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Oselusi SO, Dube P, Odugbemi AI, Akinyede KA, Ilori TL, Egieyeh E, Sibuyi NR, Meyer M, Madiehe AM, Wyckoff GJ, Egieyeh SA. The role and potential of computer-aided drug discovery strategies in the discovery of novel antimicrobials. Comput Biol Med 2024; 169:107927. [PMID: 38184864 DOI: 10.1016/j.compbiomed.2024.107927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Antimicrobial resistance (AMR) has become more of a concern in recent decades, particularly in infections associated with global public health threats. The development of new antibiotics is crucial to ensuring infection control and eradicating AMR. Although drug discovery and development are essential processes in the transformation of a drug candidate from the laboratory to the bedside, they are often very complicated, expensive, and time-consuming. The pharmaceutical sector is continuously innovating strategies to reduce research costs and accelerate the development of new drug candidates. Computer-aided drug discovery (CADD) has emerged as a powerful and promising technology that renews the hope of researchers for the faster identification, design, and development of cheaper, less resource-intensive, and more efficient drug candidates. In this review, we discuss an overview of AMR, the potential, and limitations of CADD in AMR drug discovery, and case studies of the successful application of this technique in the rapid identification of various drug candidates. This review will aid in achieving a better understanding of available CADD techniques in the discovery of novel drug candidates against resistant pathogens and other infectious agents.
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Affiliation(s)
- Samson O Oselusi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Phumuzile Dube
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Adeshina I Odugbemi
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Kolajo A Akinyede
- Department of Science Technology, Biochemistry Unit, The Federal Polytechnic P.M.B.5351, Ado Ekiti, 360231, Nigeria
| | - Tosin L Ilori
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Elizabeth Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa
| | - Nicole Rs Sibuyi
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Mervin Meyer
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Abram M Madiehe
- DSI/Mintek Nanotechnology Innovation Centre (NIC), Biolabels Node, Department of Biotechnology, University of the Western Cape, Private Bag X17, Bellville, Cape Town, 7535, South Africa
| | - Gerald J Wyckoff
- School of Pharmacy, Division of Pharmacology and Pharmaceutical Sciences, University of Missouri, Kansas City, MO, 64110-2446, United States
| | - Samuel A Egieyeh
- School of Pharmacy, University of the Western Cape, Bellville, Cape Town, 7535, South Africa.
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Xu Z, Chughtai H, Tian L, Liu L, Roy JF, Bayen S. Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis. Talanta 2023; 253:123861. [PMID: 36095943 DOI: 10.1016/j.talanta.2022.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/13/2022]
Abstract
Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.
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Affiliation(s)
- Ziyun Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Hamza Chughtai
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Tian
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lan Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
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Pflégr V, Stolaříková J, Vinšová J, Krátký M. Synthesis and Antimycobacterial Activity of Isoniazid Derivatives Tethered with Aliphatic Amines. Curr Top Med Chem 2022; 22:2695-2706. [PMID: 35929626 DOI: 10.2174/1568026622666220805152811] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/27/2022] [Accepted: 05/07/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND There is an urgent need for new antitubercular compounds. Modification of antimycobacterial isonicotinohydrazide at hydrazide N2 provided antimycobacterial active compounds. OBJECTIVE Combining this scaffold with various aliphatic amines that are also frequently present in antitubercular compounds, we have designed, synthesized, and evaluated twenty-three N- (cyclo)alkyl-2-(2-isonicotinoylhydrazineylidene)propanamides and their analogues as potential antimycobacterial compounds. By increasing lipophilicity, we intended to facilitate the penetration of mycobacteria's highly impermeable cell wall. METHODS The target amides were prepared via condensation of isoniazid and pyruvic acid, followed by carbodiimide-mediated coupling with yields from 35 to 98 %. The compounds were screened against Mycobacterium tuberculosis H37Rv and two nontuberculous mycobacteria (M. avium, M. kansasii). RESULTS All the derivatives exhibited low minimum inhibitory concentrations (MIC) from ≤0.125 and 2 μM against M. tuberculosis and nontuberculous mycobacteria, respectively. The most active molecules were substituted by a longer n-alkyl from C8 to C14. Importantly, the compounds showed comparable or even several-fold lower MIC than parent isonicotinohydrazide. Based on in silico predictions, a vast majority of the derivatives share suitable physicochemical properties and structural features for drug-likeness. CONCLUSION Presented amides are promising antimycobacterial agents.
