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Melo-Filho CC, Su G, Liu K, Muratov EN, Tropsha A, Liu J. Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis. Glycobiology 2024; 34:cwae039. [PMID: 38836441 PMCID: PMC11180703 DOI: 10.1093/glycob/cwae039] [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: 03/17/2024] [Revised: 04/30/2024] [Accepted: 05/29/2024] [Indexed: 06/06/2024] Open
Abstract
Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity. Using fibroblast growth factor 2 (FGF2) as a model protein, we assembled an in-house dataset of HS oligosaccharides on microarrays and developed two structural representations: a standard representation with all atoms explicit and a simplified representation with disaccharide units as "quasi-atoms." Predictive Quantitative Structure-Activity Relationship (QSAR) models for FGF2 affinity were developed using the Random Forest (RF) algorithm. The resulting models, considering the applicability domain, demonstrated high predictivity, with a correct classification rate of 0.81-0.80 and improved positive predictive values (PPV) up to 0.95. Virtual screening of 40 new oligosaccharides using the simplified model identified 15 computational hits, 11 of which were experimentally validated for high FGF2 affinity. This hybrid approach marks a significant step toward the targeted design of oligosaccharides with desired protein interactions, providing a foundation for broader applications in glycobiology.
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Affiliation(s)
- Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Guowei Su
- Glycan Therapeutics, 617 Hutton Street, Raleigh, NC 27606, United States
| | - Kevin Liu
- Glycan Therapeutics, 617 Hutton Street, Raleigh, NC 27606, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, 301 Beard Hall, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Jian Liu
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, 1044 Genetic Medicine Bldg., University of North Carolina, Chapel Hill, NC 27599, United States
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Singh A, Kumar S, Kapoor A, Kumar P, Kumar A. Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors. Toxicol Mech Methods 2023; 33:222-232. [PMID: 36042574 DOI: 10.1080/15376516.2022.2118092] [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: 10/14/2022]
Abstract
The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R2 = 0.8053, 0.7591, and 0.8283) and robustness (Q2LOO = 0.7507, 0.7227, and 0.8194; Q2LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R2EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.
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Affiliation(s)
- Ayushi Singh
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Sunil Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Archana Kapoor
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
| | - Parvin Kumar
- Department of Chemistry, Kurukshetra University, Kurukshetra, India
| | - Ashwani Kumar
- Department of Pharmaceutical Sciences, Guru Jambheshwar University of Science and Technology, Hisar, India
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Tan H, Wu J, Zhang R, Zhang C, Li W, Chen Q, Zhang X, Yu H, Shi W. Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12391-12403. [PMID: 35960020 DOI: 10.1021/acs.est.2c02242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.
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Affiliation(s)
- Haoyue Tan
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Jinqiu Wu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Rong Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Chi Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Li
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Qinchang Chen
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
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Prediction of aquatic toxicity of energetic materials using genetic function approximation. FIREPHYSCHEM 2022. [DOI: 10.1016/j.fpc.2022.07.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Jakovljević I, Šimić I, Mendaš G, Sever Štrukil Z, Žužul S, Gluščić V, Godec R, Pehnec G, Bešlić I, Milinković A, Bakija Alempijević S, Šala M, Ogrizek M, Frka S. Pollution levels and deposition processes of airborne organic pollutants over the central Adriatic area: Temporal variabilities and source identification. MARINE POLLUTION BULLETIN 2021; 172:112873. [PMID: 34428622 DOI: 10.1016/j.marpolbul.2021.112873] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/13/2021] [Accepted: 08/14/2021] [Indexed: 05/27/2023]
Abstract
First data on polycyclic aromatic hydrocarbons (PAHs) and nitro-aromatic compounds (NACs) in aerosols as well as of PAHs, polychlorinated biphenyls (PCBs) and NACs in bulk and wet atmospheric deposition samples were simultaneously obtained during 6-month-long field campaign at the costal central Adriatic area. Special attention was given to open-fire biomass burning episodes as extreme events common for the overall Mediterranean coastal area in order to gain a better understanding of the atmospheric variabilities and potential sources of trace organic pollutants in coastal environments. Diesel and gasoline combustion related to land and maritime traffic as well as occasional open-fire episodes (forest fires) were found to be the dominant pollution sources of PAHs in PM10 particles. NACs were determined almost exclusively in samples affected by biomass burning episodes. Open-fire episodes had a strong contribution to the overall NACs atmospheric deposition fluxes. Several chlorinated congeners of PCBs were predominantly contributed in deposition samples.
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Affiliation(s)
- Ivana Jakovljević
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Iva Šimić
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia.
