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Mora Lagares L, Vračko M. Ecotoxicological Evaluation of Bisphenol A and Alternatives: A Comprehensive In Silico Modelling Approach. J Xenobiot 2023; 13:719-739. [PMID: 38132707 PMCID: PMC10744758 DOI: 10.3390/jox13040046] [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: 10/19/2023] [Revised: 11/17/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
Bisphenol A (BPA), a compound widely used in industrial applications, has raised concerns due to its environmental impact. As a key component in the manufacture of polycarbonate plastics and epoxy resins used in many consumer products, concerns about potential harm to human health and the environment are unavoidable. This study seeks to address these concerns by evaluating a range of potential BPA alternatives, focusing on their ecotoxicological properties. The research examines 76 bisphenols, including BPA derivatives, using a variety of in silico ecotoxicological models, although it should be noted that these models were not developed exclusively for this particular class of compounds. Consequently, interpretations should be made with caution. The results of this study highlight specific compounds of potential environmental concern and underscore the need to develop more specific models for BPA alternatives that will allow for more accurate and reliable assessment.
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Affiliation(s)
- Liadys Mora Lagares
- Laboratory for Cheminformatics, Theory Department, National Institute of Chemistry, 1000 Ljubljana, Slovenia;
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2
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Zushi Y. Direct Prediction of Physicochemical Properties and Toxicities of Chemicals from Analytical Descriptors by GC-MS. Anal Chem 2022; 94:9149-9157. [PMID: 35700270 PMCID: PMC9246259 DOI: 10.1021/acs.analchem.2c01667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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With advances in
machine learning (ML) techniques, the quantitative
structure–activity relationship (QSAR) approach is becoming
popular for evaluating chemicals. However, the QSAR approach requires
that the chemical structure of the target compound is known and that
it should be convertible to molecular descriptors. These requirements
lead to limitations in predicting the properties and toxicities of
chemicals distributed in the environment as in the PubChem database;
the structural information on only 14% of compounds is available.
This study proposes a new ML-based QSAR approach that can predict
the properties and toxicities of compounds using analytical descriptors
of mass spectrum and retention index obtained via gas chromatography–mass
spectrometry without requiring exact structural information. The model
was developed based on the XGBoost ML method. The root-mean-square
errors (RMSEs) for log Ko-w, log (molecular weight), melting point,
boiling point, log (vapor pressure), log (water solubility), log (LD50) (rat, oral), and log (LD50) (mouse, oral) are
0.97, 0.052, 51, 23, 0.74, 1.1, 0.74, and 0.6, respectively. The model
performed well on a chemical standard mixture measurement, with similar
results to those of model validation. It also performed well on a
measurement of contaminated oil with spectral deconvolution. These
results indicate that the model is suitable for investigating unknown-structured
chemicals detected in measurements. Any online user can execute the
model through a web application named Detective-QSAR (http://www.mixture-platform.net/Detective_QSAR_Med_Open/). The analytical descriptor-based approach is expected to create
new opportunities for the evaluation of unknown chemicals around us.
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Affiliation(s)
- Yasuyuki Zushi
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology, 16-1 Onogawa, Tsukuba, Ibaraki 305-8506, Japan.,Graduate School of Science and Technology, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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3
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Golbaz S, Yaghmaeian K, Isazadeh S, Zamanzadeh M. Environmental risk assessments of multiclass pharmaceutical active compounds: selection of high priority concern pharmaceuticals using entropy-utility functions. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:59745-59770. [PMID: 34146330 DOI: 10.1007/s11356-021-14693-w] [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: 11/30/2020] [Accepted: 05/31/2021] [Indexed: 06/12/2023]
Abstract
This research aimed to identify high-risk pharmaceutically active compounds (PhACs) by analyzing occurrence (O), persistence (P), bioaccumulation (B), and toxicity (T) of 62 drugs which are widely used in Iran. A comprehensive approach was taken in risk assessment of the selected PhACs and in their prioritization using multiple-criteria decision analysis (MCDA) such as utility functions and principal component analysis (PCA). In practice, assigning weight to each criterion (i.e., O, P, B, and T) for risk assessment of PhACs is a challenge. In this research, the impact of giving both equal and unequal weight to each criterion by using a quantitative entropy method was studied. For risk assessment, two exposure approaches (consumption rate and occurrence of PhACs) and three MCDA approaches (PCA and utility functions with and without equal weights for each criterion) were compared. The utility function using equal weights for all O, P, B, and T criteria showed that thioridazine, pimozide, chlorpromazine, sertraline, clomipramine, and aripiprazole were at the highest level of risk, with concern score of 0.75, 0.75, 0.67, 0.58, 0.58, and 0.58, respectively. Unequal weight approach included additional compounds such as fluoxetine, citalopram, and methadone as a priority. All three MCDA approaches showed that sedatives and antidepressants were prevalent PhACs in the risk-based priority lists. However, the exposure-based approaches showed antibiotics and analgesics as the pharmaceutical of the highest priority. Overall, selection of the high priority concern pharmaceuticals depends on the prioritization approach employed. However, the utility function using unequal weights is a more conservative and effective approach for prioritization.
