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Hernández-Silva D, Alcaraz-Pérez F, Pérez-Sánchez H, Cayuela ML. Virtual screening and zebrafish models in tandem, for drug discovery and development. Expert Opin Drug Discov 2022:1-13. [DOI: 10.1080/17460441.2022.2147503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- David Hernández-Silva
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Structural Bioinformatics and High-Performance Computing Research Group (BIOHPC), Computer Engineering Department, Universidad Católica de Murcia (UCAM), Guadalupe, 30107 Murcia, Spain
| | - Francisca Alcaraz-Pérez
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Horacio Pérez-Sánchez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Maria Luisa Cayuela
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
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PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors. Biomedicines 2022; 10:biomedicines10020491. [PMID: 35203699 PMCID: PMC8962338 DOI: 10.3390/biomedicines10020491] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/10/2022] [Accepted: 02/15/2022] [Indexed: 02/07/2023] Open
Abstract
Pancreatic cancer (PANC) is a dangerous type of cancer that is a major cause of mortality worldwide and exhibits a remarkably poor prognosis. To date, discovering anti-PANC agents remains a very complex and expensive process. Computational approaches can accelerate the search for anti-PANC agents. We report for the first time two models that combined perturbation theory with machine learning via a multilayer perceptron network (PTML-MLP) to perform the virtual design and prediction of molecules that can simultaneously inhibit multiple PANC cell lines and PANC-related proteins, such as caspase-1, tumor necrosis factor-alpha (TNF-alpha), and the insulin-like growth factor 1 receptor (IGF1R). Both PTML-MLP models exhibited accuracies higher than 78%. Using the interpretation from one of the PTML-MLP models as a guideline, we extracted different molecular fragments desirable for the inhibition of the PANC cell lines and the aforementioned PANC-related proteins and then assembled some of those fragments to form three new molecules. The two PTML-MLP models predicted the designed molecules as potentially versatile anti-PANC agents through inhibition of the three PANC-related proteins and multiple PANC cell lines. Conclusions: This work opens new horizons for the application of the PTML modeling methodology to anticancer research.
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A Comprehensive In Silico Exploration of Pharmacological Properties, Bioactivities, Molecular Docking, and Anticancer Potential of Vieloplain F from Xylopia vielana Targeting B-Raf Kinase. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27030917. [PMID: 35164181 PMCID: PMC8839023 DOI: 10.3390/molecules27030917] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/17/2022] [Accepted: 01/25/2022] [Indexed: 01/21/2023]
Abstract
Compounds derived from plants have several anticancer properties. In the current study, one guaiane-type sesquiterpene dimer, vieloplain F, isolated from Xylopia vielana species, was tested against B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma. A comprehensive in silico analysis was conducted in this research to understand the pharmacological properties of a compound encompassing absorption, distribution, metabolism, excretion, and toxicity (ADMET), bioactivity score predictions, and molecular docking. During ADMET estimations, the FDA-approved medicine vemurafenib was hepatotoxic, cytochrome-inhibiting, and non-cardiotoxic compared to the vieloplain F. The bioactivity scores of vieloplain F were active for nuclear receptor ligand and enzyme inhibitor. During molecular docking experiments, the compound vieloplain F has displayed a higher binding potential with −11.8 kcal/mol energy than control vemurafenib −10.2 kcal/mol. It was shown that intermolecular interaction with the B-Raf complex and the enzyme’s active gorge through hydrogen bonding and hydrophobic contacts was very accurate for the compound vieloplain F, which was then examined for MD simulations. In addition, simulations using MM-GBSA showed that vieloplain F had the greatest propensity to bind to active site residues. The vieloplain F has predominantly represented a more robust profile compared to control vemurafenib, and these results opened the road for vieloplain F for its utilization as a plausible anti-melanoma agent and anticancer drug in the next era.
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Shams Ul Hassan S, Abbas SQ, Hassan M, Jin HZ. Computational Exploration of Anti-Cancer Potential of Guaiane Dimers from Xylopia vielana by Targeting B-Raf Kinase Using Chemo-Informatics, Molecular Docking and MD Simulation Studies. Anticancer Agents Med Chem 2021; 22:731-746. [PMID: 34645380 DOI: 10.2174/1871520621666211013115500] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 02/10/2021] [Accepted: 02/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Natural products from herbs are prolific to display robust anticancer activities. OBJECTIVES In the current study, B-Raf kinase protein (PDB: 3OG7), a potent target for melanoma, was tested against two guaiane-type sesquiterpene dimers, xylopin E-F, obtained from Xylopia vielana. METHODS In this work, a systematic in silico study using ADMET analysis, bioactivity score forecasts, molecular docking, and its simulations were conducted to understand compounds' pharmacological properties. RESULTS During ADMET predictions of both the compounds, Xylopin E-F has displayed a safer profile in hepatotoxicity, cytochrome inhibition, and only xylopin F displayed as non-cardiotoxic compared to FDA approved drug vemurafenib. Both the compounds were proceeded to molecular docking experiments using Autodock docking software and both the compounds Xylopin E-F have displayed higher binding potential with -11.5Kcal/mol energy compared to control vemurafenib -10.2 Kcal/mol. All the compounds were further evaluated for their MD simulations and their molecular interactions with the B-Raf kinase complex displayed precise interactions with the active gorge of the enzyme by hydrogen bonding. CONCLUSIONS Overall, xylopin F had a better profile relative to xylopin E and vemurafenib, and these findings indicated that this bio-molecule could be used as an anti-melanoma agent and as a possible anticancer drug in the future. Therefore, this is a systematic optimized in silico approach to creating an anticancer pathway for guaiane dimers against the backdrop of its potential for future drug development.
