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Dodd-O J, Acevedo-Jake AM, Azizogli AR, Mulligan VK, Kumar VA. How to Design Peptides. Methods Mol Biol 2023; 2597:187-216. [PMID: 36374423 DOI: 10.1007/978-1-0716-2835-5_15] [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] [Indexed: 11/16/2022]
Abstract
Novel design of proteins to target receptors for treatment or tissue augmentation has come to the fore owing to advancements in computing power, modeling frameworks, and translational successes. Shorter proteins, or peptides, can offer combinatorial synergies with dendrimer, polymer, or other peptide carriers for enhanced local signaling, which larger proteins may sterically hinder. Here, we present a generalized method for designing a novel peptide. We first show how to create a script protocol that can be used to iteratively optimize and screen novel peptide sequences for binding a target protein. We present a step-by-step introduction to utilizing file repositories, data bases, and the Rosetta software suite. RosettaScripts, an .xml interface that allows for sequential functions to be performed, is used to order the functions for repeatable performance. These strategies may lead to more groups venturing into computational design, which may result in synergies from artificial intelligence/machine learning (AI/ML) to phage display and screening. Importantly, the beginner is expected to be able to design their first peptide ligand and begin their journey in peptide drug discovery. Generally, these peptides potentially could be used to interact with any enzyme or receptor, for example, in the study of chemokines and their interactions with glycosoaminoglycans and their receptors.
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Affiliation(s)
- Joseph Dodd-O
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Amanda M Acevedo-Jake
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | | | | | - Vivek A Kumar
- York Center for Environmental Engineering and Science, New Jersey Institute of Technology, Newark, NJ, USA.
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Saarimäki LA, Morikka J, Pavel A, Korpilähde S, del Giudice G, Federico A, Fratello M, Serra A, Greco D. Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2203984. [PMID: 36479815 PMCID: PMC9839874 DOI: 10.1002/advs.202203984] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/09/2022] [Indexed: 05/25/2023]
Abstract
Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.
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Affiliation(s)
- Laura Aliisa Saarimäki
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Jack Morikka
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Alisa Pavel
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Seela Korpilähde
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Giusy del Giudice
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Antonio Federico
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Michele Fratello
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
| | - Angela Serra
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
- Tampere Institute for Advanced StudyTampere UniversityKalevantie 4Tampere33100Finland
| | - Dario Greco
- Finnish Hub for Development and Validation of Integrated Approaches (FHAIVE)Faculty of Medicine and Health TechnologyTampere UniversityArvo Ylpön katu 34Tampere33520Finland
- Institute of BiotechnologyUniversity of HelsinkiP.O.Box 56HelsinkiUusimaa00014Finland
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Wang X, Kong F, Lin Z. Cromolyn prevents cerebral vasospasm and dementia by targeting WDR43. Front Aging Neurosci 2023; 15:1132733. [PMID: 37122373 PMCID: PMC10133528 DOI: 10.3389/fnagi.2023.1132733] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Background Cerebral vasospasm (CV) can cause inflammation and damage to neuronal cells in the elderly, leading to dementia. Purpose This study aimed to investigate the genetic mechanisms underlying dementia caused by CV in the elderly, identify preventive and therapeutic drugs, and evaluate their efficacy in treating neurodegenerative diseases. Methods Genes associated with subarachnoid hemorrhage and CV were acquired and screened for differentially expressed miRNAs (DEmiRNAs) associated with aneurysm rupture. A regulatory network of DEmiRNAs and mRNAs was constructed, and virtual screening was performed to evaluate possible binding patterns between Food and Drug Administration (FDA)-approved drugs and core proteins. Molecular dynamics simulations were performed on the optimal docked complexes. Optimally docked drugs were evaluated for efficacy in the treatment of neurodegenerative diseases through cellular experiments. Results The study found upregulated genes (including WDR43 and THBS1) and one downregulated gene associated with aneurysm rupture. Differences in the expression of these genes indicate greater disease risk. DEmiRNAs associated with ruptured aortic aneurysm were identified, of which two could bind to THBS1 and WDR43. Cromolyn and lanoxin formed the best docking complexes with WDR43 and THBS1, respectively. Cellular experiments showed that cromolyn improved BV2 cell viability and enhanced Aβ42 uptake, suggesting its potential as a therapeutic agent for inflammation-related disorders. Conclusion The findings suggest that WDR43 and THBS1 are potential targets for preventing and treating CV-induced dementia in the elderly. Cromolyn may have therapeutic value in the treatment of Alzheimer's disease and dementia.
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Luo Y, Liu L, He Z, Zhang S, Huo P, Wang Z, Jiaxin Q, Zhao L, Wu Y, Zhang D, Bu D, Chen R, Zhao Y. TREAT: Therapeutic RNAs exploration inspired by artificial intelligence technology. Comput Struct Biotechnol J 2022; 20:5680-5689. [PMID: 36320935 PMCID: PMC9589171 DOI: 10.1016/j.csbj.2022.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 10/04/2022] [Accepted: 10/05/2022] [Indexed: 11/08/2022] Open
Abstract
Recent advances in RNA engineering have enabled the development of RNA-based therapeutics for a broad spectrum of applications. Developing RNA therapeutics start with targeted RNA screening and move to the drug design and optimization. However, existing target screening tools ignore noncoding RNAs and their disease-relevant regulatory relationships. And designing therapeutic RNAs encounters high computational complexity of multi-objective optimization to overcome the immunogenicity, instability and inefficient translational production. To unlock the therapeutic potential of noncoding RNAs and enable one-stop screening and design of therapeutic RNAs, we have built the platform TREAT. It incorporates 43,087,953 regulatory relationships between coding and noncoding genes from 81 biological networks under different physiological conditions. TREAT introduces graph representation learning with Random Walk Diffusions to perform disease-relevant target screening, in addition to the commonly utilized Topological Degree and PageRank algorithms. Design and optimization of large RNAs or interfering RNAs are both available. To reduce the computational complexity of multi-objective optimization for large RNA, we stratified the features into local and global features. The local features are evaluated on the fixed-length or dynamic-length local bins, whereas the latter are inspired by AI language models of protein sequence. Then the global assessment is performed on refined candidates, thus reducing the enormous search space. Overall, TREAT is a one-stop platform for the screening and designing of therapeutic RNAs, with particular attention to noncoding RNAs and cutting-edge AI technology embedded, leading the progress of innovative therapeutics for challenging diseases. TREAT is freely accessible at https://rna.org.cn/treat.
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Affiliation(s)
- Yufan Luo
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liu Liu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Zihao He
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Shanshan Zhang
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Peipei Huo
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Zhihao Wang
- Luoyang Zhongke Information Industry Research Institute, Luoyang, China
| | - Qin Jiaxin
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Lianhe Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Yang Wu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Dongdong Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Dechao Bu
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,Hwa Mei Hospital, University of Chinese Academy of Sciences, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China,Shenzhen Institute of Nucleic Acid Drug Research, Shenzhen Bay Laboratory Pingshan Translational Medicine Center, Shenzhen 510800, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China,Correspondence authors at: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Y. Zhao).
