1
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Fomina AD, Uvarova VI, Kozlovskaya LI, Palyulin VA, Osolodkin DI, Ishmukhametov AA. Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors. Mol Inform 2024; 43:e202300279. [PMID: 38973780 DOI: 10.1002/minf.202300279] [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: 10/16/2023] [Revised: 02/21/2024] [Accepted: 03/03/2024] [Indexed: 07/09/2024]
Abstract
During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.
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Affiliation(s)
- Anastasia D Fomina
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Department of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Victoria I Uvarova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
| | - Liubov I Kozlovskaya
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| | - Vladimir A Palyulin
- Department of Chemistry, Lomonosov Moscow State University, 119991, Moscow, Russia
| | - Dmitry I Osolodkin
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
| | - Aydar A Ishmukhametov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), 108819, Moscow, Russia
- Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, 119991, Moscow, Russia
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2
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Osolodkin DI, Kozlovskaya LI, Iusupov IR, Kurkin AV, Shustova EY, Orlov AA, Khvatov EV, Mutnykh ES, Kurashova SS, Vetrova AN, Yatsenko DO, Goryashchenko AS, Ivanov VN, Lukyanenko ER, Karpova EV, Stepanova DA, Volok VP, Sotskova SE, Dzagurova TK, Karganova GG, Lukashev AN, Ishmukhametov AA. Phenotypic assessment of antiviral activity for spiro-annulated oxepanes and azepenes. Chem Biol Drug Des 2024; 103:e14553. [PMID: 38789394 DOI: 10.1111/cbdd.14553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/02/2024] [Accepted: 05/07/2024] [Indexed: 05/26/2024]
Abstract
Evolutionary potential of viruses can result in outbreaks of well-known viruses and emergence of novel ones. Pharmacological methods of intervening the reproduction of various less popular, but not less important viruses are not available, as well as the spectrum of antiviral activity for most known compounds. In the framework of chemical biology paradigm, characterization of antiviral activity spectrum of new compounds allows to extend the antiviral chemical space and provides new important structure-activity relationships for data-driven drug discovery. Here we present a primary assessment of antiviral activity of spiro-annulated derivatives of seven-membered heterocycles, oxepane and azepane, in phenotypic assays against viruses with different genomes, virion structures, and genome realization schemes: orthoflavivirus (tick-borne encephalitis virus, TBEV), enteroviruses (poliovirus, enterovirus A71, echovirus 30), adenovirus (human adenovirus C5), hantavirus (Puumala virus). Hit compounds inhibited reproduction of adenovirus C5, the only DNA virus in the studied set, in the yield reduction assay, and did not inhibit reproduction of RNA viruses.
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Affiliation(s)
- Dmitry I Osolodkin
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | - Liubov I Kozlovskaya
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - Ildar R Iusupov
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | - Alexander V Kurkin
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | - Elena Y Shustova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Alexey A Orlov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | - Evgeny V Khvatov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Elena S Mutnykh
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | | | - Anna N Vetrova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Darya O Yatsenko
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | | | - Vladimir N Ivanov
- Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia
| | | | - Evgenia V Karpova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Daria A Stepanova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Viktor P Volok
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Svetlana E Sotskova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Tamara K Dzagurova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Galina G Karganova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexander N Lukashev
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
| | - Aydar A Ishmukhametov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
- Sechenov First Moscow State Medical University, Moscow, Russia
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3
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Tarasova OA. Current Perspectives in Antiviral Research. Int J Mol Sci 2023; 24:14555. [PMID: 37833992 PMCID: PMC10572941 DOI: 10.3390/ijms241914555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Studies on virus-host interactions are of high significance for a number of reasons [...].