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Affiliation(s)
- Syed Shams Ul Hassan
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240. China
| | - Syed Qamar Abbas
- Department of Pharmacy, Sarhad University of Science and Technology, Peshawar. Pakistan
| | - Mubashir Hassan
- Institute of Molecular Biology and Biotechnology, The University of Lahore. Pakistan
| | - Hui-Zi Jin
- Shanghai Key Laboratory for Molecular Engineering of Chiral Drugs, School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240. China
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Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors. Antibiotics (Basel) 2021; 10:antibiotics10081005. [PMID: 34439055 PMCID: PMC8388932 DOI: 10.3390/antibiotics10081005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.
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A novel indazole derivative, compound Cyy-272, attenuates LPS-induced acute lung injury by inhibiting JNK phosphorylation. Toxicol Appl Pharmacol 2021; 428:115648. [PMID: 34280409 DOI: 10.1016/j.taap.2021.115648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 06/25/2021] [Accepted: 07/09/2021] [Indexed: 11/20/2022]
Abstract
Acute lung injury (ALI) is a diffuse lung dysfunction disease characterized by high prevalence and high mortality. Thus far, no effective pharmacological treatment has been made for ALI in clinics. Inflammation is critical to the development of ALI. Therefore, anti-inflammation may be a potential therapy strategy for ALI. Indazole-containing derivatives, representing one of the most important heterocycles in drug molecules, are endowed with a broad range of biological properties, such as anti-cancer and anti-inflammation. In the current study, we investigated the biological effects of Cyy-272, a newly synthesized indazole compound, on LPS-induced ALI both in vivo and in vitro. Results show that Cyy-272 can inhibit the release of inflammatory cytokines in LPS-stimulated macrophage and alleviate LPS induced ALI. Further experiment revealed that Cyy-272 exhibit anti-inflammation activity by inhibiting JNK phosphorylation. Overall, our studies show that an indazole derivative, Cyy-272, is effective in suppressing LPS-induced JNK activation and inflammatory signaling.
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Pensado-López A, Fernández-Rey J, Reimunde P, Crecente-Campo J, Sánchez L, Torres Andón F. Zebrafish Models for the Safety and Therapeutic Testing of Nanoparticles with a Focus on Macrophages. NANOMATERIALS 2021; 11:nano11071784. [PMID: 34361170 PMCID: PMC8308170 DOI: 10.3390/nano11071784] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/05/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022]
Abstract
New nanoparticles and biomaterials are increasingly being used in biomedical research for drug delivery, diagnostic applications, or vaccines, and they are also present in numerous commercial products, in the environment and workplaces. Thus, the evaluation of the safety and possible therapeutic application of these nanomaterials has become of foremost importance for the proper progress of nanotechnology. Due to economical and ethical issues, in vitro and in vivo methods are encouraged for the testing of new compounds and/or nanoparticles, however in vivo models are still needed. In this scenario, zebrafish (Danio rerio) has demonstrated potential for toxicological and pharmacological screenings. Zebrafish presents an innate immune system, from early developmental stages, with conserved macrophage phenotypes and functions with respect to humans. This fact, combined with the transparency of zebrafish, the availability of models with fluorescently labelled macrophages, as well as a broad variety of disease models offers great possibilities for the testing of new nanoparticles. Thus, with a particular focus on macrophage-nanoparticle interaction in vivo, here, we review the studies using zebrafish for toxicological and biodistribution testing of nanoparticles, and also the possibilities for their preclinical evaluation in various diseases, including cancer and autoimmune, neuroinflammatory, and infectious diseases.
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Affiliation(s)
- Alba Pensado-López
- Department of Zoology, Genetics and Physical Anthropology, Campus de Lugo, Universidade de Santiago de Compostela, 27002 Lugo, Spain; (A.P.-L.); (J.F.-R.)
- Center for Research in Molecular Medicine & Chronic Diseases (CIMUS), Campus Vida, Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
| | - Juan Fernández-Rey
- Department of Zoology, Genetics and Physical Anthropology, Campus de Lugo, Universidade de Santiago de Compostela, 27002 Lugo, Spain; (A.P.-L.); (J.F.-R.)
- Center for Research in Molecular Medicine & Chronic Diseases (CIMUS), Campus Vida, Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
| | - Pedro Reimunde
- Department of Physiotherapy, Medicine and Biomedical Sciences, Universidade da Coruña, Campus de Oza, 15006 A Coruña, Spain;
- Department of Neurosurgery, Hospital Universitario Lucus Augusti, 27003 Lugo, Spain
| | - José Crecente-Campo
- Center for Research in Molecular Medicine & Chronic Diseases (CIMUS), Campus Vida, Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
| | - Laura Sánchez
- Department of Zoology, Genetics and Physical Anthropology, Campus de Lugo, Universidade de Santiago de Compostela, 27002 Lugo, Spain; (A.P.-L.); (J.F.-R.)
- Correspondence: (L.S.); (F.T.A.)
| | - Fernando Torres Andón
- Center for Research in Molecular Medicine & Chronic Diseases (CIMUS), Campus Vida, Universidade de Santiago de Compostela, 15706 Santiago de Compostela, Spain;
- Correspondence: (L.S.); (F.T.A.)
