1
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Diana M, Felipe-Sotelo M, Bond T. Disinfection byproducts potentially responsible for the association between chlorinated drinking water and bladder cancer: A review. WATER RESEARCH 2019; 162:492-504. [PMID: 31302365 DOI: 10.1016/j.watres.2019.07.014] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 07/05/2019] [Accepted: 07/06/2019] [Indexed: 06/10/2023]
Abstract
Epidemiological studies have consistently associated the consumption of chlorinated drinking water with an enhanced risk of bladder cancer. While this suggests that some disinfection byproducts (DBPs) are bladder carcinogens, causal agents are unknown. This study aims to highlight likely candidates. To achieve this, structures of known and hypothesised DBPs were compared with 76 known bladder carcinogens. The latter are dominated by nitrogenous and aromatic compounds; only 10 are halogenated. Under 10% of the chlorine applied during drinking water treatment is converted into identified halogenated byproducts; most of the chlorine is likely to be consumed during the generation of unidentified non-halogenated oxidation products. Six nitrosamines are among the nine most potent bladder carcinogens, and two of them are known to be DBPs: N-nitrosodiphenylamine and nitrosodibutylamine. However, these and other nitrosamines are formed in insufficiently low concentrations in chlorinated drinking water to account for the observed bladder cancer risk. Furthermore, although not proven bladder carcinogens, certain amines, haloamides, halocyclopentenoic acids, furans and haloquinones are potential candidates. At present, most identified bladder carcinogens are nitrogenous, whereas >90% of natural organic matter is not. Therefore, non-nitrogenous DBPs are likely to contribute to the bladder cancer risk. Given the high proportion of DBPs that remains uncharacterised, it is important that future research prioritises compounds believed to be potent toxicants.
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Affiliation(s)
- Marine Diana
- Department of Civil and Environmental Engineering, University of Surrey, Guildford, GU2 7XH, UK
| | | | - Tom Bond
- Department of Civil and Environmental Engineering, University of Surrey, Guildford, GU2 7XH, UK.
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2
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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3
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Song F, Zhang A, Liang H, Cui L, Li W, Si H, Duan Y, Zhai H. QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:E1141. [PMID: 27854309 PMCID: PMC5129351 DOI: 10.3390/ijerph13111141] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 10/21/2016] [Accepted: 10/24/2016] [Indexed: 11/27/2022]
Abstract
A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.
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Affiliation(s)
- Fucheng Song
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Anling Zhang
- Modern Educational Technology Center, Qingdao University, Qingdao 266071, China.
| | - Hui Liang
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Lianhua Cui
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Wenlian Li
- Department of Public Health, Qingdao University Medical College, Qingdao 266071, China.
| | - Hongzong Si
- Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China.
| | - Yunbo Duan
- Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China.
| | - Honglin Zhai
- Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
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Zhang H, Cao ZX, Li M, Li YZ, Peng C. Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals. Food Chem Toxicol 2016; 97:141-149. [PMID: 27597133 DOI: 10.1016/j.fct.2016.09.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 08/02/2016] [Accepted: 09/01/2016] [Indexed: 02/05/2023]
Abstract
The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (MW), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development.
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Affiliation(s)
- Hui Zhang
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China.
| | - Zhi-Xing Cao
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, 610041, PR China
| | - Meng Li
- College of Life Science, Northwest Normal University, Lanzhou, Gansu, 730070, PR China
| | - Yu-Zhi Li
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China
| | - Cheng Peng
- Pharmacy College, Chengdu University of Traditional Chinese Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province-key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chendu, Sichuan, PR China
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5
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Casalegno M, Sello G. Carcinogenicity prediction of noncongeneric chemicals by augmented top priority fragment classification. Comput Biol Chem 2016; 61:145-54. [DOI: 10.1016/j.compbiolchem.2016.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Revised: 12/15/2015] [Accepted: 01/26/2016] [Indexed: 10/22/2022]
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Singh KP, Gupta S, Rai P. Predicting carcinogenicity of diverse chemicals using probabilistic neural network modeling approaches. Toxicol Appl Pharmacol 2013; 272:465-75. [PMID: 23856075 DOI: 10.1016/j.taap.2013.06.029] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2013] [Accepted: 06/22/2013] [Indexed: 01/31/2023]
Abstract
Robust global models capable of discriminating positive and non-positive carcinogens; and predicting carcinogenic potency of chemicals in rodents were developed. The dataset of 834 structurally diverse chemicals extracted from Carcinogenic Potency Database (CPDB) was used which contained 466 positive and 368 non-positive carcinogens. Twelve non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals and nonlinearity in the data were evaluated using Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Probabilistic neural network (PNN) and generalized regression neural network (GRNN) models were constructed for classification and function optimization problems using the carcinogenicity end point in rat. Validation of the models was performed using the internal and external procedures employing a wide series of statistical checks. PNN constructed using five descriptors rendered classification accuracy of 92.09% in complete rat data. The PNN model rendered classification accuracies of 91.77%, 80.70% and 92.08% in mouse, hamster and pesticide data, respectively. The GRNN constructed with nine descriptors yielded correlation coefficient of 0.896 between the measured and predicted carcinogenic potency with mean squared error (MSE) of 0.44 in complete rat data. The rat carcinogenicity model (GRNN) applied to the mouse and hamster data yielded correlation coefficient and MSE of 0.758, 0.71 and 0.760, 0.46, respectively. The results suggest for wide applicability of the inter-species models in predicting carcinogenic potency of chemicals. Both the PNN and GRNN (inter-species) models constructed here can be useful tools in predicting the carcinogenicity of new chemicals for regulatory purposes.