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Affiliation(s)
- Somayeh Golbaz
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamyar Yaghmaeian
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
| | - Siavash Isazadeh
- Research and Development, American Water Works Co., Delran, NJ, 08075, USA
| | - Mirzaman Zamanzadeh
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
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4
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Dearden JC, Hewitt M. Prediction of Human Lethal Doses and Concentrations of MEIC Chemicals from Rodent LD 50 Values: An Attempt to Make Some Reparation. Altern Lab Anim 2021; 49:10-21. [PMID: 33626883 DOI: 10.1177/0261192921994754] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The prediction of human toxicities from animal toxicity tests is often poor, and is now discouraged and in some cases banned, especially those involving the LD50 test. However, there is a vast number of historical LD50 data in both public and in-house repositories that are being put to little use. This study examined the correlations between human lethality (doses and concentrations) of 36 MEIC chemicals and the median values of a large number of mouse and rat LD50 values obtained for four different routes of administration. The best correlations were found with mouse and rat intraperitoneal LD50 values (r2 = 0.838 and 0.810 for human lethal dose, and r2 = 0.753 and 0.785 for human lethal concentration). The results show that excellent prediction of human lethal dose and concentration can be made, for this series of chemicals at least, by using uncurated rodent LD50 values, thus offering some reparation for the millions of rodent lives sacrificed in LD50 testing.
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Affiliation(s)
- John C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, UK
| | - Mark Hewitt
- School of Pharmacy, University of Wolverhampton, Wolverhampton, UK
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5
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Dias AR, Costa-Rodrigues J, Teixeira C, Prudêncio C, Gomes P, Ferraz R. Ionic Liquids for Topical Delivery in Cancer. Curr Med Chem 2020; 26:7520-7532. [DOI: 10.2174/0929867325666181026110227] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/08/2018] [Accepted: 08/12/2018] [Indexed: 11/22/2022]
Abstract
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The unique properties of ionic liquids make them quite appealing for diverse applications,
from “green” solvents (1st generation ionic liquids) to finely tuned materials (2nd generation
ionic liquids). A decade ago, a 3rd generation of ionic liquids emerged which is focused
on their prospective clinical applications, either as drugs per se or as adjuvants in drug formulations.
In recent years, research focused on the use of ionic liquids for topical drug delivery
has been increasing and holds great promise towards clinical application against skin cancers.
This article highlights the growing relevance of ionic liquids in medicinal chemistry and pharmaceutical
technology, which is opening new windows of opportunity.
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Affiliation(s)
- Ana Rita Dias
- Ciências Químicas e das Biomoléculas, Centro de Investigação em Saúde e Ambiente, Escola Superior de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal
| | - João Costa-Rodrigues
- Ciências Químicas e das Biomoléculas, Centro de Investigação em Saúde e Ambiente, Escola Superior de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal
| | - Cátia Teixeira
- Ciências Químicas e das Biomoléculas, Centro de Investigação em Saúde e Ambiente, Escola Superior de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal
| | - Cristina Prudêncio
- Ciências Químicas e das Biomoléculas, Centro de Investigação em Saúde e Ambiente, Escola Superior de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal
| | - Paula Gomes
- LAQV-REQUIMTE, Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto, Portugal
| | - Ricardo Ferraz
- Ciências Químicas e das Biomoléculas, Centro de Investigação em Saúde e Ambiente, Escola Superior de Saúde do Porto, Instituto Politécnico do Porto, Porto, Portugal
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6
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Lunghini F, Marcou G, Azam P, Horvath D, Patoux R, Van Miert E, Varnek A. Consensus models to predict oral rat acute toxicity and validation on a dataset coming from the industrial context. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:879-897. [PMID: 31607169 DOI: 10.1080/1062936x.2019.1672089] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 09/21/2019] [Indexed: 06/10/2023]
Abstract
We report predictive models of acute oral systemic toxicity representing a follow-up of our previous work in the framework of the NICEATM project. It includes the update of original models through the addition of new data and an external validation of the models using a dataset relevant for the chemical industry context. A regression model for LD50 and multi-class classification model for toxicity classes according to the Global Harmonized System categories were prepared. ISIDA descriptors were used to encode molecular structures. Machine learning algorithms included support vector machine (SVM), random forest (RF) and naïve Bayesian. Selected individual models were combined in consensus. The different datasets were compared using the generative topographic mapping approach. It appeared that the NICEATM datasets were lacking some relevant chemotypes for chemical industry. The new models trained on enlarged data sets have applicability domains (AD) sufficiently large to accommodate industrial compounds. The fraction of compounds inside the models' AD increased from 58% (NICEATM model) to 94% (new model). The increase of training sets improved models' prediction performance: RMSE values decreased from 0.56 to 0.47 and balanced accuracies increased from 0.69 to 0.71 for NICEATM and new models, respectively.