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Kleandrova VV, Scotti L, Bezerra Mendonça Junior FJ, Muratov E, Scotti MT, Speck-Planche A. QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites. Front Chem 2021; 9:634663. [PMID: 33777898 PMCID: PMC7987820 DOI: 10.3389/fchem.2021.634663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 01/22/2021] [Indexed: 11/21/2022] Open
Abstract
Parasitic diseases remain as unresolved health issues worldwide. While for some parasites the treatments involve drug combinations with serious side effects, for others, chemical therapies are inefficient due to the emergence of drug resistance. This urges the search for novel antiparasitic agents able to act through multiple mechanisms of action. Here, we report the first multi-target model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mt-QSAR-MLP) to virtually design and predict versatile inhibitors of proteins involved in the survival and/or infectivity of different pathogenic parasites. The mt-QSAR-MLP model exhibited high accuracy (>80%) in both training and test sets for the classification/prediction of protein inhibitors. Several fragments were directly extracted from the physicochemical and structural interpretations of the molecular descriptors in the mt-QSAR-MLP model. Such interpretations enabled the generation of four molecules that were predicted as multi-target inhibitors against at least three of the five parasitic proteins reported here with two of the molecules being predicted to inhibit all the proteins. Docking calculations converged with the mt-QSAR-MLP model regarding the multi-target profile of the designed molecules. The designed molecules exhibited drug-like properties, complying with Lipinski’s rule of five, as well as Ghose’s filter and Veber’s guidelines.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Moscow, Russian Federation
| | - Luciana Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
| | | | - Eugene Muratov
- Laboratory for Molecular Modeling, The UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Marcus T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
| | - Alejandro Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
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Sampaio-Dias IE, Rodríguez-Borges JE, Yáñez-Pérez V, Arrasate S, Llorente J, Brea JM, Bediaga H, Viña D, Loza MI, Caamaño O, García-Mera X, González-Díaz H. Synthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML). ACS Chem Neurosci 2021; 12:203-215. [PMID: 33347281 DOI: 10.1021/acschemneuro.0c00687] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
This work describes the synthesis and pharmacological evaluation of 2-furoyl-based Melanostatin (MIF-1) peptidomimetics as dopamine D2 modulating agents. Eight novel peptidomimetics were tested for their ability to enhance the maximal effect of tritiated N-propylapomorphine ([3H]-NPA) at D2 receptors (D2R). In this series, 2-furoyl-l-leucylglycinamide (6a) produced a statistically significant increase in the maximal [3H]-NPA response at 10 pM (11 ± 1%), comparable to the effect of MIF-1 (18 ± 9%) at the same concentration. This result supports previous evidence that the replacement of proline residue by heteroaromatic scaffolds are tolerated at the allosteric binding site of MIF-1. Biological assays performed for peptidomimetic 6a using cortex neurons from 19-day-old Wistar-Kyoto rat embryos suggest that 6a displays no neurotoxicity up to 100 μM. Overall, the pharmacological and toxicological profile and the structural simplicity of 6a makes this peptidomimetic a potential lead compound for further development and optimization, paving the way for the development of novel modulating agents of D2R suitable for the treatment of CNS-related diseases. Additionally, the pharmacological and biological data herein reported, along with >20 000 outcomes of preclinical assays, was used to seek a general model to predict the allosteric modulatory potential of molecular candidates for a myriad of target receptors, organisms, cell lines, and biological activity parameters based on perturbation theory (PT) ideas and machine learning (ML) techniques, abbreviated as ALLOPTML. By doing so, ALLOPTML shows high specificity Sp = 89.2/89.4%, sensitivity Sn = 71.3/72.2%, and accuracy Ac = 86.1%/86.4% in training/validation series, respectively. To the best of our knowledge, ALLOPTML is the first general-purpose chemoinformatic tool using a PTML-based model for the multioutput and multicondition prediction of allosteric compounds, which is expected to save both time and resources during the early drug discovery of allosteric modulators.
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Affiliation(s)
- Ivo E. Sampaio-Dias
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - José E. Rodríguez-Borges
- LAQV/REQUIMTE, Dept. of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Víctor Yáñez-Pérez
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Sonia Arrasate
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Javier Llorente
- Dept. of Pharmacology, Faculty of Medicine and Nursing, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Pharmacology, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - José M. Brea
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Harbil Bediaga
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Dept. of Physical Chemistry, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
| | - Dolores Viña
- Dept. of Pharmacology, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
- Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - María Isabel Loza
- Innopharma Screening Platform, Biofarma Research group, Centre of Research in Molecular Medicine and Chronic Diseases CIMUS, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Olga Caamaño
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Xerardo García-Mera
- Dept. of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Humberto González-Díaz
- Dept. of Organic Chemistry II, University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- Basque Center for Biophysics (CSIC UPV/EHU), University of Basque Country (UPV-EHU), 48940 Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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Kleandrova VV, Scotti MT, Scotti L, Nayarisseri A, Speck-Planche A. Cell-based multi-target QSAR model for design of virtual versatile inhibitors of liver cancer cell lines. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:815-836. [PMID: 32967475 DOI: 10.1080/1062936x.2020.1818617] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
Liver cancers are one of the leading fatal diseases among malignant neoplasms. Current chemotherapeutic treatments used to fight these illnesses have become less efficient in terms of both efficacy and safety. Therefore, there is a great need of search for new anti-liver cancer agents and this can be accelerated by using computer-aided drug discovery approaches. In this work, we report the development of the first cell-based multi-target model based on quantitative structure-activity relationships (CBMT-QSAR) for the design and prediction of chemicals as anticancer agents against 17 liver cancer cell lines. While having a good quality and predictive power (accuracy higher than 80%) in the training and test sets, respectively, the CBMT-QSAR model was employed as a tool to directly extract suitable fragments from the physicochemical and structural interpretations of the molecular descriptors. Some of these desirable fragments were assembled, leading to the virtual design of eight molecules with drug-like properties, with six of them being predicted as versatile anticancer agents against the 17 liver cancer cell lines reported here.
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Affiliation(s)
- V V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production , Moscow, Russian Federation
| | - M T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
| | - L Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
| | - A Nayarisseri
- In Silico Research Laboratory, Eminent Biosciences , Indore, Madhya Pradesh, India
| | - A Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba , João Pessoa, Brazil
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Kleandrova VV, Speck-Planche A. PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6. Curr Top Med Chem 2020; 20:1661-1676. [DOI: 10.2174/1568026620666200607190951] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/12/2019] [Accepted: 01/05/2020] [Indexed: 01/23/2023]
Abstract
Background:
Alzheimer’s disease is characterized by a progressive pattern of cognitive and
functional impairment, which ultimately leads to death. Computational approaches have played an important
role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational
models reported to date have been focused on only one protein associated with Alzheimer's,
while relying on small datasets of structurally related molecules.
