1
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Iduoku K, Ngongang M, Kulathunga J, Daghighi A, Casanola-Martin G, Simsek S, Rasulev B. Phenolic Acid-β-Cyclodextrin Complexation Study to Mask Bitterness in Wheat Bran: A Machine Learning-Based QSAR Study. Foods 2024; 13:2147. [PMID: 38998653 PMCID: PMC11241027 DOI: 10.3390/foods13132147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/14/2024] Open
Abstract
The need to solvate and encapsulate hydro-sensitive molecules drives noticeable trends in the applications of cyclodextrins in the pharmaceutical industry, in foods, polymers, materials, and in agricultural science. Among them, β-cyclodextrin is one of the most used for the entrapment of phenolic acid compounds to mask the bitterness of wheat bran. In this regard, there is still a need for good data and especially for a robust predictive model that assesses the bitterness masking capabilities of β-cyclodextrin for various phenolic compounds. This study uses a dataset of 20 phenolic acids docked into the β-cyclodextrin cavity to generate three different binding constants. The data from the docking study were combined with topological, topographical, and quantum-chemical features from the ligands in a machine learning-based structure-activity relationship study. Three different models for each binding constant were computed using a combination of the genetic algorithm (GA) and multiple linear regression (MLR) approaches. The developed ML/QSAR models showed a very good performance, with high predictive ability and correlation coefficients of 0.969 and 0.984 for the training and test sets, respectively. The models revealed several factors responsible for binding with cyclodextrin, showing positive contributions toward the binding affinity values, including such features as the presence of six-membered rings in the molecule, branching, electronegativity values, and polar surface area.
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Affiliation(s)
- Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Marvellous Ngongang
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Jayani Kulathunga
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Department of Multidisciplinary Studies, Faculty of Urban and Aquatic Bioresources, University of Sri Jayewardenepura, Gangodawila, Nugegoda 10250, Sri Lanka
| | - Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Senay Simsek
- Cereal Science Graduate Program, Department of Plant Sciences, North Dakota State University, Fargo, ND 58102, USA (S.S.)
- Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, West Lafayette, IN 47907, USA
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
- Biomedical Engineering Program, North Dakota State University, Fargo, ND 58102, USA
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2
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Groover KE, Randall JR, Davies BW. Development of a Selective and Stable Antimicrobial Peptide. ACS Infect Dis 2024; 10:2151-2160. [PMID: 38712889 PMCID: PMC11185160 DOI: 10.1021/acsinfecdis.4c00142] [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: 02/23/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Antimicrobial peptides (AMPs) are presented as potential scaffolds for antibiotic development due to their desirable qualities including broad-spectrum activity, rapid action, and general lack of susceptibility to current resistance mechanisms. However, they often lose antibacterial activity under physiological conditions and/or display mammalian cell toxicity, which limits their potential use. Identification of AMPs that overcome these barriers will help develop rules for how this antibacterial class can be developed to treat infection. Here we describe the development of our novel synthetic AMP, from discovery through in vivo application. Our evolved AMP, DTr18-dab, has broad-spectrum antibacterial activity and is nonhemolytic. It is active against planktonic bacteria and biofilm, is unaffected by colistin resistance, and importantly is active in both human serum and a Galleria mellonella infection model. Several modifications, including the incorporation of noncanonical amino acids, were used to arrive at this robust sequence. We observed that the impact on antibacterial activity with noncanonical amino acids was dependent on assay conditions and therefore not entirely predictable. Overall, our results demonstrate how a relatively weak lead can be developed into a robust AMP with qualities important for potential therapeutic translation.