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Affiliation(s)
- Kunwar P Singh
- Academy of Scientific and Innovative Research, Council of Scientific & Industrial Research, New Delhi, India; Environmental Chemistry Division, CSIR-Indian Institute of Toxicology Research, Post Box 80, Mahatma Gandhi Marg, Lucknow 226 001, India.
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7
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Quintero FA, Patel SJ, Muñoz F, Sam Mannan M. Review of Existing QSAR/QSPR Models Developed for Properties Used in Hazardous Chemicals Classification System. Ind Eng Chem Res 2012. [DOI: 10.1021/ie301079r] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Flor A. Quintero
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
- Departamento de
Ingeniería Química, Universidad de los Andes, Cr.1 Este #19 A-40, Bogotá D.C.,
Colombia
| | - Suhani J. Patel
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
| | - Felipe Muñoz
- Departamento de
Ingeniería Química, Universidad de los Andes, Cr.1 Este #19 A-40, Bogotá D.C.,
Colombia
| | - M. Sam Mannan
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University System, College Station, Texas 77843-3122, United States
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8
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Fjodorova N, Vračko M, Novič M, Roncaglioni A, Benfenati E. New public QSAR model for carcinogenicity. Chem Cent J 2010; 4 Suppl 1:S3. [PMID: 20678182 PMCID: PMC2913330 DOI: 10.1186/1752-153x-4-s1-s3] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. RESULTS Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. CONCLUSION Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.
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Affiliation(s)
- Natalja Fjodorova
- National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Marjan Vračko
- National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Marjana Novič
- National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia
| | - Alessandra Roncaglioni
- Institute for Pharmacological Research "Mario Negri", Via La Masa 19, 20156 Milan, Italy
| | - Emilio Benfenati
- Institute for Pharmacological Research "Mario Negri", Via La Masa 19, 20156 Milan, Italy
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9
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Fjodorova N, Vracko M, Novic M, Roncaglioni A, Benfenati E. New public QSAR model for carcinogenicity. Chem Cent J 2010. [PMID: 20678182 DOI: 10.1186/1752–153x–4–s1–s3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fulfill the gaps in data concerned with properties of chemicals affecting the human health. (Q)SAR models are accepted as a suitable source of information. The EU funded CAESAR project aimed to develop models for prediction of 5 endpoints for regulatory purposes. Carcinogenicity is one of the endpoints under consideration. RESULTS Models for prediction of carcinogenic potency according to specific requirements of Chemical regulation were developed. The dataset of 805 non-congeneric chemicals extracted from Carcinogenic Potency Database (CPDBAS) was used. Counter Propagation Artificial Neural Network (CP ANN) algorithm was implemented. In the article two alternative models for prediction carcinogenicity are described. The first model employed eight MDL descriptors (model A) and the second one twelve Dragon descriptors (model B). CAESAR's models have been assessed according to the OECD principles for the validation of QSAR. For the model validity we used a wide series of statistical checks. Models A and B yielded accuracy of training set (644 compounds) equal to 91% and 89% correspondingly; the accuracy of the test set (161 compounds) was 73% and 69%, while the specificity was 69% and 61%, respectively. Sensitivity in both cases was equal to 75%. The accuracy of the leave 20% out cross validation for the training set of models A and B was equal to 66% and 62% respectively. To verify if the models perform correctly on new compounds the external validation was carried out. The external test set was composed of 738 compounds. We obtained accuracy of external validation equal to 61.4% and 60.0%, sensitivity 64.0% and 61.8% and specificity equal to 58.9% and 58.4% respectively for models A and B. CONCLUSION Carcinogenicity is a particularly important endpoint and it is expected that QSAR models will not replace the human experts opinions and conventional methods. However, we believe that combination of several methods will provide useful support to the overall evaluation of carcinogenicity. In present paper models for classification of carcinogenic compounds using MDL and Dragon descriptors were developed. Models could be used to set priorities among chemicals for further testing. The models at the CAESAR site were implemented in java and are publicly accessible.