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Affiliation(s)
- Václav Pflégr
- Department of Organic and Bioorganic Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203, 500 05, Hradec Králové, Czech Republic
| | - Jiřina Stolaříková
- Laboratory for Mycobacterial Diagnostics and Tuberculosis, Regional Institute of Public Health in Ostrava, Partyzánské náměstí 7, 702 00, Ostrava, Czech Republic
| | - Jarmila Vinšová
- Department of Organic and Bioorganic Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203, 500 05, Hradec Králové, Czech Republic
| | - Martin Krátký
- Department of Organic and Bioorganic Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Akademika Heyrovského 1203, 500 05, Hradec Králové, Czech Republic
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Kerner J, Dogan A, von Recum H. Machine learning and big data provide crucial insight for future biomaterials discovery and research. Acta Biomater 2021; 130:54-65. [PMID: 34087445 DOI: 10.1016/j.actbio.2021.05.053] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 05/24/2021] [Accepted: 05/25/2021] [Indexed: 02/06/2023]
Abstract
Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.
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Affiliation(s)
- Jacob Kerner
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Alan Dogan
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
| | - Horst von Recum
- Case Western Reserve University; 10900 Euclid Ave., Cleveland Ohio 44106.
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Badura A, Krysiński J, Nowaczyk A, Buciński A. Prediction of the antimicrobial activity of quaternary ammonium salts against Staphylococcus aureus using artificial neural networks. ARAB J CHEM 2021. [DOI: 10.1016/j.arabjc.2021.103233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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Meftahi N, Walker ML, Smith BJ. Predicting aqueous solubility by QSPR modeling. J Mol Graph Model 2021; 106:107901. [PMID: 33857890 DOI: 10.1016/j.jmgm.2021.107901] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 12/26/2022]
Abstract
The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R2 greater than 0.7 and Q2 greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R2 and standard deviation.
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Affiliation(s)
- Nastaran Meftahi
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Michael L Walker
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Brian J Smith
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia.
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Winkler DA. Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases. Front Chem 2021; 9:614073. [PMID: 33791277 PMCID: PMC8005575 DOI: 10.3389/fchem.2021.614073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/18/2021] [Indexed: 12/11/2022] Open
Abstract
Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.
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Affiliation(s)
- David A Winkler
- Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC, Australia.,Latrobe Institute for Molecular Science, La Trobe University, Bundoora, VIC, Australia.,School of Pharmacy, University of Nottingham, Nottingham, United Kingdom.,CSIRO Data61, Pullenvale, QLD, Australia
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Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6662779. [PMID: 33727951 PMCID: PMC7937476 DOI: 10.1155/2021/6662779] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 12/10/2020] [Accepted: 02/18/2021] [Indexed: 02/08/2023]
Abstract
Introduction A Noninvasive diagnosis model for digestive diseases is the vital issue for the current clinical research. Our systematic review is aimed at demonstrating diagnosis accuracy between the BP-ANN algorithm and linear regression in digestive disease patients, including their activation function and data structure. Methods We reported the systematic review according to the PRISMA guidelines. We searched related articles from seven electronic scholarly databases for comparison of the diagnosis accuracy focusing on BP-ANN and linear regression. The characteristics, patient number, input/output marker, diagnosis accuracy, and results/conclusions related to comparison were extracted independently based on inclusion criteria. Results Nine articles met all the criteria and were enrolled in our review. Of those enrolled articles, the publishing year ranged from 1991 to 2017. The sample size ranged from 42 to 3222 digestive disease patients, and all of the patients showed comparable biomarkers between the BP-ANN algorithm and linear regression. According to our study, 8 literature demonstrated that the BP-ANN model is superior to linear regression in predicting the disease outcome based on AUROC results. One literature reported linear regression to be superior to BP-ANN for the early diagnosis of colorectal cancer. Conclusion The BP-ANN algorithm and linear regression both had high capacity in fitting the diagnostic model and BP-ANN displayed more prediction accuracy for the noninvasive diagnosis model of digestive diseases. We compared the activation functions and data structure between BP-ANN and linear regression for fitting the diagnosis model, and the data suggested that BP-ANN was a comprehensive recommendation algorithm.
