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Arora S, Chettri S, Percha V, Kumar D, Latwal M. Artifical intelligence: a virtual chemist for natural product drug discovery. J Biomol Struct Dyn 2024; 42:3826-3835. [PMID: 37232451 DOI: 10.1080/07391102.2023.2216295] [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: 01/16/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023]
Abstract
Nature is full of a bundle of medicinal substances and its product perceived as a prerogative structure to collaborate with protein drug targets. The natural product's (NPs) structure heterogeneity and eccentric characteristics inspired scientists to work on natural product-inspired medicine. To gear NP drug-finding artificial intelligence (AI) to confront and excavate unexplored opportunities. Natural product-inspired drug discoveries based on AI to act as an innovative tool for molecular design and lead discovery. Various models of machine learning produce quickly synthesizable mimetics of the natural products templates. The invention of novel natural products mimetics by computer-assisted technology provides a feasible strategy to get the natural product with defined bio-activities. AI's hit rate makes its high importance by improving trail patterns such as dose selection, trail life span, efficacy parameters, and biomarkers. Along these lines, AI methods can be a successful tool in a targeted way to formulate advanced medicinal applications for natural products. 'Prediction of future of natural product based drug discovery is not magic, actually its artificial intelligence'Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Shefali Arora
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Sukanya Chettri
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
| | - Versha Percha
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Deepak Kumar
- Department of Pharmaceutical Chemistry, Dolphin(PG) Institute of Biomedical and Natural Sciences, Dehradun, Uttarakhand, India
| | - Mamta Latwal
- Department of Chemistry, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
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2
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Sabotič J, Bayram E, Ezra D, Gaudêncio SP, Haznedaroğlu BZ, Janež N, Ktari L, Luganini A, Mandalakis M, Safarik I, Simes D, Strode E, Toruńska-Sitarz A, Varamogianni-Mamatsi D, Varese GC, Vasquez MI. A guide to the use of bioassays in exploration of natural resources. Biotechnol Adv 2024; 71:108307. [PMID: 38185432 DOI: 10.1016/j.biotechadv.2024.108307] [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: 07/24/2023] [Revised: 12/05/2023] [Accepted: 01/01/2024] [Indexed: 01/09/2024]
Abstract
Bioassays are the main tool to decipher bioactivities from natural resources thus their selection and quality are critical for optimal bioprospecting. They are used both in the early stages of compounds isolation/purification/identification, and in later stages to evaluate their safety and efficacy. In this review, we provide a comprehensive overview of the most common bioassays used in the discovery and development of new bioactive compounds with a focus on marine bioresources. We present a comprehensive list of practical considerations for selecting appropriate bioassays and discuss in detail the bioassays typically used to explore antimicrobial, antibiofilm, cytotoxic, antiviral, antioxidant, and anti-ageing potential. The concept of quality control and bioassay validation are introduced, followed by safety considerations, which are critical to advancing bioactive compounds to a higher stage of development. We conclude by providing an application-oriented view focused on the development of pharmaceuticals, food supplements, and cosmetics, the industrial pipelines where currently known marine natural products hold most potential. We highlight the importance of gaining reliable bioassay results, as these serve as a starting point for application-based development and further testing, as well as for consideration by regulatory authorities.
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Affiliation(s)
- Jerica Sabotič
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia.