Objective:
We introduce the first model combining perturbation theory and machine learning based on
artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three
Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase
1 (HDAC1), and histone deacetylase 6 (HDAC6).
Methods:
The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on
a classification approach to predict chemicals as active or inactive.
Results:
The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training
and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model
permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget
inhibitory activity. Based on this information, we assembled ten molecules from several fragments
with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the
remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of
the designed molecules complied with Lipinski’s rule of five and its variants.
Conclusion:
This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's
therapies.
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Affiliation(s)
- Valeria V. Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080, Moscow, Russian Federation
| | - Alejandro Speck-Planche
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. Box 8940577, San Joaquin, Santiago, Chile
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Ahmed S, Moni DA, Sonawane KD, Paek KY, Shohael AM. A comprehensive in silico exploration of pharmacological properties, bioactivities and COX-2 inhibitory potential of eleutheroside B from Eleutherococcus senticosus (Rupr. & Maxim.) Maxim. J Biomol Struct Dyn 2020; 39:6553-6566. [DOI: 10.1080/07391102.2020.1803135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Sium Ahmed
- Cell Genetics and Plant Biotechnology Laboratory, Department of Biotechnology and Genetic Engineering, Jahangirnagar University, Savar, Bangladesh
| | - Dil Afroj Moni
- Cell Genetics and Plant Biotechnology Laboratory, Department of Biotechnology and Genetic Engineering, Jahangirnagar University, Savar, Bangladesh
| | - Kailas Dashrath Sonawane
- Department of Microbiology, Shivaji University, Kolhapur, Maharashtra, India
- Structural Bioinformatics Unit, Department of Biochemistry, Shivaji University, Kolhapur, Maharashtra, India
| | - Kee Yoeup Paek
- Research Center for the Development of Advanced Horticultural Technology, Chungbuk National University, Cheongju, Republic of Korea
| | - Abdullah Mohammad Shohael
- Cell Genetics and Plant Biotechnology Laboratory, Department of Biotechnology and Genetic Engineering, Jahangirnagar University, Savar, Bangladesh
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Santana R, Zuluaga R, Gañán P, Arrasate S, Onieva Caracuel E, González-Díaz H. PTML Model of ChEMBL Compounds Assays for Vitamin Derivatives. ACS COMBINATORIAL SCIENCE 2020; 22:129-141. [PMID: 32011854 DOI: 10.1021/acscombsci.9b00166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Determining the biological activity of vitamin derivatives is needed given that organic synthesis of analogs of vitamins is an active field of interest for medicinal chemistry, pharmaceuticals, and food additives. Accordingly, scientists from different disciplines perform preclinical assays (nij) with a considerable combination of assay conditions (cj). Indeed, the ChEMBL platform contains a database that includes results from 36 220 different biological activity bioassays of 21 240 different vitamins and vitamin derivatives. These assays present are heterogeneous in terms of assay combinations of cj. They are focused on >500 different biological activity parameters (c0), >340 different targets (c1), >6200 types of cell (c2), >120 organisms of assay (c3), and >60 assay strains (c4). It includes a total of >1850 niacin assays, >1580 tretinoin assays, >1580 retinol assays, 857 ascorbic acid assays, etc. Given the complexity of this combinatorial data in terms of being assimilated by researchers, we propose to build a model by combining perturbation theory (PT) and machine learning (ML). Through this study, we propose a PTML (PT + ML) combinatorial model for ChEMBL results on biological activity of vitamins and vitamins derivatives. The linear discriminant analysis (LDA) model presented the following results for training subset a: specificity (%) = 90.38, sensitivity (%) = 87.51, and accuracy (%) = 89.89. The model showed the following results for the external validation subset: specificity (%) = 90.58, sensitivity (%) = 87.72, and accuracy (%) = 90.09. Different types of linear and nonlinear PTML models, such as logistic regression (LR), classification tree (CT), näive Bayes (NB), and random Forest (RF), were applied to contrast the capacity of prediction. The PTML-LDA model predicts with more accuracy by applying combinatorial descriptors. In addition, a PCA experiment with chemical structure descriptors allowed us to characterize the high structural diversity of the chemical space studied. In any case, PTML models using chemical structure descriptors do not improve the performance of the PTML-LDA model based on ALOGP and PSA. We can conclude that the three variable PTML-LDA model is a simplified and adaptable tool for the prediction, for different experiment combinations, the biological activity of derivative vitamins.
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Affiliation(s)
- Ricardo Santana
- DeustoTech-Fundación Deusto, Avda. Universidades, 24, 48007 Bilbao, Spain
- Grupo de Investigación sobre Nuevos Materiales, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Robin Zuluaga
- Facultad de Ingeniería Agroindustrial, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Piedad Gañán
- Facultad de Ingeniería Química, Universidad Pontificia Bolivariana UPB, 050031, Medellín, Colombia
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | | | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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14
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Carracedo-Reboredo P, Corona R, Martinez-Nunes M, Fernandez-Lozano C, Tsiliki G, Sarimveis H, Aranzamendi E, Arrasate S, Sotomayor N, Lete E, Munteanu CR, González-Díaz H. MCDCalc: Markov Chain Molecular Descriptors Calculator for Medicinal Chemistry. Curr Top Med Chem 2019; 20:305-317. [PMID: 31878856 DOI: 10.2174/1568026620666191226092431] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 09/17/2019] [Accepted: 09/17/2019] [Indexed: 11/22/2022]
Abstract
AIMS Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). BACKGROUND Cheminformatics models are able to predict different outputs (activity, property, chemical reactivity) in single molecules or complex molecular systems (catalyzed organic synthesis, metabolic reactions, nanoparticles, etc.). OBJECTIVE Cheminformatics prediction of complex catalytic enantioselective reactions is a major goal in organic synthesis research and chemical industry. Markov Chain Molecular Descriptors (MCDs) have been largely used to solve Cheminformatics problems. There are different types of Markov chain descriptors such as Markov-Shannon entropies (Shk), Markov Means (Mk), Markov Moments (πk), etc. However, there are other possible MCDs that have not been used before. In addition, the calculation of MCDs is done very often using specific software not always available for general users and there is not an R library public available for the calculation of MCDs. This fact, limits the availability of MCMDbased Cheminformatics procedures. METHODS We studied the enantiomeric excess ee(%)[Rcat] for 324 α-amidoalkylation reactions. These reactions have a complex mechanism depending on various factors. The model includes MCDs of the substrate, solvent, chiral catalyst, product along with values of time of reaction, temperature, load of catalyst, etc. We tested several Machine Learning regression algorithms. The Random Forest regression model has R2 > 0.90 in training and test. Secondly, the biological activity of 5644 compounds against colorectal cancer was studied. RESULTS We developed very interesting model able to predict with Specificity and Sensitivity 70-82% the cases of preclinical assays in both training and validation series. CONCLUSION The work shows the potential of the new tool for computational studies in organic and medicinal chemistry.
