1
|
Subramanian G, Fanai HL, Chand J, Ahmad SF, Attia SM, Emran TB. System biology-based assessment of the molecular mechanism of IMPHY000797 in Parkinson's disease: a network pharmacology and in-silico evaluation. Sci Rep 2024; 14:23414. [PMID: 39379677 PMCID: PMC11461797 DOI: 10.1038/s41598-024-75603-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 10/07/2024] [Indexed: 10/10/2024] Open
Abstract
IMPHY000797 derivatives have been well known for their efficacy in various diseases. Moreover, IMPHY000797 derivatives have been found to modulate such genes involved in multiple neurological disorders. Hence, this study seeks to identify such genes and the probable molecular mechanism that could be involved in the pathogenesis of Parkinson's disease. The study utilized various biological tools such as DisGeNET, STRING, Swiss target predictor, Cytoscape, AutoDock 4.2, Schrodinger suite, ClueGo, and GUSAR. All the reported genes were obtained using DisGeNET, and further, the common genes were incorporated into the STRING to get the KEGG pathway, and all the data was converted to a protein/pathway network via Cytoscape. The clustering of the genes was performed for the gene-enriched data using two-sided hypergeometrics (p-value). The binding affinity of the IMPHY000797 was verified with the highest regulated 25 proteins via utilizing the "Monte Carlo iterated search technique" and the "Emodel and Glide score" function. Three thousand five hundred eighty-three genes were identified for Parkinson's disease and 31 genes for IMPHY000797 compound, among which 25 common genes were identified. Further, the "FOXO-signaling pathway" was identified to be a modulated pathway. Among the 25 proteins, the highest modulated genes and highest binding affinity were exhibited by SIRT3, FOXO1, and PPARGC1A with the compound IMPHY000797. Further, rat toxicity analysis provided the efficacy and safety of the compound. The study was required to identify the probable molecular mechanism, which needs more confirmation from other studies, which is still a significant hit-back.
Collapse
Affiliation(s)
- Gomathy Subramanian
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, 643001, Tamil Nadu, India
| | - Hannah Lalengzuali Fanai
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, 643001, Tamil Nadu, India
| | - Jagdish Chand
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, 643001, Tamil Nadu, India.
| | - Sheikh F Ahmad
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Sabry M Attia
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Talha Bin Emran
- Department of Pathology and Laboratory Medicine, Warren Alpert Medical School, Brown University, Providence, RI, 02912, USA.
- Legorreta Cancer Center, Brown University, Providence, RI, 02912, USA.
- Department of Pharmacy, Faculty of Allied Health Sciences, Daffodil International University, Dhaka, 1207, Bangladesh.
| |
Collapse
|
2
|
Boulaamane Y, Kandpal P, Chandra A, Britel MR, Maurady A. Chemical library design, QSAR modeling and molecular dynamics simulations of naturally occurring coumarins as dual inhibitors of MAO-B and AChE. J Biomol Struct Dyn 2024; 42:1629-1646. [PMID: 37199265 DOI: 10.1080/07391102.2023.2209650] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 04/05/2023] [Indexed: 05/19/2023]
Abstract
Coumarins are a highly privileged scaffold in medicinal chemistry. It is present in many natural products and is reported to display various pharmacological properties. A large plethora of compounds based on the coumarin ring system have been synthesized and were found to possess biological activities such as anticonvulsant, antiviral, anti-inflammatory, antibacterial, antioxidant as well as neuroprotective properties. Despite the wide activity spectrum of coumarins, its naturally occurring derivatives are yet to be investigated in detail. In the current study, a chemical library was created to assemble all chemical information related to naturally occurring coumarins from the literature. Additionally, a multi-stage virtual screening combining QSAR modeling, molecular docking, and ADMET prediction was conducted against monoamine oxidase B and acetylcholinesterase, two relevant targets known for their neuroprotective properties and 'disease-modifying' potential in Parkinson's and Alzheimer's disease. Our findings revealed ten coumarin derivatives that may act as dual-target drugs against MAO-B and AChE. Two coumarin candidates were selected from the molecular docking study: CDB0738 and CDB0046 displayed favorable interactions for both proteins as well as suitable ADMET profiles. The stability of the selected coumarins was assessed through 100 ns molecular dynamics simulations which revealed promising stability through key molecular interactions for CDB0738 to act as dual inhibitor of MAO-B and AChE. However, experimental studies are necessary to evaluate the bioactivity of the proposed candidate. The current results may generate an increasing interest in bioprospecting naturally occurring coumarins as potential candidates against relevant macromolecular targets by encouraging virtual screening studies against our chemical library.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Yassir Boulaamane
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
| | | | | | - Mohammed Reda Britel
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
| | - Amal Maurady
- Laboratory of Innovative Technologies, National School of Applied Sciences of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
- Faculty of Sciences and Techniques of Tangier, Abdelmalek Essaadi University, Tetouan, Morocco
| |
Collapse
|
3
|