| | - Engin Bayram
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - David Ezra
- Department of Plant Pathology and Weed Research, ARO, The Volcani Institute, P.O.Box 15159, Rishon LeZion 7528809, Israel
| | - Susana P Gaudêncio
- Associate Laboratory i4HB - Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal; UCIBIO - Applied Biomolecular Sciences Unit, Department of Chemistry, Blue Biotechnology & Biomedicine Lab, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Berat Z Haznedaroğlu
- Institute of Environmental Sciences, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Nika Janež
- Department of Biotechnology, Jožef Stefan Institute, 1000 Ljubljana, Slovenia
| | - Leila Ktari
- B3Aqua Laboratory, National Institute of Marine Sciences and Technologies, Carthage University, Tunis, Tunisia
| | - Anna Luganini
- Department of Life Sciences and Systems Biology, University of Turin, 10123 Turin, Italy
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | - Ivo Safarik
- Department of Nanobiotechnology, Biology Centre, ISBB, CAS, Na Sadkach 7, 370 05 Ceske Budejovice, Czech Republic; Regional Centre of Advanced Technologies and Materials, Czech Advanced Technology and Research Institute, Palacky University, Slechtitelu 27, 783 71 Olomouc, Czech Republic
| | - Dina Simes
- Centre of Marine Sciences (CCMAR), Universidade do Algarve, 8005-139 Faro, Portugal; 2GenoGla Diagnostics, Centre of Marine Sciences (CCMAR), Universidade do Algarve, Faro, Portugal
| | - Evita Strode
- Latvian Institute of Aquatic Ecology, Agency of Daugavpils University, Riga LV-1007, Latvia
| | - Anna Toruńska-Sitarz
- Department of Marine Biology and Biotechnology, Faculty of Oceanography and Geography, University of Gdańsk, 81-378 Gdynia, Poland
| | - Despoina Varamogianni-Mamatsi
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, 71500 Heraklion, Greece
| | | | - Marlen I Vasquez
- Department of Chemical Engineering, Cyprus University of Technology, 3036 Limassol, Cyprus
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3
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Yuan Y, Shi C, Zhao H. Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products. ACS Synth Biol 2023; 12:2650-2662. [PMID: 37607352 PMCID: PMC10615616 DOI: 10.1021/acssynbio.3c00234] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.
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Affiliation(s)
- Yujie Yuan
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Chengyou Shi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
- Departments of Chemistry, Biochemistry, and Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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4
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Gaudêncio SP, Bayram E, Lukić Bilela L, Cueto M, Díaz-Marrero AR, Haznedaroglu BZ, Jimenez C, Mandalakis M, Pereira F, Reyes F, Tasdemir D. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar Drugs 2023; 21:md21050308. [PMID: 37233502 DOI: 10.3390/md21050308] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/27/2023] Open
Abstract
Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.
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Affiliation(s)
- Susana P Gaudêncio
- Associate Laboratory i4HB-Institute for Health and Bioeconomy, NOVA School of Science and Technology, NOVA University Lisbon, 2819-516 Caparica, Portugal
- UCIBIO-Applied Molecular Biosciences Unit, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Engin Bayram
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Lada Lukić Bilela
- Department of Biology, Faculty of Science, University of Sarajevo, 71000 Sarajevo, Bosnia and Herzegovina
| | - Mercedes Cueto
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
| | - Ana R Díaz-Marrero
- Instituto de Productos Naturales y Agrobiología-CSIC, 38206 La Laguna, Spain
- Instituto Universitario de Bio-Orgánica (IUBO), Universidad de La Laguna, 38206 La Laguna, Spain
| | - Berat Z Haznedaroglu
- Institute of Environmental Sciences, Room HKC-202, Hisar Campus, Bogazici University, Bebek, Istanbul 34342, Turkey
| | - Carlos Jimenez
- CICA- Centro Interdisciplinar de Química e Bioloxía, Departamento de Química, Facultade de Ciencias, Universidade da Coruña, 15071 A Coruña, Spain
| | - Manolis Mandalakis
- Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, HCMR Thalassocosmos, 71500 Gournes, Crete, Greece
| | - Florbela Pereira
- LAQV, REQUIMTE, Chemistry Department, NOVA School of Science and Technology, NOVA University of Lisbon, 2819-516 Caparica, Portugal
| | - Fernando Reyes
- Fundación MEDINA, Avda. del Conocimiento 34, 18016 Armilla, Spain
| | - Deniz Tasdemir
- GEOMAR Centre for Marine Biotechnology (GEOMAR-Biotech), Research Unit Marine Natural Products Chemistry, GEOMAR Helmholtz Centre for Ocean Research Kiel, Am Kiel-Kanal 44, 24106 Kiel, Germany
- Faculty of Mathematics and Natural Science, Kiel University, Christian-Albrechts-Platz 4, 24118 Kiel, Germany