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Affiliation(s)
- Natalja Fjodorova
- National Institute of Chemistry, Hajdrihova 19, SI-1001 Ljubljana, Slovenia.
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10
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Fjodorova N, Vracko M, Jezierska A, Novic M. Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2010; 21:57-75. [PMID: 20373214 DOI: 10.1080/10629360903563250] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
One of the main goals of the new chemical regulation REACH (Registration, Evaluation and Authorization of Chemicals) is to fill the gaps on the toxicological properties of chemicals that affect human health. Carcinogenicity is one of the endpoints under consideration. The information obtained from (quantitative) structure-activity relationship ((Q)SAR) models is accepted as an alternative solution to avoid expensive and time-consuming animal tests. The reported results were obtained within the framework of the European project 'Computer Assisted Evaluation of industrial chemical Substances According to Regulations (CAESAR)'. In this article, we demonstrate intermediate results for counter propagation artificial neural network (CP ANN) models for the prediction category of the carcinogenic potency using two-dimensional (2D) descriptors from different software programs. A total of 805 non-congeneric chemicals were extracted from the Carcinogenic Potency Database (CPDBAS). The resulting models had prediction accuracies for internal (training) and external (test) sets as high as 91-93% and 68-70%, respectively. The sensitivity and specificity of the test set were 69-73 and 63-72% correspondingly. High specificity is critical in models for regulatory use that are aimed at ensuring public safety. Thus, the errors that give rise to false negatives are much more relevant. We discuss how we can increase the number of correctly predicted carcinogens using the correlation between the threshold and the values of the sensitivity and specificity.
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Affiliation(s)
- N Fjodorova
- National Institute of Chemistry, Ljubljana, Slovenia.
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11
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Pérez-Garrido A, Helguera AM, López GC, Cordeiro MNDS, Escudero AG. A topological substructural molecular design approach for predicting mutagenesis end-points of alpha, beta-unsaturated carbonyl compounds. Toxicology 2009; 268:64-77. [PMID: 20004227 DOI: 10.1016/j.tox.2009.11.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2009] [Revised: 11/29/2009] [Accepted: 11/30/2009] [Indexed: 11/18/2022]
Abstract
Chemically reactive, alpha, beta-unsaturated carbonyl compounds are common environmental pollutants able to produce a wide range of adverse effects, including, e.g. mutagenicity. This toxic property can often be related to chemical structure, in particular to specific molecular substructures or fragments (alerts), which can then be used in specialized software or expert systems for predictive purposes. In the past, there have been many attempts to predict the mutagenicity of alpha, beta-unsaturated carbonyl compounds through quantitative structure activity relationships (QSAR) but considering only one exclusive endpoint: the Ames test. Besides, even though those studies give a comprehensive understanding of the phenomenon, they do not provide substructural information that could be useful forward improving expert systems based on structural alerts (SAs). This work reports an evaluation of classification models to probe the mutagenic activity of alpha, beta-unsaturated carbonyl compounds over two endpoints--the Ames and mammalian cell gene mutation tests--based on linear discriminant analysis along with the topological Substructure molecular design (TOPS-MODE) approach. The obtained results showed the better ability of the TOPS-MODE approach in flagging structural alerts for the mutagenicity of these compounds compared to the expert system TOXTREE. Thus, the application of the present QSAR models can aid toxicologists in risk assessment and in prioritizing testing, as well as in the improvement of expert systems, such as the TOXTREE software, where SAs are implemented.
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Affiliation(s)
- Alfonso Pérez-Garrido
- Enviromental Engineering and Toxicology Dpt., Catholic University of San Antonio, Guadalupe, Murcia, C.P. 30107, Spain.