Pogodin PV, Salina EG, Semenov VV, Raihstat MM, Druzhilovskiy DS, Filimonov DA, Poroikov VV. Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:53-69. [PMID: 38282553 DOI: 10.1080/1062936x.2024.2304803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/07/2024] [Indexed: 01/30/2024]
Abstract
Novel antimycobacterial compounds are needed to expand the existing toolbox of therapeutic agents, which sometimes fail to be effective. In our study we extracted, filtered, and aggregated the diverse data on antimycobacterial activity of chemical compounds from the ChEMBL database version 24.1. These training sets were used to create the classification and regression models with PASS and GUSAR software. The IOC chemical library consisting of approximately 200,000 chemical compounds was screened using these (Q)SAR models to select novel compounds potentially having antimycobacterial activity. The QikProp tool (Schrödinger) was used to predict ADME properties and find compounds with acceptable ADME profiles. As a result, 20 chemical compounds were selected for further biological evaluation, of which 13 were the Schiff bases of isoniazid. To diversify the set of selected compounds we applied substructure filtering and selected an additional 10 compounds, none of which were Schiff bases of isoniazid. Thirty compounds selected using virtual screening were biologically evaluated in a REMA assay against the M. tuberculosis strain H37Rv. Twelve compounds demonstrated MIC below 20 µM (ranging from 2.17 to 16.67 µM) and 18 compounds demonstrated substantially higher MIC values. The discovered antimycobacterial agents represent different chemical classes.
Collapse
Affiliation(s)
- P V Pogodin
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - E G Salina
- Group of Biochemistry of Adaptation of Microorganisms, Bach Institute of Biochemistry, Research Center of Biotechnology of the Russian Academy of Sciences, Moscow, Russia
| | - V V Semenov
- Laboratory of Medicinal Chemistry (N17), N. D. Zelinsky Institute of Organic Chemistry RAS, Moscow, Russia
| | - M M Raihstat
- Laboratory of Medicinal Chemistry (N17), N. D. Zelinsky Institute of Organic Chemistry RAS, Moscow, Russia
| | - D S Druzhilovskiy
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - D A Filimonov
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - V V Poroikov
- Laboratory of Structure-Function Based Drug Design, Department for Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| |
Collapse
|
4
|
Khairullina V, Martynova Y. Quantitative Structure-Activity Relationship in the Series of 5-Ethyluridine, N2-Guanine, and 6-Oxopurine Derivatives with Pronounced Anti-Herpetic Activity. Molecules 2023; 28:7715. [PMID: 38067446 PMCID: PMC10708366 DOI: 10.3390/molecules28237715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 12/18/2023] Open
Abstract
A quantitative analysis of the relationship between the structure and inhibitory activity against the herpes simplex virus thymidine kinase (HSV-TK) was performed for the series of 5-ethyluridine, N2-guanine, and 6-oxopurines derivatives with pronounced anti-herpetic activity (IC50 = 0.09 ÷ 160,000 μmol/L) using the GUSAR 2019 software. On the basis of the MNA and QNA descriptors and whole-molecule descriptors using the self-consistent regression, 12 statistically significant consensus models for predicting numerical pIC50 values were constructed. These models demonstrated high predictive accuracy for the training and test sets. Molecular fragments of HSV-1 and HSV-2 TK inhibitors that enhance or diminish the anti-herpetic activity are considered. Virtual screening of the ChEMBL database using the developed QSAR models revealed 42 new effective HSV-1 and HSV-2 TK inhibitors. These compounds are promising for further research. The obtained data open up new opportunities for developing novel effective inhibitors of TK.
Collapse
Affiliation(s)
- Veronika Khairullina
- Institute of Chemistry and Defence in Emergency Situations, Ufa University of Science and Technology, 50076 Ufa, Russia;
| | | |
Collapse
|
5
|
Smajić A, Rami I, Sosnin S, Ecker GF. Identifying Differences in the Performance of Machine Learning Models for Off-Targets Trained on Publicly Available and Proprietary Data Sets. Chem Res Toxicol 2023; 36:1300-1312. [PMID: 37439496 PMCID: PMC10445286 DOI: 10.1021/acs.chemrestox.3c00042] [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: 02/13/2023] [Indexed: 07/14/2023]
Abstract
Each year, publicly available databases are updated with new compounds from different research institutions. Positive experimental outcomes are more likely to be reported; therefore, they account for a considerable fraction of these entries. Established publicly available databases such as ChEMBL allow researchers to use information without constrictions and create predictive tools for a broad spectrum of applications in the field of toxicology. Therefore, we investigated the distribution of positive and nonpositive entries within ChEMBL for a set of off-targets and its impact on the performance of classification models when applied to pharmaceutical industry data sets. Results indicate that models trained on publicly available data tend to overpredict positives, and models based on industry data sets predict negatives more often than those built using publicly available data sets. This is strengthened even further by the visualization of the prediction space for a set of 10,000 compounds, which makes it possible to identify regions in the chemical space where predictions converge. Finally, we highlight the utilization of these models for consensus modeling for potential adverse events prediction.
