < 10 ppm) or nontoxic were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance and multi-task GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https://github.com/ChemPredict/ChemicalAquaticToxicity).
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Affiliation(s)
- Minjie Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hongbin Yang
- 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
| | - Weihua Li
- 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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Sedykh A, Choksi NY, Allen DG, Casey WM, Shah R, Kleinstreuer NC. Mixtures-Inclusive In Silico Models of Ocular Toxicity Based on United States and International Hazard Categories. Chem Res Toxicol 2022; 35:992-1000. [PMID: 35549170 DOI: 10.1021/acs.chemrestox.1c00443] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Computational modeling grounded in reliable experimental data can help design effective non-animal approaches to predict the eye irritation and corrosion potential of chemicals. The National Toxicology Program (NTP) Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM) has compiled and curated a database of in vivo eye irritation studies from the scientific literature and from stakeholder-provided data. The database contains 810 annotated records of 593 unique substances, including mixtures, categorized according to UN GHS and US EPA hazard classifications. This study reports a set of in silico models to predict EPA and GHS hazard classifications for chemicals and mixtures, accounting for purity by setting thresholds of 100% and 10% concentration. We used two approaches to predict classification of mixtures: conventional and mixture-based. Conventional models evaluated substances based on the chemical structure of its major component. These models achieved balanced accuracy in the range of 68-80% and 87-96% for the 100% and 10% test concentration thresholds, respectively. Mixture-based models, which accounted for all known components in the substance by weighted feature averaging, showed similar or slightly higher accuracy of 72-79% and 89-94% for the respective thresholds. We also noted a strong trend between the pH feature metric calculated for each substance and its activity. Across all the models, the calculated pH of inactive substances was within one log10 unit of neutral pH, on average, while for active substances, pH varied from neutral by at least 2 log10 units. This pH dependency is especially important for complex mixtures. Additional evaluation on an external test set of 673 substances obtained from ECHA dossiers achieved balanced accuracies of 64-71%, which suggests that these models can be useful in screening compounds for ocular irritation potential. Negative predictive value was particularly high and indicates the potential application of these models in a bottom-up approach to identify nonirritant substances.
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Affiliation(s)
- Alexander Sedykh
- Sciome LLC, 1920 E NC 54 Hwy, Suite 510, Durham, North Carolina 27713, United States
| | - Neepa Y Choksi
- Integrated Laboratory Systems Inc, 601 Keystone Park Drive, Suite 200, Morrisville, North Carolina 27560, United States
| | - David G Allen
- Integrated Laboratory Systems Inc, 601 Keystone Park Drive, Suite 200, Morrisville, North Carolina 27560, United States
| | - Warren M Casey
- NIH/NIEHS/DNTP/NICEATM, 530 Davis Drive, Morrisville, North Carolina 27560, United States
| | - Ruchir Shah
- Sciome LLC, 1920 E NC 54 Hwy, Suite 510, Durham, North Carolina 27713, United States
| | - Nicole C Kleinstreuer
- NIH/NIEHS/DNTP/NICEATM, 530 Davis Drive, Morrisville, North Carolina 27560, United States
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13
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Mahajan A, Coelho A, Pissurlenkar R, Fernandes A, Tagalpallewar A. OUP accepted manuscript. J Chromatogr Sci 2022; 61:393-401. [PMID: 35543315 DOI: 10.1093/chromsci/bmac039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Indexed: 11/14/2022]
Abstract
BACKGROUND Proparacaine hydrochloric ophthalmic solution (0.5%) is widely used as ophthalmic anesthetic for short conjunctival and corneal procedures. In connection to this, present research work was conducted to investigate ocular toxicity of its probable degradation products (DPs), which were generated after treating drug with stressors mentioned under International Conference on Harmonization (ICH) guideline Q1A (R2). RESULT The probable DPs were generated by placing drug under various stress conditions to generate similarity of the effect of stressor which drug supposed to face during its life cycle. The hydrolytic stress condition under acidic, basic and neutral environment formed two major DPs, while the drug was stable and did not generate any DP when subjected to oxidative, dry heat and photolytic stress. The separation of DPs was carried out using high performance liquid chromatographic system. The structural identification of DPs was carried out by subjecting degradation samples to liquid chromatography coupled with mass spectrometry. These DPs were screened using in-silico ocular toxicity prediction models Pub-SVM to predict their potential to cause irritation/corrosion to eye. CONCLUSIONS The DPs identified were DP 1 (2-(diethyl amino) ethanol) and DP 2 (3-amino-4-propoxybenzoic acid). The in-silico ocular toxicity study predicted that DP 1 has potential of eye corrosion (EC) and eye irritation (EI), whereas DP 2 has a potential EI. The drug was found safe and devoid of any EC and EI potential.
