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Daghighi A, Casanola-Martin GM, Iduoku K, Kusic H, González-Díaz H, Rasulev B. Multi-Endpoint Acute Toxicity Assessment of Organic Compounds Using Large-Scale Machine Learning Modeling. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:10116-10127. [PMID: 38797941 DOI: 10.1021/acs.est.4c01017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
In recent years, alternative animal testing methods such as computational and machine learning approaches have become increasingly crucial for toxicity testing. However, the complexity and scarcity of available biomedical data challenge the development of predictive models. Combining nonlinear machine learning together with multicondition descriptors offers a solution for using data from various assays to create a robust model. This work applies multicondition descriptors (MCDs) to develop a QSTR (Quantitative Structure-Toxicity Relationship) model based on a large toxicity data set comprising more than 80,000 compounds and 59 different end points (122,572 data points). The prediction capabilities of developed single-task multi-end point machine learning models as well as a novel data analysis approach with the use of Convolutional Neural Networks (CNN) are discussed. The results show that using MCDs significantly improves the model and using them with CNN-1D yields the best result (R2train = 0.93, R2ext = 0.70). Several structural features showed a high level of contribution to the toxicity, including van der Waals surface area (VSA), number of nitrogen-containing fragments (nN+), presence of S-P fragments, ionization potential, and presence of C-N fragments. The developed models can be very useful tools to predict the toxicity of various compounds under different conditions, enabling quick toxicity assessment of new compounds.
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Affiliation(s)
- Amirreza Daghighi
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Gerardo M Casanola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Kweeni Iduoku
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
| | - Hrvoje Kusic
- Faculty of Chemical Engineering and Technology, University of Zagreb, Marulicev Trg 19, Zagreb 10000, Croatia
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, Leioa 48940, Spain
- BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, Leioa 48940, Spain
- IKERBASQUE, Basque Foundation for Science,Bilbao, Biscay 48011, Spain
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
- Biomedical Engineering Program, North Dakota State University, Fargo, North Dakota 58102, United States
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2
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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3
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Kleandrova VV, Speck-Planche A. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling. Mini Rev Med Chem 2021; 20:1357-1374. [PMID: 32013845 DOI: 10.2174/1389557520666200204123156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
Abstract
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
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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
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation
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4
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Applications of artificial intelligence to drug design and discovery in the big data era: a comprehensive review. Mol Divers 2021; 25:1643-1664. [PMID: 34110579 DOI: 10.1007/s11030-021-10237-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Artificial intelligence (AI) renders cutting-edge applications in diverse sectors of society. Due to substantial progress in high-performance computing, the development of superior algorithms, and the accumulation of huge biological and chemical data, computer-assisted drug design technology is playing a key role in drug discovery with its advantages of high efficiency, fast speed, and low cost. Over recent years, due to continuous progress in machine learning (ML) algorithms, AI has been extensively employed in various drug discovery stages. Very recently, drug design and discovery have entered the big data era. ML algorithms have progressively developed into a deep learning technique with potent generalization capability and more effectual big data handling, which further promotes the integration of AI technology and computer-assisted drug discovery technology, hence accelerating the design and discovery of the newest drugs. This review mainly summarizes the application progression of AI technology in the drug discovery process, and explores and compares its advantages over conventional methods. The challenges and limitations of AI in drug design and discovery have also been discussed.
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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6
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Concu R, Cordeiro MNDS. Alignment-Free Method to Predict Enzyme Classes and Subclasses. Int J Mol Sci 2019; 20:ijms20215389. [PMID: 31671806 PMCID: PMC6862210 DOI: 10.3390/ijms20215389] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 10/21/2019] [Accepted: 10/23/2019] [Indexed: 01/03/2023] Open
Abstract
The Enzyme Classification (EC) number is a numerical classification scheme for enzymes, established using the chemical reactions they catalyze. This classification is based on the recommendation of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology. Six enzyme classes were recognised in the first Enzyme Classification and Nomenclature List, reported by the International Union of Biochemistry in 1961. However, a new enzyme group was recently added as the six existing EC classes could not describe enzymes involved in the movement of ions or molecules across membranes. Such enzymes are now classified in the new EC class of translocases (EC 7). Several computational methods have been developed in order to predict the EC number. However, due to this new change, all such methods are now outdated and need updating. In this work, we developed a new multi-task quantitative structure-activity relationship (QSAR) method aimed at predicting all 7 EC classes and subclasses. In so doing, we developed an alignment-free model based on artificial neural networks that proved to be very successful.