| | - Gordana Mendaš
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | | | - Silva Žužul
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Valentina Gluščić
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Ranka Godec
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Gordana Pehnec
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Ivan Bešlić
- Institute for Medical Research and Occupational Health, 10000 Zagreb, Croatia
| | - Andrea Milinković
- Laboratory for Marine and Atmospheric Biogeochemistry, Division for Marine and Environmental Research, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Saranda Bakija Alempijević
- Laboratory for Marine and Atmospheric Biogeochemistry, Division for Marine and Environmental Research, Ruđer Bošković Institute, 10000 Zagreb, Croatia
| | - Martin Šala
- Analytical Chemistry Laboratory, National Institute of Chemistry, 1000 Ljubljana, Slovenia
| | - Monika Ogrizek
- Analytical Chemistry Laboratory, National Institute of Chemistry, 1000 Ljubljana, Slovenia; Jožef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia
| | - Sanja Frka
- Laboratory for Marine and Atmospheric Biogeochemistry, Division for Marine and Environmental Research, Ruđer Bošković Institute, 10000 Zagreb, Croatia
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Qin C, Abrell L, Troya D, Hunt E, Taylor S, Dontsova K. Outdoor dissolution and photodegradation of insensitive munitions formulations IMX-101 and IMX-104: Photolytic transformation pathway and mechanism study. CHEMOSPHERE 2021; 280:130672. [PMID: 33964749 DOI: 10.1016/j.chemosphere.2021.130672] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/16/2021] [Accepted: 04/22/2021] [Indexed: 06/12/2023]
Abstract
New munition compounds have been developed to replace traditional explosives to prevent unintended detonations. However, insensitive munitions (IM) can leave large proportion of unexploded charge in the field, where it is subjected to photodegradation and dissolution in precipitation. The photolytic reactions occurring on the surfaces of IMX-101 and IMX-104 formulations and the subsequent fate of photolytic products in the environment were thoroughly investigated. The constituents of IMX-101 and IMX-104 formulations dissolve sequentially under rainfall in the order of aqueous solubility: 3-nitro-1,2,4-triazol-5-one (NTO) > nitroguanidine (NQ) > 2,4-dinitroanisole (DNAN) > 1,3,5-hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX). A linear relationship between DNAN dissolution and rainwater volume was observed (r2: 0.86-0.99). It was estimated that it would take 16-228 years to completely dissolve these formulation particles under natural environmental conditions in Oracle, AZ. We used LC/MS/MS and GC/MS to examine the dissolution samples from IMX-101 and 104 particles exposed to rainfall and sunlight and found six DNAN photo-transformation products including 2-methoxy-5-nitrophenol, 4-methoxy-3-nitrophenol, 4-methoxy-3-nitroaniline, 2-methoxy-5-nitroaniline, 2,4-dinitrophenol, and methoxy-dinitrophenol, which are in good agreement with computational modeling results of bond strengths. The main DNAN photodegradation pathways are therefore proposed. Predicted eco-toxicity values suggested that the parent compound DNAN, methoxy-nitrophenols, methoxy-nitroanilines and the other two products (2,4-dinitrophenol and methoxy-dinitrophenol) would be harmful to fish and daphnid. Our study provides improved insight about the rain dissolution and photochemical behavior of IM formulations under natural conditions, which helps to form target-oriented strategies to mitigate explosive contamination in military training sites.
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Affiliation(s)
- Chao Qin
- College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, 210095, China; Biosphere 2, University of Arizona, Oracle, AZ, 85721-0158, United States.
| | - Leif Abrell
- Department of Environmental Science, University of Arizona, Tucson, AZ, 85721-0038, United States
| | - Diego Troya
- Department of Chemistry, Virginia Tech, Blacksburg, VA, 24061, United States
| | - Edward Hunt
- Biosphere 2, University of Arizona, Oracle, AZ, 85721-0158, United States
| | - Susan Taylor
- U.S. Army Engineer Research and Development Center, Hanover, NH, 03755-1290, United States
| | - Katerina Dontsova
- Biosphere 2, University of Arizona, Oracle, AZ, 85721-0158, United States; Department of Environmental Science, University of Arizona, Tucson, AZ, 85721-0038, United States.
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Masand VH, Zaki MEA, Al-Hussain SA, Ghorbal AB, Akasapu S, Lewaa I, Ghosh A, Jawarkar RD. Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata. AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 239:105962. [PMID: 34525418 DOI: 10.1016/j.aquatox.2021.105962] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 08/10/2021] [Accepted: 09/01/2021] [Indexed: 06/13/2023]
Abstract
In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC50 for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R2 = 0.72, Q2LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati, Maharashtra, 444 602, India
| | - Magdi E A Zaki
- Department of Chemistry, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | - Sami A Al-Hussain
- Department of Chemistry, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | - Anis Ben Ghorbal
- Department of Mathematics and Statistics, Faculty of Science, College of Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia.
| | | | - Israa Lewaa
- Assistant Lecturer of Statistics, Faculty of Business Administration, Department of Business Administration, Economics and Political Science, The British University in Egypt, Cairo, Egypt.
| | - Arabinda Ghosh
- Microbiology Division, Department of Botany, Gauhati University, Guwahati, Assam, 781014, India
| | - Rahul D Jawarkar
- Department of Medicinal Chemistry, Dr. Rajendra Gode Institute of Pharmacy, Amravati, Maharashtra, India
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Huang T, Sun G, Zhao L, Zhang N, Zhong R, Peng Y. Quantitative Structure-Activity Relationship (QSAR) Studies on the Toxic Effects of Nitroaromatic Compounds (NACs): A Systematic Review. Int J Mol Sci 2021; 22:8557. [PMID: 34445263 PMCID: PMC8395302 DOI: 10.3390/ijms22168557] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 08/05/2021] [Accepted: 08/05/2021] [Indexed: 01/22/2023] Open
Abstract
Nitroaromatic compounds (NACs) are ubiquitous in the environment due to their extensive industrial applications. The recalcitrance of NACs causes their arduous degradation, subsequently bringing about potential threats to human health and environmental safety. The problem of how to effectively predict the toxicity of NACs has drawn public concern over time. Quantitative structure-activity relationship (QSAR) is introduced as a cost-effective tool to quantitatively predict the toxicity of toxicants. Both OECD (Organization for Economic Co-operation and Development) and REACH (Registration, Evaluation and Authorization of Chemicals) legislation have promoted the use of QSAR as it can significantly reduce living animal testing. Although numerous QSAR studies have been conducted to evaluate the toxicity of NACs, systematic reviews related to the QSAR modeling of NACs toxicity are less reported. The purpose of this review is to provide a thorough summary of recent QSAR studies on the toxic effects of NACs according to the corresponding classes of toxic response endpoints.
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Affiliation(s)
- Tao Huang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Guohui Sun
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Lijiao Zhao
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Na Zhang
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Rugang Zhong
- Key Laboratory of Environmental and Viral Oncology, College of Life Science and Chemistry, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China; (T.H.); (L.Z.); (N.Z.); (R.Z.)