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Carraro C, Bonaguro L, Schulte-Schrepping J, Horne A, Oestreich M, Warnat-Herresthal S, Helbing T, De Franco M, Haendler K, Mukherjee S, Ulas T, Gandin V, Goettlich R, Aschenbrenner AC, Schultze JL, Gatto B. Decoding mechanism of action and sensitivity to drug candidates from integrated transcriptome and chromatin state. eLife 2022; 11:e78012. [PMID: 36043458 PMCID: PMC9433094 DOI: 10.7554/elife.78012] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
Omics-based technologies are driving major advances in precision medicine, but efforts are still required to consolidate their use in drug discovery. In this work, we exemplify the use of multi-omics to support the development of 3-chloropiperidines, a new class of candidate anticancer agents. Combined analyses of transcriptome and chromatin accessibility elucidated the mechanisms underlying sensitivity to test agents. Furthermore, we implemented a new versatile strategy for the integration of RNA- and ATAC-seq (Assay for Transposase-Accessible Chromatin) data, able to accelerate and extend the standalone analyses of distinct omic layers. This platform guided the construction of a perturbation-informed basal signature predicting cancer cell lines' sensitivity and to further direct compound development against specific tumor types. Overall, this approach offers a scalable pipeline to support the early phases of drug discovery, understanding of mechanisms, and potentially inform the positioning of therapeutics in the clinic.
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Affiliation(s)
- Caterina Carraro
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Arik Horne
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Marie Oestreich
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
| | - Stefanie Warnat-Herresthal
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
| | - Tim Helbing
- Institute of Organic Chemistry, Justus Liebig University GiessenGiessenGermany
| | - Michele De Franco
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Kristian Haendler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
- Institute of Human Genetics, University of LübeckLübeckGermany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- MRC Biostatistics Unit, University of CambridgeCambridgeUnited Kingdom
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
| | - Valentina Gandin
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
| | - Richard Goettlich
- Institute of Organic Chemistry, Justus Liebig University GiessenGiessenGermany
| | - Anna C Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical CenterNijmegenNetherlands
| | - Joachim L Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V.BonnGermany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of BonnBonnGermany
- PRECISE Platform for Genomics and Epigenomics, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) e.V. and University of BonnBonnGermany
| | - Barbara Gatto
- Department of Pharmaceutical and Pharmacological Sciences, University of PadovaPadovaItaly
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Lu D, Pan R, Wu W, Zhang Y, Li S, Xu H, Huang J, Xia J, Wang Q, Luan X, Lv C, Zhang W, Meng G. FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis. Brief Bioinform 2022; 23:6632928. [PMID: 35794722 DOI: 10.1093/bib/bbac263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 06/01/2022] [Accepted: 06/06/2022] [Indexed: 11/12/2022] Open
Abstract
Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner. This method was built based on a hypothesis that cells under a status of homeostasis would take responses to drug perturbation by activating feedback loops. Therefore, the drug interacting targets can be predicted by analyzing the network responses after drug perturbation. We evaluated this method using the expression data of estrogen stimulation, gene manipulation and drug perturbation and validated its good performance to identify the annotated drug targets. Using STAT3 as a target protein, we applied this method to drug perturbation data of 500 natural compounds and predicted five compounds with STAT3 interacting activities. Experimental assay validated the STAT3-interacting activities of four compounds. Overall, our evaluation suggests that FL-DTD predicts the drug interacting targets with good accuracy and can be used for drug target discovery.
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Affiliation(s)
- Dong Lu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Rongrong Pan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Wenxuan Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Yanyan Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Shensuo Li
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Hong Xu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Jialan Huang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Jianhua Xia
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Qun Wang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Xin Luan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Chao Lv
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
| | - Guofeng Meng
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Cailun 1200, 201203, Shanghai, China
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Rintala TJ, Ghosh A, Fortino V. Network approaches for modeling the effect of drugs and diseases. Brief Bioinform 2022; 23:6608969. [PMID: 35704883 PMCID: PMC9294412 DOI: 10.1093/bib/bbac229] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 04/29/2022] [Accepted: 05/17/2021] [Indexed: 12/12/2022] Open
Abstract
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug’s MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).
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Affiliation(s)
- T J Rintala
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - Arindam Ghosh
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
| | - V Fortino
- Institute of Biomedicine, University of Eastern Finland, 70210 Kuopio, Finland
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Fuellen G, Jünemann A. Gene Expression Data for Investigating Glaucoma Treatment Options and Pharmacology in the Anterior Segment, State-of-the-Art and Future Directions. Front Neurosci 2022; 16:912043. [PMID: 35757536 PMCID: PMC9213806 DOI: 10.3389/fnins.2022.912043] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Accepted: 05/20/2022] [Indexed: 11/30/2022] Open
Abstract
Glaucoma treatment options as well as its etiology are far from understood. Gene expression (transcriptomics) data of the anterior segment of the eye can help by elucidating the molecular-mechanistic underpinnings, and we present an up-to-date description and discussion of what gene expression data are publicly available, and for which purposes these can be used. We feature the few resources covering all segments of the eye, and we then specifically focus on the anterior segment, and provide an extensive list of the Gene Expression Omnibus data that may be useful. We also feature single-cell data of relevance, particularly three datasets from tissues of relevance to aqueous humor outflow. We describe how the data have been used by researchers, by following up resource citations and data re-analyses. We discuss datasets and analyses pertaining to fibrosis following glaucoma surgery, and to glaucoma resulting from the use of steroids. We conclude by pointing out the current lack and underutilization of ocular gene expression data, and how the state of the art is expected to improve in the future.
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Affiliation(s)
- Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
| | - Anselm Jünemann
- Institute for Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Rostock, Germany
- Department of Ophthalmology, Rostock University Medical Center, Rostock, Germany
- Department of General Ophthalmology and Pediatric Ophthalmology Service, Medical University of Lublin, Lublin, Poland
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From single-omics to interactomics: How can ligand-induced perturbations modulate single-cell phenotypes? ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2022; 131:45-83. [PMID: 35871896 DOI: 10.1016/bs.apcsb.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Cells suffer from perturbations by different stimuli, which, consequently, rise to individual alterations in their profile and function that may end up affecting the tissue as a whole. This is no different if we consider the effect of a therapeutic agent on a biological system. As cells are exposed to external ligands their profile can change at different single-omics levels. Detecting how these changes take place through different sequencing technologies is key to a better understanding of the effects of therapeutic agents. Single-cell RNA-sequencing stands out as one of the most common approaches for cell profiling and perturbation analysis. As a result, single-cell transcriptomics data can be integrated with other omics data sources, such as proteomics and epigenomics data, to clarify the perturbation effects and mechanism at the cell level. Appropriate computational tools are key to process and integrate the available information. This chapter focuses on the recent advances on ligand-induced perturbation and single-cell omics computational tools and algorithms, their current limitations, and how the deluge of data can be used to improve the current process of drug research and development.