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Affiliation(s)
- Riccardo Concu
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal.
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7
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 340] [Impact Index Per Article: 68.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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8
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Barreiro E, Munteanu CR, Cruz-Monteagudo M, Pazos A, González-Díaz H. Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems. Sci Rep 2018; 8:12340. [PMID: 30120369 PMCID: PMC6098100 DOI: 10.1038/s41598-018-30637-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 07/24/2018] [Indexed: 11/09/2022] Open
Abstract
Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Shk) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.
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Affiliation(s)
- Enrique Barreiro
- Department of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain.,Center for Computational Science (CCS), University of Miami (UM), Miami, 33136, FL, USA.,West Coast University, Miami Campus, 33178, FL, USA
| | - Cristian R Munteanu
- Department of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain
| | - Maykel Cruz-Monteagudo
- Center for Computational Science (CCS), University of Miami (UM), Miami, 33136, FL, USA.,West Coast University, Miami Campus, 33178, FL, USA
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain
| | - Humbert González-Díaz
- Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Biscay, Spain. .,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain.
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9
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Hmamouchi R, Larif M, Chtita S, Adad A, Bouachrine M, Lakhlifi T. Predictive modelling of the LD50 activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT. JOURNAL OF TAIBAH UNIVERSITY FOR SCIENCE 2018. [DOI: 10.1016/j.jtusci.2015.06.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Affiliation(s)
- Rachid Hmamouchi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco
| | - Majdouline Larif
- Separation Process Laboratories, Faculty of Science, University Ibn Tofail, Kenitra, Morocco
| | - Samir Chtita
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco
| | - Azeddine Adad
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco
| | | | - Tahar Lakhlifi
- Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, University Moulay Ismail, Meknes, Morocco
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10
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Liu J, Ming B, Gong GH, Wang D, Bao GL, Yu LJ. Current research on anti-breast cancer synthetic compounds. RSC Adv 2018. [DOI: 10.1039/c7ra12912b] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Breast cancer (BC) is the most common cancer for females and its incidence tends to increase year by year.
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Affiliation(s)
- Jia Liu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Bian Ming
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Guo-Hua Gong
- First Clinical Medical of Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
| | - Di Wang
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Gui-Lan Bao
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Li-Jun Yu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
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11
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Speck-Planche A, Cordeiro MNDS. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1936-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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12
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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13
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Vázquez-Prieto S, Paniagua E, Ubeira FM, González-Díaz H. QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development. Int J Pept Res Ther 2016. [DOI: 10.1007/s10989-016-9524-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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14
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Toropova AP, Schultz TW, Toropov AA. Building up a QSAR model for toxicity toward Tetrahymena pyriformis by the Monte Carlo method: A case of benzene derivatives. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2016; 42:135-145. [PMID: 26851376 DOI: 10.1016/j.etap.2016.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2015] [Revised: 01/12/2016] [Accepted: 01/14/2016] [Indexed: 06/05/2023]
Abstract
Data on toxicity toward Tetrahymena pyriformis is indicator of applicability of a substance in ecologic and pharmaceutical aspects. Quantitative structure-activity relationships (QSARs) between the molecular structure of benzene derivatives and toxicity toward T. pyriformis (expressed as the negative logarithms of the population growth inhibition dose, mmol/L) are established. The available data were randomly distributed three times into the visible training and calibration sets, and invisible validation sets. The statistical characteristics for the validation set are the following: r(2)=0.8179 and s=0.338 (first distribution); r(2)=0.8682 and s=0.341 (second distribution); r(2)=0.8435 and s=0.323 (third distribution). These models are built up using only information on the molecular structure: no data on physicochemical parameters, 3D features of the molecular structure and quantum mechanics descriptors are involved in the modeling process.
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Affiliation(s)
- Alla P Toropova
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy.