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, College of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China;
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Tinkov OV, Grigorev VY, Grigoreva LD. QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:541-571. [PMID: 34157880 DOI: 10.1080/1062936x.2021.1932583] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
Avermectins have been effectively used in medicine, veterinary medicine, and agriculture as antiparasitic agents for many years. However, there are still no reliable data on the main ecotoxicological characteristics of most individual avermectins. Although many QSAR models have been proposed to describe the acute toxicity of organic compounds towards Tetrahymena pyriformis (T. pyriformis), avermectins are outside the applicability domain of these models. The influence of the molecular structures of various organic compounds on the acute toxicity towards T. pyriformis was studied using the OCHEM web platform (https://ochem.eu). A data set of 1792 toxicants was used to create models. The QSAR (Quantitative Structure-Activity Relationship) models were developed using the molecular descriptors Dragon, ISIDA, CDK, PyDescriptor, alvaDesc, and SIRMS and machine learning methods, such as Least Squares Support Vector Machine and Transformer Convolutional Neural Network. The HYBOT descriptors and Random Forest were used for a comparative QSAR investigation. Since the best predictive ability was demonstrated by the Transformer Convolutional Neural Network model, it was used to predict the toxicity of individual avermectins towards T. pyriformis. During a structural interpretation of the developed QSAR model, we determined the significant molecular transformations that increase and decrease the acute toxicity of organic compounds.
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Affiliation(s)
- O V Tinkov
- Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Shevchenko Transnistria State University, Tiraspol, Moldova
- Department of Computer Science, Military Institute of the Ministry of Defense, Tiraspol, Moldova
| | - V Y Grigorev
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Science, Chernogolovka, Russia
| | - L D Grigoreva
- Department of Fundamental Physicochemical Engineering, Moscow State University, Moscow, Russia
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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11
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Yang ZY, Yang ZJ, Zhao Y, Yin MZ, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. PySmash: Python package and individual executable program for representative substructure generation and application. Brief Bioinform 2021; 22:6168498. [PMID: 33709154 DOI: 10.1093/bib/bbab017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/06/2021] [Accepted: 01/12/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.
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Affiliation(s)
- Zi-Yi Yang
- Department of Pharmacy, Xiangya Hospital, Central South University and the Xiangya School of Pharmaceutical Sciences, Central South University, Sichuan, China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Hunan, China
| | - Yue Zhao
- Xiangya School of Pharmaceutical Sciences, Central South University (Changsha), Sichuan, China
| | - Ming-Zhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Hunan
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Hunan
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Hunan
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
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12
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Maurya R, Pandey AK. Importance of protozoa Tetrahymena in toxicological studies: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 741:140058. [PMID: 32599397 DOI: 10.1016/j.scitotenv.2020.140058] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 06/11/2023]
Abstract
Tetrahymena is a single-cell eukaryotic organism present in all aquatic environments and can easily be maintained in laboratory conditions in a cost-effective manner. This review gives a brief description of the physiology of Tetrahymena, culture handling, and maintenance of Tetrahymena species. The review article focuses on various toxicological bioassays at different biological organizational (biochemical, individual, population, and community) levels. Furthermore, some techniques such as single cell gel electrophoresis (SCGE) and microcalorimetry assay are also available to investigate the effect of xenobiotics on the integrity of DNA and metabolic state of Tetrahymena species respectively. The article also discusses how the general physiology, behavioural activities and different organelles of Tetrahymena could be useful in toxicological studies. The strength and limitations of Tetrahymena over other model organisms are also discussed. This article also provides suggestions to overcome some problems related to toxicity assessment. Various aspects associated with variability in results, toxicity endpoints, characteristics of organisms and responses against xenobiotic substances (old and new emerging toxicants) are considered.
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Affiliation(s)
- Renuka Maurya
- CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh 201002, India
| | - Alok Kumar Pandey
- CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow, Uttar Pradesh, India.
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13
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Tinkov O, Polishchuk P, Matveieva M, Grigorev V, Grigoreva L, Porozov Y. The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Mol Inform 2020; 40:e2000209. [PMID: 33029954 DOI: 10.1002/minf.202000209] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/01/2020] [Indexed: 12/28/2022]
Abstract
Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.
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Affiliation(s)
- Oleg Tinkov
- Department of Computer Science, Military Institute of the Ministry of Defense, 3300, Gogol str. 2"B", Tiraspol, Transdniestria, Moldova.,Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Transnistrian State University, 3300, October 25 str. 128, Tiraspol, Transdniestria, Moldova
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Mariia Matveieva
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Veniamin Grigorev
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432, Severniy proezd 1, Chernogolovka, Moscow region, Russia
| | - Ludmila Grigoreva
- Department of Fundamental Physical and Chemical Engineering, Moscow State University, 119991, Leninskiye Gory 1/51, Moscow, Russia
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Computational Biology, Sirius University of Science and Technology, 354340, Olympic Ave 1, Sochi, Russia
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14
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Alves VM, Bobrowski T, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. QSAR Modeling of SARS-CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Mol Inform 2020; 40:e2000113. [PMID: 33405340 DOI: 10.1002/minf.202000113] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).
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Affiliation(s)
- Vinicius M Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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15
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Borba JVB, Braga RC, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Andrade CH. Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers. Chem Res Toxicol 2020; 34:258-267. [PMID: 32673477 DOI: 10.1021/acs.chemrestox.0c00186] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
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Affiliation(s)
- Joyce V B Borba
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.,Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | | | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.,Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, Paraíba 58059, Brazil
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil
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16
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Bobrowski T, Alves VM, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12153594. [PMID: 32511287 PMCID: PMC7252448 DOI: 10.26434/chemrxiv.12153594] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 04/22/2020] [Indexed: 01/09/2023]
Abstract
The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation. .