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Affiliation(s)
- Paula Carracedo-Reboredo
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain.,Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Ramiro Corona
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Mikel Martinez-Nunes
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Carlos Fernandez-Lozano
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Georgia Tsiliki
- Institute for the Management of Information Systems, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, Zografou, Campus, 15780, Athens, Greece.,Pharma-Informatics Unit, ATHENA Research and Innovation Centre, 15125, Athens, Greece
| | - Eider Aranzamendi
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Nuria Sotomayor
- Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Esther Lete
- Department of Organic Chemistry II, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain
| | - Cristian Robert Munteanu
- Department of Computer Science and Information Technologies, Faculty of Computer Science, University of A Coruña, CITIC, Campus Elviña s/n, 15071, A Coruña, Spain.,Group of Artificial Neural Networks and Adaptative Systems, Medical Imaging, and Diagnostic Radiology (RNASA-IMEDIR), Institute of Biomedical Research of Coruna (INIBIC), Hospital Complex of University of A Coruna (CHUAC), Sergas, University of Coruna (UDC), Xubias de arriba 84, 15006, A Coruna, Spain
| | - Humbert González-Díaz
- Basque Center for Biophysics, University of the Basque Country UPV/EHU, 48940, Leioa, Bilbao, Spain.,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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15
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Bioactive Molecules and Their Mechanisms of Action. Molecules 2019; 24:molecules24203752. [PMID: 31635224 PMCID: PMC6832559 DOI: 10.3390/molecules24203752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Accepted: 10/14/2019] [Indexed: 11/17/2022] Open
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16
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Caballero-Alfonso AY, Cruz-Monteagudo M, Tejera E, Benfenati E, Borges F, Cordeiro MND, Armijos-Jaramillo V, Perez-Castillo Y. Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity. Curr Top Med Chem 2019; 19:957-969. [DOI: 10.2174/1568026619666190510100313] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 01/25/2019] [Accepted: 03/27/2019] [Indexed: 11/22/2022]
Abstract
Background:
Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium
genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre.
This pathology is considered one of the first causes of death in tropical countries and, despite several
existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure-
Activity Relationship studies have been widely used in drug design work flows.
Objective:
The main goal of the current research is to develop computational models for the identification
of antimalarial hit compounds.
Materials and Methods:
For this, a data set suitable for the modeling of the antimalarial activity of
chemical compounds was compiled from the literature and subjected to a thorough curation process. In
addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated
and one of these ensembles was selected as the most suitable for the identification of antimalarial
hits based on its virtual screening performance. Data curation was conducted to minimize noise.
Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection
of the base classifiers and Majority Vote for their aggregation showed the best performance.
Results:
Our results also show that ensemble modeling is an effective strategy for the QSAR modeling
of highly heterogeneous datasets in the discovery of potential antimalarial compounds.
Conclusion:
It was determined that the best performing ensembles were those that use Genetic Algorithms
as a method of selection of base models and Majority Vote as the aggregation method.
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Affiliation(s)
- Ana Yisel Caballero-Alfonso
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche "Mario Negri" - IRCCS, Milano, Italy
| | - Maykel Cruz-Monteagudo
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias. Universidade do Porto. Porto, Portugal
| | - Eduardo Tejera
- Bio-Cheminformatics Research Group. Universidad de Las Americas. Quito, Ecuador
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche "Mario Negri" - IRCCS, Milano, Italy
| | - Fernanda Borges
- CIQUP/Departamento de Quimica e Bioquimica, Faculdade de Ciencias. Universidade do Porto. Porto, Portugal
| | - M. Natália D.S. Cordeiro
- REQUIMTE/Departamento de Quimica e Bioquimica, Faculdade de Ciencias, Universidade do Porto. Porto, Portugal
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Nocedo-Mena D, Cornelio C, Camacho-Corona MDR, Garza-González E, Waksman de Torres N, Arrasate S, Sotomayor N, Lete E, González-Díaz H. Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. J Chem Inf Model 2019; 59:1109-1120. [PMID: 30802402 DOI: 10.1021/acs.jcim.9b00034] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN s) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.