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Tao Xue H, Stanley-Baker M, Wai Kin Kong A, Leung Li H, Wen Bin Goh W. Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discov Today 2022; 27:2235-2243. [DOI: 10.1016/j.drudis.2022.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022]
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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Patrício RPS, Videira PA, Pereira F. A computer-aided drug design approach to discover tumour suppressor p53 protein activators for colorectal cancer therapy. Bioorg Med Chem 2022; 53:116530. [PMID: 34861473 DOI: 10.1016/j.bmc.2021.116530] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/02/2021] [Accepted: 11/19/2021] [Indexed: 02/03/2023]
Abstract
Colorectal cancer (CRC) is the third most detected cancer and the second foremost cause of cancer deaths in the world. Intervention targeting p53 provides potential therapeutic strategies, but thus far no p53-based therapy has been successfully translated into clinical cancer treatment. Here we developed a Quantitative Structure-Activity Relationships (QSAR) classification models using empirical molecular descriptors and fingerprints to predict the activity against the p53 protein, using the potency value with the active or inactive label, were developed. These models were built using in total 10,505 molecules that were extracted from the ChEMBL, ZINC and Reaxys® databases, and recent literature. Three machine learning (ML) techniques e.g., Random Forest, Support Vector Machine, Convolutional Neural Network were explored to build models for p53 inhibitor prediction. The performances of the models were successfully evaluated by internal and external validation. Moreover, based on the best in silico p53 model, a virtual screening campaign was carried out using 1443 FDA-approved drugs that were extracted from the ZINC database. A list of virtual screening hits was assented on base of some limits established in this approach, such as: (1) probability of being active against p53; (2) applicability domain; (3) prediction of the affinity between the p53, and ligands, through molecular docking. The most promising according to the limits established above was dihydroergocristine. This compound revealed cytotoxic activity against a p53-expressing CRC cell line with an IC50 of 56.8 µM. This study demonstrated that the computer-aided drug design approach can be used to identify previously unknown molecules for targeting p53 protein with anti-cancer activity and thus pave the way for the study of a therapeutic solution for CRC.
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Affiliation(s)
- Rui P S Patrício
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal; UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Paula A Videira
- UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
| | - Florbela Pereira
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal.
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8
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Zhang R, Li X, Zhang X, Qin H, Xiao W. Machine learning approaches for elucidating the biological effects of natural products. Nat Prod Rep 2021; 38:346-361. [PMID: 32869826 DOI: 10.1039/d0np00043d] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Covering: 2000 to 2020 Machine learning (ML) is an efficient tool for the prediction of bioactivity and the study of structure-activity relationships. Over the past decade, an emerging trend for combining these approaches with the study of natural products (NPs) has developed in order to manage the challenge of the discovery of bioactive NPs. In the present review, we will introduce the basic principles and protocols for using the ML approach to investigate the bioactivity of NPs, citing a series of practical examples regarding the study of anti-microbial, anti-cancer, and anti-inflammatory NPs, etc. ML algorithms manage a variety of classification and regression problems associated with bioactive NPs, from those that are linear to non-linear and from pure compounds to plant extracts. Inspired by cases reported in the literature and our own experience, a number of key points have been emphasized for reducing modeling errors, including dataset preparation and applicability domain analysis.
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Affiliation(s)
- Ruihan Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xiaoli Li
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Xingjie Zhang
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Huayan Qin
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
| | - Weilie Xiao
- Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, Yunnan Research & Development Center for Natural Products, School of Chemical Science and Technology, Yunnan University, 2 Rd Cuihubei, P. R. China.
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Gaudêncio SP, Pereira F. A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition. Mar Drugs 2020; 18:E633. [PMID: 33322052 PMCID: PMC7764804 DOI: 10.3390/md18120633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/04/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure-activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.