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Affiliation(s)
- Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Cleber C. Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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17
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Mondal D, Ghosh K, Baidya ATK, Gantait AM, Gayen S. Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics. Toxicol Mech Methods 2020; 30:257-265. [DOI: 10.1080/15376516.2019.1709238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Dipayan Mondal
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Kalyan Ghosh
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | - Anurag T. K. Baidya
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
| | | | - Shovanlal Gayen
- Laboratory of Drug Design and Discovery, Department of Pharmaceutical Sciences, Dr. HarisinghGour University, Sagar, Madhya Pradesh, India
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18
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Roy JK, Kar S, Leszczynski J. Optoelectronic Properties of C 60 and C 70 Fullerene Derivatives: Designing and Evaluating Novel Candidates for Efficient P3HT Polymer Solar Cells. MATERIALS (BASEL, SWITZERLAND) 2019; 12:E2282. [PMID: 31315218 PMCID: PMC6678454 DOI: 10.3390/ma12142282] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 07/11/2019] [Accepted: 07/12/2019] [Indexed: 11/21/2022]
Abstract
Ten novel fullerene-derivatives (FDs) of C60 and C70 had been designed as acceptor for polymer solar cell (PSC) by employing the quantitative structure-property relationship (QSPR) model, which was developed strategically with a reasonably big pool of experimental power conversion efficiency (PCE) data. The QSPR model was checked and validated with stringent parameter and reliability of predicted PCE values of all designed FDs. They were assessed by the applicability domain (AD) and process randomization test. The predicted PCE of FDs range from 7.96 to 23.01. The obtained encouraging results led us to the additional theoretical analysis of the energetics and UV-Vis spectra of isolated dyes employing Density functional theory (DFT) and Time-dependent-DFT (TD-DFT) calculations using PBE/6-31G(d,p) and CAM-B3LYP/6-311G(d,p) level calculations, respectively. The FD4 is the best C60-derivatives candidates for PSCs as it has the lowest exciton binding energy, up-shifted lowest unoccupied molecular orbital (LUMO) energy level to increase open-circuit voltage (VOC) and strong absorption in the UV region. In case of C70-derivatives, FD7 is potential candidate for future PSCs due to its strong absorption in UV-Vis region and lower exciton binding energy with higher VOC. Our optoelectronic results strongly support the developed QSPR model equation. Analyzing QSPR model and optoelectronic parameters, we concluded that the FD1, FD2, FD4, and FD10 are the most potential candidates for acceptor fragment of fullerene-based PSC. The outcomes of tactical molecular design followed by the investigation of optoelectronic features are suggested to be employed as a significant resource for the synthesis of FDs as an acceptor of PSCs.
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Affiliation(s)
- Juganta K Roy
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA
| | - Supratik Kar
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA.
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, MS 39217, USA.
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19
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Ojha PK, Kar S, Roy K, Leszczynski J. Toward comprehension of multiple human cells uptake of engineered nano metal oxides: quantitative inter cell line uptake specificity (QICLUS) modeling. Nanotoxicology 2018; 13:14-34. [PMID: 30354872 DOI: 10.1080/17435390.2018.1529836] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
To address the nanomaterial exposure threat, it is imperative to understand how nanomaterials are recognized, internalized, and distributed within diverse cell systems. Targeting of nanomaterials to a specific cell type is generally attained through the modification of the nanoparticle (NP) surface leading to required cellular uptake. The enhanced cellular uptake to normal cells can direct to the higher interaction of NPs with subcellular organelles resulting the provocation of various signaling pathways. The successes of NPs rely on the prospect for the synthesis of functionalized NPs with necessary properties and their enhanced potential for cellular uptake for specific targeting. In the present study, we have modeled the cellular uptake of 109 surface modifiers of metal oxide nanoparticles (MNPs) for three different cell lines: HUVEC (Human endothelial cells), U937 (human macrophage cells), and PaCa2 (cancer cell lines). Along with the quantitative structure-activity relationship (QSAR) models, for the very first time we have developed and performed quantitative inter cell line uptake specificity (QICLUS) modeling to identify the physicochemical properties, as well as majorly structural fragments responsible for cellular uptake differences between two specific cell lines. The present work provides a comprehensive understanding of the cellular uptake of MNPs and the underlying structural parameters controlling the nano-cellular interactions. This phenomenon has also been analyzed from the QSAR and QICLUS models that concluded the functional groups of surface modifiers like amine, anhydride, halogen atoms, nitro group, acids have the dominating roles for the uptake of MNPs into the cell lines. Thus, the developed models may be used for designing of novel surface modifiers of MNPs of desired characteristics for proper cell-NPs interactions, as well as in the context of virtual screening aspect. Moreover, the MNP-cell interactions can give some idea about the toxicity for target-specific drug delivery treatment as higher cellular uptake is required for specific cells to treat the disease and lower uptake to the neighboring cells for lower toxicity.