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12
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Pérez-Garrido A, Helguera AM, Cordeiro MND, Escudero AG. QSPR modelling with the topological substructural molecular design approach: β-cyclodextrin complexation. J Pharm Sci 2009; 98:4557-76. [DOI: 10.1002/jps.21747] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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13
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Quantitative and qualitative models for carcinogenicity prediction for non-congeneric chemicals using CP ANN method for regulatory uses. Mol Divers 2009; 14:581-94. [DOI: 10.1007/s11030-009-9190-4] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2009] [Accepted: 07/26/2009] [Indexed: 10/20/2022]
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14
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Saiz-Urra L, Racero JC, Macías-Sáchez AJ, Hernández-Galán R, Hanson JR, Perez-Gonzalez M, Collado IG. Synthesis and quantitative structure-antifungal activity relationships of clovane derivatives against Botrytis cinerea. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2009; 57:2420-2428. [PMID: 19220016 DOI: 10.1021/jf8033978] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Twenty-three clovane derivatives, nine described here for the first time, bearing substituents on carbon C-2, have been synthesized and evaluated for their in vitro antifungal activity against the phytopathogenic fungus Botrytis cinerea. The results showed that compounds 9, 14, 16, and 18 bearing nitrogen atoms in the chain attached at C-2 displayed potent antifungal activity, whereas mercapto derivatives 13, 19, and 22 displayed low activity. The antifungal activity showed a clear structure-activity relationship (SAR) trend, which confirmed the importance of the nature of the C-2 chain on the antifungal activity. On the basis of these observations, the metabolism of compounds 8 and 14 by the fungus B. cinerea, and the metabolism of other clovanes by this fungus, described previously, a pro-drug action mechanism for 2-alkoxyclovane compounds is proposed. Quantitative structure-activity relationship (QSAR) studies were performed to rationalize the results and to suggest further optimization, using a topological sub-structural molecular design (TOPS-MODE) approach. The model displayed good fit and predictive capability, describing 85.5% of the experimental variance, with a standard deviation of 9.502 and yielding high values of cross-validation determination coefficients (q2CV-LOO = 0.784 and q2boot = 0.673). The most significant variables were the spectral moments weighted by bond dipole moment (Dip), hydrophobicity (Hyd), and the combined dipolarity/polarizability Abraham molecular descriptor (Ab-pi2H).
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Affiliation(s)
- Liane Saiz-Urra
- Departamento de Qumica Orgánica, Facultad de Ciencias, Universidad de Cádiz, Puerto Real, Cádiz, Spain
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15
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Combes R, Grindon C, Cronin MTD, Roberts DW, Garrod JF. Integrated decision-tree testing strategies for mutagenicity and carcinogenicity with respect to the requirements of the EU REACH legislation. Altern Lab Anim 2009; 36 Suppl 1:43-63. [PMID: 19025331 DOI: 10.1177/026119290803601s05] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for mutagenicity (genotoxicity) and carcinogenicity testing--two toxicity endpoints, which, together with reproductive toxicity, are of pivotal importance for the REACH system. The manuscript critically discusses well-established testing approaches, and in particular, the requirement for short-term in vivo tests for confirming positive mutagenicity, and the need for the rodent bioassay for detecting non-genotoxic carcinogens. Recently-proposed testing strategies focusing on non-animal approaches are also considered, and our own testing scheme is presented and supported with background information. This scheme makes maximum use of pre-existing data, computer (in silico) and in vitro methods, with weight-of-evidence assessments at each major stage. The need for the improvement of in vitro methods, to reduce the generation of false-positive results, is also discussed. Lastly, ways in which reduction and refinement measures can be used are also considered, and some recommendations are made for future research to facilitate the implementation of the proposed testing scheme.
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16
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A TOPological Sub-structural Molecular Design (TOPS-MODE)-QSAR approach for modeling the antiproliferative activity against murine leukemia tumor cell line (L1210). Bioorg Med Chem 2009; 17:537-47. [DOI: 10.1016/j.bmc.2008.11.084] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2008] [Revised: 11/25/2008] [Accepted: 11/29/2008] [Indexed: 11/22/2022]
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17
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Tan NX, Rao HB, Li ZR, Li XY. Prediction of chemical carcinogenicity by machine learning approaches. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2009; 20:27-75. [PMID: 19343583 DOI: 10.1080/10629360902724085] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.