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Affiliation(s)
- Anand Mahajan
- Department of Pharmaceutical Analysis, Goa College of Pharmacy, 18th June Road, Panaji 403001, Goa, India
| | - Allan Coelho
- Department of Pharmaceutical Analysis, Goa College of Pharmacy, 18th June Road, Panaji 403001, Goa, India
| | - Raghuvir Pissurlenkar
- Department of Pharmaceutical Chemistry, Goa College of Pharmacy, 18th June Road, Panaji 403001, Goa, India
| | - Adison Fernandes
- Department of Pharmaceutical Analysis, Goa College of Pharmacy, 18th June Road, Panaji 403001, Goa, India
| | - Amol Tagalpallewar
- Department of Pharmaceutics, School of Pharmacy, Dr. Vishwanath Karad MIT World Peace University, Pune 411038, Maharashtra, India
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Pastor M, Sanz F, Bringezu F. Development of In Silico Methods for Toxicity Prediction in Collaboration Between Academia and the Pharmaceutical Industry. Methods Mol Biol 2022; 2425:119-131. [PMID: 35188630 DOI: 10.1007/978-1-0716-1960-5_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The pharmaceutical industry would benefit from the collaboration with academic groups in the development of predictive safety models using the newest computational technologies. However, this collaboration is sometimes hampered by the handling of confidential proprietary information and different working practices in both environments. In this manuscript, we propose a strategy for facilitating this collaboration, based on the use of modeling frameworks developed for facilitating the use of sensitive data, as well as the development, interchange, hosting, and use of predictive models in production. The strategy is illustrated with a real example in which we used Flame, an open-source modeling framework developed in our group, for the development of an in silico eye irritation model. The model was based on bibliographic data, refined during the company-academic group collaboration, and enriched with the incorporation of confidential data, yielding a useful model that was validated experimentally.
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Affiliation(s)
- Manuel Pastor
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
| | - Ferran Sanz
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Frank Bringezu
- Chemical and Preclinical Safety, Merck Healthcare KGaA, Darmstadt, Germany
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15
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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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16
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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: 22] [Impact Index Per Article: 7.3] [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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17
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Wu Z, Jiang D, Wang J, Hsieh CY, Cao D, Hou T. Mining Toxicity Information from Large Amounts of Toxicity Data. J Med Chem 2021; 64:6924-6936. [PMID: 33961429 DOI: 10.1021/acs.jmedchem.1c00421] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety is a main reason for drug failures, and therefore, the detection of compound toxicity and potential adverse effects in the early stage of drug development is highly desirable. However, accurate prediction of many toxicity endpoints is extremely challenging due to low accessibility of sufficient and reliable toxicity data, as well as complicated and diversified mechanisms related to toxicity. In this study, we proposed the novel multitask graph attention (MGA) framework to learn the regression and classification tasks simultaneously. MGA has shown excellent predictive power on 33 toxicity data sets and has the capability to extract general toxicity features and generate customized toxicity fingerprints. In addition, MGA provides a new way to detect structural alerts and discover the relationship between different toxicity tasks, which will be quite helpful to mine toxicity information from large amounts of toxicity data.