Collapse
Affiliation(s)
- Aljoša Smajić
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Iris Rami
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Sergey Sosnin
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Sciences, University of Vienna, Josef-Holaubek-Platz 2, 1090 Vienna, Austria
| |
Collapse
|
6
|
Stolbov LA, Filimonov DA, Poroikov VV. SAR based on self consistent classifier. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2022; 33:793-804. [PMID: 36369710 DOI: 10.1080/1062936x.2022.2139751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 10/20/2022] [Indexed: 06/16/2023]
Abstract
The accuracy and performance of (Q)SAR models depend significantly on the data used for training. Datasets prepared on the basis of publicly available databases contain structures belonging to different chemical classes and have a highly imbalanced actives/inactives ratio. Currently, hundreds of structural descriptors are used in (Q)SAR studies. The abundance of structural descriptors gives rise to the problem of the constructed (Q)SAR models stability. The methods frequently used for the selection of a small fraction of the 'best' descriptors usually do not have sufficient mathematical justification. We propose a new approach to a self-consistent classifier for SAR analysis in order to overcome these problems. Logistic (SCLC) and extreme (SCEC) extensions of self-consistent regression (SCR) were implemented to enhance the classification capabilities of SCR. The approach was applied to classification models' development for inhibiting activity endpoints in HIV-1-related data and toxicity endpoints with subsequent fivefold cross-validation to estimate the models' performance. Comparison of the proposed SCLC and SCEC models with those developed using the original SCR and support vector machine demonstrated the comparable accuracy. Advantages in feature selection using our approach provide more generalizable (Q)SAR models. In particular, the crucial factors responsible for the observed value are determined unambiguously.
Collapse
Affiliation(s)
- L A Stolbov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russian Federation
| | - D A Filimonov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russian Federation
| | - V V Poroikov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russian Federation
| |
Collapse
|
7
|
Sun X, Tamura R, Sumita M, Mori K, Terayama K, Tsuda K. Integrating Incompatible Assay Data Sets with Deep Preference Learning. ACS Med Chem Lett 2022; 13:70-75. [PMID: 35047110 PMCID: PMC8762726 DOI: 10.1021/acsmedchemlett.1c00439] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/27/2021] [Indexed: 11/30/2022] Open
Abstract
A large amount of bioactivity assay data is already accumulated in public databases, but the integration of these data sets for quantitative structure-activity relationship (QSAR) studies is not straightforward due to differences in experimental methods and settings. We present an efficient deep-learning-based approach called Deep Preference Data Integration (DPDI). For integrating outcome variables of different assay types, a surrogate variable is introduced, and a neural network is trained such that the total order induced by the surrogate variable is maximally consistent with given data sets. In a task of predicting efficacy of factor Xa inhibitors, DPDI successfully integrated 2959 molecules distributed in 129 assay data sets. In most of our experiments, data integration improved prediction accuracy strongly in interpolation and extrapolation tasks, indicating that DPDI is an effective tool for QSAR studies.
Collapse
Affiliation(s)
- Xiaolin Sun
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Ryo Tamura
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Masato Sumita
- International
Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| | - Kenichi Mori
- Astellas
Pharma Inc., Tsukuba, Ibaraki 305-8585, Japan
| | - Kei Terayama
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
- Graduate
School of Medical Life Science, Yokohama
City University, Yokohama 230-0045, Japan
| | - Koji Tsuda
- Graduate
School of Frontier Sciences, The University
of Tokyo, Kashiwa, Chiba 277-8561, Japan
- Research
and Services Division of Materials Data and Integrated System, National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan
- RIKEN
Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
| |
Collapse
|
8
|
Matsumoto K, Miyao T, Funatsu K. Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration. ACS OMEGA 2021; 6:11964-11973. [PMID: 34056351 PMCID: PMC8154010 DOI: 10.1021/acsomega.1c00463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/21/2021] [Indexed: 05/15/2023]
Abstract
In ligand-based drug design, quantitative structure-activity relationship (QSAR) models play an important role in activity prediction. One of the major end points of QSAR models is half-maximal inhibitory concentration (IC50). Experimental IC50 data from various research groups have been accumulated in publicly accessible databases, providing an opportunity for us to use such data in predictive QSAR models. In this study, we focused on using a ranking-oriented QSAR model as a predictive model because relative potency strength within the same assay is solid information that is not based on any mechanical assumptions. We conducted rigorous validation using the ChEMBL database and previously reported data sets. Ranking support vector machine (ranking-SVM) models trained on compounds from similar assays were as good as support vector regression (SVR) with the Tanimoto kernel trained on compounds from all the assays. As effective ways of data integration, for ranking-SVM, integrated compounds should be selected from only similar assays in terms of compounds. For SVR with the Tanimoto kernel, entire compounds from different assays can be incorporated.