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Affiliation(s)
- N X Tan
- College of Chemical Engineering and State Key Laboratory of Biotherapy, Sichuan University, Chengdu 610065, People's Republic of China
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Saíz-Urra L, Pérez-Castillo Y, Pérez González M, Molina Ruiz R, Cordeiro M, Rodríguez-Borges J, García-Mera X. Theoretical Prediction of Antiproliferative Activity against Murine Leukemia Tumor Cell Line (L1210). 3D-Morse Descriptor and its Application in Computational Chemistry. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200860060] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Estrada E. Quantum-Chemical Foundations of the Topological Substructural Molecular Design. J Phys Chem A 2008; 112:5208-17. [DOI: 10.1021/jp8010712] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Ernesto Estrada
- Complex Systems Research Group, RIAIDT & Department of Organic Chemistry, Faculty of Pharmacy, Edificio CACTUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
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Pérez-Garrido A, González MP, Escudero AG. Halogenated derivatives QSAR model using spectral moments to predict haloacetic acids (HAA) mutagenicity. Bioorg Med Chem 2008; 16:5720-32. [DOI: 10.1016/j.bmc.2008.03.070] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2007] [Revised: 02/29/2008] [Accepted: 03/25/2008] [Indexed: 10/22/2022]
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Prado-Prado FJ, González-Díaz H, de la Vega OM, Ubeira FM, Chou KC. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg Med Chem 2008; 16:5871-80. [PMID: 18485714 DOI: 10.1016/j.bmc.2008.04.068] [Citation(s) in RCA: 104] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2008] [Revised: 04/22/2008] [Accepted: 04/25/2008] [Indexed: 10/22/2022]
Abstract
Several pathogen parasite species show different susceptibilities to different antiparasite drugs. Unfortunately, almost all structure-based methods are one-task or one-target Quantitative Structure-Activity Relationships (ot-QSAR) that predict the biological activity of drugs against only one parasite species. Consequently, multi-tasking learning to predict drugs activity against different species by a single model (mt-QSAR) is vitally important. In the two previous works of the present series we reported two single mt-QSAR models in order to predict the antimicrobial activity against different fungal (Bioorg. Med. Chem.2006, 14, 5973-5980) or bacterial species (Bioorg. Med. Chem.2007, 15, 897-902). These mt-QSARs offer a good opportunity (unpractical with ot-QSAR) to construct drug-drug similarity Complex Networks and to map the contribution of sub-structures to function for multiple species. These possibilities were unattended in our previous works. In the present work, we continue this series toward other important direction of chemotherapy (antiparasite drugs) with the development of an mt-QSAR for more than 500 drugs tested in the literature against different parasites. The data were processed by Linear Discriminant Analysis (LDA) classifying drugs as active or non-active against the different tested parasite species. The model correctly classifies 212 out of 244 (87.0%) cases in training series and 207 out of 243 compounds (85.4%) in external validation series. In order to illustrate the performance of the QSAR for the selection of active drugs we carried out an additional virtual screening of antiparasite compounds not used in training or predicting series; the model recognized 97 out of 114 (85.1%) of them. We also give the procedures to construct back-projection maps and to calculate sub-structures contribution to the biological activity. Finally, we used the outputs of the QSAR to construct, by the first time, a multi-species Complex Networks of antiparasite drugs. The network predicted has 380 nodes (compounds), 634 edges (pairs of compounds with similar activity). This network allows us to cluster different compounds and identify on average three known compounds similar to a new query compound according to their profile of biological activity. This is the first attempt to calculate probabilities of antiparasitic action of drugs against different parasites.
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Morales Helguera A, Pérez González M, Dias Soeiro Cordeiro MN, Cabrera Pérez MÁ. Quantitative Structure−Carcinogenicity Relationship for Detecting Structural Alerts in Nitroso Compounds: Species, Rat; Sex, Female; Route of Administration, Gavage. Chem Res Toxicol 2008; 21:633-42. [DOI: 10.1021/tx700336n] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Aliuska Morales Helguera
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Maykel Pérez González
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Maria Natália Dias Soeiro Cordeiro
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Miguel Ángel Cabrera Pérez
- Department of Chemistry and Molecular Simulation and Drug Design Group, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba, and REQUIMTE, Chemistry Department, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Combes R, Grindon C, Cronin MTD, Roberts DW, Garrod J. Proposed integrated decision-tree testing strategies for mutagenicity and carcinogenicity in relation to the EU REACH legislation. Altern Lab Anim 2007; 35:267-87. [PMID: 17559315 DOI: 10.1177/026119290703500201] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Liverpool John Moores University and FRAME recently conducted a research project sponsored by Defra, on the status of alternatives to animal testing with regard to the European Union REACH (Registration, Evaluation and Authorisation of Chemicals) system for the safety testing and risk assessment of chemicals. The project covered all the main toxicity endpoints associated with the REACH system. This paper focuses on the prospects for using alternative methods (both in vitro and in silico) for mutagenicity (genotoxicity) and carcinogenicity testing - two toxicity endpoints, which, together with reproductive toxicity, are of pivotal importance for the REACH system. The manuscript critically discusses well-established testing approaches, and in particular, the requirement for short-term in vivo tests for confirming positive mutagenicity, and the need for the rodent bioassay for detecting non-genotoxic carcinogens. Recently-proposed testing strategies focusing on non-animal approaches are also considered, and our own testing scheme is presented and supported with background information. This scheme makes maximum use of pre-existing data, computer (in silico) and in vitro methods, with weight-of-evidence assessments at each major stage. The need for the improvement of in vitro methods, to reduce the generation of false-positive results, is also discussed. Lastly, ways in which reduction and refinement measures can be used are also considered, and some recommendations are made for future research to facilitate the implementation of the proposed testing scheme.