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Affiliation(s)
- Zhenxing Wu
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Dejun Jiang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
| | - Jike Wang
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan 430072 Hubei, P. R. China
| | - Chang-Yu Hsieh
- Tencent Quantum Laboratory, Tencent, Shenzhen 518057 Guangdong, P. R. China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410004 Hunan, P. R. China
| | - Tingjun Hou
- Innovation Institute for Artificial Intelligence in Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China.,State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058 Zhejiang, P. R. China
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18
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Jeong MH, Han Y, Oh IS, Kim DM, Son DW, Jung MS, Yang H, Lee K, Shin JY, Kim HR, Chung KH. Pre-validation of a Calu-3 epithelium cytotoxicity assay for predicting acute inhalation toxicity of chemicals. Toxicol In Vitro 2021; 75:105136. [PMID: 33675894 DOI: 10.1016/j.tiv.2021.105136] [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: 09/11/2020] [Revised: 02/25/2021] [Accepted: 03/01/2021] [Indexed: 10/22/2022]
Abstract
Although in vivo inhalation toxicity tests have been widely conducted, the testing of many chemicals is limited for economic and ethical reasons. Therefore, we previously developed an in vitro acute inhalation toxicity test method. The goal of the present pre-validation study was to evaluate the transferability, reproducibility, and predictive capacity of this method. After confirming the transferability of the Calu-3 epithelium cytotoxicity assay, reproducibility was evaluated using 20 test substances at three independent institutions. Cytotoxicity data were analyzed using statistical methods, including the intra-class correlation coefficient and Bland-Altman plots for within- and between-laboratory reproducibility. The assay for the 20 test substances showed excellent agreement within and between laboratories. To evaluate the predictive capacity, 77 test substances were analyzed for acute inhalation toxicity. Accuracy was measured using a cutoff of 40%, and the relevance was analyzed as a receiver-operating characteristic (ROC) curve. An accuracy of 72.73% was obtained, and the area under the ROC curve was 0.77, indicating moderate performance. In this study, we found that the in vitro acute inhalation toxicity test method demonstrated good reliability and relevance for predicting the acute toxicity of inhalable chemicals. Hence, this assay has potential as an alternative test for screening acutely toxic inhalants.
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Affiliation(s)
- Mi Ho Jeong
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Yubin Han
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - In-Sun Oh
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Dong Min Kim
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Dong-Won Son
- Biotoxtech, 53, Cheongju, Chungcheongbuk-do 28115, Republic of Korea
| | - Mi-Sook Jung
- Biotoxtech, 53, Cheongju, Chungcheongbuk-do 28115, Republic of Korea
| | - Hyoseon Yang
- National Center for Efficacy Evaluation of Respiratory Disease Product, Korea Institute of Toxicology, Jeongeup 56212, Republic of Korea
| | - Kyuhong Lee
- National Center for Efficacy Evaluation of Respiratory Disease Product, Korea Institute of Toxicology, Jeongeup 56212, Republic of Korea
| | - Ju-Young Shin
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea
| | - Ha Ryong Kim
- College of Pharmacy, Daegu Catholic University, Gyeongsan, Gyeongsangbuk-do 38430, Republic of Korea.
| | - Kyu Hyuck Chung
- School of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do 16419, Republic of Korea.
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19
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Jiao Z, Hu P, Xu H, Wang Q. Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications. ACS CHEMICAL HEALTH & SAFETY 2020. [DOI: 10.1021/acs.chas.0c00075] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Zeren Jiao
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Pingfan Hu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Hongfei Xu
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
| | - Qingsheng Wang
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843-3122, United States
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20
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Cui X, Yang R, Li S, Liu J, Wu Q, Li X. Modeling and insights into molecular basis of low molecular weight respiratory sensitizers. Mol Divers 2020; 25:847-859. [PMID: 32166484 DOI: 10.1007/s11030-020-10069-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Accepted: 03/03/2020] [Indexed: 01/10/2023]
Abstract
Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.
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Affiliation(s)
- Xueyan Cui
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Rui Yang
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Siwen Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Juan Liu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Qiuyun Wu
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China
| | - Xiao Li
- Department of Clinical pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, 250014, China. .,Department of Clinical pharmacy, The First Affiliated Hospital of Shandong First Medical University, Shandong First Medical University, Jinan, 250014, China.