| | - Terry W Schultz
- College of Veterinary Medicine, The University of Tennessee, 2407 River Drive, Knoxville, TN 37996-4543, United States
| | - Andrey A Toropov
- IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, Italy
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15
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Wang T, Wu MB, Lin JP, Yang LR. Quantitative structure–activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discov 2015; 10:1283-300. [DOI: 10.1517/17460441.2015.1083006] [Citation(s) in RCA: 68] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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16
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Exploring the role of quantum chemical descriptors in modeling acute toxicity of diverse chemicals to Daphnia magna. J Mol Graph Model 2015; 61:89-101. [PMID: 26188798 DOI: 10.1016/j.jmgm.2015.06.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Revised: 06/04/2015] [Accepted: 06/20/2015] [Indexed: 11/18/2022]
Abstract
Various quantum-mechanically computed molecular and thermodynamic descriptors along with physico-chemical, electrostatic and topological descriptors are compared while developing quantitative structure-activity relationships (QSARs) for the acute toxicity of 252 diverse organic chemicals towards Daphnia magna. QSAR models based on the quantum-chemical descriptors, computed with routinely employed advanced semi-empirical and ab-initio methods, along with the electron-correlation contribution (CORR) of the descriptors, are analyzed for the external predictivity of the acute toxicity. The models with reliable internal stability and external predictivity are found to be based on the HOMO energy along with the physico-chemical, electrostatic and topological descriptors. Besides this, the total energy and electron-correlation energy are also observed as highly reliable descriptors, suggesting that the intra-molecular interactions between the electrons play an important role in the origin of the acute toxicity, which is in fact an unexplored phenomenon. The models based on quantum-chemical descriptors such as chemical hardness, absolute electronegativity, standard Gibbs free energy and enthalpy are also observed to be reliable. A comparison of the robust models based on the quantum-chemical descriptors computed with various quantum-mechanical methods suggests that the advanced semi-empirical methods such as PM7 can be more reliable than the ab-initio methods which are computationally more expensive.
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Casañola-Martin GM, Le-Thi-Thu H, Pérez-Giménez F, Marrero-Ponce Y, Merino-Sanjuán M, Abad C, González-Díaz H. Multi-output model with Box–Jenkins operators of linear indices to predict multi-target inhibitors of ubiquitin–proteasome pathway. Mol Divers 2015; 19:347-56. [DOI: 10.1007/s11030-015-9571-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2014] [Accepted: 02/14/2015] [Indexed: 12/29/2022]
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18
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Speck-Planche A, Cordeiro MNDS. Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov 2015; 10:245-56. [DOI: 10.1517/17460441.2015.1006195] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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19
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Speck-Planche A, Cordeiro MNDS. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol Biol 2015; 1260:45-64. [PMID: 25502375 DOI: 10.1007/978-1-4939-2239-0_4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.
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Affiliation(s)
- A Speck-Planche
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
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20
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Surowka AD, Adamek D, Szczerbowska-Boruchowska M. The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors. Analyst 2015; 140:2428-38. [DOI: 10.1039/c4an01867b] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Protein-related changes associated with the development of human brain gliomas are of increasing interest in modern neuro-oncology.
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Affiliation(s)
- A. D. Surowka
- AGH University of Science and Technology
- Faculty of Physics and Applied Computer Science
- 30-059 Krakow
- Poland
| | - D. Adamek
- Jagiellonian University
- Faculty of Medicine
- Department of Neuropathology
- Chair of Pathomorphology
- Krakow
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21
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Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med Chem 2014; 6:2013-28. [DOI: 10.4155/fmc.14.136] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.
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22
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Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates. Int J Mol Sci 2014; 15:17035-64. [PMID: 25255029 PMCID: PMC4200850 DOI: 10.3390/ijms150917035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 08/19/2014] [Accepted: 08/21/2014] [Indexed: 11/25/2022] Open
Abstract
In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.
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23
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Speck-Planche A, Cordeiro MNDS. Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening. ACS COMBINATORIAL SCIENCE 2014; 16:78-84. [PMID: 24383958 DOI: 10.1021/co400115s] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto, 4169-007 Porto, Portugal
| | - M. N. D. S. Cordeiro
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto, 4169-007 Porto, Portugal
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24
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Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. J Immunol Res 2014; 2014:768515. [PMID: 24741624 PMCID: PMC3987976 DOI: 10.1155/2014/768515] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 11/17/2013] [Indexed: 11/17/2022] Open
Abstract
Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.
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25
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Tenorio-Borroto E, Peñuelas-Rivas CG, Vásquez-Chagoyán JC, Castañedo N, Prado-Prado FJ, García-Mera X, González-Díaz H. Model for high-throughput screening of drug immunotoxicity – Study of the anti-microbial G1 over peritoneal macrophages using flow cytometry. Eur J Med Chem 2014; 72:206-20. [DOI: 10.1016/j.ejmech.2013.08.035] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2012] [Revised: 08/29/2013] [Accepted: 08/31/2013] [Indexed: 10/26/2022]
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26
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Alonso N, Caamaño O, Romero-Duran FJ, Luan F, D. S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 2013; 4:1393-403. [PMID: 23855599 DOI: 10.1021/cn400111n] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.