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Farajtabar A, Zhao H. Equilibrium solubility of 7-amino-4-methylcoumarin in several aqueous co-solvent mixtures revisited: Transfer property, solute-solvent and solvent-solvent interactions and preferential solvation. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.114407] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Suay‐Garcia B, Bueso‐Bordils JI, Falcó A, Pérez‐Gracia MT, Antón‐Fos G, Alemán‐López P. Quantitative structure–activity relationship methods in the discovery and development of antibacterials. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1472] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Beatriz Suay‐Garcia
- Departamento de Matemáticas, Física y Ciencias Tecnológicas Universidad Cardenal Herrera‐CEU, CEU Universities Alfara del Patriarca, Valencia Spain
| | - Jose Ignacio Bueso‐Bordils
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Antonio Falcó
- Departamento de Matemáticas, Física y Ciencias Tecnológicas Universidad Cardenal Herrera‐CEU, CEU Universities Alfara del Patriarca, Valencia Spain
| | - María Teresa Pérez‐Gracia
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Gerardo Antón‐Fos
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
| | - Pedro Alemán‐López
- Departamento de Farmacia, Universidad Cardenal Herrera‐CEU CEU Universities Alfara del Patriarca, Valencia Spain
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Teixeira C, Ventura C, Gomes JRB, Gomes P, Martins F. Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure-Activity Relationship Studies. Molecules 2020; 25:molecules25030456. [PMID: 31973244 PMCID: PMC7037561 DOI: 10.3390/molecules25030456] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 01/15/2020] [Accepted: 01/17/2020] [Indexed: 11/22/2022] Open
Abstract
Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), remains one of the top ten causes of death worldwide and the main cause of mortality from a single infectious agent. The upsurge of multi- and extensively-drug resistant tuberculosis cases calls for an urgent need to develop new and more effective antitubercular drugs. As the cinnamoyl scaffold is a privileged and important pharmacophore in medicinal chemistry, some studies were conducted to find novel cinnamic acid derivatives (CAD) potentially active against tuberculosis. In this context, we have engaged in the setting up of a quantitative structure–activity relationships (QSAR) strategy to: (i) derive through multiple linear regression analysis a statistically significant model to describe the antitubercular activity of CAD towards wild-type Mtb; and (ii) identify the most relevant properties with an impact on the antitubercular behavior of those derivatives. The best-found model involved only geometrical and electronic CAD related properties and was successfully challenged through strict internal and external validation procedures. The physicochemical information encoded by the identified descriptors can be used to propose specific structural modifications to design better CAD antitubercular compounds.
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Affiliation(s)
- Cátia Teixeira
- LAQV-REQUIMTE, Departamento de Química e Bioquímica da Faculdade de Ciências da Universidade do Porto, P-4169-007 Porto, Portugal
- Correspondence: (C.T.); (F.M.)
| | - Cristina Ventura
- Instituto Superior de Educação e Ciências, P-1750-142 Lisboa, Portugal
| | - José R. B. Gomes
- CICECO, Departamento de Química, Universidade de Aveiro, P-3810-193 Aveiro, Portugal
| | - Paula Gomes
- LAQV-REQUIMTE, Departamento de Química e Bioquímica da Faculdade de Ciências da Universidade do Porto, P-4169-007 Porto, Portugal
| | - Filomena Martins
- Centro de Química e Bioquímica (CQB), Centro de Química Estrutural (CQE), Faculdade de Ciências da Universidade de Lisboa, P-1749-016 Lisboa, Portugal
- Correspondence: (C.T.); (F.M.)