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21
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Rim KT. In silico prediction of toxicity and its applications for chemicals at work. TOXICOLOGY AND ENVIRONMENTAL HEALTH SCIENCES 2020; 12:191-202. [PMID: 32421081 PMCID: PMC7223298 DOI: 10.1007/s13530-020-00056-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/21/2020] [Indexed: 04/14/2023]
Abstract
OBJECTIVE AND METHODS This study reviewed the concept of in silico prediction of chemical toxicity for prevention of occupational cancer and future prospects in workers' health. In this review, a new approach to determine the credibility of in silico predictions with raw data is explored, and the method of determining the confidence level of evaluation based on the credibility of data is discussed. I searched various papers and books related to the in silico prediction of chemical toxicity and carcinogenicity. The intention was to utilize the most recent reports after 2015 regarding in silico prediction. RESULTS AND CONCLUSION The application of in silico methods is increasing with the prediction of toxic risks to human and the environment. The various toxic effects of industrial chemicals have triggered the recognition of the importance of using a combination of in silico models in the risk assessments. In silico occupational exposure models, industrial accidents, and occupational cancers are effectively managed and chemicals evaluated. It is important to identify and manage hazardous substances proactively through the rigorous evaluation of chemicals.
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Affiliation(s)
- Kyung-Taek Rim
- Chemicals Research Bureau, Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency, Daejeon, Korea
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22
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Wang Y, Jafari M, Tang Y, Tang J. Predicting Meridian in Chinese traditional medicine using machine learning approaches. PLoS Comput Biol 2019; 15:e1007249. [PMID: 31765369 PMCID: PMC6876772 DOI: 10.1371/journal.pcbi.1007249] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/20/2019] [Indexed: 12/26/2022] Open
Abstract
Plant-derived nature products, known as herb formulas, have been commonly used in Traditional Chinese Medicine (TCM) for disease prevention and treatment. The herbs have been traditionally classified into different categories according to the TCM Organ systems known as Meridians. Despite the increasing knowledge on the active components of the herbs, the rationale of Meridian classification remains poorly understood. In this study, we took a machine learning approach to explore the classification of Meridian. We determined the molecule features for 646 herbs and their active components including structure-based fingerprints and ADME properties (absorption, distribution, metabolism and excretion), and found that the Meridian can be predicted by machine learning approaches with a top accuracy of 0.83. We also identified the top compound features that were important for the Meridian prediction. To the best of our knowledge, this is the first time that molecular properties of the herb compounds are associated with the TCM Meridians. Taken together, the machine learning approach may provide novel insights for the understanding of molecular evidence of Meridians in TCM.
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Affiliation(s)
- Yinyin Wang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Mohieddin Jafari
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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23
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He L, Xiao K, Zhou C, Li G, Yang H, Li Z, Cheng J. Insights into pesticide toxicity against aquatic organism: QSTR models on Daphnia Magna. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2019; 173:285-292. [PMID: 30776561 DOI: 10.1016/j.ecoenv.2019.02.014] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2018] [Revised: 01/30/2019] [Accepted: 02/04/2019] [Indexed: 06/09/2023]
Abstract
The toxicities of agrochemicals to non-target aquatic organisms are key items in chemical ecological risk assessment. However, it is still an urgent need to develop new tools to assess the agrochemical aquatic toxicity efficiently and accurately. In this work, QSTR studies were performed on a data set containing 639 diverse pesticides with measured EC50 toxicity against Daphnia magna, by using five machine learning methods combined with seven fingerprints and a set of molecular descriptors. The imbalance problem of the data set was successfully solved by clustering analysis. The top-10 QSTR models displayed greater predicative abilities than ECOSAR. The optimal model, Ext-SVM, showed the best performance in 10-fold cross validation (Qhigh=0.807, Qmoderate=0.806, Qlow=0.755, Qtotal=0.794), and also in the test set verification (Qhigh=0.865, Qmoderate=0.783, Qlow=0.931, Qtotal=0.848). The relevance of the key physical-chemical properties with the toxicity was also investigated, in which the MW, a_np, logP(o/w), GCUT_SLOGP_1, chilv and SMR_VSA7 values displayed positive correlation with Daphnia magna toxicity, whereas the logS and a_don showed negative correlation. The robust QSTR models provided efficient tools for assessing agrochemical aquatic toxicity, and the revealed different physical-chemical properties between the high and low toxic compounds might be useful in the discovery and design of low aquatic toxic pesticides.