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Affiliation(s)
- Susana P. Gaudêncio
- Blue Biotechnology & Biomedicine Lab, UCIBIO—Applied Biomolecular Sciences Unit, Department of Chemistry, Faculty of Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal;
| | - Florbela Pereira
- LAQV, Department of Chemistry, Faculty for Sciences and Technology, NOVA University of Lisbon, 2829-516 Caparica, Portugal
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Kumar MS, Sharma SA. Toxicological effects of marine seaweeds: a cautious insight for human consumption. Crit Rev Food Sci Nutr 2020; 61:500-521. [PMID: 32188262 DOI: 10.1080/10408398.2020.1738334] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Marine environment is a rich and diverse source for many biologically active substances including functional foods and nutraceuticals. It is well exploited for useful compounds, natural products and aquaculture industry; and seaweeds is one of the major contributors in terms of both food security and healthy nutrition. They are well-known due to their enormous benefits and is consumed globally in many countries. However, there is lack of attention toward their toxicity reports which might be due toxic chemical compounds from seaweed, epiphytic bacteria or harmful algal bloom and absorbed heavy metals from seawater. The excess of these components might lead to harmful interactions with drugs and hormone levels in the human body. Due to their global consumption and to meet increasing demands, it is necessary to address their hazardous and toxic aspects. In this review, we have done extensive literature for healthy seaweeds, their nutritional composition while summarizing the toxic effects of selected seaweeds from red, brown and green group which includes- Gracilaria, Acanthophora, Caulerpa, Cladosiphon, and Laminaria sp. Spirulina, a microalgae (cyanobacteria) biomass is also included in toxicity discussion as it an important food supplement and many times shows adverse reactions and drug interactions. The identified compounds from seaweeds were concluded to be toxic to humans, though they exhibited certain beneficial effects too. They have an easy access in food chain and thus invade the higher trophic level organisms. This review will create an awareness among scientific and nonscientific community, as well as government organization to regulate edible seaweed consumption and keep them under surveillance for their beneficial and safe consumption.
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Affiliation(s)
- Maushmi S Kumar
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, Mumbai, India
| | - Simran A Sharma
- Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM's NMIMS, Mumbai, India
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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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12
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Engineering enzymatic assembly lines to produce new antibiotics. Curr Opin Microbiol 2019; 51:88-96. [PMID: 31743841 PMCID: PMC6908967 DOI: 10.1016/j.mib.2019.10.007] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 10/17/2019] [Accepted: 10/18/2019] [Indexed: 02/07/2023]
Abstract
Many clinical antibiotics are natural products produced by thiotemplate-based assembly line biosynthetic pathways. Assembly line pathways provide an opportunity for rational bioengineering to modify complex natural product structures. New, rule-based mix and match strategies facilitate the engineering of non-ribosomal peptide assembly line synthetases. Evolutionary guided approaches highlight new avenues for polyketide synthase assembly line reprogramming.
Numerous important therapeutic agents, including widely-used antibiotics, anti-cancer drugs, immunosuppressants, agrochemicals and other valuable compounds, are produced by microorganisms. Many of these are biosynthesised by modular enzymatic assembly line polyketide synthases, non-ribosomal peptide synthetases, and hybrids thereof. To alter the backbone structure of these valuable but difficult to modify compounds, the respective enzymatic machineries can be engineered to create even more valuable molecules with improved properties and/or to bypass resistance mechanisms. In the past, many attempts to achieve assembly line pathway engineering failed or led to enzymes with compromised activity. Recently our understanding of assembly line structural biology, including an appreciation of the conformational changes that occur during the catalytic cycle, have improved hugely. This has proven to be a driving force for new approaches and several recent examples have demonstrated the production of new-to-nature molecules, including anti-infectives. We discuss the developments of the last few years and highlight selected, illuminating examples of assembly line engineering.