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Affiliation(s)
- Olga A Tarasova
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 Bldg. 8, Pogodinskaya Str., 119121 Moscow, Russia
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4
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Sosnina EA, Sosnin S, Fedorov MV. Improvement of multi-task learning by data enrichment: application for drug discovery. J Comput Aided Mol Des 2023; 37:183-200. [PMID: 36943645 DOI: 10.1007/s10822-023-00500-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/21/2023] [Indexed: 03/23/2023]
Abstract
Multi-task learning in deep neural networks has become a topic of growing importance in many research fields, including drug discovery. However, applying multi-task learning poses new challenges in improving prediction performance. This study investigated the potential of training data enrichment to enhance multi-task model prediction quality in drug discovery. The study evaluated four scenarios with varying degrees of information capacity of the training data and applied two types of test data to evaluate prediction performance. We used three datasets: ViralChEMBL, which consisted of binary activities of compounds against viral species, was applied for the classification task; pQSAR(159) and pQSAR(4267), which consisted of bio-activities of compounds and assays from the research of the profile-QSAR method, were applied for regression tasks. We built multi-task models based on the feed-forward DNNs using the PyTorch framework. Our findings showed that training data enrichment could be an effective means of enhancing prediction performance in multi-task learning, but the degree of improvement depends on the quality of the training data. The more unique compounds and targets the training data included, the more new compound-target interactions are required for prediction improvement. Also, we found out that even using multi-task learning, one could not predict the interactions of compounds that are highly dissimilar from those used for model training. The study provides some recommendations for effectively employing multi-task learning in drug discovery to improve prediction accuracy and facilitate the discovery of novel drug candidates.
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Affiliation(s)
- Ekaterina A Sosnina
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow, Russia, 143026.
| | - Sergey Sosnin
- Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1190, Vienna, Austria
| | - Maxim V Fedorov
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow, Russia, 143026
- Sirius University of Science and Technology, Olympiisky Prospect 1, Sochi, Russia, 354340
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5
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Goryashchenko AS, Uvarova VI, Osolodkin DI, Ishmukhametov AA. Discovery of small molecule antivirals targeting tick-borne encephalitis virus. ANNUAL REPORTS IN MEDICINAL CHEMISTRY 2022. [DOI: 10.1016/bs.armc.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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6
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Zakharova MY, Kuznetsova AA, Uvarova VI, Fomina AD, Kozlovskaya LI, Kaliberda EN, Kurbatskaia IN, Smirnov IV, Bulygin AA, Knorre VD, Fedorova OS, Varnek A, Osolodkin DI, Ishmukhametov AA, Egorov AM, Gabibov AG, Kuznetsov NA. Pre-Steady-State Kinetics of the SARS-CoV-2 Main Protease as a Powerful Tool for Antiviral Drug Discovery. Front Pharmacol 2021; 12:773198. [PMID: 34938188 PMCID: PMC8686763 DOI: 10.3389/fphar.2021.773198] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 10/29/2021] [Indexed: 02/05/2023] Open
Abstract
The design of effective target-specific drugs for COVID-19 treatment has become an intriguing challenge for modern science. The SARS-CoV-2 main protease, Mpro, responsible for the processing of SARS-CoV-2 polyproteins and production of individual components of viral replication machinery, is an attractive candidate target for drug discovery. Specific Mpro inhibitors have turned out to be promising anticoronaviral agents. Thus, an effective platform for quantitative screening of Mpro-targeting molecules is urgently needed. Here, we propose a pre-steady-state kinetic analysis of the interaction of Mpro with inhibitors as a basis for such a platform. We examined the kinetic mechanism of peptide substrate binding and cleavage by wild-type Mpro and by its catalytically inactive mutant C145A. The enzyme induces conformational changes of the peptide during the reaction. The inhibition of Mpro by boceprevir, telaprevir, GC-376, PF-00835231, or thimerosal was investigated. Detailed pre-steady-state kinetics of the interaction of the wild-type enzyme with the most potent inhibitor, PF-00835231, revealed a two-step binding mechanism, followed by covalent complex formation. The C145A Mpro mutant interacts with PF-00835231 approximately 100-fold less effectively. Nevertheless, the binding constant of PF-00835231 toward C145A Mpro is still good enough to inhibit the enzyme. Therefore, our results suggest that even noncovalent inhibitor binding due to a fine conformational fit into the active site is sufficient for efficient inhibition. A structure-based virtual screening and a subsequent detailed assessment of inhibition efficacy allowed us to select two compounds as promising noncovalent inhibitor leads of SARS-CoV-2 Mpro.