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Affiliation(s)
- Robert Combes
- FRAME, Russell & Burch House, 96-98 North Sherwood Street, Nottingham, NG1 4EE, UK.
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Cruz-Monteagudo M, González-Díaz H, Agüero-Chapín G, Santana L, Borges F, Domínguez ER, Podda G, Uriarte E. Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems. J Comput Chem 2007; 28:1909-23. [PMID: 17405109 DOI: 10.1002/jcc.20730] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Predicting tissue and environmental distribution of chemicals is of major importance for environmental and life sciences. Most of the molecular descriptors used in computational prediction of chemicals partition behavior consider molecular structure but ignore the nature of the partition system. Consequently, computational models derived up-to-date are restricted to the specific system under study. Here, a free energy-based descriptor (DeltaG(k)) is introduced, which circumvent this problem. Based on DeltaG(k), we developed for the first time a single linear classification model to predict the partition behavior of a broad number of structurally diverse drugs and other chemicals (1300) for 38 different partition systems of biological and environmental significance. The model presented training/predicting set accuracies of 91.79/88.92%. Parametrical assumptions were checked. Desirability analysis was used to explore the levels of the predictors that produce the most desirable partition properties. Finally, inversion of the partition direction for each one of the 38 partition systems evidences that our models correctly classified 89.08% of compounds with an uncertainty of only +/-0.17% independently of the direction of the partition process used to seek the model. Other 10 different classification models (linear, neural networks, and genetic algorithms) were also tested for the same purposes. None of these computational models favorably compare with respect to the linear model indicating that our approach capture the main aspects that govern chemicals partition in different systems.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- Physico-Chemical Molecular Research Unit, Department of Organic Chemistry, Faculty of Pharmacy, University of Porto 4050-047, Porto, Portugal
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Helguera AM, González MP, D S Cordeiro MN, Pérez MAC. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol Appl Pharmacol 2007; 221:189-202. [PMID: 17477948 DOI: 10.1016/j.taap.2007.02.021] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2007] [Revised: 02/16/2007] [Accepted: 02/21/2007] [Indexed: 02/01/2023]
Abstract
Prevention of environmentally induced cancers is a major health problem of which solutions depend on the rapid and accurate screening of potential chemical hazards. Lately, theoretical approaches such as the one proposed here - Quantitative Structure-Activity Relationship (QSAR) - are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the Topological Substructural Molecular Design (TOPS-MODE) approach, aiming at predicting the rodent carcinogenicity of a set of nitroso-compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises nitrosoureas (14 chemicals), N-nitrosamines (18 chemicals) C-nitroso-compounds (1 chemical), nitrosourethane (1 chemical) and nitrosoguanidine (1 chemical), which have been bioassayed in male rat using gavage as the route of administration. Here we are especially concerned in gathering the role of both parameters on the carcinogenic activity of this family of compounds. First, the regression model was derived, upon removal of one identified nitrosamine outlier, and was able to account for more than 84% of the variance in the experimental activity. Second, the TOPS-MODE approach afforded the bond contributions -- expressed as fragment contributions to the carcinogenic activity -- that can be interpreted and provide tools for better understanding the mechanisms of carcinogenesis. Finally, and most importantly, we demonstrate the potentialities of this approach towards the recognition of structural alerts for carcinogenicity predictions.
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Affiliation(s)
- Aliuska Morales Helguera
- Department of Chemistry, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
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Vilar S, Quezada E, Alcaide C, Orallo F, Santana L, Uriarte E. Quantitative Structure Vasodilatory Activity Relationship – Synthesis and “In Silico” and “In Vitro” Evaluation of Resveratrol-Coumarin Hybrids. ACTA ACUST UNITED AC 2007. [DOI: 10.1002/qsar.200630006] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Saíz-Urra L, González MP, Fall Y, Gómez G. Quantitative structure–activity relationship studies of HIV-1 integrase inhibition. 1. GETAWAY descriptors. Eur J Med Chem 2007; 42:64-70. [PMID: 17030481 DOI: 10.1016/j.ejmech.2006.08.005] [Citation(s) in RCA: 39] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2006] [Revised: 08/11/2006] [Accepted: 08/14/2006] [Indexed: 11/21/2022]
Abstract
The GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) approach has been applied to the study of the HIV-1 integrase inhibition of 172 compounds that belong to 11 different chemistry families. A model able to describe more than 68.5% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of the five different approaches, including the use of Randić Molecular Profiles, Geometrical, RDF, 3D-MORSE and WHIM descriptors was able to explain more than 62.4% of the variance in the mentioned property with the same number of variables in the equation. Finally, after extracting five compounds considered by us as outliers the model was able to describe more than 72.5% of the variance in the experimental activity.