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Furxhi I, Murphy F, Mullins M, Poland CA. Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index. Toxicol Lett 2019; 312:157-166. [PMID: 31102714 DOI: 10.1016/j.toxlet.2019.05.016] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 04/12/2019] [Accepted: 05/13/2019] [Indexed: 01/22/2023]
Abstract
Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads have been made in our understanding of the risks posed to human health and the environment by NPs but this area will require continuous research and monitoring. In recent years Machine Learning (ML) techniques have exploited large datasets and computation power to create breakthroughs in diverse fields from facial recognition to genomics. More recently, ML techniques have been applied to nanotoxicology with very encouraging results. In this study, categories of ML classifiers (rules, trees, lazy, functions and bayes) were compared using datasets from the Safe and Sustainable Nanotechnology (S2NANO) database to investigate their performance in predicting NPs in vitro toxicity. Physicochemical properties, toxicological and quantum-mechanical attributes and in vitro experimental conditions were used as input variables to predict the toxicity of NPs based on cell viability. Voting, an ensemble meta-classifier, was used to combine base models to optimize the classification prediction of toxicity. To facilitate inter-comparison, a Copeland Index was applied that ranks the classifiers according to their performance and suggested the optimal classifier. Neural Network (NN) and Random forest (RF) showed the best performance in the majority of the datasets used in this study. However, the combination of classifiers demonstrated an improved prediction resulting meta-classifier to have higher indices. This proposed Copeland Index can now be used by researchers to identify and clearly prioritize classifiers in order to achieve more accurate classification predictions for NP toxicity for a given dataset.
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Affiliation(s)
- Irini Furxhi
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Finbarr Murphy
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Martin Mullins
- Dept. of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93, Ireland.
| | - Craig A Poland
- ELEGI/Colt Laboratory, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, Scotland, United Kingdom.
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Sarigiannis DA, Karakitsios S, Dominguez-Romero E, Papadaki K, Brochot C, Kumar V, Schuhmacher M, Sy M, Mielke H, Greiner M, Mengelers M, Scheringer M. Physiology-based toxicokinetic modelling in the frame of the European Human Biomonitoring Initiative. ENVIRONMENTAL RESEARCH 2019; 172:216-230. [PMID: 30818231 DOI: 10.1016/j.envres.2019.01.045] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Revised: 01/25/2019] [Accepted: 01/28/2019] [Indexed: 06/09/2023]
Abstract
Given the opportunities provided by internal dosimetry modelling in the interpretation of human biomonitoring (HBM) data, the assessment of the links between exposure to chemicals and observed HBM data can be effectively supported by PBTK modelling. This paper gives a comprehensive review of available human PBTK models for compounds selected as a priority by the European Human Biomonitoring Initiative (HBM4EU). We highlight their advantages and deficiencies and suggest steps for advanced internal dose modelling. The review of the available PBTK models highlighted the conceptual differences between older models compared to the ones developed recently, reflecting commensurate differences in research questions. Due to the lack of coordinated strategies for deriving useful biomonitoring data for toxicokinetic properties, significant problems in model parameterisation still remain; these are further increased by the lack of human toxicokinetic data due to ethics issues. Finally, questions arise as well as to the extent they are really representative of interindividual variability. QSARs for toxicokinetic properties is a complementary approach for PBTK model parameterisation, especially for data poor chemicals. This approach could be expanded to model chemico-biological interactions such as intestinal absorption and renal clearance; this could serve the development of more complex generic PBTK models that could be applied to newly derived chemicals. Another gap identified is the framework for mixture interaction terms among compounds that could eventually interact in metabolism. From the review it was concluded that efforts should be shifted toward the development of generic multi-compartmental and multi-route models, supported by targeted biomonitoring coupled with parameterisation by both QSAR approach and experimental (in-vivo and in-vitro) data for newly developed and data poor compounds.