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 74] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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Sharma A, Yadav D, Rao P, Sinha S, Goswami D, Rawal RM, Shrivastava N. Identification of potential therapeutic targets associated with diagnosis and prognosis of colorectal cancer patients based on integrated bioinformatics analysis. Comput Biol Med 2022; 146:105688. [DOI: 10.1016/j.compbiomed.2022.105688] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 05/27/2022] [Accepted: 05/30/2022] [Indexed: 01/04/2023]
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Winter MJ, Ono Y, Ball JS, Walentinsson A, Michaelsson E, Tochwin A, Scholpp S, Tyler CR, Rees S, Hetheridge MJ, Bohlooly-Y M. A Combined Human in Silico and CRISPR/Cas9-Mediated in Vivo Zebrafish Based Approach to Provide Phenotypic Data for Supporting Early Target Validation. Front Pharmacol 2022; 13:827686. [PMID: 35548346 PMCID: PMC9082939 DOI: 10.3389/fphar.2022.827686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 02/16/2022] [Indexed: 12/29/2022] Open
Abstract
The clinical heterogeneity of heart failure has challenged our understanding of the underlying genetic mechanisms of this disease. In this respect, large-scale patient DNA sequencing studies have become an invaluable strategy for identifying potential genetic contributing factors. The complex aetiology of heart failure, however, also means that in vivo models are vital to understand the links between genetic perturbations and functional impacts as part of the process for validating potential new drug targets. Traditional approaches (e.g., genetically-modified mice) are optimal for assessing small numbers of genes, but less practical when multiple genes are identified. The zebrafish, in contrast, offers great potential for higher throughput in vivo gene functional assessment to aid target prioritisation, by providing more confidence in target relevance and facilitating gene selection for definitive loss of function studies undertaken in mice. Here we used whole-exome sequencing and bioinformatics on human patient data to identify 3 genes (API5, HSPB7, and LMO2) suggestively associated with heart failure that were also predicted to play a broader role in disease aetiology. The role of these genes in cardiovascular system development and function was then further investigated using in vivo CRISPR/Cas9-mediated gene mutation analysis in zebrafish. We observed multiple impacts in F0 knockout zebrafish embryos (crispants) following effective somatic mutation, including changes in ventricle size, pericardial oedema, and chamber malformation. In the case of lmo2, there was also a significant impact on cardiovascular function as well as an expected reduction in erythropoiesis. The data generated from both the human in silico and zebrafish in vivo assessments undertaken supports further investigation of the potential roles of API5, HSPB7, and LMO2 in human cardiovascular disease. The data presented also supports the use of human in silico genetic variant analysis, in combination with zebrafish crispant phenotyping, as a powerful approach for assessing gene function as part of an integrated multi-level drug target validation strategy.
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Affiliation(s)
- Matthew J Winter
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Yosuke Ono
- Living Systems Institute, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Jonathan S Ball
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Anna Walentinsson
- Translational Science and Experimental Medicine, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Erik Michaelsson
- Early Clinical Development, Research and Early Development, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anna Tochwin
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Steffen Scholpp
- Living Systems Institute, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Charles R Tyler
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Steve Rees
- Discovery Biology, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Malcolm J Hetheridge
- Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Mohammad Bohlooly-Y
- Translational Genomics, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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63
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The emerging role of mass spectrometry-based proteomics in drug discovery. Nat Rev Drug Discov 2022; 21:637-654. [PMID: 35351998 DOI: 10.1038/s41573-022-00409-3] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2022] [Indexed: 12/14/2022]
Abstract
Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.
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64
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Cha HK, Cheon S, Kim H, Lee KM, Ryu HS, Han D. Discovery of Proteins Responsible for Resistance to Three Chemotherapy Drugs in Breast Cancer Cells Using Proteomics and Bioinformatics Analysis. Molecules 2022; 27:molecules27061762. [PMID: 35335125 PMCID: PMC8954867 DOI: 10.3390/molecules27061762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/19/2022] [Accepted: 03/02/2022] [Indexed: 02/04/2023] Open
Abstract
Chemoresistance is a daunting obstacle to the effective treatment of breast cancer patients receiving chemotherapy. Although the mechanism of chemotherapy drug resistance has been explored broadly, the precise mechanism at the proteome level remains unclear. Especially, comparative studies between widely used anticancer drugs in breast cancer are very limited. In this study, we employed proteomics and bioinformatics approaches on chemoresistant breast cancer cell lines to understand the underlying resistance mechanisms that resulted from doxorubicin (DR), paclitaxel (PR), and tamoxifen (TAR). In total, 10,385 proteins were identified and quantified from three TMT 6-plex and one TMT 10-plex experiments. Bioinformatics analysis showed that Notch signaling, immune response, and protein re-localization processes were uniquely associated with DR, PR, and TAR resistance, respectively. In addition, proteomic signatures related to drug resistance were identified as potential targets of many FDA-approved drugs. Furthermore, we identified potential prognostic proteins with significant effects on overall survival. Representatively, PLXNB2 expression was associated with a highly significant increase in risk, and downregulation of ACOX3 was correlated with a worse overall survival rate. Consequently, our study provides new insights into the proteomic aspects of the distinct mechanisms underlying chemoresistance in breast cancer.
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Affiliation(s)
- Hyo Kyeong Cha
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul 03080, Korea; (H.K.C.); (H.K.)
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
| | - Seongmin Cheon
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
| | - Hyeyoon Kim
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul 03080, Korea; (H.K.C.); (H.K.)
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
| | - Kyung-Min Lee
- Center for Medical Innovation, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
| | - Han Suk Ryu
- Center for Medical Innovation, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
- Department of Pathology, Seoul National University Hospital, Seoul 03080, Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul 03080, Korea
- Correspondence: (H.S.R.); (D.H.)
| | - Dohyun Han
- Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul 03080, Korea; (H.K.C.); (H.K.)
- Proteomics Core Facility, Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Korea;
- Correspondence: (H.S.R.); (D.H.)
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65
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Gu R, Liang A, Liao G, To I, Shehu A, Ma X. Roles of co-factors in drug-induced liver injury: drug metabolism and beyond. Drug Metab Dispos 2022; 50:646-654. [PMID: 35221288 PMCID: PMC9132098 DOI: 10.1124/dmd.121.000457] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 02/22/2022] [Indexed: 11/22/2022] Open
Abstract
Drug-induced liver injury (DILI) remains one of the major concerns for healthcare providers and patients. Unfortunately, it is difficult to predict and prevent DILI in the clinic because detailed mechanisms of DILI are largely unknown. Many risk factors have been identified for both "intrinsic" and "idiosyncratic" DILI, suggesting that cofactors are an important aspect in understanding DILI. This review outlines the cofactors that potentiate DILI and categorizes them into two types: (1) the specific cofactors that target metabolic enzymes, transporters, antioxidation defense, immune response, and liver regeneration; and (2) the general cofactors that include inflammation, age, gender, comorbidity, gut microbiota, and lifestyle. The underlying mechanisms by which cofactors potentiate DILI are also discussed. SIGNIFICANCE STATEMENT: This review summarizes the risk factors for DILI, which can be used to predict and prevent DILI in the clinic. This work also highlights the gaps in the DILI field and provides future perspectives on the roles of cofactors in DILI.
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Affiliation(s)
- Ruizhi Gu
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Alina Liang
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Grace Liao
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Isabelle To
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Amina Shehu
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Xiaochao Ma
- Center for Pharmacogenetics, Department of Pharmaceutical Sciences (R.G., A.S., X.M.) and School of Pharmacy (A.L., G.L., I.T.), University of Pittsburgh, Pittsburgh, Pennsylvania
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Affiliation(s)
| | - Stefano Peluso
- Department of Statistics and Quantitative Methods, Università degli Studi di Milano-Bicocca, Milan
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67
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John Cremin C, Dash S, Huang X. Big Data: Historic Advances and Emerging Trends in Biomedical Research. CURRENT RESEARCH IN BIOTECHNOLOGY 2022. [DOI: 10.1016/j.crbiot.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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68
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Langston JC, Rossi MT, Yang Q, Ohley W, Perez E, Kilpatrick LE, Prabhakarpandian B, Kiani MF. Omics of endothelial cell dysfunction in sepsis. VASCULAR BIOLOGY (BRISTOL, ENGLAND) 2022; 4:R15-R34. [PMID: 35515704 PMCID: PMC9066943 DOI: 10.1530/vb-22-0003] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 04/07/2022] [Indexed: 12/19/2022]
Abstract
During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.