Collapse
Affiliation(s)
- Katsuhisa Matsumoto
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5
Takayama-cho, Ikoma, Nara, 630-0192, Japan
| | - Kimito Funatsu
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5
Takayama-cho, Ikoma, Nara, 630-0192, Japan
- Department
of Chemical System Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan
- E-mail: . Phone: +81-3-5841-7751. Fax: +81-3-5841-7771
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Sakai M, Nagayasu K, Shibui N, Andoh C, Takayama K, Shirakawa H, Kaneko S. Prediction of pharmacological activities from chemical structures with graph convolutional neural networks. Sci Rep 2021; 11:525. [PMID: 33436854 PMCID: PMC7803991 DOI: 10.1038/s41598-020-80113-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Accepted: 12/17/2020] [Indexed: 01/29/2023] Open
Abstract
Many therapeutic drugs are compounds that can be represented by simple chemical structures, which contain important determinants of affinity at the site of action. Recently, graph convolutional neural network (GCN) models have exhibited excellent results in classifying the activity of such compounds. For models that make quantitative predictions of activity, more complex information has been utilized, such as the three-dimensional structures of compounds and the amino acid sequences of their respective target proteins. As another approach, we hypothesized that if sufficient experimental data were available and there were enough nodes in hidden layers, a simple compound representation would quantitatively predict activity with satisfactory accuracy. In this study, we report that GCN models constructed solely from the two-dimensional structural information of compounds demonstrated a high degree of activity predictability against 127 diverse targets from the ChEMBL database. Using the information entropy as a metric, we also show that the structural diversity had less effect on the prediction performance. Finally, we report that virtual screening using the constructed model identified a new serotonin transporter inhibitor with activity comparable to that of a marketed drug in vitro and exhibited antidepressant effects in behavioural studies.
Collapse
Affiliation(s)
- Miyuki Sakai
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan ,Medical Database Ltd., 2-5-5 Sumitomoshibadaimon building, Shibadaimon, Minato-ku, Tokyo, 105-0012 Japan
| | - Kazuki Nagayasu
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Norihiro Shibui
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Chihiro Andoh
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Kaito Takayama
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Hisashi Shirakawa
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Shuji Kaneko
- grid.258799.80000 0004 0372 2033Department of Molecular Pharmacology, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshida-Shimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| |
Collapse
|
11
|
Biziukova N, Tarasova O, Ivanov S, Poroikov V. Automated Extraction of Information From Texts of Scientific Publications: Insights Into HIV Treatment Strategies. Front Genet 2021; 11:618862. [PMID: 33414815 PMCID: PMC7783389 DOI: 10.3389/fgene.2020.618862] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2020] [Accepted: 11/26/2020] [Indexed: 12/16/2022] Open
Abstract
Text analysis can help to identify named entities (NEs) of small molecules, proteins, and genes. Such data are very important for the analysis of molecular mechanisms of disease progression and development of new strategies for the treatment of various diseases and pathological conditions. The texts of publications represent a primary source of information, which is especially important to collect the data of the highest quality due to the immediate obtaining information, in comparison with databases. In our study, we aimed at the development and testing of an approach to the named entity recognition in the abstracts of publications. More specifically, we have developed and tested an algorithm based on the conditional random fields, which provides recognition of NEs of (i) genes and proteins and (ii) chemicals. Careful selection of abstracts strictly related to the subject of interest leads to the possibility of extracting the NEs strongly associated with the subject. To test the applicability of our approach, we have applied it for the extraction of (i) potential HIV inhibitors and (ii) a set of proteins and genes potentially responsible for viremic control in HIV-positive patients. The computational experiments performed provide the estimations of evaluating the accuracy of recognition of chemical NEs and proteins (genes). The precision of the chemical NEs recognition is over 0.91; recall is 0.86, and the F1-score (harmonic mean of precision and recall) is 0.89; the precision of recognition of proteins and genes names is over 0.86; recall is 0.83; while F1-score is above 0.85. Evaluation of the algorithm on two case studies related to HIV treatment confirms our suggestion about the possibility of extracting the NEs strongly relevant to (i) HIV inhibitors and (ii) a group of patients i.e., the group of HIV-positive individuals with an ability to maintain an undetectable HIV-1 viral load overtime in the absence of antiretroviral therapy. Analysis of the results obtained provides insights into the function of proteins that can be responsible for viremic control. Our study demonstrated the applicability of the developed approach for the extraction of useful data on HIV treatment.