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Affiliation(s)
- Dimosthenis A Sarigiannis
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece.
| | - Spyros Karakitsios
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece; HERACLES Research Center on the Exposome and Health, Center for Interdisciplinary Research and Innovation, Balkan Center, Bldg. B, 10th km Thessaloniki-Thermi Road, 57001, Greece
| | | | - Krystalia Papadaki
- Aristotle University of Thessaloniki, Department of Chemical Engineering, Environmental Engineering Laboratory, University Campus, Thessaloniki 54124, Greece
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Safder U, Nam K, Kim D, Heo S, Yoo C. A real time QSAR-driven toxicity evaluation and monitoring of iron containing fine particulate matters in indoor subway stations. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2019; 169:361-369. [PMID: 30458403 DOI: 10.1016/j.ecoenv.2018.11.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 10/30/2018] [Accepted: 11/07/2018] [Indexed: 06/09/2023]
Abstract
A fine particulate matter less than 2.5 µm (PM2.5) in the underground subway system are the cause of many diseases. The iron containing PMs frequently confront in underground stations, which ultimately have an impact on the health of living beings especially in children. Hence, it is necessary to conduct toxicity assessment of chemical species and regularized the indoor air pollutants to ensure the good health of children. Therefore, in this study, a new indoor air quality (IAQ) index is proposed based on toxicity assessment by quantitative structure-activity relationship (QSAR) model. The new indices called comprehensive indoor air toxicity (CIAT) and cumulative comprehensive indoor air toxicity (CCIAT) suggests the new standards based on toxicity assessment of PM2.5. QSAR based deep neural network (DNN) exhibited the best model in predicting the toxicity assessment of chemical species in particulate matters, which yield lowest RMSE and QF32 values of 0.6821 and 0.8346, respectively, in the test phase. After integration with a standard concentration of PM2.5, two health risk indices of CIAT and CCIAT are introduced based on toxicity assessment results, which can be use as the toxicity standard of PM2.5 for detail IAQ management in a subway station. These new health risk indices suggest more sensitive air pollutant level of iron containing fine particulate matters or molecular level contaminants in underground spaces, alerting the health risk of adults and children in "unhealthy for sensitive group".
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Affiliation(s)
- Usman Safder
- Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea
| | - KiJeon Nam
- Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea
| | - Dongwoo Kim
- Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea
| | - SungKu Heo
- Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea
| | - ChangKyoo Yoo
- Dept. of Environmental Science and Engineering, College of Engineering, Center for Environmental Studies, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Republic of Korea.
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15
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Sheikhpour R, Sarram MA, Sheikhpour E. Semi-supervised sparse feature selection via graph Laplacian based scatter matrix for regression problems. Inf Sci (N Y) 2018. [DOI: 10.1016/j.ins.2018.08.035] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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16
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Meftahi N, Walker ML, Enciso M, Smith BJ. Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling. Sci Rep 2018; 8:9779. [PMID: 29950681 PMCID: PMC6021403 DOI: 10.1038/s41598-018-28105-6] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 06/15/2018] [Indexed: 11/28/2022] Open
Abstract
The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublimation; here we describe several models that build upon the previous models for predicting the enthalpy of sublimation. The most robust and predictive model constructed using multiple linear regression, with the fewest number of descriptors for estimating this property, was obtained with an R2 of the training set of 0.71, an R2 of the test set of 0.62, and a standard deviation of 9.1 kJ mol−1. This model could be improved by training using a neural network, yielding an R2 of the training and test sets of 0.80 and 0.63, respectively, and a standard deviation of 8.9 kJ mol−1.
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Affiliation(s)
- Nastaran Meftahi
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Michael L Walker
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Marta Enciso
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia
| | - Brian J Smith
- La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria, 3086, Australia.
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17
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Saini B, Srivastava S. Nanotoxicity prediction using computational modelling - review and future directions. ACTA ACUST UNITED AC 2018. [DOI: 10.1088/1757-899x/348/1/012005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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18
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Dynamic Analysis of Ecological Environment Quality Combined with Water Conservation Changes in National Key Ecological Function Areas in China. SUSTAINABILITY 2018. [DOI: 10.3390/su10041202] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Sheikhpour R, Sarram MA, Rezaeian M, Sheikhpour E. QSAR modelling using combined simple competitive learning networks and RBF neural networks. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2018; 29:257-276. [PMID: 29372662 DOI: 10.1080/1062936x.2018.1424030] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Accepted: 01/02/2018] [Indexed: 06/07/2023]
Abstract
The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.