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Affiliation(s)
- F Lunghini
- Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg, France
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., St. Fons, France
| | - G Marcou
- Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg, France
| | - P Azam
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., St. Fons, France
| | - D Horvath
- Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg, France
| | - R Patoux
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., St. Fons, France
| | - E Van Miert
- Toxicological and Environmental Risk Assessment unit, Solvay S.A., St. Fons, France
| | - A Varnek
- Laboratory of Chemoinformatics, University of Strasbourg, Strasbourg, France
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7
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Myatt GJ, Ahlberg E, Akahori Y, Allen D, Amberg A, Anger LT, Aptula A, Auerbach S, Beilke L, Bellion P, Benigni R, Bercu J, Booth ED, Bower D, Brigo A, Burden N, Cammerer Z, Cronin MTD, Cross KP, Custer L, Dettwiler M, Dobo K, Ford KA, Fortin MC, Gad-McDonald SE, Gellatly N, Gervais V, Glover KP, Glowienke S, Van Gompel J, Gutsell S, Hardy B, Harvey JS, Hillegass J, Honma M, Hsieh JH, Hsu CW, Hughes K, Johnson C, Jolly R, Jones D, Kemper R, Kenyon MO, Kim MT, Kruhlak NL, Kulkarni SA, Kümmerer K, Leavitt P, Majer B, Masten S, Miller S, Moser J, Mumtaz M, Muster W, Neilson L, Oprea TI, Patlewicz G, Paulino A, Lo Piparo E, Powley M, Quigley DP, Reddy MV, Richarz AN, Ruiz P, Schilter B, Serafimova R, Simpson W, Stavitskaya L, Stidl R, Suarez-Rodriguez D, Szabo DT, Teasdale A, Trejo-Martin A, Valentin JP, Vuorinen A, Wall BA, Watts P, White AT, Wichard J, Witt KL, Woolley A, Woolley D, Zwickl C, Hasselgren C. In silico toxicology protocols. Regul Toxicol Pharmacol 2018; 96:1-17. [PMID: 29678766 DOI: 10.1016/j.yrtph.2018.04.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2017] [Revised: 03/16/2018] [Accepted: 04/16/2018] [Indexed: 10/17/2022]
Abstract
The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.
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Affiliation(s)
- Glenn J Myatt
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA.