Collapse
Affiliation(s)
- Nadezhda Biziukova
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Olga Tarasova
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Sergey Ivanov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Faculty of Biomedicine, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Vladimir Poroikov
- Laboratory of Structure-Function Based Drug Design, Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 327] [Impact Index Per Article: 81.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
Collapse
Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
14
|
Computer-aided prediction of biological activity spectra for organic compounds: the possibilities and limitations. Russ Chem Bull 2020. [DOI: 10.1007/s11172-019-2683-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
15
|
Tarasova O, Biziukova N, Kireev D, Lagunin A, Ivanov S, Filimonov D, Poroikov V. A Computational Approach for the Prediction of Treatment History and the Effectiveness or Failure of Antiretroviral Therapy. Int J Mol Sci 2020; 21:ijms21030748. [PMID: 31979356 PMCID: PMC7037494 DOI: 10.3390/ijms21030748] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/20/2020] [Accepted: 01/21/2020] [Indexed: 02/01/2023] Open
Abstract
Human Immunodeficiency Virus Type 1 (HIV-1) infection is associated with high mortality if no therapy is provided. Currently, the treatment of an HIV-1 positive patient requires that several drugs should be taken simultaneously. The resistance of the virus to an antiretroviral drug may lead to treatment failure. Our approach focuses on predicting the exposure of a particular viral variant to an antiretroviral drug or drug combination. It also aims at the prediction of drug treatment success or failure. We utilized nucleotide sequences of HIV-1 encoding protease and reverse transcriptase to perform such types of prediction. The PASS (Prediction of Activity Spectra for Substances) algorithm based on the naive Bayesian classifier was used to make a prediction. We calculated the probability of whether a sequence belonged (P1) or did not belong (P0) to the class associated with exposure of the viral sequence to the set of drugs that can be associated with resistance to the set of drugs. The accuracy calculated as the average Area Under the ROC (Receiver Operating Characteristic) Curve (AUC/ROC) for classifying exposure of the sequence to the HIV-1 protease inhibitors was 0.81 (±0.07), and for HIV-1 reverse transcriptase, it was 0.83 (±0.07). To predict cases of treatment effectiveness or failure, we used P1 and P0 values, obtained in PASS, along with the binary vector constructed based on short nucleotide descriptors and the applied random forest classifier. Average AUC/ROC prediction accuracy for the prediction of treatment effectiveness or failure for the combinations of HIV-1 protease inhibitors was 0.82 (±0.06) and of HIV-1 reverse transcriptase was 0.76 (±0.09).
Collapse
Affiliation(s)
- Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Correspondence:
| | - Nadezhda Biziukova
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Dmitry Kireev
- Central Research Institute of Epidemiology, 111123 Moscow, Russia;
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
- Department of Bioinformatics, Pirogov Russian National Research Medical University, 117997 Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| | - Vladimir Poroikov
- Department of Bioinformatics, Institute of Biomedical Chemistry, 119121 Moscow, Russia; (N.B.); (A.L.); (S.I.); (D.F.); (V.P.)
| |
Collapse
|
16
|
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.
Collapse
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:
| |
Collapse
|
17
|
Tarasova OA, Biziukova NY, Filimonov DA, Poroikov VV, Nicklaus MC. Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications. J Chem Inf Model 2019; 59:3635-3644. [PMID: 31453694 DOI: 10.1021/acs.jcim.9b00164] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.
Collapse
Affiliation(s)
- Olga A Tarasova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Nadezhda Yu Biziukova
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Vladimir V Poroikov
- Department of Bioinformatics , Institute of Biomedical Chemistry , 10 Building 8, Pogodinskaya Street , Moscow 119121 , Russia
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research , National Cancer Institute , Frederick , Maryland 21702 , United States
| |
Collapse
|
18
|
Dmitriev AV, Lagunin AA, Karasev DА, Rudik AV, Pogodin PV, Filimonov DA, Poroikov VV. Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Curr Top Med Chem 2019; 19:319-336. [PMID: 30674264 DOI: 10.2174/1568026619666190123160406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
Collapse
Affiliation(s)
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russian Federation.,Pirogov Russian National Research Medical University, Moscow, RussiaN Federation
| | | | | | - Pavel V Pogodin
- Institute of Biomedical Chemistry, Moscow, Russian Federation
| | | | | |
Collapse
|
19
|
Lagunin AA, Geronikaki A, Eleftheriou P, Pogodin PV, Zakharov AV. Rational Use of Heterogeneous Data in Quantitative Structure-Activity Relationship (QSAR) Modeling of Cyclooxygenase/Lipoxygenase Inhibitors. J Chem Inf Model 2019; 59:713-730. [PMID: 30688458 DOI: 10.1021/acs.jcim.8b00617] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Numerous studies have been published in recent years with acceptable quantitative structure-activity relationship (QSAR) modeling based on heterogeneous data. In many cases, the training sets for QSAR modeling were constructed from compounds tested by different biological assays, contradicting the opinion that QSAR modeling should be based on the data measured by a single protocol. We attempted to develop approaches that help to determine how heterogeneous data should be used for the creation of QSAR models on the basis of different sets of compounds tested by different experimental methods for the same target and the same endpoint. To this end, more than 100 QSAR models for the IC50 values of ligands interacting with cyclooxygenase 1,2 (COX) and seed lipoxygenase (LOX), obtained from ChEMBL database were created using the GUSAR software. The QSAR models were tested on the external set, including 26 new thiazolidinone derivatives, which were experimentally tested for COX-1,2/LOX inhibition. The IC50 values of the derivatives varied from 89 μM to 26 μM for LOX, from 200 μM to 0.018 μM for COX-1, and from 210 μM to 1 μM for COX-2. This study showed that the accuracy of the models is dependent on the distribution of IC50 values of low activity compounds in the training sets. In the most cases, QSAR models created based on the combined training sets had advantages in comparison with QSAR models, based on a single publication. We introduced a new method of combination of quantitative data from different experimental studies based on the data of reference compounds, which was called "scaling".