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Affiliation(s)
- Deyani Nocedo-Mena
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Carlos Cornelio
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - María Del Rayo Camacho-Corona
- Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Elvira Garza-González
- Servicio de Gastroenterología, Hospital Universitario, Dr. Eleuterio González , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Noemi Waksman de Torres
- Facultad de Medicina , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Sonia Arrasate
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Nuria Sotomayor
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Esther Lete
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Biscay , Spain
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18
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Speck-Planche A. Combining Ensemble Learning with a Fragment-Based Topological Approach To Generate New Molecular Diversity in Drug Discovery: In Silico Design of Hsp90 Inhibitors. ACS OMEGA 2018; 3:14704-14716. [PMID: 30555986 PMCID: PMC6289491 DOI: 10.1021/acsomega.8b02419] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/23/2018] [Indexed: 05/05/2023]
Abstract
Machine learning methods have revolutionized modern science, providing fast and accurate solutions to multiple problems. However, they are commonly treated as "black boxes". Therefore, in important scientific fields such as medicinal chemistry and drug discovery, machine learning methods are restricted almost exclusively to the task of performing predictions of large and heterogeneous data sets of chemicals. The lack of interpretability prevents the full exploitation of the machine learning models as generators of new chemical knowledge. This work focuses on the development of an ensemble learning model for the prediction and design of potent dual heat shock protein 90 (Hsp90) inhibitors. The model displays accuracy higher than 80% in both training and test sets. To use the ensemble model as a generator of new chemical knowledge, three steps were followed. First, a physicochemical and/or structural interpretation was provided for each molecular descriptor present in the ensemble learning model. Second, the term "pseudolinear equation" was introduced within the context of machine learning to calculate the relative quantitative contributions of different molecular fragments to the inhibitory activity against the two Hsp90 isoforms studied here. Finally, by assembling the fragments with positive contributions, new molecules were designed, being predicted as potent Hsp90 inhibitors. According to Lipinski's rule of five, the designed molecules were found to exhibit potentially good oral bioavailability, a primordial property that chemicals must have to pass early stages in drug discovery. The present approach based on the combination of ensemble learning and fragment-based topological design holds great promise in drug discovery, and it can be adapted and applied to many different scientific disciplines.
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19
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Bediaga H, Arrasate S, González-Díaz H. PTML Combinatorial Model of ChEMBL Compounds Assays for Multiple Types of Cancer. ACS COMBINATORIAL SCIENCE 2018; 20:621-632. [PMID: 30240186 DOI: 10.1021/acscombsci.8b00090] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Determining the target proteins of new anticancer compounds is a very important task in Medicinal Chemistry. In this sense, chemists carry out preclinical assays with a high number of combinations of experimental conditions (c j). In fact, ChEMBL database contains outcomes of 65 534 different anticancer activity preclinical assays for 35 565 different chemical compounds (1.84 assays per compound). These assays cover different combinations of c j formed from >70 different biological activity parameters ( c0), >300 different drug targets ( c1), >230 cell lines ( c2), and 5 organisms of assay ( c3) or organisms of the target ( c4). It include a total of 45 833 assays in leukemia, 6227 assays in breast cancer, 2499 assays in ovarian cancer, 3499 in colon cancer, 3159 in lung cancer, 2750 in prostate cancer, 601 in melanoma, etc. This is a very complex data set with multiple Big Data features. This data is hard to be rationalized by researchers to extract useful relationships and predict new compounds. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. In this work, we report a PTML (PT + ML) model for ChEMBL data set of preclinical assays of anticancer compounds. This is a simple linear model with only three variables. The model presented values of area under receiver operating curve = AUROC = 0.872, specificity = Sp(%) = 90.2, sensitivity = Sn(%) = 70.6, and overall accuracy = Ac(%) = 87.7 in training series. The model also have Sp(%) = 90.1, Sn(%) = 71.4, and Ac(%) = 87.8 in external validation series. The model use PT operators based on multicondition moving averages to capture all the complexity of the data set. We also compared the model with nonlinear artificial neural network (ANN) models obtaining similar results. This confirms the hypothesis of a linear relationship between the PT operators and the classification as anticancer compounds in different combinations of assay conditions. Last, we compared the model with other PTML models reported in the literature concluding that this is the only one PTML model able to predict activity against multiple types of cancer. This model is a simple but versatile tool for the prediction of the targets of anticancer compounds taking into consideration multiple combinations of experimental conditions in preclinical assays.
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Affiliation(s)
- Harbil Bediaga
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Sonia Arrasate
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II, University of Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
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20
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BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models. Mol Divers 2018; 23:555-572. [PMID: 30421269 DOI: 10.1007/s11030-018-9890-8] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/30/2018] [Indexed: 12/17/2022]
Abstract
Epigenetics has become a focus of interest in drug discovery. In this sense, bromodomain-containing proteins have emerged as potential epigenetic targets in cancer research and other therapeutic areas. Several computational approaches have been applied to the prediction of bromodomain inhibitors. Nevertheless, such approaches have several drawbacks such as the fact that they predict activity against only one bromodomain-containing protein, using structurally related compounds. Also, there are no reports focused on meaningfully analyzing the physicochemical/structural features that are necessary for the design of a bromodomain inhibitor. This work describes the development of two different multi-target models based on quantitative structure-activity relationships (mt-QSAR) for the prediction and in silico design of multi-target bromodomain inhibitors against the proteins BRD2, BRD3, and BRD4. The first model relied on linear discriminant analysis (LDA) while the second focused on artificial neural networks. Both models exhibited accuracies higher than 85% in the dataset. Several molecular fragments were extracted, and their contributions to the inhibitory activity against the three BET proteins were calculated by the LDA model. Six molecules were designed by assembling the fragments with positive contributions, and they were predicted as multi-target BET bromodomain inhibitors by the two mt-QSAR models. Molecular docking calculations converged with the predictions performed by the mt-QSAR models, suggesting that the designed molecules can exhibit potent activity against the three BET proteins. These molecules complied with the Lipinski's rule of five.