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Affiliation(s)
- Jordan C Langston
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
| | | | - Qingliang Yang
- Department of Mechanical Engineering, Temple University, Philadelphia, Pennsylvania, USA
| | - William Ohley
- Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Edwin Perez
- Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Laurie E Kilpatrick
- Center for Inflammation and Lung Research, Department of Microbiology, Immunology and Inflammation, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Balabhaskar Prabhakarpandian
- Center for Inflammation and Lung Research, Department of Microbiology, Immunology and Inflammation, Lewis Katz School of Medicine, Temple University, Philadelphia, Pennsylvania, USA
| | - Mohammad F Kiani
- Department of Bioengineering, Temple University, Philadelphia, Pennsylvania, USA
- Department of Mechanical Engineering, Temple University, Philadelphia, Pennsylvania, USA
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69
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Singh RS, Angra V, Singh A, Masih GD, Medhi B. Integrative omics - An arsenal for drug discovery. Indian J Pharmacol 2022; 54:1-6. [PMID: 35343200 PMCID: PMC9012413 DOI: 10.4103/ijp.ijp_53_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Affiliation(s)
- Rahul Soloman Singh
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Vani Angra
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Ashutosh Singh
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Gladson David Masih
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Bikash Medhi
- Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
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70
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Singh S, Singh DB, Gautam B, Singh A, Yadav N. Pharmacokinetics and pharmacodynamics analysis of drug candidates. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00001-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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71
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The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:3859338. [PMID: 34868339 PMCID: PMC8642006 DOI: 10.1155/2021/3859338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/30/2021] [Indexed: 11/23/2022]
Abstract
Purpose Heart failure (HF) is a clinical syndrome caused by ventricular insufficiency. In order to further explore the biomarkers related to HF, we apply the high-throughput database. Materials and Methods The GSE21610 was applied for the differentially expressed gene (DEG) analysis. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The Gene Set Enrichment Analysis (GSEA) was used for gene expression profile GSE21610. The Protein-Protein Interaction (PPI) network and modules were also constructed for research. These hub gene function pathways were estimated in HF progression. Result We have identified 434 DEGs in total, including 304 downregulated DEGs and 130 upregulated DEGs. GO and KEGG illustrated that DEGs in HF were significantly enriched in G protein-coupled receptor binding, peroxisome, and cAMP signaling pathway. GSEA results showed gene set GSE21610 was gathered in lipid digestion, defense response to fungus, and intestinal lipid absorption. Finally, through analyzing the PPI network, we screened hub genes CDH1, TFRC, CCL2, BUB1B, and CD19 by the Cytoscape software. Conclusion This study uses a series of bioinformatics technologies to obtain hug genes and key pathways related to HF. These analysis results provide us with new ideas for finding biomarkers and treatment methods for HF.
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Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells. Cells 2021; 10:cells10113139. [PMID: 34831362 PMCID: PMC8621084 DOI: 10.3390/cells10113139] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/06/2021] [Accepted: 11/09/2021] [Indexed: 12/28/2022] Open
Abstract
Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.
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73
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Deng J, Yang Z, Ojima I, Samaras D, Wang F. Artificial intelligence in drug discovery: applications and techniques. Brief Bioinform 2021; 23:6420092. [PMID: 34734228 DOI: 10.1093/bib/bbab430] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/02/2021] [Accepted: 09/18/2021] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.
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Affiliation(s)
- Jianyuan Deng
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA
| | - Zhibo Yang
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
| | - Iwao Ojima
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11790, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA.,Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
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Venkateswaran MR, Vadivel TE, Jayabal S, Murugesan S, Rajasekaran S, Periyasamy S. A review on network pharmacology based phytotherapy in treating diabetes- An environmental perspective. ENVIRONMENTAL RESEARCH 2021; 202:111656. [PMID: 34265348 DOI: 10.1016/j.envres.2021.111656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 06/19/2021] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
Diabetes has become common lifestyle disorder associated with obesity and cardiovascular diseases. Environmental factors like physical inactivity, polluted surroundings and unhealthy dieting also plays a vital role in diabetes pathogenesis. As the current anti-diabetic drugs possess unprecedented side effects, traditional herbal medicine can be used an alternative therapy. The paramount challenge with the herbal formulation usage is the lack of standardized procedure, entangled with little knowledge on drug safety and mechanism of drug action. Heavy metal contamination is a major environmental hazard where plants tend to accumulate toxic metals like nickel, chromium and lead through industrial and agricultural activities. It becomes inappropriate to use these plants for phytotherapy as it may affect the human health on long term consumption. This review discuss about the environmental risk factors related to diabetes and better implication of medicinal plants in anti-diabetic therapy using network pharmacology. It is an in silico analytical tool that helps to unravel the multi-targeted action of herbal formulations rich in secondary metabolites. Also, a special focus is attempted to pool the databases regarding the medicinal plants for diabetes and associated diseases, their bioactive compounds, possible diabetic targets, drug-target interaction and toxicology reports that may open an aisle in safer, effective and toxicity-free drug discovery.
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Affiliation(s)
- Meenakshi R Venkateswaran
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Tamil Elakkiya Vadivel
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Sasidharan Jayabal
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Selvakumar Murugesan
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India
| | - Subbiah Rajasekaran
- Department of Biochemistry, ICMR-National Institute for Research in Environmental Health, Bhopal, India.
| | - Sureshkumar Periyasamy
- Department of Biotechnology, Anna University, BIT-Campus, Tiruchirappalli, 620024, Tamil Nadu, India.
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Cheng CT, Wang TY, Chen PR, Wu WH, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer. BIOLOGY 2021; 10:biology10111115. [PMID: 34827109 PMCID: PMC8614794 DOI: 10.3390/biology10111115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 01/21/2023]
Abstract
Simple Summary Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. Abstract The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Tsun-Yu Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Jin-Mei Lai
- Department of Life Science, Fu-Jen Catholic University, New Taipei City 24205, Taiwan;
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei 11217, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry, Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan;
| | - Chi-Ying F. Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan;
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
- Correspondence: ; Tel.: +886-5-2720411 (ext. 33404)
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Zhou Y, Zhang Y, Lian X, Li F, Wang C, Zhu F, Qiu Y, Chen Y. Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res 2021; 50:D1398-D1407. [PMID: 34718717 PMCID: PMC8728281 DOI: 10.1093/nar/gkab953] [Citation(s) in RCA: 292] [Impact Index Per Article: 97.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Chaoxin Wang
- Department of Computer Science, Kansas State University, Manhattan 66506, USA
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, 79 QingChun Road, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China.,Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
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77
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Arici MK, Tuncbag N. Performance Assessment of the Network Reconstruction Approaches on Various Interactomes. Front Mol Biosci 2021; 8:666705. [PMID: 34676243 PMCID: PMC8523993 DOI: 10.3389/fmolb.2021.666705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.
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Affiliation(s)
- M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,School of Medicine, Koc University, Istanbul, Turkey
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78
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Ye M, Lin Y, Pan S, Wang ZW, Zhu X. Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis. Front Oncol 2021; 11:745808. [PMID: 34631583 PMCID: PMC8497990 DOI: 10.3389/fonc.2021.745808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/08/2021] [Indexed: 12/29/2022] Open
Abstract
Ovarian cancer ranks as the fifth most common cause of cancer-related death in females. The molecular mechanisms of ovarian carcinogenesis need to be explored in order to identify effective clinical therapies for ovarian cancer. Recently, multi-omics approaches have been applied to determine the mechanisms of ovarian oncogenesis at genomics (DNA), transcriptomics (RNA), proteomics (proteins), and metabolomics (metabolites) levels. Multi-omics approaches can identify some diagnostic and prognostic biomarkers and therapeutic targets for ovarian cancer, and these molecular signatures are beneficial for clarifying the development and progression of ovarian cancer. Moreover, the discovery of molecular signatures and targeted therapy strategies could noticeably improve the prognosis of ovarian cancer patients.