| | - Ernst Ahlberg
- Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden
| | - Yumi Akahori
- Chemicals Evaluation and Research Institute, 1-4-25 Kouraku, Bunkyo-ku, Tokyo 112-0004 Japan
| | - David Allen
- Integrated Laboratory Systems, Inc., Research Triangle Park, NC, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Lennart T Anger
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | - Aynur Aptula
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | | | | | - Joel Bercu
- Gilead Sciences, 333 Lakeside Drive, Foster City, CA, USA
| | - Ewan D Booth
- Syngenta, Product Safety Department, Jealott's Hill International Research Centre, Bracknell, Berkshire, RG42 6EY, UK
| | - Dave Bower
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Alessandro Brigo
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Natalie Burden
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Zoryana Cammerer
- Janssen Research & Development, 1400 McKean Road, Spring House, PA 19477, USA
| | - Mark T D Cronin
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Kevin P Cross
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Laura Custer
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Krista Dobo
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Kevin A Ford
- Global Blood Therapeutics, South San Francisco, CA 94080, USA
| | - Marie C Fortin
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers, The State University of New Jersey, 170 Frelinghuysen Rd, Piscataway, NJ 08855, USA
| | | | - Nichola Gellatly
- National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs), Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | | | - Kyle P Glover
- Defense Threat Reduction Agency, Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD 21010, USA
| | - Susanne Glowienke
- Novartis Pharma AG, Pre-Clinical Safety, Werk Klybeck, CH-4057, Basel, Switzerland
| | - Jacky Van Gompel
- Janssen Pharmaceutical Companies of Johnson & Johnson, 2340 Beerse, Belgium
| | - Steve Gutsell
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Barry Hardy
- Douglas Connect GmbH, Technology Park Basel, Hochbergerstrasse 60C, CH-4057 Basel / Basel-Stadt, Switzerland
| | - James S Harvey
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Jedd Hillegass
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Jui-Hua Hsieh
- Kelly Government Solutions, Research Triangle Park, NC 27709, USA
| | - Chia-Wen Hsu
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Kathy Hughes
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | | | - Robert Jolly
- Toxicology Division, Eli Lilly and Company, Indianapolis, IN, USA
| | - David Jones
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Ray Kemper
- Vertex Pharmaceuticals Inc., Discovery and Investigative Toxicology, 50 Northern Ave, Boston, MA, USA
| | - Michelle O Kenyon
- Pfizer Global Research & Development, 558 Eastern Point Road, Groton, CT 06340, USA
| | - Marlene T Kim
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Naomi L Kruhlak
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | - Sunil A Kulkarni
- Existing Substances Risk Assessment Bureau, Health Canada, Ottawa, ON, K1A 0K9, Canada
| | - Klaus Kümmerer
- Institute for Sustainable and Environmental Chemistry, Leuphana University Lüneburg, Scharnhorststraße 1/C13.311b, 21335 Lüneburg, Germany
| | - Penny Leavitt
- Bristol-Myers Squibb, Drug Safety Evaluation, 1 Squibb Dr, New Brunswick, NJ 08903, USA
| | | | - Scott Masten
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Scott Miller
- Leadscope, Inc., 1393 Dublin Rd, Columbus, OH 43215, USA
| | - Janet Moser
- Chemical Security Analysis Center, Department of Homeland Security, 3401 Ricketts Point Road, Aberdeen Proving Ground, MD 21010-5405, USA; Battelle Memorial Institute, 505 King Avenue, Columbus, OH 43210, USA
| | - Moiz Mumtaz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Wolfgang Muster
- Roche Pharmaceutical Research & Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Switzerland
| | - Louise Neilson
- British American Tobacco, Research and Development, Regents Park Road, Southampton, Hampshire, SO15 8TL, UK
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, Health Sciences Center, The University of New Mexico, NM, USA
| | - Grace Patlewicz
- U.S. Environmental Protection Agency, National Center for Computational Toxicology, Research Triangle Park, NC 27711, USA
| | - Alexandre Paulino
- SAPEC Agro, S.A., Avenida do Rio Tejo, Herdade das Praias, 2910-440 Setúbal, Portugal
| | - Elena Lo Piparo
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | - Mark Powley
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - Andrea-Nicole Richarz
- European Commission, Joint Research Centre, Directorate for Health, Consumers and Reference Materials, Chemical Safety and Alternative Methods Unit, Via Enrico Fermi 2749, 21027 Ispra, VA, Italy
| | - Patricia Ruiz
- Agency for Toxic Substances and Disease Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Benoit Schilter
- Chemical Food Safety Group, Nestlé Research Center, Lausanne, Switzerland
| | | | - Wendy Simpson
- Unilever, Safety and Environmental Assurance Centre, Colworth, Beds, UK
| | - Lidiya Stavitskaya
- FDA Center for Drug Evaluation and Research, Silver Spring, MD 20993, USA
| | | | | | - David T Szabo
- RAI Services Company, 950 Reynolds Blvd., Winston-Salem, NC 27105, USA
| | | | | | | | | | - Brian A Wall
- Colgate-Palmolive Company, Piscataway, NJ 08854, USA
| | - Pete Watts
- Bibra, Cantium House, Railway Approach, Wallington, Surrey, SM6 0DZ, UK
| | - Angela T White
- GlaxoSmithKline Pre-Clinical Development, Park Road, Ware, Hertfordshire, SG12 0DP, UK
| | - Joerg Wichard