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Affiliation(s)
- Maria Yu Zakharova
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia.,Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Alexandra A Kuznetsova
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch (SB) of RAS, Novosibirsk, Russia
| | - Victoria I Uvarova
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia
| | - Anastasiia D Fomina
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia.,Lomonosov Moscow State University, Moscow, Russia
| | - Liubov I Kozlovskaya
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia.,Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Elena N Kaliberda
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Inna N Kurbatskaia
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Ivan V Smirnov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia.,Lomonosov Moscow State University, Moscow, Russia
| | - Anatoly A Bulygin
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch (SB) of RAS, Novosibirsk, Russia
| | - Vera D Knorre
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia
| | - Olga S Fedorova
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch (SB) of RAS, Novosibirsk, Russia
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique, UMR 7140 CNRS, Université de Strasbourg, Strasbourg, France
| | - Dmitry I Osolodkin
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia.,Lomonosov Moscow State University, Moscow, Russia.,Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Aydar A Ishmukhametov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia.,Institute of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexey M Egorov
- FSASI "Chumakov FSC R&D IBP RAS" (Institute of Poliomyelitis), Moscow, Russia.,Lomonosov Moscow State University, Moscow, Russia
| | - Alexander G Gabibov
- Institute of Bioorganic Chemistry, Russian Academy of Sciences (RAS), Moscow, Russia.,Lomonosov Moscow State University, Moscow, Russia.,Department of Biology and Biotechnology, Higher School of Economics, Moscow, Russia
| | - Nikita A Kuznetsov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch (SB) of RAS, Novosibirsk, Russia.,Department of Natural Sciences, Novosibirsk State University, Novosibirsk, Russia
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7
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Titov IY, Stroylov VS, Rusina P, Svitanko IV. Preliminary modelling as the first stage of targeted organic synthesis. RUSSIAN CHEMICAL REVIEWS 2021. [DOI: 10.1070/rcr5012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.
The bibliography includes 353 references.
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8
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Serafim MSM, Dos Santos Júnior VS, Gertrudes JC, Maltarollo VG, Honorio KM. Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade. Expert Opin Drug Discov 2021; 16:961-975. [PMID: 33957833 DOI: 10.1080/17460441.2021.1918098] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the biological potential of chemicals and accelerate the discovery of drugs over the past decade, new methods and their combinations have improved their performance and established promising perspectives regarding ML in the search for new antivirals.Areas covered: The authors consider some interesting areas that deal with different ML techniques applied to antivirals. Recent innovative studies on ML and antivirals were selected and analyzed in detail. Also, the authors provide a brief look at the past to the present to detect advances and bottlenecks in the area.Expert opinion: From classical ML techniques, it was possible to boost the searches for antivirals. However, from the emergence of new algorithms and the improvement in old approaches, promising results will be achieved every day, as we have observed in the case of SARS-CoV-2. Recent experience has shown that it is possible to use ML to discover new antiviral candidates from virtual screening and drug repurposing.
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Affiliation(s)
- Mateus Sá Magalhães Serafim
- Departamento de Microbiologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Jadson Castro Gertrudes
- Departamento de Computação, Instituto de Ciências Exatas e Biológicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Kathia Maria Honorio
- Escola de Artes, Ciências e Humanidades, Universidade de São Paulo (USP), São Paulo, Brazil.,Centro de Ciências Naturais e Humanas, Universidade Federal do ABC (UFABC), Santo André, Brazil
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9
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Tarasova O, Poroikov V. Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy. Curr Med Chem 2021; 28:7840-7861. [PMID: 33949929 DOI: 10.2174/0929867328666210504114351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/13/2021] [Accepted: 02/24/2021] [Indexed: 11/22/2022]
Abstract
Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.