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Affiliation(s)
- R Sheikhpour
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - M A Sarram
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - M Rezaeian
- a Department of Computer Engineering , Yazd University , Yazd , Iran
| | - E Sheikhpour
- b Hematology and Oncology Research Center , Shahid Sadoughi University of Medical Sciences , Yazd , Iran
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20
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Barigye SJ, Freitas MP, Ausina P, Zancan P, Sola-Penna M, Castillo-Garit JA. Discrete Fourier Transform-Based Multivariate Image Analysis: Application to Modeling of Aromatase Inhibitory Activity. ACS COMBINATORIAL SCIENCE 2018; 20:75-81. [PMID: 29297675 DOI: 10.1021/acscombsci.7b00155] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
We recently generalized the formerly alignment-dependent multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) method through the application of the discrete Fourier transform (DFT), allowing for its application to noncongruent and structurally diverse chemical compound data sets. Here we report the first practical application of this method in the screening of molecular entities of therapeutic interest, with human aromatase inhibitory activity as the case study. We developed an ensemble classification model based on the two-dimensional (2D) DFT MIA-QSAR descriptors, with which we screened the NCI Diversity Set V (1593 compounds) and obtained 34 chemical compounds with possible aromatase inhibitory activity. These compounds were docked into the aromatase active site, and the 10 most promising compounds were selected for in vitro experimental validation. Of these compounds, 7419 (nonsteroidal) and 89 201 (steroidal) demonstrated satisfactory antiproliferative and aromatase inhibitory activities. The obtained results suggest that the 2D-DFT MIA-QSAR method may be useful in ligand-based virtual screening of new molecular entities of therapeutic utility.
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Affiliation(s)
- Stephen J. Barigye
- Department
of Chemistry, McGill University, 801 Sherbrooke Street West, Montréal, QC H3A 0B8, Canada
| | - Matheus P. Freitas
- Department
of Chemistry, Federal University of Lavras, P.O. Box 3037, 37200-000 Lavras-MG Brazil
| | - Priscila Ausina
- Laboratório
de Enzimologia e Controle do Metabolismo (LabECoM), Departamento de
Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de
Janeiro-RJ, Brazil
| | - Patricia Zancan
- Laboratório
de Oncobiologia Molecular (LabOMol), Departamento de Biotecnologia
Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de Janeiro-RJ, Brazil
| | - Mauro Sola-Penna
- Laboratório
de Enzimologia e Controle do Metabolismo (LabECoM), Departamento de
Biotecnologia Farmacêutica, Faculdade de Farmácia, Universidade Federal do Rio de Janeiro, 21941-902 Rio de
Janeiro-RJ, Brazil
| | - Juan A. Castillo-Garit
- Unidad
de Toxicología Experimental, Universidad de Ciencias Médicas “Serafín Ruiz de Zárate Ruiz”, Santa Clara, 50200 Villa Clara, Cuba
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21
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A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities. J Comput Aided Mol Des 2017; 32:375-384. [DOI: 10.1007/s10822-017-0094-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Accepted: 12/15/2017] [Indexed: 10/18/2022]
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22
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Development of QSARs for parameterizing Physiology Based ToxicoKinetic models. Food Chem Toxicol 2017; 106:114-124. [DOI: 10.1016/j.fct.2017.05.029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 04/13/2017] [Accepted: 05/14/2017] [Indexed: 11/23/2022]
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23
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Gomes MN, Braga RC, Grzelak EM, Neves BJ, Muratov E, Ma R, Klein LL, Cho S, Oliveira GR, Franzblau SG, Andrade CH. QSAR-driven design, synthesis and discovery of potent chalcone derivatives with antitubercular activity. Eur J Med Chem 2017; 137:126-138. [PMID: 28582669 DOI: 10.1016/j.ejmech.2017.05.026] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 05/04/2017] [Accepted: 05/08/2017] [Indexed: 10/19/2022]
Abstract
New anti-tuberculosis (anti-TB) drugs are urgently needed to battle drug-resistant Mycobacterium tuberculosis strains and to shorten the current 6-12-month treatment regimen. In this work, we have continued the efforts to develop chalcone-based anti-TB compounds by using an in silico design and QSAR-driven approach. Initially, we developed SAR rules and binary QSAR models using literature data for targeted design of new heteroaryl chalcone compounds with anti-TB activity. Using these models, we prioritized 33 compounds for synthesis and biological evaluation. As a result, 10 heteroaryl chalcone compounds (4, 8, 9, 11, 13, 17-20, and 23) were found to exhibit nanomolar activity against replicating mycobacteria, low micromolar activity against nonreplicating bacteria, and nanomolar and micromolar against rifampin (RMP) and isoniazid (INH) monoresistant strains (rRMP and rINH) (<1 μM and <10 μM, respectively). The series also show low activity against commensal bacteria and generally show good selectivity toward M. tuberculosis, with very low cytotoxicity against Vero cells (SI = 11-545). Our results suggest that our designed heteroaryl chalcone compounds, due to their high potency and selectivity, are promising anti-TB agents.