- Bayer Pharma AG, Investigational Toxicology, Muellerstr. 178, D-13353 Berlin, Germany
| | - Kristine L Witt
- The National Institute of Environmental Health Sciences, Division of the National Toxicology Program, Research Triangle Park, NC 27709, USA
| | - Adam Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
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8
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Camacho-Mendoza RL, Aquino-Torres E, Cordero-Pensado V, Cruz-Borbolla J, Alvarado-Rodríguez JG, Thangarasu P, Gómez-Castro CZ. A new computational model for the prediction of toxicity of phosphonate derivatives using QSPR. Mol Divers 2018. [PMID: 29532429 DOI: 10.1007/s11030-018-9819-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Structural and electronic properties of a series of 25 phosphonate derivatives were analyzed applying density functional theory, with the exchange-correlation functional PBEPBE in combination with the 6-311++G** basis set for all atoms. The chemical reactivity of these derivatives has been interpreted using quantum descriptors such as frontier molecular orbitals (HOMO, LUMO), Hirshfeld charges, molecular electrostatic potential, and the dual descriptor [[Formula: see text]]. These descriptors are directly related to experimental median lethal dose ([Formula: see text], expressed as its decimal logarithm [[Formula: see text]([Formula: see text]] through a multiple linear regression equation. The proposed model predicts the toxicity of phosphonates in function of the volume (V), the load of the most electronegative atom of the molecule (q), and the eigenvalue of the molecular orbital HOMO ([Formula: see text]. The obtained values in the internal validation of the model are: [Formula: see text]%, [Formula: see text]%, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text]%. The toxicity of nine phosphonate derivatives used as test molecules was adequately predicted by the model. The theoretical results indicate that the oxygen atom of the O=P group plays an important role in the interaction mechanism between the phosphonate and the acetylcholinesterase enzyme, inhibiting the removal of the proton of the ser-200 residue by the his-440 residue.
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Affiliation(s)
- Rosa L Camacho-Mendoza
- Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, km 4.5 Carretera Pachuca-Tulancingo, C.P. 42184, Mineral de la Reforma Hidalgo, Mexico
| | - Eliazar Aquino-Torres
- Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, km 4.5 Carretera Pachuca-Tulancingo, C.P. 42184, Mineral de la Reforma Hidalgo, Mexico
| | - Viviana Cordero-Pensado
- Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, km 4.5 Carretera Pachuca-Tulancingo, C.P. 42184, Mineral de la Reforma Hidalgo, Mexico
| | - Julián Cruz-Borbolla
- Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, km 4.5 Carretera Pachuca-Tulancingo, C.P. 42184, Mineral de la Reforma Hidalgo, Mexico.
| | - José G Alvarado-Rodríguez
- Área Académica de Química, Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, km 4.5 Carretera Pachuca-Tulancingo, C.P. 42184, Mineral de la Reforma Hidalgo, Mexico
| | - Pandiyan Thangarasu
- Facultad de Química, Universidad Nacional Autónoma de México, Ciudad Universitaria, C.P. 04510, Mexico City, Mexico
| | - Carlos Z Gómez-Castro
- Universidad Autónoma del Estado de Hidalgo, Ciudad del Conocimiento, C.P. 42184, Mineral de la Reforma, Hidalgo, Mexico
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9
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Li JJ, Zhang XJ, Yang Y, Huang T, Li C, Su L, Zhao YH, Cronin MTD. Development of thresholds of excess toxicity for environmental species and their application to identification of modes of acute toxic action. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 616-617:491-499. [PMID: 29127803 DOI: 10.1016/j.scitotenv.2017.10.308] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 10/23/2017] [Accepted: 10/29/2017] [Indexed: 06/07/2023]
Abstract
The acute toxicity of organic pollutants to fish, Daphnia magna, Tetrahymena pyriformis, and Vibrio fischeri was investigated. The results indicated that the Toxicity Ratio (TR) threshold of log TR =1, which has been based on the distribution of toxicity data to fish, can also be used to discriminate reactive or specifically acting compounds from baseline narcotics for Daphnia magna and Vibrio fischeri. A log TR=0.84 is proposed for Tetrahymena pyriformis following investigation of the relationships between the species sensitivity and the absolute averaged residuals (AAR) between the predicted baseline toxicity and the experimental toxicity. Less inert compounds exhibit relatively higher toxicity to the lower species (Tetrahymena pyriformis and Vibrio fischeri) than the higher species (fish and Daphnia magna). A greater number of less inert compounds with log TR greater than the thresholds was observed for Tetrahymena pyriformis and Vibrio fischeri. This may be attributed to the hydrophilic compounds which may pass more easily through cell membranes than the skin or exoskeleton of organisms and have higher bioconcentration factors in the lower species, leading to higher toxicity. Most of classes of chemical associated with excess toxicity to one species also exhibited excess toxicity to other species, however, a few classes with excess toxicity to one species exhibiting narcotic toxicity to other species and thus may have different MOAs between species. Some ionizable compounds have log TR much lower than one because of the over-estimated log KOW. The factors that influence the toxicity ratio calculated from baseline level are discussed in this paper.