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Affiliation(s)
- Liane Saíz-Urra
- Chemical Bioactive Center, Central University of Las Villas, Santa Clara, Villa Clara, CP 54830, Cuba
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González-Díaz H, Olazábal E, Santana L, Uriarte E, González-Díaz Y, Castañedo N. QSAR study of anticoccidial activity for diverse chemical compounds: Prediction and experimental assay of trans-2-(2-nitrovinyl)furan. Bioorg Med Chem 2007; 15:962-8. [PMID: 17081758 DOI: 10.1016/j.bmc.2006.10.032] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2006] [Revised: 10/03/2006] [Accepted: 10/17/2006] [Indexed: 11/21/2022]
Abstract
In this work we report a QSAR model that discriminates between chemically heterogeneous classes of anticoccidial and non-anticoccidial compounds. For this purpose we used the Markovian Chemicals in silico Design (MARCH-INSIDE) approach J. Mol. Mod.2002, 8, 237-245; J. Mol. Mod.2003, 9, 395-407]. Linear discriminant analysis allowed us to fit the discriminant function. This function correctly classifies 86.67% of anticoccidial compounds and 96.23% of inactive compounds in the training series. Overall classification is 94.12%. We validated the model by means of an external predicting series, with 86.96% of global predictability. Remarkably, the present model is based on topological as well as configuration-dependent molecular descriptors. Therefore, the model performs timely calculations and allows discrimination between Z/E and chiral isomers. Finally, to exemplify the use of the model in practice we report the prediction and experimental assay of trans-2-(2-nitrovinyl)furan. It is notable that lesion control was 72.86% at mg/kg of body weight with respect to 60% at 125 mg/kg for amprolium (control drug). The back-projection map for this compound predicts a high level of importance for the double bond and for the nitro group in the trans position. We conclude that the MARCH-INSIDE approach enables the accurate fast track identification of anticoccidial hits. Moreover, trans-2-(2-nitrovinyl)furan seems to be a promising drug for the treatment of coccidiosis.
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Affiliation(s)
- Humberto González-Díaz
- Department of Organic Chemistry & Institute of Industrial Pharmacy, Faculty of Pharmacy, University of Santiago de Compostela, Santiago 15782, Spain.
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González MP, Puente M, Fall Y, Gómez G. In silico studies using Radial Distribution Function approach for predicting affinity of 1 alpha,25-dihydroxyvitamin D(3) analogues for Vitamin D receptor. Steroids 2006; 71:510-27. [PMID: 16566955 DOI: 10.1016/j.steroids.2006.02.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2005] [Revised: 02/02/2006] [Accepted: 02/04/2006] [Indexed: 01/28/2023]
Abstract
The Radial Distribution Function (RDF) approach has been applied to the study of the chick intestinal VDR affinity of 49 Vitamin D analogues. A model able to describe more than 77.5% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of four different approaches, including the use of Topological, BCUT, Randić molecular profiles and Geometrical descriptors were able to explain more than 55% of the variance in the mentioned property, with the same number of variables in the equation.
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González MP, Helguera AM, Collado IG. A topological substructural molecular design to predict soil sorption coefficients for pesticides. Mol Divers 2006; 10:109-18. [PMID: 16710808 DOI: 10.1007/s11030-005-9004-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2005] [Accepted: 10/19/2005] [Indexed: 11/25/2022]
Abstract
A TOPological Sub-structural MOlecular DEsign (TOPS-MODE) approach was used to predict the soil sorption coefficients for a set of pesticide compounds. The obtained model accounted for more than 85% of the data variance and demonstrated the importance of the dipole moment, the standard distance, the polarizability, and the hydrophobicity in describing the property under study. In addition, we compared this new model to a previous one using different descriptors such as WHIM and molecular connectivity indices. Finally, the TOPS-MODE was used to calculate the contribution of different fragments to the soil sorption coefficient of the compounds studied. The present approximation proved to be a good method for studying the soil sorption coefficient for pesticides, but it could also be extended to other series of chemicals.