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Affiliation(s)
- Lujue He
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Keya Xiao
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Cong Zhou
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guanglong Li
- Shanghai Key Laboratory of Chemical Biology, 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
| | - Zhong Li
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Jiagao Cheng
- Shanghai Key Laboratory of Chemical Biology, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China; Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
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24
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Sun G, Fan T, Sun X, Hao Y, Cui X, Zhao L, Ren T, Zhou Y, Zhong R, Peng Y. In Silico Prediction of O⁶-Methylguanine-DNA Methyltransferase Inhibitory Potency of Base Analogs with QSAR and Machine Learning Methods. Molecules 2018; 23:E2892. [PMID: 30404161 PMCID: PMC6278368 DOI: 10.3390/molecules23112892] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Revised: 11/04/2018] [Accepted: 11/06/2018] [Indexed: 12/24/2022] Open
Abstract
O⁶-methylguanine-DNA methyltransferase (MGMT), a unique DNA repair enzyme, can confer resistance to DNA anticancer alkylating agents that modify the O⁶-position of guanine. Thus, inhibition of MGMT activity in tumors has a great interest for cancer researchers because it can significantly improve the anticancer efficacy of such alkylating agents. In this study, we performed a quantitative structure activity relationship (QSAR) and classification study based on a total of 134 base analogs related to their ED50 values (50% inhibitory concentration) against MGMT. Molecular information of all compounds were described by quantum chemical descriptors and Dragon descriptors. Genetic algorithm (GA) and multiple linear regression (MLR) analysis were combined to develop QSAR models. Classification models were generated by seven machine-learning methods based on six types of molecular fingerprints. Performances of all developed models were assessed by internal and external validation techniques. The best QSAR model was obtained with Q²Loo = 0.83, R² = 0.87, Q²ext = 0.67, and R²ext = 0.69 based on 84 compounds. The results from QSAR studies indicated topological charge indices, polarizability, ionization potential (IP), and number of primary aromatic amines are main contributors for MGMT inhibition of base analogs. For classification studies, the accuracies of 10-fold cross-validation ranged from 0.750 to 0.885 for top ten models. The range of accuracy for the external test set ranged from 0.800 to 0.880 except for PubChem-Tree model, suggesting a satisfactory predictive ability. Three models (Ext-SVM, Ext-Tree and Graph-RF) showed high and reliable predictive accuracy for both training and external test sets. In addition, several representative substructures for characterizing MGMT inhibitors were identified by information gain and substructure frequency analysis method. Our studies might be useful for further study to design and rapidly identify potential MGMT inhibitors.
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Affiliation(s)
- Guohui Sun
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Tengjiao Fan
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Xiaodong Sun
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Yuxing Hao
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Xin Cui
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Lijiao Zhao
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Ting Ren
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Yue Zhou
- State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 2A Nanwei Road, Beijing 100050, China.
| | - Rugang Zhong
- Beijing Key Laboratory of Environmental & Viral Oncology, College of Life Science & Bioengineering, Beijing University of Technology, Beijing 100124, China.
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment & Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, China.
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25
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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: 105] [Impact Index Per Article: 17.5] [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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26
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Li X, Chen Y, Song X, Zhang Y, Li H, Zhao Y. The development and application of in silico models for drug induced liver injury. RSC Adv 2018; 8:8101-8111. [PMID: 35542036 PMCID: PMC9078522 DOI: 10.1039/c7ra12957b] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 02/09/2018] [Indexed: 11/23/2022] Open
Abstract
Drug-induced liver injury (DILI), caused by drugs, herbal agents or nutritional supplements, is a major issue for patients and the pharmaceutical industry. It has been a leading cause of clinical trials failure and withdrawal of FDA approval. In this research, we focused on in silico estimation of chemical DILI potential on humans based on structurally diverse organic chemicals. We developed a series of binary classification models using five different machine learning methods and eight different feature reduction methods. The model, developed with the support vector machine (SVM) and the MACCS fingerprint, performed best both on the test set and external validation. It achieved a prediction accuracy of 80.39% on the test set and 82.78% on external validation. We made this model available at http://opensource.vslead.com/. The user can freely predict the DILI potential of molecules. Furthermore, we analyzed the difference of distributions of 12 key physical-chemical properties between DILI-positive and DILI-negative compounds and 20 privileged substructures responsible for DILI were identified from the Klekota-Roth fingerprint. Moreover, since traditional Chinese medicine (TCM)-induced liver injury is also one of the major concerns among the toxic effects, we evaluated the DILI potential of TCM ingredients using the MACCS_SVM model developed in this study. We hope the model and privileged substructures could be useful complementary tools for chemical DILI evaluation.