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Affiliation(s)
- Jin J Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China; College of Marine Ecology and Environment, Shanghai Ocean University, Shanghai, 201306, PR China
| | - Xu J Zhang
- College of Geographical Science, Harbin Normal University, Harbin, Heilongjiang 150028, PR China
| | - Yi Yang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Tao Huang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Chao Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Limin Su
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
| | - Yuan H Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China.
| | - Mark T D Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L3 3AF, UK.
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10
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Ferraz R, Costa-Rodrigues J, Fernandes MH, Santos MM, Marrucho IM, Rebelo LPN, Prudêncio C, Noronha JP, Petrovski Ž, Branco LC. Antitumor Activity of Ionic Liquids Based on Ampicillin. ChemMedChem 2015; 10:1480-3. [PMID: 26190053 DOI: 10.1002/cmdc.201500142] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2015] [Revised: 05/05/2015] [Indexed: 11/09/2022]
Abstract
Significant antiproliferative effects against various tumor cell lines were observed with novel ampicillin salts as ionic liquids. The combination of anionic ampicillin with appropriate ammonium, imidazolium, phosphonium, and pyridinium cations yielded active pharmaceutical ingredient ionic liquids (API-ILs) that show potent antiproliferative activities against five different human cancer cell lines: T47D (breast), PC3 (prostate), HepG2 (liver), MG63 (osteosarcoma), and RKO (colon). Some API-ILs showed IC50 values between 5 and 42 nM, activities that stand in dramatic contrast to the negligible cytotoxic activity level shown by the ampicillin sodium salt. Moreover, very low cytotoxicity against two primary cell lines-skin (SF) and gingival fibroblasts (GF)-indicates that the majority of these API-ILs are nontoxic to normal human cell lines. The most promising combination of antitumor activity and low toxicity toward healthy cells was observed for the 1-hydroxyethyl-3-methylimidazolium-ampicillin pair ([C2 OHMIM][Amp]), making this the most suitable lead API-IL for future studies.
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Affiliation(s)
- Ricardo Ferraz
- Departamento de Química, REQUIMTE-CQFB, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Caparica (Portugal).,Ciências Químicas e das Biomoléculas, Escola Superior de Tecnologia da Saúde do Porto do Instituto Politécnico do Porto, Rua Valente Perfeito 322, 4400-330 Vila Nova de Gaia (Portugal)
| | - João Costa-Rodrigues
- Laboratório de Farmacologia e Biocompatibilidade Celular, Faculdade de Medicina Dentária, Universidade do Porto, Rua Dr. Manuel Pereira da Silva, 4200-393 Porto (Portugal).
| | - Maria H Fernandes
- Laboratório de Farmacologia e Biocompatibilidade Celular, Faculdade de Medicina Dentária, Universidade do Porto, Rua Dr. Manuel Pereira da Silva, 4200-393 Porto (Portugal)
| | - Miguel M Santos
- Departamento de Química, REQUIMTE-CQFB, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Caparica (Portugal)
| | - Isabel M Marrucho
- Instituto de Tecnologia Química e Biológica, ITQB, Universidade Nova de Lisboa, Av. da República Estação Agronómica Nacional, 2780-157 Oeiras (Portugal)
| | - Luís Paulo N Rebelo
- Instituto de Tecnologia Química e Biológica, ITQB, Universidade Nova de Lisboa, Av. da República Estação Agronómica Nacional, 2780-157 Oeiras (Portugal)
| | - Cristina Prudêncio
- Ciências Químicas e das Biomoléculas, Escola Superior de Tecnologia da Saúde do Porto do Instituto Politécnico do Porto, Rua Valente Perfeito 322, 4400-330 Vila Nova de Gaia (Portugal).,Centro de Farmacologia e Biopatologia Química (U38-FCT), Faculdade de Medicina da Universidade do Porto (FMUP) (Portugal)
| | - João Paulo Noronha
- Departamento de Química, REQUIMTE-CQFB, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Caparica (Portugal)
| | - Željko Petrovski
- Departamento de Química, REQUIMTE-CQFB, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Caparica (Portugal).
| | - Luís C Branco
- Departamento de Química, REQUIMTE-CQFB, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Caparica (Portugal).