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Affiliation(s)
| | | | | | - Zhi-wei Wang
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xueqiong Zhu
- Center of Uterine Cancer Diagnosis & Therapy Research of Zhejiang Province, Department of Obstetrics and Gynecology, the Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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79
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Liu G, Singha M, Pu L, Neupane P, Feinstein J, Wu HC, Ramanujam J, Brylinski M. GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data. J Cheminform 2021; 13:58. [PMID: 34380569 PMCID: PMC8356453 DOI: 10.1186/s13321-021-00540-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 07/31/2021] [Indexed: 12/22/2022] Open
Abstract
Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.
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Affiliation(s)
- Guannan Liu
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Manali Singha
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Limeng Pu
- Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Prasanga Neupane
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Joseph Feinstein
- Department of Computer Science, Brown University, Providence, RI, 02902, USA
| | - Hsiao-Chun Wu
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - J Ramanujam
- Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.,Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA
| | - Michal Brylinski
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, 70803, USA. .,Center for Computation and Technology, Louisiana State University, Baton Rouge, LA, 70803, USA.
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80
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Heo YJ, Hwa C, Lee GH, Park JM, An JY. Integrative Multi-Omics Approaches in Cancer Research: From Biological Networks to Clinical Subtypes. Mol Cells 2021; 44:433-443. [PMID: 34238766 PMCID: PMC8334347 DOI: 10.14348/molcells.2021.0042] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 04/09/2021] [Accepted: 05/12/2021] [Indexed: 11/27/2022] Open
Abstract
Multi-omics approaches are novel frameworks that integrate multiple omics datasets generated from the same patients to better understand the molecular and clinical features of cancers. A wide range of emerging omics and multi-view clustering algorithms now provide unprecedented opportunities to further classify cancers into subtypes, improve the survival prediction and therapeutic outcome of these subtypes, and understand key pathophysiological processes through different molecular layers. In this review, we overview the concept and rationale of multi-omics approaches in cancer research. We also introduce recent advances in the development of multi-omics algorithms and integration methods for multiple-layered datasets from cancer patients. Finally, we summarize the latest findings from large-scale multi-omics studies of various cancers and their implications for patient subtyping and drug development.
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Affiliation(s)
- Yong Jin Heo
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
- Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Korea
| | - Chanwoong Hwa
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Gang-Hee Lee
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Jae-Min Park
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Korea
- Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Korea
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81
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Loilome W, Dokduang H, Suksawat M, Padthaisong S. Therapeutic challenges at the preclinical level for targeted drug development for Opisthorchis viverrini-associated cholangiocarcinoma. Expert Opin Investig Drugs 2021; 30:985-1006. [PMID: 34292795 DOI: 10.1080/13543784.2021.1955102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients. AREAS COVERED We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine. EXPERT OPINION Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.
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Affiliation(s)
- Watcharin Loilome
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Hasaya Dokduang
- Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Manida Suksawat
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
| | - Sureerat Padthaisong
- Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen Thailand.,Cholangiocarcinoma Screening and Care Program (CASCAP), Khon Kaen University, Khon Kaen, Thailand.,Cholangiocarcinoma Research Institute, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand
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82
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Nigella sativa callus treated with sodium azide exhibit augmented antioxidant activity and DNA damage inhibition. Sci Rep 2021; 11:13954. [PMID: 34230566 PMCID: PMC8260798 DOI: 10.1038/s41598-021-93370-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Accepted: 06/17/2021] [Indexed: 02/06/2023] Open
Abstract
Nigella sativa L. (NS) is an herbaceous plant, possessing phytochemicals of therapeutic importance. Thymoquinone is one of the active phytochemicals of NS that confers noteworthy antioxidant properties. Sodium azide, an agent of abiotic stress, can modulates antioxidant system in plants. In the present investigation, sodium azide (0, 5 µM, 10 µM, 20 µM, 50 µM, 100 µM and 200 µM) doses administered to the in vitro NS callus cultures for production/modification of secondary metabolites with augmented activity. 200 µM sodium azide treated NS callus exhibited maximum peroxidase activity (1.286 ± 0.101 nanokatal mg-1 protein) and polyphenol oxidase activity (1.590 ± 0.110 nanokatal mg-1 protein), while 100 µM sodium azide treated NS callus for optimum catalase activity (1.250 ± 0.105 nanokatal mg-1 protein). Further, 200 µM sodium azide treated NS callus obtained significantly the highest phenolics (3.666 ± 0.475 mg g-1 callus fresh weight), 20 µM sodium azide treated NS callus, the highest flavonoids (1.308 ± 0.082 mg g-1 callus fresh weight) and 100 µM sodium azide treated NS callus, the highest carotenes (1.273 ± 0.066 mg g-1 callus fresh weight). However, NS callus exhibited a decrease in thymoquinone yield/content vis-à-vis possible emergence of its analog with 5.3 min retention time and an increase in antioxidant property. Treatment with 200 µM sodium azide registered significantly the lowest percent yield of callus extract (4.6 ± 0.36 mg g-1 callus fresh weight) and thymoquinone yield (16.65 ± 2.52 µg g-1 callus fresh weight) and content (0.36 ± 0.07 mg g-1 callus dry weight) and the highest antioxidant activity (3.873 ± 0.402%), signifying a negative correlation of the former with the latter. DNA damage inhibition (24.3 ± 1.7%) was recorded significantly maximum at 200 µM sodium azide treatment. Sodium azide treated callus also recorded emergence of a new peak at 5.3 min retention time (possibly an analog of thymoquinone with augmented antioxidant activity) whose area exhibits significantly negative correlation with callus extract yield and thymoquinone yield/content and positive correlation with antioxidant activity and in vitro DNA damage inhibition. Thus, sodium azide treatment to NS callus confers possible production of secondary metabolites or thymoquinone analog (s) responsible for elevated antioxidant property and inhibition to DNA damage. The formation of potent antioxidants through sodium azide treatment to NS could be worthy for nutraceutical and pharmaceutical industries.
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83
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Tsabouri S, Atanaskovic-Markovic M. Skin eruptions in children: Drug hypersensitivity vs viral exanthema. Pediatr Allergy Immunol 2021; 32:824-834. [PMID: 33621365 DOI: 10.1111/pai.13485] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 02/13/2021] [Accepted: 02/18/2021] [Indexed: 12/11/2022]
Abstract
Childhood rashes or exanthemas are common and are usually relatively benign. There are many causes of rash in children, including mainly viruses, and less often bacterial toxins, drugs, allergens and other diseases. Viral exanthema often appears while children are taking a medication in the course of a viral infection; it can mimic drug exanthema and is perceived as a drug allergy in 10% of cases. In the vast majority of cases, the distinction between virus-induced and drug-induced skin eruption during the acute phase is not possible. The drugs most commonly implicated are beta-lactams (BL) and non-steroidal anti-inflammatory drugs (NSAIDs). Viruses, commonly Epstein-Barr virus (EBV), human herpesvirus 6 (HHV6) and cytomegalovirus (CMV), and the bacterium, Mycoplasma pneumoniae, may cause exanthema either from the infection itself (active or latent) or because of interaction with drugs that are taken simultaneously. Determination of the exact diagnosis requires a careful clinical history and thorough physical examination. Haematological and biochemical investigations and histology are not always helpful in differentiating between the two types of exanthema. Serological and polymerase chain reaction (PCR) assays can be helpful, although a concomitant acute infection does not exclude drug hypersensitivity. A drug provocation test (DPT) is although considered the gold standard for the diagnosis and is not preferred by the patients. Skin tests are not well tolerated, and in vitro tests, such as the basophil activation test and lymphocyte transformation, are of low sensitivity and specificity and their relevance is debatable. Based on current evidence, we propose a systematic clinical approach for timely differential diagnosis and management of rashes in children who present a cutaneous eruption while receiving a drug.