Collapse
Affiliation(s)
- Alexey A Lagunin
- Pirogov Russian National Research Medical University , Ostrovitianov str. 1 , Moscow , 117997 , Russia
- Institute of Biomedical Chemistry , Pogodinskaya Str., 10/8 , Moscow , 119121 , Russia
| | - Athina Geronikaki
- School of Pharmacy , Aristotle University , Thessaloniki , 54124 , Greece
| | - Phaedra Eleftheriou
- School of Health and Medical Care , Alexander Technological Educational Institute of Thessaloniki , Thessaloniki , 57400 , Greece
| | - Pavel V Pogodin
- Institute of Biomedical Chemistry , Pogodinskaya Str., 10/8 , Moscow , 119121 , Russia
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS) , National Institutes of Health , Rockville , Maryland 20850 , United States
| |
Collapse
|
20
|
Tarasova O, Poroikov V. HIV Resistance Prediction to Reverse Transcriptase Inhibitors: Focus on Open Data. Molecules 2018; 23:E956. [PMID: 29671808 PMCID: PMC6017644 DOI: 10.3390/molecules23040956] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 04/16/2018] [Accepted: 04/17/2018] [Indexed: 12/16/2022] Open
Abstract
Research and development of new antiretroviral agents are in great demand due to issues with safety and efficacy of the antiretroviral drugs. HIV reverse transcriptase (RT) is an important target for HIV treatment. RT inhibitors targeting early stages of the virus-host interaction are of great interest for researchers. There are a lot of clinical and biochemical data on relationships between the occurring of the single point mutations and their combinations in the pol gene of HIV and resistance of the particular variants of HIV to nucleoside and non-nucleoside reverse transcriptase inhibitors. The experimental data stored in the databases of HIV sequences can be used for development of methods that are able to predict HIV resistance based on amino acid or nucleotide sequences. The data on HIV sequences resistance can be further used for (1) development of new antiretroviral agents with high potential for HIV inhibition and elimination and (2) optimization of antiretroviral therapy. In our communication, we focus on the data on the RT sequences and HIV resistance, which are available on the Internet. The experimental methods, which are applied to produce the data on HIV-1 resistance, the known data on their concordance, are also discussed.
Collapse
Affiliation(s)
- Olga Tarasova
- Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya st., Moscow 119121, Russia.
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya st., Moscow 119121, Russia.
| |
Collapse
|
21
|
Druzhilovskiy DS, Rudik AV, Filimonov DA, Gloriozova TA, Lagunin AA, Dmitriev AV, Pogodin PV, Dubovskaya VI, Ivanov SM, Tarasova OA, Bezhentsev VM, Murtazalieva KA, Semin MI, Maiorov IS, Gaur AS, Sastry GN, Poroikov VV. Computational platform Way2Drug: from the prediction of biological activity to drug repurposing. Russ Chem Bull 2018. [DOI: 10.1007/s11172-017-1954-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
|
22
|
Filimonov D, Druzhilovskiy D, Lagunin A, Gloriozova T, Rudik A, Dmitriev A, Pogodin P, Poroikov V. Computer-aided prediction of biological activity spectra for chemical compounds: opportunities and limitation. ACTA ACUST UNITED AC 2018. [DOI: 10.18097/bmcrm00004] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
An essential characteristic of chemical compounds is their biological activity since its presence can become the basis for the use of the substance for therapeutic purposes, or, on the contrary, limit the possibilities of its practical application due to the manifestation of side action and toxic effects. Computer assessment of the biological activity spectra makes it possible to determine the most promising directions for the study of the pharmacological action of particular substances, and to filter out potentially dangerous molecules at the early stages of research. For more than 25 years, we have been developing and improving the computer program PASS (Prediction of Activity Spectra for Substances), designed to predict the biological activity spectrum of substance based on the structural formula of its molecules. The prediction is carried out by the analysis of structure-activity relationships for the training set, which currently contains information on structures and known biological activities for more than one million molecules. The structure of the organic compound is represented in PASS using Multilevel Neighborhoods of Atoms descriptors; the activity prediction for new compounds is performed by the naive Bayes classifier and the structure-activity relationships determined by the analysis of the training set. We have created and improved both local versions of the PASS program and freely available web resources based on PASS (http://www.way2drug.com). They predict several thousand biological activities (pharmacological effects, molecular mechanisms of action, specific toxicity and adverse effects, interaction with the unwanted targets, metabolism and action on molecular transport), cytotoxicity for tumor and non-tumor cell lines, carcinogenicity, induced changes of gene expression profiles, metabolic sites of the major enzymes of the first and second phases of xenobiotics biotransformation, and belonging to substrates and/or metabolites of metabolic enzymes. The web resource Way2Drug is used by over 18,000 researchers from more than 90 countries around the world, which allowed them to obtain over 600,000 predictions and publish about 500 papers describing the obtained results. The analysis of the published works shows that in some cases the interpretation of the prediction results presented by the authors of these publications requires an adjustment. In this work, we provide the theoretical basis and consider, on particular examples, the opportunities and limitations of computer-aided prediction of biological activity spectra.