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Affiliation(s)
- Probir Kumar Ojha
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Supratik Kar
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
| | - Kunal Roy
- a Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology , Jadavpur University , Kolkata , India
| | - Jerzy Leszczynski
- b Interdisciplinary Nanotoxicity Center, Department of Chemistry, Physics and Atmospheric Sciences , Jackson State University , Jackson , MS , USA
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20
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Alves VM, Golbraikh A, Capuzzi SJ, Liu K, Lam WI, Korn DR, Pozefsky D, Andrade CH, Muratov EN, Tropsha A. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models. J Chem Inf Model 2018; 58:1214-1223. [PMID: 29809005 DOI: 10.1021/acs.jcim.8b00124] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Alexander Golbraikh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Kammy Liu
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Wai In Lam
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Daniel Robert Korn
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Diane Pozefsky
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa , 65000 , Ukraine
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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21
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Luan F, Wang T, Tang L, Zhang S, Cordeiro MNDS. Estimation of the Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis by QSAR Approach. Molecules 2018; 23:molecules23051002. [PMID: 29695132 PMCID: PMC6099972 DOI: 10.3390/molecules23051002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Revised: 04/20/2018] [Accepted: 04/21/2018] [Indexed: 11/16/2022] Open
Abstract
Nowadays, quantitative structure–activity relationship (QSAR) methods have been widely performed to predict the toxicity of compounds to organisms due to their simplicity, ease of implementation, and low hazards. In this study, to estimate the toxicities of substituted aromatic compounds to Tetrahymena pyriformis, the QSAR models were established by the multiple linear regression (MLR) and radial basis function neural network (RBFNN). Unlike other QSAR studies, according to the difference of functional groups (−NO2, −X), the whole dataset was divided into three groups and further modeled separately. The statistical characteristics for the models are obtained as the following: MLR: n = 36, R2 = 0.829, RMS (root mean square) = 0.192, RBFNN: n = 36, R2 = 0.843, RMS = 0.167 for Group 1; MLR: n = 60, R2 = 0.803, RMS = 0.222, RBFNN: n = 60, R2 = 0.821, RMS = 0.193 for Group 2; MLR: n = 31 R2 = 0.852, RMS = 0.192; RBFNN: n = 31, R2 = 0.885, RMS = 0.163 for Group 3, respectively. The results were within the acceptable range, and the models were found to be statistically robust with high external predictivity. Moreover, the models also gave some insight on those characteristics of the structures that most affect the toxicity.
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Affiliation(s)
- Feng Luan
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Ting Wang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Lili Tang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - Shuang Zhang
- College of Chemistry and Chemical Engineering, Yantai University, Yantai 264005, China.
| | - M Natália Dias Soeiro Cordeiro
- LAQV/REQUIMTE, Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
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22
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Satpathy R. Quantitative Structure-Activity Modelling of Toxic Compounds. ENVIRONMENTAL CHEMISTRY FOR A SUSTAINABLE WORLD 2018. [DOI: 10.1007/978-3-319-70166-0_10] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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23
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Pflieger M, Kroflič A. Acute toxicity of emerging atmospheric pollutants from wood lignin due to biomass burning. JOURNAL OF HAZARDOUS MATERIALS 2017; 338:132-139. [PMID: 28549272 DOI: 10.1016/j.jhazmat.2017.05.023] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 05/12/2017] [Accepted: 05/13/2017] [Indexed: 06/07/2023]
Abstract
Guaiacol (2-methoxyphenol) is an important atmospheric pollutant. It is the major component of wood lignin and is essentially emitted to the atmosphere during biomass burning. Its aging in the tropospheric aqueous phase leads to the generation of the following ring-retaining transformation products, also during nighttime: 4-nitroguaiacol, 6-nitroguaiacol, and dinitroguaiacol. This study presents the first toxicological data of guaiacol and its nitro derivatives and reveals their harmful potential for the ecosystem. Applying V. fischeri bioluminescence acute toxicity test, EC50 values range from 16.7 to 103mgL-1 after a 30-min incubation period, which classifies all investigated compounds as 'harmful' according to the European legislation. The investigation of environmentally relevant mixtures did not show significant joint actions between the four studied compounds. Therefore, their concentration addition can be considered for ecotoxicological purposes. However, a synergistic effect between guaiacol and a minor unidentified first-generation product of its aqueous-phase aging was observed and should be taken into account when assessing the reaction mixture toxicity. These results stress the need for further toxicological testing, including organisms of different trophic levels, to better evaluate the environmental hazard of guaiacol and especially its nitro derivatives.
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Affiliation(s)
- Maryline Pflieger
- Laboratory for Environmental Research, University of Nova Gorica, Vipavska 13, SI-5000 Nova Gorica, Slovenia.
| | - Ana Kroflič
- Department of Analytical Chemistry, National Institute of Chemistry, Hajdrihova 19, SI-1000 Ljubljana, Slovenia.
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24
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Tetko IV, Maran U, Tropsha A. Public (Q)SAR Services, Integrated Modeling Environments, and Model Repositories on the Web: State of the Art and Perspectives for Future Development. Mol Inform 2016; 36. [PMID: 27778468 DOI: 10.1002/minf.201600082] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2016] [Accepted: 10/03/2016] [Indexed: 01/08/2023]
Abstract
Thousands of (Quantitative) Structure-Activity Relationships (Q)SAR models have been described in peer-reviewed publications; however, this way of sharing seldom makes models available for the use by the research community outside of the developer's laboratory. Conversely, on-line models allow broad dissemination and application representing the most effective way of sharing the scientific knowledge. Approaches for sharing and providing on-line access to models range from web services created by individual users and laboratories to integrated modeling environments and model repositories. This emerging transition from the descriptive and informative, but "static", and for the most part, non-executable print format to interactive, transparent and functional delivery of "living" models is expected to have a transformative effect on modern experimental research in areas of scientific and regulatory use of (Q)SAR models.