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Affiliation(s)
- Sophia Tsabouri
- Child Health Department, Medical School, University of Ioannina, Ioannina, Greece
| | - Marina Atanaskovic-Markovic
- Faculty of Medicine, University of Belgrade, Belgrade, Serbia.,University Children's Hospital of Belgrade, Belgrade, Serbia
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84
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The Osteogenic Function of Danggui Buxue Tang, a Herbal Decoction Containing Astragali Radix and Angelicae Sinensis Radix, Is Optimized by Boiling the Two Herbs Together: Signaling Analyses Revealed by Systems Biology. Processes (Basel) 2021. [DOI: 10.3390/pr9071119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
The therapeutic efficacy of a herbal mixture, being multi-target, multi-function and multi-pathway, is the niche of traditional Chinese medicine (TCM). Systems biology can dissect the network of signaling mechanisms in a complex biological system. In preparing TCM decoctions, the boiling of herbs together in water is a common practice; however, the rationale of this specific preparation has not been fully revealed. An approach of mass-spectrometry-based multi-omics was employed to examine the profiles of the cellular pathways, so as to understand the pharmacological efficacy of Danggui Buxue Tang (DBT), a Chinese herbal mixture containing Astragali Radix and Angelicae Sinensis Radix, in cultured rat osteoblasts and mesenchymal stem cells. The results, generated from omics analyses, were compared from DBT-treated osteoblasts to those of treating the herbal extract by simple mixing of extracts from Astragali Radix and Angelicae Sinensis Radix, i.e., herbal mixture without boiling together. The signaling pathways responsible for energy metabolism and amino acid metabolism showed distinct activation, as triggered by DBT, in contrast to simple mixing of two herbal extracts. The result supports that boiling the herbs together is designed to maximize the osteoblastic function of DBT, such as in energy and lipid metabolism. This harmony of TCM formulation, by having interactive functions of two herbs during preparation, is being illustrated. The systems biology approach provides new and essential insights into the synergy of herbal preparation. Well-defined multiple targets and multiple pathways in different levels of omics are the key to modernizing TCM.
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85
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van Harten AM, Brakenhoff RH. Targeted Treatment of Head and Neck (Pre)Cancer: Preclinical Target Identification and Development of Novel Therapeutic Applications. Cancers (Basel) 2021; 13:2774. [PMID: 34204886 PMCID: PMC8199752 DOI: 10.3390/cancers13112774] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 12/14/2022] Open
Abstract
Head and neck squamous cell carcinomas (HNSCC) develop in the mucosal lining of the upper-aerodigestive tract. In carcinogen-induced HNSCC, tumors emerge from premalignant mucosal changes characterized by tumor-associated genetic alterations, also coined as 'fields' that are occasionally visible as leukoplakia or erythroplakia lesions but are mostly invisible. Consequently, HNSCC is generally diagnosed de novo at more advanced stages in about 70% of new diagnosis. Despite intense multimodality treatment protocols, the overall 5-years survival rate is 50-60% for patients with advanced stage of disease and seems to have reached a plateau. Of notable concern is the lack of further improvement in prognosis despite advances in treatment. This can be attributed to the late clinical presentation, failure of advanced HNSCC to respond to treatment, the deficit of effective targeted therapies to eradicate tumors and precancerous changes, and the lack of suitable markers for screening and personalized therapy. The molecular landscape of head and neck cancer has been elucidated in great detail, but the absence of oncogenic mutations hampers the identification of druggable targets for therapy to improve outcome of HNSCC. Currently, functional genomic approaches are being explored to identify potential therapeutic targets. Identification and validation of essential genes for both HNSCC and oral premalignancies, accompanied with biomarkers for therapy response, are being investigated. Attentive diagnosis and targeted therapy of the preceding oral premalignant (preHNSCC) changes may prevent the development of tumors. As classic oncogene addiction through activating mutations is not a realistic concept for treatment of HNSCC, synthetic lethality and collateral lethality need to be exploited, next to immune therapies. In recent studies it was shown that cell cycle regulation and DNA damage response pathways become significantly altered in HNSCC causing replication stress, which is an avenue that deserves further exploitation as an HNSCC vulnerability for treatment. The focus of this review is to summarize the current literature on the preclinical identification of potential druggable targets for therapy of (pre)HNSCC, emerging from the variety of gene knockdown and knockout strategies, and the testing of targeted inhibitors. We will conclude with a future perspective on targeted therapy of HNSCC and premalignant changes.
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Affiliation(s)
- Anne M. van Harten
- Cancer Center Amsterdam, Otolaryngology-Head and Neck Surgery, Tumor Biology & Immunology Section, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands; or
- Sidney Kimmel Cancer Center, Department of Cancer Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Ruud H. Brakenhoff
- Cancer Center Amsterdam, Otolaryngology-Head and Neck Surgery, Tumor Biology & Immunology Section, Vrije Universiteit Amsterdam, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands; or
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86
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Advani D, Kumar P. Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach. ACS OMEGA 2021; 6:13870-13887. [PMID: 34095679 PMCID: PMC8173619 DOI: 10.1021/acsomega.1c01526] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 05/10/2021] [Indexed: 05/08/2023]
Abstract
AIM/HYPOTHESIS The complexity and heterogeneity of multiple pathological features make Alzheimer's disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on "omics" data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD. RESULTS We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein-protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug-target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors. CONCLUSION Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.
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Affiliation(s)
- Dia Advani
- Molecular Neuroscience and Functional
Genomics Laboratory, Delhi Technological
University, Shahabad Daulatpur, Bawana Road, Delhi 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional
Genomics Laboratory, Delhi Technological
University, Shahabad Daulatpur, Bawana Road, Delhi 110042, India
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87
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Network Pharmacological Analysis through a Bioinformatics Approach of Novel NSC765600 and NSC765691 Compounds as Potential Inhibitors of CCND1/ CDK4/ PLK1/ CD44 in Cancer Types. Cancers (Basel) 2021; 13:cancers13112523. [PMID: 34063946 PMCID: PMC8196568 DOI: 10.3390/cancers13112523] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/26/2021] [Accepted: 05/18/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Around 14 million new cancer cases, rate are reported annually, with high mortality worldswide, several mechanisms are associated with complexities in cancer, which leads to resistance to current therapeutic interventions in cancer patients. The aim of this study was to identify molecular genes responsible for cancer development, progression and resistances to therapeutic intervention, and also evaluate the potency of our novel compounds NSC7565600 and NSC765691 as potential target for these oncogenes. Using bioinformatics, we successfully identified CCND1/CDK4/PLK1/CD44 as oncogenic signatures, which drives cancer progression and resistance to treatment, and as potential druggable candidates for both NSC7565600 and NSC765691 small molecules. We also showed the antiproliferative and cytotoxic effects of these compounds against a panel of NCI-60 cancer cell lines. This suggests the potential of NSC765600 and NSC765691 compounds to inhibit CCND1/CDK4/PLK1/CD44 expressions in cancer. Abstract Cyclin D1 (CCND1) and cyclin-dependent kinase 4 (CDK4) both play significant roles in regulating cell cycle progression, while polo-like kinase 1 (PLK1) regulates cell differentiation and tumor progression, and activates cancer stem cells (CSCs), with the cluster of differentiation 44 (CD44) surface marker mostly being expressed. These oncogenes have emerged as promoters of metastasis in a variety of cancer types. In this study, we employed comprehensive computational and bioinformatics analyses to predict drug targets of our novel small molecules, NSC765600 and NSC765691, respectively derived from diflunisal and fostamatinib. The target prediction tools identified CCND1/CDK4/PLK1/CD44 as target genes for NSC765600 and NSC765691 compounds. Additionally, the results of our in silico molecular docking analysis showed unique ligand–protein interactions with putative binding affinities of NSC765600 and NSC765691 with CCND1/CDK4/PLK1/CD44 oncogenic signaling pathways. Moreover, we used drug-likeness precepts as our guidelines for drug design and development, and found that both compounds passed the drug-likeness criteria of molecular weight, polarity, solubility, saturation, flexibility, and lipophilicity, and also exhibited acceptable pharmacokinetic properties. Furthermore, we used development therapeutics program (DTP) algorithms and identified similar fingerprints and mechanisms of NSC765600 and NSC765691 with synthetic compounds and standard anticancer agents in the NCI database. We found that NSC765600 and NSC765691 displayed antiproliferative and cytotoxic effects against a panel of NCI-60 cancer cell lines. Based on these finding, NSC765600 and NSC765691 exhibited satisfactory levels of safety with regard to toxicity, and met all of the required criteria for drug-likeness precepts. Currently, further in vitro and in vivo investigations in tumor-bearing mice are in progress to study the potential treatment efficacies of the novel NSC765600 and NSC765691 small molecules.