Collapse
Affiliation(s)
| | | | - A.A. Lagunin
- Institute of Biomedical Chemistry; Pirogov Russian National Research Medical University, Moscow, Russia
| | | | - A.V. Rudik
- Institute of Biomedical Chemistry, Moscow, Russia
| | | | - P.V. Pogodin
- Institute of Biomedical Chemistry, Moscow, Russia
| | | |
Collapse
|
23
|
Martin EJ, Polyakov VR, Tian L, Perez RC. Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC 50s for Realistically Novel Compounds. J Chem Inf Model 2017. [PMID: 28651433 DOI: 10.1021/acs.jcim.7b00166] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
While conventional random forest regression (RFR) virtual screening models appear to have excellent accuracy on random held-out test sets, they prove lacking in actual practice. Analysis of 18 historical virtual screens showed that random test sets are far more similar to their training sets than are the compounds project teams actually order. A new, cluster-based "realistic" training/test set split, which mirrors the chemical novelty of real-life virtual screens, recapitulates the poor predictive power of RFR models in real projects. The original Profile-QSAR (pQSAR) method greatly broadened the domain of applicability over conventional models by using as independent variables a profile of activity predictions from all historical assays in a large protein family. However, the accuracy still fell short of experiment on realistic test sets. The improved "pQSAR 2.0" method replaces probabilities of activity from naïve Bayes categorical models at several thresholds with predicted IC50s from RFR models. Unexpectedly, the high accuracy also requires removing the RFR model for the actual assay of interest from the independent variable profile. With these improvements, pQSAR 2.0 activity predictions are now statistically comparable to medium-throughput four-concentration IC50 measurements even on the realistic test set. Beyond the yes/no activity predictions from a typical high-throughput screen (HTS) or conventional virtual screen, these semiquantitative IC50 predictions allow for predicted potency, ligand efficiency, lipophilic efficiency, and selectivity against antitargets, greatly facilitating hitlist triaging and enabling virtual screening panels such as toxicity panels and overall promiscuity predictions.
Collapse
Affiliation(s)
- Eric J Martin
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Valery R Polyakov
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Li Tian
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| | - Rolando C Perez
- Novartis Institutes for Biomedical Research , 5300 Chiron Way, Emeryville, California 94608-2916, United States
| |
Collapse
|
24
|
Viira B, García-Sosa AT, Maran U. Chemical structure and correlation analysis of HIV-1 NNRT and NRT inhibitors and database-curated, published inhibition constants with chemical structure in diverse datasets. J Mol Graph Model 2017; 76:205-223. [PMID: 28738270 DOI: 10.1016/j.jmgm.2017.06.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 06/18/2017] [Accepted: 06/19/2017] [Indexed: 01/26/2023]
Abstract
Human immunodeficiency virus (HIV-1) reverse transcriptase is a major target for designing anti-HIV drugs. Developed inhibitors are divided into non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs) and nucleoside analog reverse-transcriptase inhibitors (NRTIs) depending on their mechanism. Given that many inhibitors have been studied and for many of them binding affinity constants have been calculated, it is beneficial to analyze the chemical landscape of these families of inhibitors and correlate these inhibition constants with molecular structure descriptors. For this, the HIV-1 RT data was retrieved from the ChEMBL database, carefully curated, and original literature verified, grouped into NRTIs and NNRTIs, analyzed using a hierarchical scaffold classification method and modelled with best multi-linear regression approach. Analysis of the HIV-1 NNRTIs subset results in ten different common structural parent types of oxazepanone, piperazinone, pyrazine, oxazinanone, diazinanone, pyridine, pyrrole, diazepanone, thiazole, and triazine. The same analysis for HIV-1 NRTIs groups structures into four different parent types of uracil, pyrimide, pyrimidione, and imidazole. Each scaffold tree corresponding to the parent types has been carefully analyzed and examined, and changes in chemical structure favorable to potency and stability are highlighted. For both subsets, descriptive and predictive QSAR models are derived, discussed and externally validated, revealing general trends in relationships between molecular structure and binding affinity constants in structurally diverse datasets. Data and QSAR models are available at the QsarDB repository (http://dx.doi.org/10.15152/QDB.202).