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Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow. Russian Federation
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10
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Przybyłek M. Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening. Molecules 2020; 25:E5942. [PMID: 33333961 PMCID: PMC7765417 DOI: 10.3390/molecules25245942] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 12/12/2020] [Accepted: 12/14/2020] [Indexed: 12/14/2022] Open
Abstract
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.
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Affiliation(s)
- Maciej Przybyłek
- Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland
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11
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Horvath D, Orlov A, Osolodkin DI, Ishmukhametov AA, Marcou G, Varnek A. A Chemographic Audit of anti-Coronavirus Structure-activity Information from Public Databases (ChEMBL). Mol Inform 2020; 39:e2000080. [PMID: 32363750 PMCID: PMC7267182 DOI: 10.1002/minf.202000080] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 04/26/2020] [Indexed: 01/30/2023]
Abstract
Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of "anti-CoV" map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800 M organic compounds.
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Affiliation(s)
- Dragos Horvath
- Chemoinformatics LaboratoryUMR 7140 CNRS/University of Strasbourg4, rue Blaise Pascal67000Strasbourg
| | - Alexey Orlov
- Chemoinformatics LaboratoryUMR 7140 CNRS/University of Strasbourg4, rue Blaise Pascal67000Strasbourg
- FSBSI “Chumakov FSC R&D IBP RAS”Poselok Instituta Poliomielita 8 bd. 1Poselenie MoskovskyMoscow108819Russia
| | - Dmitry I. Osolodkin
- FSBSI “Chumakov FSC R&D IBP RAS”Poselok Instituta Poliomielita 8 bd. 1Poselenie MoskovskyMoscow108819Russia
- Institute of Translational Medicine and BiotechnologySechenov First Moscow State Medical UniversityTrubetskaya ul. 8Moscow119991Russia
| | - Aydar A. Ishmukhametov
- FSBSI “Chumakov FSC R&D IBP RAS”Poselok Instituta Poliomielita 8 bd. 1Poselenie MoskovskyMoscow108819Russia
- Institute of Translational Medicine and BiotechnologySechenov First Moscow State Medical UniversityTrubetskaya ul. 8Moscow119991Russia
| | - Gilles Marcou
- Chemoinformatics LaboratoryUMR 7140 CNRS/University of Strasbourg4, rue Blaise Pascal67000Strasbourg
| | - Alexandre Varnek
- Chemoinformatics LaboratoryUMR 7140 CNRS/University of Strasbourg4, rue Blaise Pascal67000Strasbourg
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12
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Poroikov VV. Computer-Aided Drug Design: from Discovery of Novel Pharmaceutical Agents to Systems Pharmacology. BIOCHEMISTRY (MOSCOW), SUPPLEMENT SERIES B: BIOMEDICAL CHEMISTRY 2020. [DOI: 10.1134/s1990750820030117] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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13
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Sosnina EA, Sosnin S, Nikitina AA, Nazarov I, Osolodkin DI, Fedorov MV. Recommender Systems in Antiviral Drug Discovery. ACS OMEGA 2020; 5:15039-15051. [PMID: 32632398 PMCID: PMC7315437 DOI: 10.1021/acsomega.0c00857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 06/03/2020] [Indexed: 06/11/2023]
Abstract
Recommender systems (RSs), which underwent rapid development and had an enormous impact on e-commerce, have the potential to become useful tools for drug discovery. In this paper, we applied RS methods for the prediction of the antiviral activity class (active/inactive) for compounds extracted from ChEMBL. Two main RS approaches were applied: collaborative filtering (Surprise implementation) and content-based filtering (sparse-group inductive matrix completion (SGIMC) method). The effectiveness of RS approaches was investigated for prediction of antiviral activity classes ("interactions") for compounds and viruses, for which some of their interactions with other viruses or compounds are known, and for prediction of interaction profiles for new compounds. Both approaches achieved relatively good prediction quality for binary classification of individual interactions and compound profiles, as quantified by cross-validation and external validation receiver operating characteristic (ROC) score >0.9. Thus, even simple recommender systems may serve as an effective tool in antiviral drug discovery.