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Pricopie AI, Ionuț I, Marc G, Arseniu AM, Vlase L, Grozav A, Găină LI, Vodnar DC, Pîrnău A, Tiperciuc B, Oniga O. Design and Synthesis of Novel 1,3-Thiazole and 2-Hydrazinyl-1,3-Thiazole Derivatives as Anti- Candida Agents: In Vitro Antifungal Screening, Molecular Docking Study, and Spectroscopic Investigation of their Binding Interaction with Bovine Serum Albumin. Molecules 2019; 24:E3435. [PMID: 31546673 PMCID: PMC6804233 DOI: 10.3390/molecules24193435] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 08/18/2019] [Accepted: 09/20/2019] [Indexed: 02/03/2023] Open
Abstract
In the context of there being a limited number of clinically approved drugs for the treatment of Candida sp.-based infections, along with the rapid development of resistance to the existing antifungals, two novel series of 4-phenyl-1,3-thiazole and 2-hydrazinyl-4-phenyl-1,3-thiazole derivatives were synthesized and tested in vitro for their anti-Candida potential. Two compounds (7a and 7e) showed promising inhibitory activity against the pathogenic C. albicans strain, exhibiting substantially lower MIC values (7.81 μg/mL and 3.9 μg/mL, respectively) as compared with the reference drug fluconazole (15.62 μg/mL). Their anti-Candida activity is also supported by molecular docking studies, using the fungal lanosterol C14α-demethylase as the target enzyme. The interaction of the most biologically active synthesized compound 7e with bovine serum albumin was investigated through fluorescence spectroscopy, and the obtained data suggested that this molecule might efficiently bind carrier proteins in vivo in order to reach the target site.
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Affiliation(s)
- Andreea-Iulia Pricopie
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Ioana Ionuț
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Gabriel Marc
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Anca-Maria Arseniu
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
- Preclinic Department, Pharmacy Specialization, Faculty of Medicine, Lucian Blaga University of Sibiu, 2A Lucian Blaga Street, 550169 Sibiu, Romania.
| | - Laurian Vlase
- Department of Pharmaceutical Technology and Biopharmaceutics, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Adriana Grozav
- Department of Organic Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Luiza Ioana Găină
- Research Center on Fundamental and Applied Heterochemistry, Faculty of Chemistry and Chemical Engineering, "Babeş-Bolyai" University, 11 Arany Janos Street, 400028 Cluj-Napoca, Romania.
| | - Dan C Vodnar
- Department of Food Science and Technology, University of Agricultural Sciences and Veterinary Medicine, 3-5 Mănăștur Street, 400372 Cluj-Napoca, Romania.
| | - Adrian Pîrnău
- National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103 Donath Street, 400293 Cluj-Napoca, Romania.
| | - Brîndușa Tiperciuc
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
| | - Ovidiu Oniga
- Department of Pharmaceutical Chemistry, "Iuliu Hațieganu" University of Medicine and Pharmacy, 41 Victor Babeș Street, 400012 Cluj-Napoca, Romania.
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A Computer-Driven Approach to Discover Natural Product Leads for Methicillin-Resistant Staphylococcus aureus Infection Therapy. Mar Drugs 2018; 17:md17010016. [PMID: 30597893 PMCID: PMC6356832 DOI: 10.3390/md17010016] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 12/17/2018] [Accepted: 12/19/2018] [Indexed: 12/16/2022] Open
Abstract
The risk of methicillin-resistant Staphylococcus aureus (MRSA) infection is increasing in both the developed and developing countries. New approaches to overcome this problem are in need. A ligand-based strategy to discover new inhibiting agents against MRSA infection was built through exploration of machine learning techniques. This strategy is based in two quantitative structure–activity relationship (QSAR) studies, one using molecular descriptors (approach A) and the other using descriptors (approach B). In the approach A, regression models were developed using a total of 6645 molecules that were extracted from the ChEMBL, PubChem and ZINC databases, and recent literature. The performance of the regression models was successfully evaluated by internal and external validation, the best model achieved R2 of 0.68 and RMSE of 0.59 for the test set. In general natural product (NP) drug discovery is a time-consuming process and several strategies for dereplication have been developed to overcome this inherent limitation. In the approach B, we developed a new NP drug discovery methodology that consists in frontloading samples with 1D NMR descriptors to predict compounds with antibacterial activity prior to bioactivity screening for NPs discovery. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from crude extracts, fractions and pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 77% for both training and test sets.