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Affiliation(s)
- Marcelo N Gomes
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil
| | - Rodolpho C Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil
| | - Edyta M Grzelak
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Bruno J Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil; Postgraduate Program of Society, Technology and Environment, University Center of Anápolis/UniEVANGELICA, Anápolis, Goiás, 75083-515, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27955-7568, United States; Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Rui Ma
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Larry L Klein
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | - Sanghyun Cho
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States
| | | | - Scott G Franzblau
- Institute for Tuberculosis Research, University of Illinois at Chicago, 833 South Wood Street, Chicago, IL 60612, United States.
| | - Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia, Goiás 74605-510, Brazil.
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24
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Santos P, López-Vallejo F, Soto CY. In silico approaches and chemical space of anti-P-type ATPase compounds for discovering new antituberculous drugs. Chem Biol Drug Des 2017; 90:175-187. [PMID: 28111912 DOI: 10.1111/cbdd.12950] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Tuberculosis (TB) is one of the most important public health problems around the world. The emergence of multi-drug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis strains has driven the finding of alternative anti-TB targets. In this context, P-type ATPases are interesting therapeutic targets due to their key role in ion homeostasis across the plasma membrane and the mycobacterial survival inside macrophages. In this review, in silico and experimental strategies used for the rational design of new anti-TB drugs are presented; in addition, the chemical space distribution based on the structure and molecular properties of compounds with anti-TB and anti-P-type ATPase activity is discussed. The chemical space distribution compared to public compound libraries demonstrates that natural product libraries are a source of novel chemical scaffolds with potential anti-P-type ATPase activity. Furthermore, compounds that experimentally display anti-P-type ATPase activity belong to a chemical space of molecular properties comparable to that occupied by those approved for oral use, suggesting that these kinds of molecules have a good pharmacokinetic profile (drug-like) for evaluation as potential anti-TB drugs.
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Affiliation(s)
- Paola Santos
- Chemistry Department, Faculty of Sciences, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Fabian López-Vallejo
- Chemistry Department, Faculty of Sciences, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Carlos-Y Soto
- Chemistry Department, Faculty of Sciences, Universidad Nacional de Colombia, Bogotá, Colombia
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25
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Oksel C, Ma CY, Liu JJ, Wilkins T, Wang XZ. Literature Review of (Q)SAR Modelling of Nanomaterial Toxicity. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 947:103-142. [PMID: 28168667 DOI: 10.1007/978-3-319-47754-1_5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Despite the clear benefits that nanotechnology can bring to various sectors of industry, there are serious concerns about the potential health risks associated with engineered nanomaterials (ENMs), intensified by the limited understanding of what makes ENMs toxic and how to make them safe. As the use of ENMs for commercial purposes and the number of workers/end-users being exposed to these materials on a daily basis increases, the need for assessing the potential adverse effects of multifarious ENMs in a time- and cost-effective manner becomes more apparent. One strategy to alleviate the problem of testing a large number and variety of ENMs in terms of their toxicological properties is through the development of computational models that decode the relationships between the physicochemical features of ENMs and their toxicity. Such data-driven models can be used for hazard screening, early identification of potentially harmful ENMs and the toxicity-governing physicochemical properties, and accelerating the decision-making process by maximising the use of existing data. Moreover, these models can also support industrial, regulatory and public needs for designing inherently safer ENMs. This chapter is mainly concerned with the investigation of the applicability of (quantitative) structure-activity relationship ((Q)SAR) methods to modelling of ENMs' toxicity. It summarizes the key components required for successful application of data-driven toxicity prediction techniques to ENMs, the published studies in this field and the current limitations of this approach.