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Affiliation(s)
- Maykel Pérez González
- Unit of Services, Experimental Sugar Cane Station "Villa Clara-Cienfuegos", Ranchuelo, 53100, Villa Clara, Cuba.
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Morales AH, Pérez MAC, Combes RD, González MP. Quantitative structure activity relationship for the computational prediction of nitrocompounds carcinogenicity. Toxicology 2006; 220:51-62. [PMID: 16414170 DOI: 10.1016/j.tox.2005.11.024] [Citation(s) in RCA: 33] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2005] [Revised: 11/23/2005] [Accepted: 11/24/2005] [Indexed: 10/25/2022]
Abstract
Several nitrocompounds have been screened for carcinogenicity in rodents, but this is a lengthy and expensive process, taking two years and typically costing 2.5 million dollars, and uses large numbers of animals. There is, therefore, much impetus to develop suitable alternative methods. One possible way of predicting carcinogenicity is to use quantitative structure-activity relationships (QSARs). QSARs have been widely utilized for toxicity testing, thereby contributing to a reduction in the need for experimental animals. This paper describes the results of applying a TOPological substructural molecular design (TOPS-MODE) approach for predicting the rodent carcinogenicity of nitrocompounds. The model described 79.10% of the experimental variance, with a standard deviation of 0.424. The predictive power of the model was validated by leave-one-out validation, with a determination coefficient of 0.666. In addition, this approach enabled the contribution of different fragments to carcinogenic potency to be assessed, thereby making the relationships between structure and carcinogenicity to be transparent. It was found that the carcinogenic activity of the chemicals analysed was increased by the presence of a primary amine group bonded to the aromatic ring, a manner that was proportional to the ring aromaticity. The nitro group bonded to an aromatic carbon atom is a more important determinant of carcinogenicity than the nitro group bonded to an aliphatic carbon. Finally, the TOPS-MODE approach was compared with four other predictive models, but none of these could explain more than 66% of the variance in the carcinogenic potency with the same number of variables.
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Affiliation(s)
- Aliuska Helguera Morales
- Department of Chemistry, Faculty of Chemistry and Pharmacy, Central University of Las Villas, Santa Clara, Villa Clara 54830, Cuba
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Combes R, Balls M. Intelligent testing strategies for chemicals testing -- a case of more haste, less speed? Altern Lab Anim 2005; 33:289-97. [PMID: 16180981 DOI: 10.1177/026119290503300302] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The prospects for using (Q)SAR modelling, read-across (chemical) and other non-animal approaches as part of integrated testing strategies for chemical risk assessment, within the framework of the EU REACH legislation, are considered. The potential advantages and limitations of (Q)SAR modelling and read-across methods for chemical regulatory risk assessment are reviewed. It is concluded that it would be premature to base a testing strategy on chemical-based computational modelling approaches, until such time as criteria to validate them for their reliability and relevance by using independent and transparent procedures, have been agreed. This is mainly because of inherent problems in validating and accepting (Q)SARs for regulatory use in ways that are analogous to those that have been developed and applied for in vitro tests. Until this issue has been resolved, it is recommended that testing strategies should be developed which comprise the integrated use of computational and read-across approaches. These should be applied in a cautious and judicious way, in association with available tissue culture methods, and in conjunction with metabolism and biokinetic studies. Such strategies should be intelligently applied by being driven by exposure information (based on bioavailability, not merely on production volume) and hazard information needs, in preference to a tick-box approach. In the meantime, there should be increased efforts to develop improved (Q)SARs, expert systems and new in vitro methods, and, in particular, ways to expedite their validation and acceptance must be found and prospectively agreed with all major stakeholders.
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González MP, Suárez PL, Fall Y, Gómez G. Quantitative structure-activity relationship studies of vitamin D receptor affinity for analogues of 1alpha,25-dihydroxyvitamin D3. 1: WHIM descriptors. Bioorg Med Chem Lett 2005; 15:5165-9. [PMID: 16202592 DOI: 10.1016/j.bmcl.2005.08.065] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2005] [Revised: 08/10/2005] [Accepted: 08/22/2005] [Indexed: 11/24/2022]
Abstract
The weighted holistic invariant molecular (WHIM) approach has been applied to the study of the VDR affinity of 86 vitamin D analogues. A model able to describe more than 71% of the variance in the experimental activity was developed with the use of the mentioned approach. In contrast, none of three different approaches, including the use of BCUT, Galvez topological charge indices, and 2D autocorrelations descriptors, was able to explain more than 38% of the variance in the mentioned property, even with more variables in the equation.
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