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Affiliation(s)
- Kyra E. Groover
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Justin R. Randall
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
| | - Bryan W. Davies
- Department
of Molecular Biosciences, The University
of Texas at Austin, Austin, Texas 78712, United States
- John
Ring LaMontagne Center for Infectious Diseases, The University of Texas at Austin, Austin, Texas 78712, United States
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3
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Zhuravskyi Y, Iduoku K, Erickson ME, Karuth A, Usmanov D, Casanola-Martin G, Sayfiyev MN, Ziyaev DA, Smanova Z, Mikolajczyk A, Rasulev B. Quantitative Structure-Permittivity Relationship Study of a Series of Polymers. ACS MATERIALS AU 2024; 4:195-203. [PMID: 38496050 PMCID: PMC10941280 DOI: 10.1021/acsmaterialsau.3c00079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/01/2023] [Accepted: 12/13/2023] [Indexed: 03/19/2024]
Abstract
Dielectric constant is an important property which is widely utilized in many scientific fields and characterizes the degree of polarization of substances under the external electric field. In this work, a structure-property relationship of the dielectric constants (ε) for a diverse set of polymers was investigated. A transparent mechanistic model was developed with the application of a machine learning approach that combines genetic algorithm and multiple linear regression analysis, to obtain a mechanistically explainable and transparent model. Based on the evaluation conducted using various validation criteria, four- and eight-variable models were proposed. The best model showed a high predictive performance for training and test sets, with R2 values of 0.905 and 0.812, respectively. Obtained statistical performance results and selected descriptors in the best models were analyzed and discussed. With the validation procedures applied, the models were proven to have a good predictive ability and robustness for further applications in polymer permittivity prediction.
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Affiliation(s)
- Yevhenii Zhuravskyi
- Department of Technology of Organic Products, Lviv Polytechnic National University, Lviv 79013, Ukraine
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Meade E Erickson
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Anas Karuth
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Durbek Usmanov
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Institute of the Chemistry of Plant Substances AS RUz, Tashkent 100170, Uzbekistan
| | - Gerardo Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Maqsud N Sayfiyev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Dilshod A Ziyaev
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
| | - Alicja Mikolajczyk
- Laboratory of Environmental Chemometrics, Institute for Environmental and Human Health Protection, Faculty of Chemistry, University of Gdansk, Gdansk 80-308, Poland
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan
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4
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Diem-Tran PT, Ho TT, Tuan NV, Bao LQ, Phuong HT, Chau TTG, Minh HTB, Nguyen CT, Smanova Z, Casanola-Martin GM, Rasulev B, Pham-The H, Cuong LCV. Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands. TOXICS 2023; 11:595. [PMID: 37505560 PMCID: PMC10383909 DOI: 10.3390/toxics11070595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/02/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023]
Abstract
Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβML) and potentiometric sensitivity (PSML) of 200 ligands in complexes with the heavy metal ions Cu2+, Cd2+, and Pb2+. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R2) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R2 of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.
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Affiliation(s)
- Phan Thi Diem-Tran
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Tue-Tam Ho
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Nguyen-Van Tuan
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le-Quang Bao
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Ha Tran Phuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Trinh Thi Giao Chau
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Hoang Thi Binh Minh
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
| | - Cong-Truong Nguyen
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Zulayho Smanova
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
| | | | - Bakhtiyor Rasulev
- Department of Chemistry, National University of Uzbekistan after Mirzo Ulugbek, Tashkent 100012, Uzbekistan
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, USA
| | - Hai Pham-The
- Faculty of Pharmaceutical Chemistry and Technology, Hanoi University of Pharmacy, 13-15 Le Thanh Tong, Hoan Kiem, Hanoi 10000, Vietnam
| | - Le Canh Viet Cuong
- Mientrung Institute for Scientific Research, Vietnam National Museum of Nature, Vietnam Academy of Science and Technology, Hue 53000, Vietnam
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5
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Xu Z, Chughtai H, Tian L, Liu L, Roy JF, Bayen S. Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis. Talanta 2023; 253:123861. [PMID: 36095943 DOI: 10.1016/j.talanta.2022.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/13/2022]
Abstract
Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.