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Tripathi VC, Satish S, Horam S, Raj S, lal A, Arockiaraj J, Pasupuleti M, Dikshit DK. Natural products from polar organisms: Structural diversity, bioactivities and potential pharmaceutical applications. POLAR SCIENCE 2018; 18:147-166. [DOI: 10.1016/j.polar.2018.04.006] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/16/2023]
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16
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In Silico HCT116 Human Colon Cancer Cell-Based Models En Route to the Discovery of Lead-Like Anticancer Drugs. Biomolecules 2018; 8:biom8030056. [PMID: 30018273 PMCID: PMC6164384 DOI: 10.3390/biom8030056] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 07/10/2018] [Accepted: 07/11/2018] [Indexed: 01/04/2023] Open
Abstract
To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.
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Pereira F, Aires-de-Sousa J. Computational Methodologies in the Exploration of Marine Natural Product Leads. Mar Drugs 2018; 16:md16070236. [PMID: 30011882 PMCID: PMC6070892 DOI: 10.3390/md16070236] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 07/02/2018] [Accepted: 07/06/2018] [Indexed: 12/18/2022] Open
Abstract
Computational methodologies are assisting the exploration of marine natural products (MNPs) to make the discovery of new leads more efficient, to repurpose known MNPs, to target new metabolites on the basis of genome analysis, to reveal mechanisms of action, and to optimize leads. In silico efforts in drug discovery of NPs have mainly focused on two tasks: dereplication and prediction of bioactivities. The exploration of new chemical spaces and the application of predicted spectral data must be included in new approaches to select species, extracts, and growth conditions with maximum probabilities of medicinal chemistry novelty. In this review, the most relevant current computational dereplication methodologies are highlighted. Structure-based (SB) and ligand-based (LB) chemoinformatics approaches have become essential tools for the virtual screening of NPs either in small datasets of isolated compounds or in large-scale databases. The most common LB techniques include Quantitative Structure–Activity Relationships (QSAR), estimation of drug likeness, prediction of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, similarity searching, and pharmacophore identification. Analogously, molecular dynamics, docking and binding cavity analysis have been used in SB approaches. Their significance and achievements are the main focus of this review.
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Affiliation(s)
- Florbela Pereira
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
| | - Joao Aires-de-Sousa
- LAQV and REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
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18
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Pereira G, Szwarc B, Mondragão MA, Lima PA, Pereira F. A Ligand-Based Approach to the Discovery of Lead-Like Potassium Channel KV
1.3 Inhibitors. ChemistrySelect 2018. [DOI: 10.1002/slct.201702977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Gilberto Pereira
- LAQV and REQUIMTE; Departamento de Química; Faculdade de Ciências e Tecnologia; Universidade Nova de Lisboa; 2829-516 Caparica Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Beatriz Szwarc
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Miguel A. Mondragão
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Pedro A. Lima
- Sea4Us - Biotecnologia e Recursos Marinhos, Lda; Rua do Poente S/N 8650-378 Sagres Portugal
- NOVA Medical School; Laboratório de Fisiologia; Faculdade de Ciências Médicas; Universidade Nova de Lisboa; Campo dos Mártires da Pátria, 130 1169-056 Lisboa PORTUGAL
| | - Florbela Pereira
- LAQV and REQUIMTE; Departamento de Química; Faculdade de Ciências e Tecnologia; Universidade Nova de Lisboa; 2829-516 Caparica Portugal
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19
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Abstract
Natural products (NPs) have been used as traditional medicines since antiquity. With more than 1060 estimated compounds with molecular weights less than 500 Da representing chemical space, NPs occupy a very small percentage; however, they are significantly overrepresented in biologically relevant chemical space. The classical approach concentrates on identifying one or more NPs with biological activity from a source organism. There is much more to be learned from NPs than we can discover this narrow view. In this review, we discuss ways to harness the global properties of NPs.
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Affiliation(s)
- Asmaa Boufridi
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
| | - Ronald J Quinn
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Queensland 4111, Australia; ,
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20
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Moloney MG. Natural Products as a Source for Novel Antibiotics. Trends Pharmacol Sci 2016; 37:689-701. [DOI: 10.1016/j.tips.2016.05.001] [Citation(s) in RCA: 161] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2016] [Revised: 05/02/2016] [Accepted: 05/02/2016] [Indexed: 01/04/2023]
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21
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Gaudêncio SP, Pereira F. Dereplication: racing to speed up the natural products discovery process. Nat Prod Rep 2015; 32:779-810. [PMID: 25850681 DOI: 10.1039/c4np00134f] [Citation(s) in RCA: 162] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Covering: 1993-2014 (July)To alleviate the dereplication holdup, which is a major bottleneck in natural products discovery, scientists have been conducting their research efforts to add tools to their "bag of tricks" aiming to achieve faster, more accurate and efficient ways to accelerate the pace of the drug discovery process. Consequently dereplication has become a hot topic presenting a huge publication boom since 2012, blending multidisciplinary fields in new ways that provide important conceptual and/or methodological advances, opening up pioneering research prospects in this field.