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Affiliation(s)
- Ekaterina A. Sosnina
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Institute
of Physiologically Active Compounds, RAS, Severniy pr. 1, Chernogolovka 142432, Russia
| | - Sergey Sosnin
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
| | - Anastasia A. Nikitina
- Department
of Chemistry, Lomonosov Moscow State University, Leninskie Gory 1 bd. 3, Moscow 119991, Russia
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
| | - Ivan Nazarov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
| | - Dmitry I. Osolodkin
- FSBSI
“Chumakov FSC R&D IBP RAS”, Poselok Instituta Poliomielita 8
bd. 1, Poselenie Moskovsky, Moscow 108819, Russia
- Institute
of Translational Medicine and Biotechnology, Sechenov First Moscow State Medical University, Trubetskaya Ulitsa 8, Moscow 119991, Russia
| | - Maxim V. Fedorov
- Center
for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30/1, Moscow 143026, Russia
- Syntelly
LLC, Skolkovo Innovation Center, Bolshoy Boulevard 30, Moscow 121205, Russia
- Physics
John Anderson Building, University of Strathclyde, 107 Rottenrow East, Glasgow G4 0NG, U.K.
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14
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Poroikov VV. [Computer-aided drug design: from discovery of novel pharmaceutical agents to systems pharmacology]. BIOMEDIT︠S︡INSKAI︠A︡ KHIMII︠A︡ 2020; 66:30-41. [PMID: 32116224 DOI: 10.18097/pbmc20206601030] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
New drug discovery is based on the analysis of public information about the mechanisms of the disease, molecular targets, and ligands, which interaction with the target could lead to the normalization of the pathological process. The available data on diseases, drugs, pharmacological effects, molecular targets, and drug-like substances, taking into account the combinatorics of the associative relations between them, correspond to the Big Data. To analyze such data, the application of computer-aided drug design methods is necessary. An overview of the studies in this area performed by the Laboratory for Structure-Function Based Drug Design of IBMC is presented. We have developed the approaches to identifying promising pharmacological targets, predicting several thousand types of biological activity based on the structural formula of the compound, analyzing protein-ligand interactions based on assessing local similarity of amino acid sequences, identifying likely molecular mechanisms of side effects of drugs, calculating the integral toxicity of drugs taking into account their metabolism, have been developed in the human body, predicting sustainable and sensitive options strains and evaluating the effectiveness of combinations of antiretroviral drugs in patients, taking into account the molecular genetic characteristics of the clinical isolates of HIV-1. Our computer programs are implemented as the web-services freely available on the Internet, which are used by thousands of researchers from many countries of the world to select the most promising substances for the synthesis and determine the priority areas for experimental testing of their biological activity.
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Affiliation(s)
- V V Poroikov
- Institute of Biomedical Chemistry, Moscow, Russia
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15
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Stolbov LA, Druzhilovskiy DS, Filimonov DA, Nicklaus MC, Poroikov VV. (Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds. Molecules 2019; 25:molecules25010087. [PMID: 31881687 PMCID: PMC6983201 DOI: 10.3390/molecules25010087] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 12/17/2022] Open
Abstract
Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure–activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.
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Affiliation(s)
- Leonid A. Stolbov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Dmitry S. Druzhilovskiy
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Dmitry A. Filimonov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
| | - Marc C. Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD 21702, USA;
| | - Vladimir V. Poroikov
- Laboratory of Structure-Function Based Drug Design, Institute of Biomedical Chemistry, 10 bldg. 8, Pogodinskaya str., 119121 Moscow, Russia; (L.A.S.); (D.S.D.); (D.A.F.)