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Affiliation(s)
- Ziyun Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Hamza Chughtai
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Tian
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lan Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
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6
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Zwickl CM, Graham J, Jolly R, Bassan A, Ahlberg E, Amberg A, Anger LT, Barton-Maclaren T, Beilke L, Bellion P, Brigo A, Cronin MT, Custer L, Devlin A, Burleigh-Flayers H, Fish T, Glover K, Glowienke S, Gromek K, Jones D, Karmaus A, Kemper R, Piparo EL, Madia F, Martin M, Masuda-Herrera M, McAtee B, Mestre J, Milchak L, Moudgal C, Mumtaz M, Muster W, Neilson L, Patlewicz G, Paulino A, Roncaglioni A, Ruiz P, Suarez D, Szabo DT, Valentin JP, Vardakou I, Woolley D, Myatt G. Principles and Procedures for Assessment of Acute Toxicity Incorporating In Silico Methods. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2022; 24:100237. [PMID: 36818760 PMCID: PMC9934006 DOI: 10.1016/j.comtox.2022.100237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Acute toxicity in silico models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an in silico analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed. This framework combines results from different sources including in silico methods and in vitro or in vivo experiments. In silico methods that can assist the prediction of in vivo outcomes (i.e., LD50) are analyzed concluding that predictions obtained using in silico approaches are now well-suited for reliably supporting assessment of LD50-based acute toxicity for the purpose of GHS classification. A general overview is provided of the endpoints from in vitro studies commonly evaluated for predicting acute toxicity (e.g., cytotoxicity/cytolethality as well as assays targeting specific mechanisms). The increased understanding of pathways and key triggering mechanisms underlying toxicity and the increased availability of in vitro data allow for a shift away from assessments solely based on endpoints such as LD50, to mechanism-based endpoints that can be accurately assessed in vitro or by using in silico prediction models. This paper also highlights the importance of an expert review of all available information using weight-of-evidence considerations and illustrates, using a series of diverse practical use cases, how in silico approaches support the assessment of acute toxicity.
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Affiliation(s)
| | - Jessica Graham
- Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA
| | - Robert Jolly
- Eli Lilly and Company, Indianapolis, IN 46285, USA
| | - Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Ernst Ahlberg
- Universal Prediction AB, Gothenburg, Sweden
- Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Tara Barton-Maclaren
- Healthy Environments and Consumer Safety Branch, Health Canada / Government of Canada
| | - Lisa Beilke
- Toxicology Solutions, Inc., 10531 4S Commons Dr. #594, San Diego, CA 92127, USA
| | - Phillip Bellion
- Boehringer Ingelheim Animal Health, Binger Str. 128, 55216 Ingelheim am Rhein, Germany
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | | | - Amy Devlin
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | - Trevor Fish
- Nelson Laboratories, Salt Lake City, Utah, USA
| | | | | | | | - David Jones
- MHRA, 10 South Colonnade, Canary Wharf, London E14 4PU
| | - Agnes Karmaus
- Integrated Laboratory Systems, LLC, Morrisville, NC, USA
| | | | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research, Lausanne, Switzerland
| | - Federica Madia
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | | | | | | | - Jordi Mestre
- IMIM Institut Hospital Del Mar d’Investigacions Mèdiques and Universitat Pompeu Fabra, Doctor Aiguader 88, Parc de Recerca Biomèdica, 08003 Barcelona, Spain
- Chemotargets SL, Baldiri Reixac 4, Parc Científic de Barcelona, 08028 Barcelona, Spain
| | | | | | - Moiz Mumtaz