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Affiliation(s)
- Igor V Tetko
- Institute of Structural Biology, Helmholtz Zentrum München -, German Research Center for Environmental Health (GmbH), Institute of Structural Biology, Ingolstädter Landstraße 1, D-, 85764, Neuherberg, Germany.,BigChem GmbH, Ingolstädter Landstraße 1, b. 60w, D-, 85764, Neuherberg, Germany
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14A, Tartu, 50411, Estonia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Butlerov Institute of Chemistry, Kazan Federal University, Kremlyovskaya St. 18, 420008, Kazan, Russia
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25
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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26
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Yin Y, Xu C, Gu S, Li W, Liu G, Tang Y. Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow. Mol Inform 2016; 34:679-88. [PMID: 27490968 DOI: 10.1002/minf.201400119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2014] [Accepted: 03/10/2015] [Indexed: 11/08/2022]
Abstract
Rapid safety assessment is more and more needed for the increasing chemicals both in chemical industries and regulators around the world. The traditional experimental methods couldn't meet the current demand any more. With the development of the information technology and the growth of experimental data, in silico modeling has become a practical and rapid alternative for the assessment of chemical properties, especially for the toxicity prediction of organic chemicals. In this study, a quantitative regression workflow was built by KNIME to predict chemical properties. With this regression workflow, quantitative values of chemical properties can be obtained, which is different from the binary-classification model or multi-classification models that can only give qualitative results. To illustrate the usage of the workflow, two predictive models were constructed based on datasets of Tetrahymena pyriformis toxicity and Aqueous solubility. The qcv (2) and qtest (2) of 5-fold cross validation and external validation for both types of models were greater than 0.7, which implies that our models are robust and reliable, and the workflow is very convenient and efficient in prediction of various chemical properties.
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Affiliation(s)
- Yongmin Yin
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033
| | - Congying Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033
| | - Shikai Gu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033.
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, P.R. China tel: +86-21-64250811; fax: +86-21-64251033.
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27
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Banerjee S, Ghose M, Paul SS, Patra S, Mukherjea KK. A gadolinium(III) complex: synthesis, structure, photophysical profile and its role in the degradation of nitroaromatics. J COORD CHEM 2016. [DOI: 10.1080/00958972.2015.1125475] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
| | - Madhulika Ghose
- Department of Chemistry, Jadavpur University, Kolkata, India
| | | | - Swarup Patra
- Department of Chemistry, Jadavpur University, Kolkata, India
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28
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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29
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Evaluating the role of electron-correlation in the external prediction of the toxicity of nitrobenzenes towards Tetrahymena pyriformis. NEW J CHEM 2016. [DOI: 10.1039/c5nj02552d] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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30
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Yilmaz H, Sizochenko N, Rasulev B, Toropov A, Guzel Y, Kuz'min V, Leszczynska D, Leszczynski J. Amino substituted nitrogen heterocycle ureas as kinase insert domain containing receptor (KDR) inhibitors: Performance of structure–activity relationship approaches. J Food Drug Anal 2015; 23:168-175. [PMID: 28911371 PMCID: PMC9351780 DOI: 10.1016/j.jfda.2015.03.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
A quantitative structure–activity relationship (QSAR) study was performed on a set of amino-substituted nitrogen heterocyclic urea derivatives. Two novel approaches were applied: (1) the simplified molecular input-line entry systems (SMILES) based optimal descriptors approach; and (2) the fragment-based simplex representation of molecular structure (SiRMS) approach. Comparison with the classic scheme of building up the model and balance of correlation (BC) for optimal descriptors approach shows that the BC scheme provides more robust predictions than the classic scheme for the considered pIC50 of the heterocyclic urea derivatives. Comparison of the SMILES-based optimal descriptors and SiRMS approaches has confirmed good performance of both techniques in prediction of kinase insert domain containing receptor (KDR) inhibitory activity, expressed as a logarithm of inhibitory concentration (pIC50) of studied compounds.
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Affiliation(s)
- Hayriye Yilmaz
- Kayseri Vocational School, Biomedical Devices and Technologies, Erciyes University, 38039, Kayseri, Turkey; Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA
| | - Natalia Sizochenko
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA; Odessa I.I. Mechnikov National University, Department of Chemistry, Dvoryanskaya Street, 2, 65082, Odessa, Ukraine
| | - Bakhtiyor Rasulev
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA
| | - Andrey Toropov
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, 20156, Via La Masa 19, Milano, Italy
| | - Yahya Guzel
- Department of Chemistry, Faculty of Science, Erciyes University, 38039, Kayseri, Turkey
| | - Viktor Kuz'min
- Odessa I.I. Mechnikov National University, Department of Chemistry, Dvoryanskaya Street, 2, 65082, Odessa, Ukraine
| | - Danuta Leszczynska
- Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, 39217, USA
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, MS, 39217, USA.
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31
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacol 2015; 284:262-72. [PMID: 25560674 PMCID: PMC4546933 DOI: 10.1016/j.taap.2014.12.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/20/2022]
Abstract
Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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32
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Kroflič A, Grilc M, Grgić I. Does toxicity of aromatic pollutants increase under remote atmospheric conditions? Sci Rep 2015; 5:8859. [PMID: 25748923 PMCID: PMC4352892 DOI: 10.1038/srep08859] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Accepted: 02/05/2015] [Indexed: 01/02/2023] Open
Abstract
Aromatic compounds contribute significantly to the budget of atmospheric pollutants and represent considerable hazard to living organisms. However, they are only rarely included into atmospheric models which deviate substantially from field measurements. A powerful experimental-simulation tool for the assessment of the impact of low- and semi-volatile aromatic pollutants on the environment due to their atmospheric aqueous phase aging has been developed and introduced for the first time. The case study herein reveals that remote biotopes might be the most damaged by wet urban guaiacol-containing biomass burning aerosols. It is shown that only after the primary pollutant guaiacol has been consumed, its probably most toxic nitroaromatic product is largely formed. Revising the recent understanding of atmospheric aqueous phase chemistry, which is mostly concerned with the radical nitration mechanisms, the observed phenomenon is mainly attributed to the electrophilic nitrogen-containing reactive species. Here, their intriguing role is closely inspected and discussed from the ecological perspective.