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11
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Chavan S, Nicholls IA, Karlsson BCG, Rosengren AM, Ballabio D, Consonni V, Todeschini R. Towards global QSAR model building for acute toxicity: Munro database case study. Int J Mol Sci 2014; 15:18162-74. [PMID: 25302621 PMCID: PMC4227209 DOI: 10.3390/ijms151018162] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 09/09/2014] [Accepted: 09/17/2014] [Indexed: 11/16/2022] Open
Abstract
A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.
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Affiliation(s)
- Swapnil Chavan
- Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden.
| | - Ian A Nicholls
- Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden.
| | - Björn C G Karlsson
- Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden.
| | - Annika M Rosengren
- Bioorganic & Biophysical Chemistry Laboratory, Linnaeus University Centre for Biomaterials Chemistry and Department of Chemistry & Biomedical Sciences, Linnaeus University, Kalmar SE-391 82, Sweden.
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy.
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy.
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milano IT-20126, Italy.
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12
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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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13
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Li X, Chen L, Cheng F, Wu Z, Bian H, Xu C, Li W, Liu G, Shen X, Tang Y. In silico prediction of chemical acute oral toxicity using multi-classification methods. J Chem Inf Model 2014; 54:1061-9. [PMID: 24735213 DOI: 10.1021/ci5000467] [Citation(s) in RCA: 106] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silico methods are hence developed as an alternative. In this study, a comprehensive data set containing 12, 204 diverse compounds with median lethal dose (LD₅₀) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), κ-nearest neighbor (kNN), and naïve Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-SVM(OAO) model was 83.0% and 89.9% for external validation sets I and II, respectively, which showed reliable predictive accuracy for each class. In addition, some representative substructures responsible for acute oral toxicity were identified using information gain and substructure frequency analysis methods, which might be very helpful for further study to avoid the toxicity.
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Affiliation(s)
- Xiao Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237, China
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14
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Ortiz de García S, Pinto GP, García-Encina PA, Irusta Mata RI. Ranking of concern, based on environmental indexes, for pharmaceutical and personal care products: an application to the Spanish case. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2013; 129:384-97. [PMID: 23995140 DOI: 10.1016/j.jenvman.2013.06.035] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2013] [Revised: 06/08/2013] [Accepted: 06/17/2013] [Indexed: 05/13/2023]
Abstract
A wide range of Pharmaceuticals and Personal Care Products (PPCPs) are present in the environment, and many of their adverse effects are unknown. The emergence of new compounds or changes in regulations have led to dynamical studies of occurrence, impact and treatment, which consider geographical areas and trends in consumption and innovation in the pharmaceutical industry. A Quantitative study of Structure-Activity Relationship ((Q)SAR) was performed to assess the possible adverse effects of ninety six PPCPs and metabolites with negligible experimental data and establish a ranking of concern, which was supported by the EPA EPI Suite™ interface. The environmental and toxicological indexes, the persistence (P), the bioaccumulation (B), the toxicity (T) (extensive) and the occurrence in Spanish aquatic environments (O) (intensive) were evaluated. The most hazardous characteristics in the largest number of compounds were generated by the P index, followed by the T and B indexes. A high number of metabolites has a concern score equal to or greater than their parent compounds. Three PBT and OPBT rankings of concern were proposed using the total and partial ranking method (supported by a Hasse diagram) by the Decision Analysis by Ranking Techniques (DART) tool, which was recently recommended by the European Commission. An analysis of the sensibility of the relative weights of these indexes has been conducted. Hormones, antidepressants (and their metabolites), blood lipid regulators and all of the personal care products considered in this study were at the highest levels of risk according to the PBT and OPBT total rankings. Furthermore, when the OPBT partial ranking was performed, X-ray contrast media, H2 blockers and some antibiotics were included at the highest level of concern. It is important to improve and incorporate useful indexes for the predicted environmental impact of PPCPs and metabolites and thus focus experimental analysis on the compounds that require urgent attention.
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Affiliation(s)
- Sheyla Ortiz de García
- Department of Chemical Engineering and Environmental Technology, University of Valladolid, Calle Dr. Mergelina s/n, 47011 Valladolid, Spain(1); Department of Chemistry, Faculty of Sciences and Technology, University of Carabobo, Av. Salvador Allende, Campus Bárbula, Carabobo, Bolivarian Republic of Venezuela(3).