- Office of the Associate Director for Science, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, GA 30333, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Roche Innovation Center Basel, Grenzacherstrasse 124, 4070, Basel, Switzerland
| | | | - Grace Patlewicz
- Centre for Computational Toxicology and Exposure (CCTE), US Environmental Protection Agency, Research Triangle Park, NC, USA
| | | | - Alessandra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Patricia Ruiz
- Centers for Disease Control and Prevention (CDC), Atlanta, GA 30341, USA
| | - Diana Suarez
- FSTox Consulting LTD, 2 Brooks Road Raunds Wellingborough NN9 6NS
| | | | - Jean-Pierre Valentin
- UCB-Biopharma SRL, Development Science, Avenue de l’industrie, Braine l’Alleud, Wallonia, Belgium
| | - Ioanna Vardakou
- British American Tobacco (Investments) Ltd., R&D Centre, Southampton, Hampshire SO15 8TL, UK
| | | | - Glenn Myatt
- Instem, 1393 Dublin Rd, Columbus, OH 43215, USA
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7
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The Hydrolysis Rate of Paraoxonase-1 Q and R Isoenzymes: An In Silico Study Based on In Vitro Data. Molecules 2022; 27:molecules27206780. [PMID: 36296373 PMCID: PMC9607273 DOI: 10.3390/molecules27206780] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 09/30/2022] [Accepted: 10/05/2022] [Indexed: 11/17/2022] Open
Abstract
Human serum paraoxonase-1 (PON1) is an important hydrolase-type enzyme found in numerous tissues. Notably, it can exist in two isozyme-forms, Q and R, that exhibit different activities. This study presents an in silico (QSAR, Docking, MD and QM/MM) study of a set of compounds on the activity towards the PON1 isoenzymes (QPON1 and RPON1). Different rates of reaction for the Q and R isoenzymes were analyzed by modelling the effect of Q192R mutation on active sites. It was concluded that the Q192R mutation is not even close to the active site, while it is still changing the geometry of it. Using the combined genetic algorithm with multiple linear regression (GA-MLR) technique, several QSAR models were developed and relative activity rates of the isozymes of PON1 explained. From these, two QSAR models were selected, one each for the QPON1 and RPON1. Best selected models are four-variable MLR models for both Q and R isozymes with squared correlation coefficient R2 values of 0.87 and 0.83, respectively. In addition, the applicability domain of the models was analyzed based on the Williams plot. The results were discussed in the light of the main factors that influence the hydrolysis activity of the PON1 isozymes.
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8
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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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9
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Gooch A, Sizochenko N, Rasulev B, Gorb L, Leszczynski J. In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2017; 36:2227-2233. [PMID: 28169452 DOI: 10.1002/etc.3761] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 11/01/2016] [Accepted: 02/06/2017] [Indexed: 06/06/2023]
Abstract
The toxicity data of 90 nitroaromatic compounds related to their 50% lethal dose concentration for rats (LD50) were analyzed to develop quantitative structure-activity relationship (QSAR) models. Quantum-chemically calculated descriptors together with molecular descriptors generated by DRAGON, PaDEL, and HiT-QSAR software were utilized to build QSAR models. Quality and validity of the models were determined by internal and external validation techniques. The results show that the toxicity of nitroaromatic compounds depends on various factors, such as the number of nitro-groups, the topological state, and the presence of certain structural fragments. The developed models based on the largest (to date) dataset of nitroaromatics in vivo toxicity showed a good predictive ability. The results provide important input that could be applied in a preliminary assessment of nitroaromatic compounds' toxicity to mammals. Environ Toxicol Chem 2017;36:2227-2233. © 2017 SETAC.