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Affiliation(s)
- Ana Kroflič
- Analytical Chemistry Laboratory, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Miha Grilc
- Laboratory of Catalysis and Chemical Reaction Engineering, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Irena Grgić
- Analytical Chemistry Laboratory, National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
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33
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Tian D, Zheng W, He G, Zheng Y, Andersen ME, Tan H, Qu W. Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC-MS spectrum with partial least squares regression. ENVIRONMENTAL RESEARCH 2015; 137:391-397. [PMID: 25614340 DOI: 10.1016/j.envres.2014.12.027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Revised: 12/29/2014] [Accepted: 12/30/2014] [Indexed: 06/04/2023]
Abstract
Complex mixture exposures, such as those associated with water sources, are an important issue in health risk assessment. This study assessed the cytotoxicity of chemical mixtures extracted from water sources in regions of the Huai River Basin with high cancer incidences and built statistical models of cytotoxicity based on pollution profiles that were measured with gas chromatography-mass spectrometry (GC-MS). Both surface and ground waters were collected from rural water sources of Shenqiu County, Henan Province of China from 2008 to 2011 and extracted with XAD-2 resigns. Cytotoxicity was evaluated with Chinese hamster ovary K1 (CHO-K1) cells and compared against the pollution profiles of the extracts. IC50 of water samples ranged from 0.023 to 0.338L-eq/mL. The pollutants in waters determined by GC-MS are complex and some of the compounds that contributed to cytotoxicity lack toxicity data. A partial least squares (PLS) regression model of cytotoxicity was built based on linear aggregation of predictor variables (i.e., peaks for single compounds in the gas chromatograms). The PLS model contains 2 PLS factors extracted from 141 variables. The model was validated internally with training data permutation and externally with a test sample. The model explained 92% of the cytotoxicity in the training samples and 40% in the test sample. This approach provides a general, rapid method for relating water toxicity to GC-MS chromatograms and for predicting the compounds that contribute most to toxicity.
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Affiliation(s)
- Dajun Tian
- Key Laboratory of Public Health and Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Yi Xue Yuan Road 138, Shanghai 200032, China
| | - Weiwei Zheng
- Key Laboratory of Public Health and Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Yi Xue Yuan Road 138, Shanghai 200032, China
| | - Gengsheng He
- Key Laboratory of the Public Health Safety, Ministry of Education, Department of Nutrition and Food Hygiene, Fudan University, Shanghai 200032, China
| | - Yuxin Zheng
- Chinese Center for Disease Control and Prevention, Nan Wei Road 29, Beijing 100050, China
| | - Melvin E Andersen
- Institute for Chemical Safety Sciences, The Hamner Institutes for Health Sciences, Research Triangle Park, NC 27709, USA
| | - Hui Tan
- Key Laboratory of the Public Health Safety, Ministry of Education, Department of Childhood and Adolescent, Fudan University, Shanghai 200032, China
| | - Weidong Qu
- Key Laboratory of Public Health and Safety, Ministry of Education, Department of Environmental Health, School of Public Health, Fudan University, Yi Xue Yuan Road 138, Shanghai 200032, China.
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34
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicol Appl Pharmacol 2015; 284:273-80. [PMID: 25560673 PMCID: PMC4408226 DOI: 10.1016/j.taap.2014.12.013] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/02/2022]
Abstract
Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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Sizochenko N, Rasulev B, Gajewicz A, Mokshyna E, Kuz'min VE, Leszczynski J, Puzyn T. Causal inference methods to assist in mechanistic interpretation of classification nano-SAR models. RSC Adv 2015. [DOI: 10.1039/c5ra11399g] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Causal inference methods are helpful with finding possible biological mechanisms of nanoparticles' toxicity.
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Affiliation(s)
- Natalia Sizochenko
- Laboratory of Environmental Chemometrics
- Faculty of Chemistry
- University of Gdansk
- Gdansk
- Poland
| | - Bakhtiyor Rasulev
- Interdisciplinary Center for Nanotoxicity
- Department of Chemistry
- Jackson State University
- Jackson
- USA
| | - Agnieszka Gajewicz
- Laboratory of Environmental Chemometrics
- Faculty of Chemistry
- University of Gdansk
- Gdansk
- Poland
| | - Elena Mokshyna
- A.V. Bogatsky Physical–Chemical Institute National Academy of Sciences of Ukraine
- Odessa
- Ukraine
| | - Victor E. Kuz'min
- A.V. Bogatsky Physical–Chemical Institute National Academy of Sciences of Ukraine
- Odessa
- Ukraine
| | - Jerzy Leszczynski
- Interdisciplinary Center for Nanotoxicity
- Department of Chemistry
- Jackson State University
- Jackson
- USA
| | - Tomasz Puzyn
- Laboratory of Environmental Chemometrics
- Faculty of Chemistry
- University of Gdansk
- Gdansk
- Poland
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Sizochenko N, Rasulev B, Gajewicz A, Kuz'min V, Puzyn T, Leszczynski J. From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles. NANOSCALE 2014; 6:13986-13993. [PMID: 25317542 DOI: 10.1039/c4nr03487b] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the "liquid drop" model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.
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Affiliation(s)
- Natalia Sizochenko
- I. I. Mechnikov Odessa National University, Department of Chemistry, Dvoryanskaya str., 2, 65082, Odessa, Ukraine
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Singh KP, Gupta S. In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches. Toxicol Appl Pharmacol 2014; 275:198-212. [PMID: 24463095 DOI: 10.1016/j.taap.2014.01.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2013] [Revised: 01/04/2014] [Accepted: 01/13/2014] [Indexed: 02/03/2023]
Abstract
Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.
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Affiliation(s)
- Kunwar P Singh
- Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi 110 001, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India.
| | - Shikha Gupta
- Academy of Scientific and Innovative Research, Anusandhan Bhawan, Rafi Marg, New Delhi 110 001, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India
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Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. J Chem Inf Model 2013; 53:1915-22. [PMID: 23809015 PMCID: PMC3779696 DOI: 10.1021/ci400216q] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds toward the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in the top 10% among all the participants. In parallel, MedusaDock, designed to predict reliable docking poses, was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock- and QSAR-predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.