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15
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Keshavarz MH, Gharagheizi F, Shokrolahi A, Zakinejad S. Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes. JOURNAL OF HAZARDOUS MATERIALS 2012; 237-238:79-101. [PMID: 22959133 DOI: 10.1016/j.jhazmat.2012.07.048] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Revised: 03/30/2012] [Accepted: 07/25/2012] [Indexed: 06/01/2023]
Abstract
Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure-toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model.
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Affiliation(s)
- Mohammad Hossein Keshavarz
- Department of Chemistry, Malek-ashtar University of Technology, Shahin-shahr P.O. Box 83145/115, Isfahan, Islamic Republic of Iran.
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16
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Simple and reliable prediction of toxicological activities of benzoic acid derivatives without using any experimental data or computer codes. Med Chem Res 2012. [DOI: 10.1007/s00044-012-0134-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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17
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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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18
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Abstract
AbstractCORAL (‘CORrelation And Logic’) is freeware available on the Internet www.insilico.eu/coral The aim of this program is to establish a correlation between an endpoint and descriptors calculated with a simplified molecular input line entry system (SMILES). Three models calculated by CORAL for toxicity towards rat (-pLD50) of inorganic substances (three random splits) have shown that CORAL could be a good tool to model this endpoint. The average statistical characteristics for the CORAL models are the following: n=38, r2=0.8461, q2=0.8298, s=0.273, F=198 (subtraining set); n=37, r2=0.8144, s=0.322, F=154 (calibration set); and n=10, r2=0.8004, Rm (test)2 =0.7815, s=0.240, F=32 (validation set).
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Sazonovas A, Japertas P, Didziapetris R. Estimation of reliability of predictions and model applicability domain evaluation in the analysis of acute toxicity (LD50). SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2010; 21:127-48. [PMID: 20373217 DOI: 10.1080/10629360903568671] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
This study presents a new type of acute toxicity (LD(50)) prediction that enables automated assessment of the reliability of predictions (which is synonymous with the assessment of the Model Applicability Domain as defined by the Organization for Economic Cooperation and Development). Analysis involved nearly 75,000 compounds from six animal systems (acute rat toxicity after oral and intraperitoneal administration; acute mouse toxicity after oral, intraperitoneal, intravenous, and subcutaneous administration). Fragmental Partial Least Squares (PLS) with 100 bootstraps yielded baseline predictions that were automatically corrected for non-linear effects in local chemical spaces--a combination called Global, Adjusted Locally According to Similarity (GALAS) modelling methodology. Each prediction obtained in this manner is provided with a reliability index value that depends on both compound's similarity to the training set (that accounts for similar trends in LD(50) variations within multiple bootstraps) and consistency of experimental results with regard to the baseline model in the local chemical environment. The actual performance of the Reliability Index (RI) was proven by its good (and uniform) correlations with Root Mean Square Error (RMSE) in all validation sets, thus providing quantitative assessment of the Model Applicability Domain. The obtained models can be used for compound screening in the early stages of drug development and prioritization for experimental in vitro testing or later in vivo animal acute toxicity studies.
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Zhu H, Martin TM, Ye L, Sedykh A, Young DM, Tropsha A. Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure. Chem Res Toxicol 2009; 22:1913-21. [PMID: 19845371 PMCID: PMC2796713 DOI: 10.1021/tx900189p] [Citation(s) in RCA: 154] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.
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Affiliation(s)
- Hao Zhu
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Carolina Environmental Bioinformatics Research Center, School of Pharmacy, University of North Carolina at Chapel Hill, North Carolina 27599-7568, USA
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21
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Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A. A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents. ENVIRONMENTAL HEALTH PERSPECTIVES 2009; 117:1257-64. [PMID: 19672406 PMCID: PMC2721870 DOI: 10.1289/ehp.0800471] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2008] [Accepted: 04/03/2009] [Indexed: 05/05/2023]
Abstract
BACKGROUND Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. OBJECTIVE A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. METHODS AND RESULTS A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. CONCLUSIONS The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.
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Affiliation(s)
- Hao Zhu
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Lin Ye
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Ann Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Alexander Golbraikh
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Ivan Rusyn
- Department of Environmental Sciences and Engineering, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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22
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Study on the quantitative structure–toxicity relationships of benzoic acid derivatives in rats via oral LD50. Med Chem Res 2009. [DOI: 10.1007/s00044-009-9162-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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23
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Classification structure–activity relationship (CSAR) studies for prediction of genotoxicity of thiophene derivatives. Toxicol Lett 2008; 177:10-9. [DOI: 10.1016/j.toxlet.2007.12.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2007] [Revised: 12/12/2007] [Accepted: 12/12/2007] [Indexed: 11/20/2022]
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