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21
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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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22
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Fragment-based in silico modeling of multi-target inhibitors against breast cancer-related proteins. Mol Divers 2017; 21:511-523. [PMID: 28194627 DOI: 10.1007/s11030-017-9731-1] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2016] [Accepted: 01/16/2017] [Indexed: 10/20/2022]
Abstract
Breast cancer is the most frequent cancer reported in women, being responsible for hundreds of thousands of deaths. Chemotherapy has proven to be effective against this malignant neoplasm depending on different biological factors such as the histopathology, grade, and stage, among others. However, breast cancer cells have become resistant to current chemotherapeutic regimens, urging the discovery of new anti-breast cancer drugs. Computational approaches have the potential to offer promising alternatives to accelerate the search for potent and versatile anti-breast cancer agents. In the present work, we introduce the first multitasking (mtk) computational model devoted to the in silico fragment-based design of new molecules with high inhibitory activity against 19 different proteins involved in breast cancer. The mtk-computational model was created from a dataset formed by 24,285 cases, and it exhibited accuracy around 93% in both training and prediction (test) sets. Several molecular fragments were extracted from the molecules present in the dataset, and their quantitative contributions to the inhibitory activities against all the proteins under study were calculated. The combined use of the fragment contributions and the physicochemical interpretations of the different molecular descriptors in the mtk-computational model allowed the design of eight new molecular entities not reported in our dataset. These molecules were predicted as potent multi-target inhibitors against all the proteins, and they exhibited a desirable druglikeness according to the Lipinski's rule of five and its variants.
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23
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Yang RX, Zhao YC, Kong LB, Yan SJ, Lin J. Simple Synthesis of Multi-Halogen Pyrazino[1,2- a]indole-1,8(2 H,5 aH)-diones. B KOREAN CHEM SOC 2016. [DOI: 10.1002/bkcs.10909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Rui-Xia Yang
- Key Laboratory of Medicinal Chemistry for Natural Resource (Yunnan University), Ministry Education, School of Chemical Science and Technology; Yunnan University; Kunming 650091 PR China
| | - Yu-Cheng Zhao
- Key Laboratory of Medicinal Chemistry for Natural Resource (Yunnan University), Ministry Education, School of Chemical Science and Technology; Yunnan University; Kunming 650091 PR China
| | - Ling-Bin Kong
- Key Laboratory of Medicinal Chemistry for Natural Resource (Yunnan University), Ministry Education, School of Chemical Science and Technology; Yunnan University; Kunming 650091 PR China
| | - Sheng-Jiao Yan
- Key Laboratory of Medicinal Chemistry for Natural Resource (Yunnan University), Ministry Education, School of Chemical Science and Technology; Yunnan University; Kunming 650091 PR China
| | - Jun Lin
- Key Laboratory of Medicinal Chemistry for Natural Resource (Yunnan University), Ministry Education, School of Chemical Science and Technology; Yunnan University; Kunming 650091 PR China
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Caboni L, Gálvez-Llompart M, Gálvez J, Blanco F, Rubio-Martinez J, Fayne D, Lloyd DG. Molecular topology applied to the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a non-ligand-binding-pocket antiandrogen. J Chem Inf Model 2014; 54:2953-66. [PMID: 25233256 DOI: 10.1021/ci500324f] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
We report the discovery of 1-benzyl-2-(3-fluorophenyl)-4-hydroxy-3-(3-phenylpropanoyl)-2H-pyrrole-5-one as a novel non-ligand binding pocket (non-LBP) antagonist of the androgen receptor (AR) through the application of molecular topology techniques. This compound, validated through time-resolved fluorescence resonance energy transfer and fluorescence polarization biological assays, provides the basis for lead optimization and structure-activity relationship analysis of a new series of non-LBP AR antagonists. Induced-fit docking and molecular dynamics studies have been performed to establish a consistent hypothesis for the interaction of the new active molecule on the AR surface.
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Affiliation(s)
- Laura Caboni
- Molecular Design Group, School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin , Dublin 2, Ireland
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25
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Optimization and pharmacological validation of a leukocyte migration assay in zebrafish larvae for the rapid in vivo bioactivity analysis of anti-inflammatory secondary metabolites. PLoS One 2013; 8:e75404. [PMID: 24124487 PMCID: PMC3790782 DOI: 10.1371/journal.pone.0075404] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2013] [Accepted: 08/13/2013] [Indexed: 02/02/2023] Open
Abstract
Over the past decade, zebrafish (Danio rerio) have emerged as an attractive model for in vivo drug discovery. In this study, we explore the suitability of zebrafish larvae to rapidly evaluate the anti-inflammatory activity of natural products (NPs) and medicinal plants used in traditional medicine for the treatment of inflammatory disorders. First, we optimized a zebrafish assay for leukocyte migration. Inflammation was induced in four days post-fertilization (dpf) zebrafish larvae by tail transection and co-incubation with bacterial lipopolysaccharides (LPS), resulting in a robust recruitment of leukocytes to the zone of injury. Migrating zebrafish leukocytes were detected in situ by myeloperoxidase (MPO) staining, and anti-inflammatory activity was semi-quantitatively scored using a standardized scale of relative leukocyte migration (RLM). Pharmacological validation of this optimized assay was performed with a panel of anti-inflammatory drugs, demonstrating a concentration-responsive inhibition of leukocyte migration for both steroidal and non-steroidal anti-inflammatory drugs (SAIDs and NSAIDs). Subsequently, we evaluated the bioactivity of structurally diverse NPs with well-documented anti-inflammatory properties. Finally, we further used this zebrafish-based assay to quantify the anti-inflammatory activity in the aqueous and methanolic extracts of several medicinal plants. Our results indicate the suitability of this LPS-enhanced leukocyte migration assay in zebrafish larvae as a front-line screening platform in NP discovery, including for the bioassay-guided isolation of anti-inflammatory secondary metabolites from complex NP extracts.
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Ziarek JJ, Liu Y, Smith E, Zhang G, Peterson FC, Chen J, Yu Y, Chen Y, Volkman BF, Li R. Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction. Curr Top Med Chem 2013; 12:2727-40. [PMID: 23368099 DOI: 10.2174/1568026611212240003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2012] [Revised: 10/08/2012] [Accepted: 11/03/2012] [Indexed: 12/21/2022]
Abstract
The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.