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Affiliation(s)
- Susana P Gaudêncio
- LAQV, REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
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22
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Gupta S, Basant N, Singh KP. Predicting the hazardous dose of industrial chemicals in warm-blooded species using machine learning-based modelling approaches. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2015; 26:479-498. [PMID: 26087353 DOI: 10.1080/1062936x.2015.1051584] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The hazardous dose of a chemical (HD50) is an emerging and acceptable test statistic for the safety/risk assessment of chemicals. Since it is derived using the experimental toxicity values of the chemical in several test species, it is highly cumbersome, time and resource intensive. In this study, three machine learning-based QSARs were established for predicting the HD50 of chemicals in warm-blooded species following the OECD guidelines. A data set comprising HD50 values of 957 chemicals was used to develop SDT, DTF and DTB QSAR models. The diversity in chemical structures and nonlinearity in the data were verified. Several validation coefficients were derived to test the predictive and generalization abilities of the constructed QSARs. The chi-path descriptors were identified as the most influential in three QSARs. The DTF and DTB performed relatively better than SDT model and yielded r(2) values of 0.928 and 0.959 between the measured and predicted HD50 values in the complete data set. Substructure alerts responsible for the toxicity of the chemicals were identified. The results suggest the appropriateness of the developed QSARs for reliably predicting the HD50 values of chemicals, and they can be used for screening of new chemicals for their safety/risk assessment for regulatory purposes.
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Affiliation(s)
- S Gupta
- a Environmental Chemistry Division , CSIR-Indian Institute of Toxicology Research , Lucknow , India
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23
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Pereira F, Latino DARS, Gaudêncio SP. QSAR-assisted virtual screening of lead-like molecules from marine and microbial natural sources for antitumor and antibiotic drug discovery. Molecules 2015; 20:4848-73. [PMID: 25789820 PMCID: PMC6272462 DOI: 10.3390/molecules20034848] [Citation(s) in RCA: 20] [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: 01/29/2015] [Revised: 02/27/2015] [Accepted: 03/04/2015] [Indexed: 11/17/2022] Open
Abstract
A Quantitative Structure-Activity Relationship (QSAR) approach for classification was used for the prediction of compounds as active/inactive relatively to overall biological activity, antitumor and antibiotic activities using a data set of 1746 compounds from PubChem with empirical CDK descriptors and semi-empirical quantum-chemical descriptors. A data set of 183 active pharmaceutical ingredients was additionally used for the external validation of the best models. The best classification models for antibiotic and antitumor activities were used to screen a data set of marine and microbial natural products from the AntiMarin database-25 and four lead compounds for antibiotic and antitumor drug design were proposed, respectively. The present work enables the presentation of a new set of possible lead like bioactive compounds and corroborates the results of our previous investigations. By other side it is shown the usefulness of quantum-chemical descriptors in the discrimination of biologically active and inactive compounds. None of the compounds suggested by our approach have assigned non-antibiotic and non-antitumor activities in the AntiMarin database and almost all were lately reported as being active in the literature.
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Affiliation(s)
- Florbela Pereira
- Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal.
| | - Diogo A R S Latino
- Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal.
- Centro de Ciências Moleculares e Materiais (CCMM), Departamento de Química e Bioquímica, Faculdade de Ciências, Universida Lisboa, Campo Grande, Lisboa 1749-016, Portugal.
| | - Susana P Gaudêncio
- Centro de Química Fina e Biotecnologia (CQFB)/LAQV-REQUIMTE, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa Campus Caparica, Caparica 2829-516, Portugal.
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