- Correspondence:
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16
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Orlov AA, Zherebker A, Eletskaya AA, Chernikov VS, Kozlovskaya LI, Zhernov YV, Kostyukevich Y, Palyulin VA, Nikolaev EN, Osolodkin DI, Perminova IV. Examination of molecular space and feasible structures of bioactive components of humic substances by FTICR MS data mining in ChEMBL database. Sci Rep 2019; 9:12066. [PMID: 31427609 PMCID: PMC6700089 DOI: 10.1038/s41598-019-48000-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 07/29/2019] [Indexed: 01/08/2023] Open
Abstract
Humic substances (HS) are complex natural mixtures comprising a large variety of compounds produced during decomposition of decaying biomass. The molecular composition of HS is extremely diverse as it was demonstrated with the use of high resolution mass spectrometry. The building blocks of HS are mostly represented by plant-derived biomolecules (lignins, lipids, tannins, carbohydrates, etc.). As a result, HS show a wide spectrum of biological activity. Despite that, HS remain a 'biological activity black-box' due to unknown structures of constituents responsible for the interaction with molecular targets. In this study, we investigated the antiviral activity of eight HS fractions isolated from peat and coal, as well as of two synthetic humic-like materials. We determined molecular compositions of the corresponding samples using ultra-high resolution Fourier-transform ion cyclotron resonance mass-spectrometry (FTICR MS). Inhibitory activity of HS was studied with respect to reproduction of tick-borne encephalitis virus (TBEV), which is a representative of Flavivirus genus, and to a panel of enteroviruses (EVs). The samples of natural HS inhibited TBEV reproduction already at a concentration of 1 µg/mL, but they did not inhibit reproduction of EVs. We found that the total relative intensity of FTICR MS formulae within elemental composition range commonly attributed to flavonoid-like structures is correlating with the activity of the samples. In order to surmise on possible active structural components of HS, we mined formulae within FTICR MS assignments in the ChEMBL database. Out of 6502 formulae within FTICR MS assignments, 3852 were found in ChEMBL. There were more than 71 thousand compounds related to these formulae in ChEMBL. To support chemical relevance of these compounds to natural HS we applied the previously developed approach of selective isotopic exchange coupled to FTICR MS to obtain structural information on the individual components of HS. This enabled to propose compounds from ChEMBL, which corroborated the labeling data. The obtained results provide the first insight onto the possible structures, which comprise antiviral components of HS and, respectively, can be used for further disclosure of antiviral activity mechanism of HS.
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Affiliation(s)
- Alexey A Orlov
- FSBSI "Chumakov FSC R&D IBP RAS", Moscow, 108819, Russia
- Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Alexander Zherebker
- Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Anastasia A Eletskaya
- FSBSI "Chumakov FSC R&D IBP RAS", Moscow, 108819, Russia
- Department of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | | | - Liubov I Kozlovskaya
- FSBSI "Chumakov FSC R&D IBP RAS", Moscow, 108819, Russia
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia
| | - Yury V Zhernov
- State Research Center "Institute of Immunology" of the Federal Medical-Biological Agency of Russia, Moscow, 115478, Russia
| | - Yury Kostyukevich
- Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
| | - Vladimir A Palyulin
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Eugene N Nikolaev
- Skolkovo Institute of Science and Technology, Moscow, 143026, Russia
| | - Dmitry I Osolodkin
- FSBSI "Chumakov FSC R&D IBP RAS", Moscow, 108819, Russia.
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.
- Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
| | - Irina V Perminova
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119991, Russia.