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Affiliation(s)
- Nerea Alonso
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Francisco J. Romero-Duran
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Feng Luan
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto,
4169-007, Porto, Portugal
- Department of Applied Chemistry, Yantai University, Yantai 264005, People’s Republic
of China
| | | | - Matilde Yañez
- Department of
Pharmacology,
Faculty of Pharmacy, USC, 15782, Santiago
de Compostela, Spain
| | - Humberto González-Díaz
- Departament
of Organic Chemistry
II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
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27
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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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28
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Lynnes T, Horne SM, Prüß BM. ß-Phenylethylamine as a novel nutrient treatment to reduce bacterial contamination due to Escherichia coli O157:H7 on beef meat. Meat Sci 2013; 96:165-71. [PMID: 23896151 DOI: 10.1016/j.meatsci.2013.06.030] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2013] [Revised: 06/21/2013] [Accepted: 06/28/2013] [Indexed: 11/26/2022]
Abstract
Bacterial infection by Escherichia coli O157:H7 through the consumption of beef meat or meat products is an ongoing problem, in part because bacteria develop resistances towards chemicals aimed at killing them. In an approach that uses bacterial nutrients to manipulate bacteria into behaviors or cellular phenotypes less harmful to humans, we screened a library of 95 carbon and 95 nitrogen sources for their effect on E. coli growth, cell division, and biofilm formation. In the initial screening experiment using the Phenotype MicroArray(TM) technology from BioLog (Hayward, CA), we narrowed the 190 starting nutrients down to eight which were consecutively tested as supplements in liquid beef broth medium. Acetoacetic acid (AAA) and ß-phenylethylamine (PEA) performed best in this experiment. On beef meat pieces, PEA reduced the bacterial cell count by 90% after incubation of the PEA treated and E. coli contaminated meat pieces at 10°C for one week.
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Affiliation(s)
- Ty Lynnes
- Department of Veterinary and Microbiological Sciences, North Dakota State University, Fargo, ND 58108, United States
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29
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Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg Med Chem 2013; 21:2727-32. [PMID: 23582445 DOI: 10.1016/j.bmc.2013.03.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 03/03/2013] [Accepted: 03/04/2013] [Indexed: 11/21/2022]
Abstract
Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.
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30
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Speck-Planche A, Kleandrova VV, Cordeiro MND. New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur J Pharm Sci 2013; 48:812-8. [DOI: 10.1016/j.ejps.2013.01.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 01/05/2013] [Accepted: 01/23/2013] [Indexed: 01/11/2023]
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31
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Scholz T, Heyl CL, Bernardi D, Zimmermann S, Kattner L, Klein CD. Chemical, biochemical and microbiological properties of a brominated nitrovinylfuran with broad-spectrum antibacterial activity. Bioorg Med Chem 2012; 21:795-804. [PMID: 23266177 DOI: 10.1016/j.bmc.2012.11.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2012] [Revised: 11/07/2012] [Accepted: 11/12/2012] [Indexed: 11/16/2022]
Abstract
A di-bromo substituted nitrovinylfuran with reported broad-spectrum antibacterial activity was found to be a potent inhibitor of MurA, a key enzyme in peptidoglycan biosynthesis. Further characterization of the compound was carried out to assess its reactivity towards thiol nucleophiles, its stability and degradation under aqueous conditions, inhibitory potential at other enzymes, and antibacterial and cytotoxic activity. Our results indicate that the nitrovinylfuran derivative is reactive towards cysteine residues in proteins, as demonstrated by the irreversible inhibition of MurA and bacterial methionine aminopeptidase. Experiments with proteins and model thiols indicate that the compound forms covalent adducts with SH groups and induces intermolecular disulfide bonds, with the intermediate formation of a monobromide derivative. The parent molecule as well as most of its breakdown products are potent antibiotics with MIC values below 4 μg/mL and are active against multiresistant strains such as methicillin-resistant Staphylococcus aureus (MRSA). Further development of the bromonitrovinyl scaffold towards antibiotics with clinical relevance, however, requires optimization of the antibiotic-cytotoxic selectivity profile.
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Affiliation(s)
- Therese Scholz
- Medicinal Chemistry, Institute of Pharmacy and Molecular Biotechnology IPMB, Heidelberg University, Im Neuenheimer Feld 364, D-69120 Heidelberg, Germany
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