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88
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Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks. Genes (Basel) 2021; 12:genes12020319. [PMID: 33672419 PMCID: PMC7926953 DOI: 10.3390/genes12020319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 02/14/2021] [Accepted: 02/20/2021] [Indexed: 12/04/2022] Open
Abstract
The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.
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89
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Gilad Y, Gellerman G, Lonard DM, O’Malley BW. Drug Combination in Cancer Treatment-From Cocktails to Conjugated Combinations. Cancers (Basel) 2021; 13:669. [PMID: 33562300 PMCID: PMC7915944 DOI: 10.3390/cancers13040669] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 02/02/2021] [Indexed: 12/14/2022] Open
Abstract
It is well recognized today that anticancer drugs often are most effective when used in combination. However, the establishment of chemotherapy as key modality in clinical oncology began with sporadic discoveries of chemicals that showed antiproliferative properties and which as a first attempt were used as single agents. In this review we describe the development of chemotherapy from its origins as a single drug treatment with cytotoxic agents to polydrug therapy that includes targeted drugs. We discuss the limitations of the first chemotherapeutic drugs as a motivation for the establishment of combined drug treatment as standard practice in spite of concerns about frequent severe, dose limiting toxicities. Next, we introduce the development of targeted treatment as a concept for advancement within the broader field of small-molecule drug combination therapy in cancer and its accelerating progress that was boosted by recent scientific and technological progresses. Finally, we describe an alternative strategy of drug combinations using drug-conjugates for selective delivery of cytotoxic drugs to tumor cells that potentiates future improvement of drug combinations in cancer treatment. Overall, in this review we outline the development of chemotherapy from a pharmacological perspective, from its early stages to modern concepts of using targeted therapies for combinational treatment.
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Affiliation(s)
- Yosi Gilad
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Gary Gellerman
- Department of Chemical Sciences, Ariel University, Ariel 40700, Israel;
| | - David M. Lonard
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
| | - Bert W. O’Malley
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030, USA;
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90
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Emmerich CH, Gamboa LM, Hofmann MCJ, Bonin-Andresen M, Arbach O, Schendel P, Gerlach B, Hempel K, Bespalov A, Dirnagl U, Parnham MJ. Improving target assessment in biomedical research: the GOT-IT recommendations. Nat Rev Drug Discov 2021; 20:64-81. [PMID: 33199880 PMCID: PMC7667479 DOI: 10.1038/s41573-020-0087-3] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/25/2020] [Indexed: 02/06/2023]
Abstract
Academic research plays a key role in identifying new drug targets, including understanding target biology and links between targets and disease states. To lead to new drugs, however, research must progress from purely academic exploration to the initiation of efforts to identify and test a drug candidate in clinical trials, which are typically conducted by the biopharma industry. This transition can be facilitated by a timely focus on target assessment aspects such as target-related safety issues, druggability and assayability, as well as the potential for target modulation to achieve differentiation from established therapies. Here, we present recommendations from the GOT-IT working group, which have been designed to support academic scientists and funders of translational research in identifying and prioritizing target assessment activities and in defining a critical path to reach scientific goals as well as goals related to licensing, partnering with industry or initiating clinical development programmes. Based on sets of guiding questions for different areas of target assessment, the GOT-IT framework is intended to stimulate academic scientists' awareness of factors that make translational research more robust and efficient, and to facilitate academia-industry collaboration.
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Affiliation(s)
| | - Lorena Martinez Gamboa
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Berlin, Germany
| | - Martine C J Hofmann
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine & Pharmacology TMP, Frankfurt am Main, Germany
| | - Marc Bonin-Andresen
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Olga Arbach
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- SPARK-Validation Fund, Berlin Institute of Health, Berlin, Germany
| | - Pascal Schendel
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | | | - Katja Hempel
- Boehringer-Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
| | - Anton Bespalov
- PAASP GmbH, Heidelberg, Germany
- Valdman Institute of Pharmacology, Pavlov Medical University, St. Petersburg, Russia
| | - Ulrich Dirnagl
- Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany
- QUEST Center for Transforming Biomedical Research, Berlin Institute of Health, Berlin, Germany
| | - Michael J Parnham
- Fraunhofer Institute for Molecular Biology and Applied Ecology IME, Branch for Translational Medicine & Pharmacology TMP, Frankfurt am Main, Germany
- Faculty of Biochemistry, Chemistry & Pharmacy, J.W. Goethe University Frankfurt, Frankfurt am Main, Germany
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91
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Jasmer DP, Rosa BA, Tyagi R, Mitreva M. Rapid determination of nematode cell and organ susceptibility to toxic treatments. INTERNATIONAL JOURNAL FOR PARASITOLOGY-DRUGS AND DRUG RESISTANCE 2020; 14:167-182. [PMID: 33125935 PMCID: PMC7593349 DOI: 10.1016/j.ijpddr.2020.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 12/28/2022]
Abstract
In research focused on the intestine of parasitic nematodes, we recently identified small molecule inhibitors toxic to intestinal cells of larval Ascaris suum (nematode intestinal toxins/toxicants; “NITs”). Some NITs had anthelmintic activity across the phylogenetic diversity of the Nematoda. The whole-worm motility inhibition assay quantified anthelmintic activity, but worm responses to NITs in relation to pathology or affected molecular pathways was not acquired. In this study we extended this research to more comprehensively determine in whole larval A. suum the cells, organ systems, molecular targets, and potential cellular pathways involved in mechanisms of toxicity leading to cell death. The experimental system utilized fluorescent nuclear probes (bisbenzimide, propidium iodide), NITs, an A. suum larval parasite culture system and transcriptional responses (RNA-seq) to NITs. The approach provides for rapid resolution of NIT-induced cell death among organ systems (e.g. intestine, excretory, esophagus, hypodermis and seam cells, and nervous), discriminates among NITs based on cell death profiles, and identifies cells and organ systems with the greatest NIT sensitivity (e.g. intestine and apparent neuronal cells adjacent to the nerve ring). Application was extended to identify cells and organs sensitive to several existing anthelmintics. This approach also resolved intestinal cell death and irreparable damage induced in adult A. suum by two NITs, establishing a new model to elucidate relevant pathologic mechanisms in adult worms. RNA-seq analysis resolved A. suum genes responsive to treatments with three NITs, identifying dihydroorotate dehydrogenase (uridine synthesis) and RAB GTPase(s) (vesicle transport) as potential targets/pathways leading to cell death. A set of genes induced by all three NITs tested suggest common stress or survival responses activated by NITs. Beyond the presented specific lines of research, elements of the overall experimental system presented in this study have broad application toward systematic development of new anthelmintics. A unique rapid cell death assay was developed for parasitic nematodes. Multiple drug-like molecules cause widespread cell death in many organs of A. suum. Multiple cell and organ systems were validated as targets for anthelmintics. Potential drug targets/pathways were implicated in activating cell death processes.