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Affiliation(s)
- Ceyda Oksel
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Cai Y Ma
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Jing J Liu
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China
| | - Terry Wilkins
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK
| | - Xue Z Wang
- Institute of Particle Science and Engineering, School of Chemical and Process Engineering, University of Leeds, Leeds, LS2 9JT, UK.
- School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, 510641, China.
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26
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Chen M, Yang F, Kang J, Yang X, Lai X, Gao Y. Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking. Molecules 2016; 21:molecules21121639. [PMID: 27916850 PMCID: PMC6273961 DOI: 10.3390/molecules21121639] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 11/21/2016] [Accepted: 11/26/2016] [Indexed: 12/19/2022] Open
Abstract
In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.
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Affiliation(s)
- Meimei Chen
- College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China.
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Fafu Yang
- College of Chemistry and Chemical Engineering, Fujian Normal University, Fuzhou 350007, Fujian, China.
| | - Jie Kang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Xuemei Yang
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Xinmei Lai
- College of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China.
| | - Yuxing Gao
- College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
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27
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Biomass Resources Distribution in the Terrestrial Ecosystem of China. SUSTAINABILITY 2015. [DOI: 10.3390/su7078548] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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28
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Teixeira VH, Ventura C, Leitão R, Ràfols C, Bosch E, Martins F, Machuqueiro M. Molecular Details of INH-C10 Binding to wt KatG and Its S315T Mutant. Mol Pharm 2015; 12:898-909. [DOI: 10.1021/mp500736n] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Vitor H. Teixeira
- Centro
de Química e Bioquímica and Departamento de Química
e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Cristina Ventura
- Centro
de Química e Bioquímica and Departamento de Química
e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Instituto Superior de Educação e Ciências, Alameda das Linhas de Torres 179, 1750 Lisboa, Portugal
| | - Ruben Leitão
- Centro
de Química e Bioquímica and Departamento de Química
e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
- Área
Departamental de Engenharia Química, Instituto Superior de
Engenharia de Lisboa, Instituto Politécnico de Lisboa, R. Conselheiro
Emídio Navarro, 1, 1959-007 Lisboa, Portugal
| | - Clara Ràfols
- Departament
de Química Analítica and Institut de Biomedicina (IBUB), Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain
| | - Elisabeth Bosch
- Departament
de Química Analítica and Institut de Biomedicina (IBUB), Universitat de Barcelona, Martí i Franquès 1-11, 08028 Barcelona, Spain
| | - Filomena Martins
- Centro
de Química e Bioquímica and Departamento de Química
e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Miguel Machuqueiro
- Centro
de Química e Bioquímica and Departamento de Química
e Bioquímica, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
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29
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Ekins S, Freundlich JS, Reynolds RC. Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis. J Chem Inf Model 2014; 54:2157-65. [PMID: 24968215 DOI: 10.1021/ci500264r] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.
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Affiliation(s)
- Sean Ekins
- Collaborations in Chemistry , 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina 27526, United States
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30
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Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming. BIOMED RESEARCH INTERNATIONAL 2014; 2014:210672. [PMID: 24971318 PMCID: PMC4054925 DOI: 10.1155/2014/210672] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Accepted: 04/29/2014] [Indexed: 11/17/2022]
Abstract
Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R2) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.
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Martins F, Santos S, Ventura C, Elvas-Leitão R, Santos L, Vitorino S, Reis M, Miranda V, Correia HF, Aires-de-Sousa J, Kovalishyn V, Latino DA, Ramos J, Viveiros M. Design, synthesis and biological evaluation of novel isoniazid derivatives with potent antitubercular activity. Eur J Med Chem 2014; 81:119-38. [DOI: 10.1016/j.ejmech.2014.04.077] [Citation(s) in RCA: 73] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 03/08/2014] [Accepted: 04/26/2014] [Indexed: 11/28/2022]
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