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Affiliation(s)
- Joshua J Ziarek
- Department of Biochemistry, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
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27
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Bohni N, Cordero-Maldonado ML, Maes J, Siverio-Mota D, Marcourt L, Munck S, Kamuhabwa AR, Moshi MJ, Esguerra CV, de Witte PAM, Crawford AD, Wolfender JL. Integration of Microfractionation, qNMR and zebrafish screening for the in vivo bioassay-guided isolation and quantitative bioactivity analysis of natural products. PLoS One 2013; 8:e64006. [PMID: 23700445 PMCID: PMC3660303 DOI: 10.1371/journal.pone.0064006] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2013] [Accepted: 04/09/2013] [Indexed: 12/17/2022] Open
Abstract
Natural products (NPs) are an attractive source of chemical diversity for small-molecule drug discovery. Several challenges nevertheless persist with respect to NP discovery, including the time and effort required for bioassay-guided isolation of bioactive NPs, and the limited biomedical relevance to date of in vitro bioassays used in this context. With regard to bioassays, zebrafish have recently emerged as an effective model system for chemical biology, allowing in vivo high-content screens that are compatible with microgram amounts of compound. For the deconvolution of the complex extracts into their individual constituents, recent progress has been achieved on several fronts as analytical techniques now enable the rapid microfractionation of extracts, and microflow NMR methods have developed to the point of allowing the identification of microgram amounts of NPs. Here we combine advanced analytical methods with high-content screening in zebrafish to create an integrated platform for microgram-scale, in vivo NP discovery. We use this platform for the bioassay-guided fractionation of an East African medicinal plant, Rhynchosia viscosa, resulting in the identification of both known and novel isoflavone derivatives with anti-angiogenic and anti-inflammatory activity. Quantitative microflow NMR is used both to determine the structure of bioactive compounds and to quantify them for direct dose-response experiments at the microgram scale. The key advantages of this approach are (1) the microgram scale at which both biological and analytical experiments can be performed, (2) the speed and the rationality of the bioassay-guided fractionation – generic for NP extracts of diverse origin – that requires only limited sample-specific optimization and (3) the use of microflow NMR for quantification, enabling the identification and dose-response experiments with only tens of micrograms of each compound. This study demonstrates that a complete in vivo bioassay-guided fractionation can be performed with only 20 mg of NP extract within a few days.
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Affiliation(s)
- Nadine Bohni
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, Geneva, Switzerland
| | - María Lorena Cordero-Maldonado
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
- Faculty of Chemistry Sciences, School of Biochemistry and Pharmacy, University of Cuenca, Cuenca, Ecuador
| | - Jan Maes
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
| | - Dany Siverio-Mota
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
| | - Laurence Marcourt
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, Geneva, Switzerland
| | - Sebastian Munck
- VIB Center for the Biology of Disease, University of Leuven, Leuven, Belgium
| | - Appolinary R. Kamuhabwa
- Faculty of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Mainen J. Moshi
- Faculty of Pharmacy, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Camila V. Esguerra
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
| | - Peter A. M. de Witte
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
| | - Alexander D. Crawford
- Laboratory for Molecular Biodiscovery, Department of Pharmaceutical and Pharmacological Sciences, University of Leuven, Leuven, Belgium
- * E-mail:
| | - Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, Geneva, Switzerland
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28
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Synthesis of amidine and bis amidine derivatives and their evaluation for anti-inflammatory and anticancer activity. Eur J Med Chem 2013. [DOI: 10.1016/j.ejmech.2012.10.046] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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29
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Guo C, Wang JS, Zhang Y, Yang L, Wang PR, Kong LY. Relationship of Chemical Structure to in Vitro Anti-inflammatory Activity of Tirucallane Triterpenoids from the Stem Barks of Aphanamixis grandifolia. Chem Pharm Bull (Tokyo) 2012; 60:1003-10. [DOI: 10.1248/cpb.c12-00252] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Affiliation(s)
- Chao Guo
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
| | - Jun-Song Wang
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
| | - Yao Zhang
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
| | - Lei Yang
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
| | - Peng-Ran Wang
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
| | - Ling-Yi Kong
- State Key Laboratory of Natural Medicines, Department of Natural Medicinal Chemistry, China Pharmaceutical University
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30
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Galvez-Llompart M, Zanni R, García-Domenech R. Modeling natural anti-inflammatory compounds by molecular topology. Int J Mol Sci 2011; 12:9481-503. [PMID: 22272145 PMCID: PMC3257142 DOI: 10.3390/ijms12129481] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2011] [Revised: 12/08/2011] [Accepted: 12/09/2011] [Indexed: 12/25/2022] Open
Abstract
One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with the remaining compounds (those not used in building up the model), has been carried out. Finally, a virtual screening on natural products was performed and 74 compounds showed actual anti-inflammatory activity. From them, 54 had been previously described as anti-inflammatory in the literature. This can be seen as a plus in the model validation and as a reinforcement of the role of Molecular Topology as an efficient tool for the discovery of new anti-inflammatory natural compounds.
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Affiliation(s)
- María Galvez-Llompart
- Molecular Connectivity & Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Avenida V.A. Estelles s/n, Burjasot, Valencia 46100, Spain; E-Mails: (M.G.-L.); (R.Z.)
| | - Riccardo Zanni
- Molecular Connectivity & Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Avenida V.A. Estelles s/n, Burjasot, Valencia 46100, Spain; E-Mails: (M.G.-L.); (R.Z.)
- Department of Pharmacology, Faculty of Pharmacy, University of Bologna, Via Irnerio, Bologna 48-40126, Italy
| | - Ramón García-Domenech
- Molecular Connectivity & Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy, University of Valencia, Avenida V.A. Estelles s/n, Burjasot, Valencia 46100, Spain; E-Mails: (M.G.-L.); (R.Z.)
- Author to whom correspondence should be addressed; E-Mail: ; Tel.: +34-963544291; Fax: +34-963544892
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