Collapse
Affiliation(s)
- Birgit Viira
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu 50411, Estonia.
| |
Collapse
|
25
|
|
26
|
Tarasova O, Filimonov D, Poroikov V. PASS-based approach to predict HIV-1 reverse transcriptase resistance. J Bioinform Comput Biol 2016; 15:1650040. [PMID: 28033735 DOI: 10.1142/s0219720016500402] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
HIV reverse transcriptase (RT) inhibitors targeting the early stages of virus-host interactions are of great interest to scientists. Acquired HIV RT resistance happens due to mutations in a particular region of the pol gene encoding the HIV RT amino acid sequence. We propose an application of the previously developed PASS algorithm for prediction of amino acid substitutions potentially involved in the resistance of HIV-1 based on open data. In our work, we used more than 3200 HIV-1 RT variants from the publicly available Stanford HIV RT and protease sequence database already tested for 10 anti-HIV drugs including both nucleoside and non-nucleoside RT inhibitors. We used a particular amino acid residue and its position to describe primary structure-resistance relationships. The average balanced accuracy of the prediction obtained in 20-fold cross-validation for the Phenosense dataset was about 88% and for the Antivirogram dataset was about 79%. Thus, the PASS-based algorithm may be used for prediction of the amino acid substitutions associated with the resistance of HIV-1 based on open data. The computational approach for the prediction of HIV-1 associated resistance can be useful for the selection of RT inhibitors for the treatment of HIV infected patients in the clinical practice. Prediction of the HIV-1 RT associated resistance can be useful for the development of new anti-HIV drugs active against the resistant variants of RT. Therefore, we propose that this study can be potentially useful for anti-HIV drug development.
Collapse
Affiliation(s)
- Olga Tarasova
- 1 Department for Bioinformatics, Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya street, 119121, Moscow, Russia
| | - Dmitry Filimonov
- 1 Department for Bioinformatics, Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya street, 119121, Moscow, Russia
| | - Vladimir Poroikov
- 1 Department for Bioinformatics, Institute of Biomedical Chemistry, 10 building 8, Pogodinskaya street, 119121, Moscow, Russia
| |
Collapse
|
27
|
Santos LH, Ferreira RS, Caffarena ER. Computational drug design strategies applied to the modelling of human immunodeficiency virus-1 reverse transcriptase inhibitors. Mem Inst Oswaldo Cruz 2016; 110:847-64. [PMID: 26560977 PMCID: PMC4660614 DOI: 10.1590/0074-02760150239] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 09/08/2015] [Indexed: 01/05/2023] Open
Abstract
Reverse transcriptase (RT) is a multifunctional enzyme in the human immunodeficiency
virus (HIV)-1 life cycle and represents a primary target for drug discovery efforts
against HIV-1 infection. Two classes of RT inhibitors, the nucleoside RT inhibitors
(NRTIs) and the nonnucleoside transcriptase inhibitors are prominently used in the
highly active antiretroviral therapy in combination with other anti-HIV drugs.
However, the rapid emergence of drug-resistant viral strains has limited the
successful rate of the anti-HIV agents. Computational methods are a significant part
of the drug design process and indispensable to study drug resistance. In this
review, recent advances in computer-aided drug design for the rational design of new
compounds against HIV-1 RT using methods such as molecular docking, molecular
dynamics, free energy calculations, quantitative structure-activity relationships,
pharmacophore modelling and absorption, distribution, metabolism, excretion and
toxicity prediction are discussed. Successful applications of these methodologies are
also highlighted.
Collapse
Affiliation(s)
| | - Rafaela Salgado Ferreira
- Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brasil
| | | |
Collapse
|
28
|
Vite-Caritino H, Méndez-Lucio O, Reyes H, Cabrera A, Chávez D, Medina-Franco JL. Advances in the development of pyridinone derivatives as non-nucleoside reverse transcriptase inhibitors. RSC Adv 2016. [DOI: 10.1039/c5ra25722k] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Medicinal chemistry, computational design and biological screening have advanced pyridin-2(1H)-one derivatives as a promising class of non-nucleoside reverse transcriptase inhibitors for the treatment of HIV/AIDS.
Collapse
Affiliation(s)
- Hugo Vite-Caritino
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| | - Oscar Méndez-Lucio
- Unilever Centre for Molecular Science Informatics
- Department of Chemistry
- University of Cambridge
- Cambridge CB2 1EW
- UK
| | - Héctor Reyes
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - Alberto Cabrera
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - Daniel Chávez
- Centro de Graduados e Investigación en Química del Instituto Tecnológico de Tijuana
- Tijuana
- Mexico
| | - José L. Medina-Franco
- Facultad de Química
- Departamento de Farmacia
- Universidad Nacional Autónoma de México
- Mexico City 04510
- Mexico
| |
Collapse
|