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Affiliation(s)
- Xiao Li
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
- Beijing Key Laboratory of Cloud Computing Key Technology and Application, Beijing Computing Center, Beijing Academy of Science and Technology 7 Fengxian road Beijing 100094 China +86-10-5934-1855 +86-10-5934-1764
| | - Yaojie Chen
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
| | - Xinrui Song
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
| | - Yuan Zhang
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
| | - Huanhuan Li
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
| | - Yong Zhao
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. 7 Fengxian road Beijing 100094 China +86-10-5934-1890
- Beijing Key Laboratory of Cloud Computing Key Technology and Application, Beijing Computing Center, Beijing Academy of Science and Technology 7 Fengxian road Beijing 100094 China +86-10-5934-1855 +86-10-5934-1764
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27
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Li X, Zhang Y, Chen H, Li H, Zhao Y. Insights into the Molecular Basis of the Acute Contact Toxicity of Diverse Organic Chemicals in the Honey Bee. J Chem Inf Model 2017; 57:2948-2957. [PMID: 29161513 DOI: 10.1021/acs.jcim.7b00476] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Use of chemical pollutants, including pesticides and other industrial chemicals, has resulted in significant risks to the whole ecosystem. Therefore, ecological risk assessment of chemicals is vital and necessary. Since the honey bee (Apis mellifera) is probably among the most exposed species to the polluting chemicals, we focused on the in silico estimation of honey bee toxicity (HBT) of chemicals and the analysis of the relevance of chemical HBT and several key physical-chemical properties and structural characteristics. A total of 40 classification models were developed by combination of five machine learning methods along with seven kinds of fingerprints and a set of molecular descriptors. After 5-fold cross validation and external validation, several models showed good predictive power. The relevance of 12 key physical-chemical properties and chemical HBT was also investigated. Five properties, including AlogP, logD, molecular weight (MW), molecular surface area (MSA), and the number of rotatable bonds (nRTB), indicated positive correlation coefficients with HBT, while molecular solubility (logS) and the number of hydrogen bond donors (nHBD) indicated negative correlation coefficients. Finally, seven privileged substructures responsible for chemical HBT were identified from KRFP and SubFP fingerprints. The results of this study should provide critical information and useful tools for chemical HBT estimation in environmental risk assessment.
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Affiliation(s)
- Xiao Li
- Beijing Computing Center, Beijing Academy of Science and Technology , 7 Fengxian road, Beijing 100094, China.,Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. , 7 Fengxian road, Beijing 100094, China
| | - Yuan Zhang
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. , 7 Fengxian road, Beijing 100094, China
| | - Hongna Chen
- Tigermed Consulting Co., Ltd. , 20 Chaowai Street, Beijing 100020, China
| | - Huanhuan Li
- Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. , 7 Fengxian road, Beijing 100094, China
| | - Yong Zhao
- Beijing Computing Center, Beijing Academy of Science and Technology , 7 Fengxian road, Beijing 100094, China.,Beijing Beike Deyuan Bio-Pharm Technology Co. Ltd. , 7 Fengxian road, Beijing 100094, China
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28
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Li X, Zhang Y, Chen H, Li H, Zhao Y. In silico prediction of chronic toxicity with chemical category approaches. RSC Adv 2017. [DOI: 10.1039/c7ra08415c] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.
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Affiliation(s)
- Xiao Li
- Beijing Computing Center
- Beijing Academy of Science and Technology
- Beijing 100094
- China
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd
| | - Yuan Zhang
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd
- Beijing 100094
- China
| | - Hongna Chen
- Tigermed Consulting Co., Ltd
- Beijing 100020
- China
| | - Huanhuan Li
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd
- Beijing 100094
- China
| | - Yong Zhao
- Beijing Computing Center
- Beijing Academy of Science and Technology
- Beijing 100094
- China
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd
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