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Affiliation(s)
- Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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Doucet JP, Doucet-Panaye A, Devillers J. Structure-activity relationship study of trifluoromethylketones: inhibitors of insect juvenile hormone esterase. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:481-499. [PMID: 23721304 DOI: 10.1080/1062936x.2013.792499] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The juvenile hormone esterase (JHE) regulates juvenile hormone titre in insect hemolymph during its larval development. It has been suggested that JHE could be targeted for use in insect control. This enzyme can also be considered as involved in the phenomenon of endocrine disruption by xenobiotics in beneficial insects. Consequently, there is a need to know the characteristics of the molecules able to act on the JHE. Trifluoromethylketones (TFKs) are the most potent JHE inhibitors found to date and different quantitative structure-activity relationships (QSARs) have been derived for this group of chemicals. In this context, a set of 181 TFKs (118 active and 63 inactive compounds), tested on Trichoplusia ni for their JHE inhibition activity and described by physico-chemical descriptors, was split into different training and test sets to derive structure-activity relationship (SAR) models from support vector classification (SVC). A SVC model including 88 descriptors and derived from a Gaussian kernel was selected for its predictive performances. Another model computed only with 13 descriptors was also selected due to its mechanistic interpretability. This study clearly illustrates the difficulty in capturing the essential structural characteristics of the TFKs explaining their JHE inhibitory activity.
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Affiliation(s)
- J P Doucet
- ITODYS, UMR 7086, Université Paris 7, Paris, France.
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Payne MP, Button WG. Prediction of acute aquatic toxicity in Tetrahymena pyriformis--'Eco-Derek', a knowledge-based system approach. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:439-460. [PMID: 23600431 DOI: 10.1080/1062936x.2013.783507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
A 'proof-of-concept' version of a software tool for making transparent predictions of acute aquatic toxicity has been developed. It is primarily limited to semi-quantitative predictions in one species, the ciliated protozoan, Tetrahymena pyriformis. A freely available system, 'Eco-Derek', was derived by adapting a well-established, knowledge-based structure-activity and reasoning platform (Derek for Windows, Lhasa Limited). The Derek reasoning code was modified to express potency rather than confidence. Structure-activity relationship (SAR) development utilised a curated version of a published dataset, supplemented with the CADASTER Challenge datasets. Forty-five structural alerts were produced. The dependence on log P was examined for each alert and entered into the system as qualitative reasoning rules specifying the predicted potency as Very Low, Low, Moderate, High or Very High. Evaluation studies showed: (a) moderate accuracy for the training set but low accuracy for an external test set; (b) non-linearity in the toxicity-log P relationship for chemicals without identified structural alerts; (c) insufficient differentiation of substituent effects in some of the reactivity-based structural alerts resulting in too few chemicals predicted with Very High toxicity; and (d) the need for additional structural alerts covering polar narcosis and less common reactive or metabolically activated chemical functionality.
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Devillers J, Pandard P, Richard B. External validation of structure-biodegradation relationship (SBR) models for predicting the biodegradability of xenobiotics. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:979-993. [PMID: 24313438 DOI: 10.1080/1062936x.2013.848632] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Biodegradation is an important mechanism for eliminating xenobiotics by biotransforming them into simple organic and inorganic products. Faced with the ever growing number of chemicals available on the market, structure-biodegradation relationship (SBR) and quantitative structure-biodegradation relationship (QSBR) models are increasingly used as surrogates of the biodegradation tests. Such models have great potential for a quick and cheap estimation of the biodegradation potential of chemicals. The Estimation Programs Interface (EPI) Suite™ includes different models for predicting the potential aerobic biodegradability of organic substances. They are based on different endpoints, methodologies and/or statistical approaches. Among them, Biowin 5 and 6 appeared the most robust, being derived from the largest biodegradation database with results obtained only from the Ministry of International Trade and Industry (MITI) test. The aim of this study was to assess the predictive performances of these two models from a set of 356 chemicals extracted from notification dossiers including compatible biodegradation data. Another set of molecules with no more than four carbon atoms and substituted by various heteroatoms and/or functional groups was also embodied in the validation exercise. Comparisons were made with the predictions obtained with START (Structural Alerts for Reactivity in Toxtree). Biowin 5 and Biowin 6 gave satisfactorily prediction results except for the prediction of readily degradable chemicals. A consensus model built with Biowin 1 allowed the diminution of this tendency.
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Cassidy-Hanley DM. Tetrahymena in the laboratory: strain resources, methods for culture, maintenance, and storage. Methods Cell Biol 2012; 109:237-76. [PMID: 22444147 PMCID: PMC3608402 DOI: 10.1016/b978-0-12-385967-9.00008-6] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2022]
Abstract
The ciliated protozoan Tetrahymena thermophila has been an important model system for biological research for many years. During that time, a variety of useful strains, including highly inbred stocks, a collection of diverse mutant strains, and wild cultivars from a variety of geographical locations have been identified. In addition, thanks to the efforts of many different laboratories, optimal conditions for growth, maintenance, and storage of Tetrahymena have been worked out. To facilitate the efficient use of Tetrahymena, especially by those new to the system, this chapter presents a brief description of many available Tetrahymena strains and lists possible resources for obtaining viable cultures of T. thermophila and other Tetrahymena species. Descriptions of commonly used media, methods for cell culture and maintenance, and protocols for short- and long-term storage are also presented.
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Affiliation(s)
- Donna M Cassidy-Hanley
- Department of Microbiology and Immunology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA
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Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. J Chem Inf Model 2010; 50:1189-204. [PMID: 20572635 DOI: 10.1021/ci100176x] [Citation(s) in RCA: 470] [Impact Index Per Article: 33.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Denis Fourches
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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