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Affiliation(s)
- Aminah Gooch
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| | - Natalia Sizochenko
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
| | - Bakhtiyor Rasulev
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota, USA
| | - Leonid Gorb
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
- HX5, Vicksburg, Mississippi, USA
| | - Jerzy Leszczynski
- Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, USA
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10
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Wang Y, Wang S, Feng XN, Yan LC, Zheng SS, Wang Y, Zhao YH. The impact of exposure route for class-based compounds: a comparative approach of lethal toxicity data in rodent models. Drug Chem Toxicol 2017; 41:95-104. [DOI: 10.1080/01480545.2017.1320405] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Yu Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
- School of Chemistry and Environmental Engineering, Yancheng Teachers University, Yancheng, Jiangsu, China
| | - Shuo Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
| | - Xiao N. Feng
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
| | - Li C. Yan
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
| | - Shan S. Zheng
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
| | - Yue Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
| | - Yuan H. Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin, China
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Recent Developments in 3D QSAR and Molecular Docking Studies of Organic and Nanostructures. HANDBOOK OF COMPUTATIONAL CHEMISTRY 2017. [PMCID: PMC7123761 DOI: 10.1007/978-3-319-27282-5_54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
The development of quantitative structure–activity relationship (QSAR) methods is going very fast for the last decades. OSAR approach already plays an important role in lead structure optimization, and nowadays, with development of big data approaches and computer power, it can even handle a huge amount of data associated with combinatorial chemistry. One of the recent developments is a three-dimensional QSAR, i.e., 3D QSAR. For the last two decades, 3D-OSAR has already been successfully applied to many datasets, especially of enzyme and receptor ligands. Moreover, quite often 3D QSAR investigations are going together with protein–ligand docking studies and this combination works synergistically. In this review, we outline recent advances in development and applications of 3D QSAR and protein–ligand docking approaches, as well as combined approaches for conventional organic compounds and for nanostructured materials, such as fullerenes and carbon nanotubes.
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Rasulev B, Jabeen F, Stafslien S, Chisholm BJ, Bahr J, Ossowski M, Boudjouk P. Polymer Coating Materials and Their Fouling Release Activity: A Cheminformatics Approach to Predict Properties. ACS APPLIED MATERIALS & INTERFACES 2017; 9:1781-1792. [PMID: 27982587 DOI: 10.1021/acsami.6b12766] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
A novel cheminformatics-based approach has been employed to investigate a set of polymer coating materials designed to mitigate the accumulation of marine biofouling on surfaces immersed in the sea. Specifically, a set of 27 nontoxic, amphiphilic polysiloxane-based polymer coatings was synthesized using a combinatorial, high-throughput approach and characterized for fouling-release (FR) activity toward a number of relevant marine fouling organisms, including bacteria, microalgae, and adult barnacles. In order to model these complex systems adequately, a new computational technique was used in which all investigated polymer-based coating materials were considered as mixture systems comprising several compositional variables at a range of concentrations. By applying a combination of methodologies for mixture systems and a quantitative structure-activity relationship approach (QSAR), seven unique QSAR models were developed that were able to successfully predict the desired FR properties. Furthermore, the developed models identified several significant descriptors responsible for FR activity of investigated polymer-based coating materials, with correlation coefficients ranging from rtest2 = 0.63 to 0.94. The computational models derived from this study may serve as a powerful set of tools to predict optimal combinations of source components to produce amphiphilic polysiloxane-based coating systems with effective, broad-spectrum FR properties.
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Affiliation(s)
- Bakhtiyor Rasulev
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota, United States
| | - Farukh Jabeen
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
| | - Shane Stafslien
- Research and Creative Activities, North Dakota State University , Fargo, North Dakota, United States
| | - Bret J Chisholm
- Department of Coatings and Polymeric Materials, North Dakota State University , Fargo, North Dakota, United States
| | - James Bahr
- Research and Creative Activities, North Dakota State University , Fargo, North Dakota, United States
| | - Martin Ossowski
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
| | - Philip Boudjouk
- Center for Computationally Assisted Science and Technology, North Dakota State University , Fargo, North Dakota, United States
- Department of Chemistry and Biochemistry, North Dakota State University , Fargo, North Dakota, United States
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Hamadache M, Benkortbi O, Hanini S, Amrane A, Khaouane L, Si Moussa C. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction. JOURNAL OF HAZARDOUS MATERIALS 2016; 303:28-40. [PMID: 26513561 DOI: 10.1016/j.jhazmat.2015.09.021] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 09/07/2015] [Accepted: 09/09/2015] [Indexed: 06/05/2023]
Abstract
Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.