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17
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Orlov AA, Khvatov EV, Koruchekov AA, Nikitina AA, Zolotareva AD, Eletskaya AA, Kozlovskaya LI, Palyulin VA, Horvath D, Osolodkin DI, Varnek A. Getting to Know the Neighbours with GTM: The Case of Antiviral Compounds. Mol Inform 2019; 38:e1800166. [DOI: 10.1002/minf.201800166] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 02/02/2019] [Indexed: 01/16/2023]
Affiliation(s)
- Alexey A. Orlov
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Lomonosov Moscow State University Moscow 119991 Russia
| | | | - Alexander A. Koruchekov
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Lomonosov Moscow State University Moscow 119991 Russia
| | - Anastasia A. Nikitina
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Lomonosov Moscow State University Moscow 119991 Russia
| | - Anastasia D. Zolotareva
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Sechenov First Moscow State Medical University Moscow 119991 Russia
| | - Anastasia A. Eletskaya
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Lomonosov Moscow State University Moscow 119991 Russia
| | - Liubov I. Kozlovskaya
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Sechenov First Moscow State Medical University Moscow 119991 Russia
| | | | - Dragos Horvath
- Laboratory of Chemoinformatics, Faculty of ChemistryUniversity of Strasbourg Strasbourg 67081 France
| | - Dmitry I. Osolodkin
- FSBSI “Chumakov FSC R&D IBP RAS” Moscow 108819 Russia
- Lomonosov Moscow State University Moscow 119991 Russia
- Sechenov First Moscow State Medical University Moscow 119991 Russia
| | - Alexandre Varnek
- Laboratory of Chemoinformatics, Faculty of ChemistryUniversity of Strasbourg Strasbourg 67081 France
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18
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Tick-borne encephalitis in Europe and Russia: Review of pathogenesis, clinical features, therapy, and vaccines. Antiviral Res 2019; 164:23-51. [PMID: 30710567 DOI: 10.1016/j.antiviral.2019.01.014] [Citation(s) in RCA: 211] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Revised: 12/10/2018] [Accepted: 01/22/2019] [Indexed: 02/07/2023]
Abstract
Tick-borne encephalitis (TBE) is an illness caused by tick-borne encephalitis virus (TBEV) infection which is often limited to a febrile illness, but may lead to very aggressive downstream neurological manifestations. The disease is prevalent in forested areas of Europe and northeastern Asia, and is typically caused by infection involving one of three TBEV subtypes, namely the European (TBEV-Eu), the Siberian (TBEV-Sib), or the Far Eastern (TBEV-FE) subtypes. In addition to the three main TBEV subtypes, two other subtypes; i.e., the Baikalian (TBEV-Bkl) and the Himalayan subtype (TBEV-Him), have been described recently. In Europe, TBEV-Eu infection usually results in only mild TBE associated with a mortality rate of <2%. TBEV-Sib infection also results in a generally mild TBE associated with a non-paralytic febrile form of encephalitis, although there is a tendency towards persistent TBE caused by chronic viral infection. TBE-FE infection is considered to induce the most severe forms of TBE. Importantly though, viral subtype is not the sole determinant of TBE severity; both mild and severe cases of TBE are in fact associated with infection by any of the subtypes. In keeping with this observation, the overall TBE mortality rate in Russia is ∼2%, in spite of the fact that TBEV-Sib and TBEV-FE subtypes appear to be inducers of more severe TBE than TBEV-Eu. On the other hand, TBEV-Sib and TBEV-FE subtype infections in Russia are associated with essentially unique forms of TBE rarely seen elsewhere if at all, such as the hemorrhagic and chronic (progressive) forms of the disease. For post-exposure prophylaxis and TBE treatment in Russia and Kazakhstan, a specific anti-TBEV immunoglobulin is currently used with well-documented efficacy, but the use of specific TBEV immunoglobulins has been discontinued in Europe due to concerns regarding antibody-enhanced disease in naïve individuals. Therefore, new treatments are essential. This review summarizes available data on the pathogenesis and clinical features of TBE, plus different vaccine preparations available in Europe and Russia. In addition, new treatment possibilities, including small molecule drugs and experimental immunotherapies are reviewed. The authors caution that their descriptions of approved or experimental therapies should not be considered to be recommendations for patient care.
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