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Affiliation(s)
- Douglas P Jasmer
- Department of Veterinary Microbiology and Pathology, Washington State University, Pullman, WA, 99164, USA
| | - Bruce A Rosa
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Rahul Tyagi
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Makedonka Mitreva
- Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, St. Louis, MO, 63110, USA; McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri, 63108, USA.
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92
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Trajanoska K, Rivadeneira F. Genomic Medicine: Lessons Learned From Monogenic and Complex Bone Disorders. Front Endocrinol (Lausanne) 2020; 11:556610. [PMID: 33162933 PMCID: PMC7581702 DOI: 10.3389/fendo.2020.556610] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/21/2020] [Indexed: 12/11/2022] Open
Abstract
Current genetic studies of monogenic and complex bone diseases have broadened our understanding of disease pathophysiology, highlighting the need for medical interventions and treatments tailored to the characteristics of patients. As genomic research progresses, novel insights into the molecular mechanisms are starting to provide support to clinical decision-making; now offering ample opportunities for disease screening, diagnosis, prognosis and treatment. Drug targets holding mechanisms with genetic support are more likely to be successful. Therefore, implementing genetic information to the drug development process and a molecular redefinition of skeletal disease can help overcoming current shortcomings in pharmaceutical research, including failed attempts and appalling costs. This review summarizes the achievements of genetic studies in the bone field and their application to clinical care, illustrating the imminent advent of the genomic medicine era.
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93
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Elechalawar CK, Hossen MN, McNally L, Bhattacharya R, Mukherjee P. Analysing the nanoparticle-protein corona for potential molecular target identification. J Control Release 2020; 322:122-136. [PMID: 32165239 PMCID: PMC7675788 DOI: 10.1016/j.jconrel.2020.03.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 12/18/2022]
Abstract
When nanoparticles are introduced into biological systems, host proteins tend to associate on the particle surface to form a protein layer termed the "protein corona" (PC). Identifying the proteins that constitute the PC can yield useful information about nanoparticle processing, bio-distribution, toxicity and clearance. Similarly, characterizing and identifying proteins within the PC from patient samples provides opportunities to probe disease proteomes and identify molecules that influence the disease process. Thus, nanoparticles represent unique probing tools for discovery of molecular targets for diseases. Here, we report a first review on target identification using nanoparticles in biological samples based on analysing physico chemical interactions. We also summarize the evolution of the PC surrounding various nano-systems, comment on PC signature, address PC complexity in fluids, and outline challenges associated with analysing the PC. In addition, the influence on PC formation of various nanoparticle parameters is summarized; nanoparticle characteristics considered include size, charge, temperature, and surface modifications for both organic and inorganic nanomaterials. We also discuss the advantages of nanotechnology, over other more invasive and laborious methods, for identifying potential diagnostic and therapeutic targets.
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Affiliation(s)
| | - Md Nazir Hossen
- Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Lacey McNally
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA
| | - Resham Bhattacharya
- Department of Obstetrics and Gynecology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Priyabrata Mukherjee
- Department of Pathology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Peggy and Charles Stephenson Cancer Center, The University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
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94
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Koch K, Hartmann R, Tsiampali J, Uhlmann C, Nickel AC, He X, Kamp MA, Sabel M, Barker RA, Steiger HJ, Hänggi D, Willbold D, Maciaczyk J, Kahlert UD. A comparative pharmaco-metabolomic study of glutaminase inhibitors in glioma stem-like cells confirms biological effectiveness but reveals differences in target-specificity. Cell Death Discov 2020; 6:20. [PMID: 32337072 PMCID: PMC7162917 DOI: 10.1038/s41420-020-0258-3] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/27/2020] [Accepted: 03/26/2020] [Indexed: 12/14/2022] Open
Abstract
Cancer cells upregulate anabolic processes to maintain high rates of cellular turnover. Limiting the supply of macromolecular precursors by targeting enzymes involved in biosynthesis is a promising strategy in cancer therapy. Several tumors excessively metabolize glutamine to generate precursors for nonessential amino acids, nucleotides, and lipids, in a process called glutaminolysis. Here we show that pharmacological inhibition of glutaminase (GLS) eradicates glioblastoma stem-like cells (GSCs), a small cell subpopulation in glioblastoma (GBM) responsible for therapy resistance and tumor recurrence. Treatment with small molecule inhibitors compound 968 and CB839 effectively diminished cell growth and in vitro clonogenicity of GSC neurosphere cultures. However, our pharmaco-metabolic studies revealed that only CB839 inhibited GLS enzymatic activity thereby limiting the influx of glutamine derivates into the TCA cycle. Nevertheless, the effects of both inhibitors were highly GLS specific, since treatment sensitivity markedly correlated with GLS protein expression. Strikingly, we found GLS overexpressed in in vitro GSC models as compared with neural stem cells (NSC). Moreover, our study demonstrates the usefulness of in vitro pharmaco-metabolomics to score target specificity of compounds thereby refining drug development and risk assessment.
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Affiliation(s)
- Katharina Koch
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Rudolf Hartmann
- Institute of Complex Systems (ICS-6) Structural Biochemistry and JuStruct: Juelich Center for Structural Biology, Forschungszentrum Juelich, 52425 Juelich, Germany
| | - Julia Tsiampali
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Constanze Uhlmann
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Ann-Christin Nickel
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Xiaoling He
- John van Geest Centre for Brain Repair and WT/MRC Cambridge Stem Cell Institute, Department of Clinical Neurosciences, University of Cambridge, CB2 0PY Cambridge, UK
| | - Marcel A. Kamp
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Michael Sabel
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Roger A. Barker
- John van Geest Centre for Brain Repair and WT/MRC Cambridge Stem Cell Institute, Department of Clinical Neurosciences, University of Cambridge, CB2 0PY Cambridge, UK
| | - Hans-Jakob Steiger
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Daniel Hänggi
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
| | - Dieter Willbold
- Institute of Complex Systems (ICS-6) Structural Biochemistry and JuStruct: Juelich Center for Structural Biology, Forschungszentrum Juelich, 52425 Juelich, Germany
- Institut für Physikalische Biologie, Heinrich Heine University Duesseldorf, 40225 Duesseldorf, Germany
| | - Jaroslaw Maciaczyk
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
- Neurosurgery Department, University Hospital Bonn, 53127 Bonn, Germany
| | - Ulf D. Kahlert
- Neurosurgery Department, University Hospital Duesseldorf, 40225 Duesseldorf, Germany
- German Cancer Consortium (DKTK), Essen/Duesseldorf, Germany
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95
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Singh N, Chaput L, Villoutreix BO. Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace. Brief Bioinform 2020; 22:1790-1818. [PMID: 32187356 PMCID: PMC7986591 DOI: 10.1093/bib/bbaa034] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.
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Affiliation(s)
- Natesh Singh
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Ludovic Chaput
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
| | - Bruno O Villoutreix
- Univ. Lille, Inserm, Institut Pasteur de Lille, U1177 Drugs and Molecules for Living Systems, F-59000 Lille, France
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