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Affiliation(s)
- Mabrouk Hamadache
- Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
| | - Othmane Benkortbi
- Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
| | - Salah Hanini
- Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
| | - Abdeltif Amrane
- Ecole Nationale Supérieure de Chimie de Rennes, Université de Rennes 1, CNRS, UMR 6226, 11 allée de Beaulieu, CS 50837, 35708 Rennes Cedex 7, France.
| | - Latifa Khaouane
- Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
| | - Cherif Si Moussa
- Laboratoire des Biomatériaux et Phénomènes de Transport (LBMPT), Université de Médéa, Quartier Ain D'heb, 26000 Medea, Algeria.
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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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15
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A new approach for accurate prediction of toxicity of amino compounds. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2014. [DOI: 10.1007/s13738-014-0506-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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16
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Turabekova MA, Rasulev BF, Dzhakhangirov FN, Toropov AA, Leszczynska D, Leszczynski J. Aconitum and delphinium diterpenoid alkaloids of local anesthetic activity: comparative QSAR analysis based on GA-MLRA/PLS and optimal descriptors approach. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2014; 32:213-238. [PMID: 25226219 DOI: 10.1080/10590501.2014.938886] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The duration of anesthesia (related to protein binding of a drug) and the onset time (determined by the pKa) are important characteristics in assessment of local anesthetic agents. They are known to be affected by a number of factors. Early studies of antiarrhythmic diterpenoid alkaloids from plants Aconitum and Delphinium suggested that they possess local anesthetic activity due to their ability to suppress sodium currents of excited membranes. In this study we utilized toxicity, duration, and onset of action as endpoints to construct Quantitative Structure-Activity Relationship (QSAR) models for the series of 34 diterpenoid alkaloids characterized by local anesthetic activity using genetic algorithm-based multiple linear regression analysis/partial least squares and simplified molecular input line entry system (SMILES)-based optimal descriptors approach. The developed QSAR models correctly reflected factors that determine three endpoints of interest. Toxicity correlates with descriptors describing partition and reactivity of compounds. The duration of anesthesia was encoded by the parameters defining the ability of a compound to bind at the receptor site. The size and number of H-bond acceptor atoms were found not to favor the speed of onset, while topographic electronic descriptor demonstrated strong positive effect on it. SMILES-based optimal descriptors approach resulted in overall improvement of models. This approach was shown to be more sensitive to structural peculiarities of molecules than regression methods. The results clearly indicate that obtained QSARs are able to provide distinct rationales for compounds optimization with respect to particular endpoint.
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Affiliation(s)
- M A Turabekova
- a Interdisciplinary Center for Nanotoxicity , Jackson State University , Jackson , Mississippi , USA
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Juretic D, Kusic H, Papic A, Smidt M, Jezovita O, Peternel I, Bozic AL. Modeling of photodegradation kinetics of aromatic pollutants in water matrix. J Photochem Photobiol A Chem 2013. [DOI: 10.1016/j.jphotochem.2013.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Kusic H, Koprivanac N, Bozic AL. Environmental aspects on the photodegradation of reactive triazine dyes in aqueous media. J Photochem Photobiol A Chem 2013. [DOI: 10.1016/j.jphotochem.2012.11.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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19
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Rasulev B, Turabekova M, Gorska M, Kulig K, Bielejewska A, Lipkowski J, Leszczynski J. Use of quantitative structure-enantioselective retention relationship for the liquid chromatography chiral separation prediction of the series of pyrrolidin-2-one compounds. Chirality 2011; 24:72-7. [DOI: 10.1002/chir.21028] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Revised: 07/27/2011] [Accepted: 08/14/2011] [Indexed: 11/05/2022]
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20
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Koleva YK, Cronin MT, Madden JC, Schwöbel JA. Modelling acute oral mammalian toxicity. 1. Definition of a quantifiable baseline effect. Toxicol In Vitro 2011; 25:1281-93. [DOI: 10.1016/j.tiv.2011.04.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2010] [Revised: 03/10/2011] [Accepted: 04/14/2011] [Indexed: 11/24/2022]
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