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Wu JW, Gao W, Shen LP, Chen YL, Du SQ, Du ZY, Zhao XD, Lu XJ. Leonurus japonicus Houtt. modulates neuronal apoptosis in intracerebral hemorrhage: Insights from network pharmacology and molecular docking. JOURNAL OF ETHNOPHARMACOLOGY 2024; 330:118223. [PMID: 38642624 DOI: 10.1016/j.jep.2024.118223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/09/2024] [Accepted: 04/17/2024] [Indexed: 04/22/2024]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Leonurus japonicus Houtt. (Labiatae), commonly known as Chinese motherwort, is a herbaceous flowering plant that is native to Asia. It is widely acknowledged in traditional medicine for its diuretic, hypoglycemic, antiepileptic properties and neuroprotection. Currently, Leonurus japonicus (Leo) is included in the Pharmacopoeia of the People's Republic of China. Traditional Chinese Medicine (TCM) recognizes Leo for its myriad pharmacological attributes, but its efficacy against ICH-induced neuronal apoptosis is unclear. AIMS OF THE STUDY This study aimed to identify the potential targets and regulatory mechanisms of Leo in alleviating neuronal apoptosis after ICH. MATERIALS AND METHODS The study employed network pharmacology, UPLC-Q-TOF-MS technique, molecular docking, pharmacodynamic studies, western blotting, and immunofluorescence techniques to explore its potential mechanisms. RESULTS Leo was found to assist hematoma absorption, thus improving the neurological outlook in an ICH mouse model. Importantly, molecular docking highlighted JAK as Leo's potential therapeutic target in ICH scenarios. Further experimental evidence demonstrated that Leo adjusts JAK1 and STAT1 phosphorylation, curbing Bax while augmenting Bcl-2 expression. CONCLUSION Leo showcases potential in mitigating neuronal apoptosis post-ICH, predominantly via the JAK/STAT mechanism.
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Affiliation(s)
- Jia-Wei Wu
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China
| | - Wei Gao
- Department of Neurology, Wuxi Ninth People's Hospital Affiliated to Soochow University, Wuxi, Jiangsu Province, 214122, PR China
| | - Li-Ping Shen
- Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China
| | - Yong-Lin Chen
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China
| | - Shi-Qing Du
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China
| | - Zhi-Yong Du
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China
| | - Xu-Dong Zhao
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China.
| | - Xiao-Jie Lu
- Neuroscience Center, Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu Province, 214122, PR China; Department of Neurosurgery, Jiangnan University Medical Center, Wuxi, Jiangsu Province, 214122, PR China; Wuxi Neurosurgical Institute, Wuxi, Jiangsu Province, 214122, PR China.
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Fu Y, Xu H, Bao Y, Wang J, Liu X, Huang Q. Unveiling the hidden potential: Above-ground parts of Paris yunnanensis Franch. Is promise as an anti-acne therapeutic beyond traditional medicinal sites. Fitoterapia 2024; 178:106179. [PMID: 39128555 DOI: 10.1016/j.fitote.2024.106179] [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: 05/27/2024] [Revised: 07/17/2024] [Accepted: 08/08/2024] [Indexed: 08/13/2024]
Abstract
The dried rhizomes of Paris yunnanensis Franch. have been extensively utilized in traditional Chinese medicine as hemostatic, antitumor, and antimicrobial agents. An examination of classical texts and renowned Chinese medical formulations showcased its efficacy in acne treatment. Presently, there is a significant scarcity of Paris resources. Consider directing attention towards the non-medicinal parts of Paris to mitigate the strain on medicinal resources within this realm. To address these resource limitations, this study investigated the bioactivity and pharmacodynamics of the above-ground parts of Paris (AGPP). A synergistic approach integrating network pharmacology, molecular docking (in silico validation), and animal experimentation (in vivo validation) was employed to elucidate the potential mechanisms underlying the efficacy of AGPP against acne vulgaris in this study. The active constituents in AGPP extracts were identified via UHPLC-Q-Orbitrap HRMS analysis, with their targets extracted for network pharmacological analysis. KEGG pathway analysis unveiled potential therapeutic mechanisms, validated through molecular docking and rat auricular acne model experiments. Comprehensive chemical characterization revealed fifty constituents, including steroidal saponins, flavonoids, amino acids, organic acids, phytohormones, phenolic acids, and alkaloids. Diosgenin, Quercetin, Kaempferol, Ecdysone, and α-linolenic acid were identified as main constituents with acne-treating potential. Core targets included SRC, MAPK3, and MAPK1, with key signaling pathways implicated. Histologically, AGPP mitigated acne-induced follicular dilatation and inflammation, inhibiting inflammatory cytokine production (IL-6, IL-1β, TNF-α). This study offers insight into AGPP's mechanism for acne treatment, laying groundwork for Paris development and drug discovery.
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Affiliation(s)
- Yue Fu
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China
| | - Huiyuan Xu
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China
| | - Yuchen Bao
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China
| | - Jin Wang
- School of Ethnic Medicine, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China
| | - Xianwu Liu
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China.
| | - Qinwan Huang
- College of Pharmacy, Chengdu University of Traditional Chinese Medicine, No.1166, Liutai Avenue, Wenjiang District, Chengdu, Sichuan, China.
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Garaci E, Paci M, Matteucci C, Costantini C, Puccetti P, Romani L. Phenotypic drug discovery: a case for thymosin alpha-1. Front Med (Lausanne) 2024; 11:1388959. [PMID: 38903817 PMCID: PMC11187271 DOI: 10.3389/fmed.2024.1388959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 05/28/2024] [Indexed: 06/22/2024] Open
Abstract
Phenotypic drug discovery (PDD) involves screening compounds for their effects on cells, tissues, or whole organisms without necessarily understanding the underlying molecular targets. PDD differs from target-based strategies as it does not require knowledge of a specific drug target or its role in the disease. This approach can lead to the discovery of drugs with unexpected therapeutic effects or applications and allows for the identification of drugs based on their functional effects, rather than through a predefined target-based approach. Ultimately, disease definitions are mostly symptom-based rather than mechanism-based, and the therapeutics should be likewise. In recent years, there has been a renewed interest in PDD due to its potential to address the complexity of human diseases, including the holistic picture of multiple metabolites engaging with multiple targets constituting the central hub of the metabolic host-microbe interactions. Although PDD presents challenges such as hit validation and target deconvolution, significant achievements have been reached in the era of big data. This article explores the experiences of researchers testing the effect of a thymic peptide hormone, thymosin alpha-1, in preclinical and clinical settings and discuss how its therapeutic utility in the precision medicine era can be accommodated within the PDD framework.
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Affiliation(s)
| | - Maurizio Paci
- Department of Chemical Sciences and Technologies, University of Rome “Tor Vergata”, Rome, Italy
| | - Claudia Matteucci
- Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy
| | - Claudio Costantini
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Paolo Puccetti
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Luigina Romani
- San Raffaele Sulmona, L’Aquila, Italy
- Department of Medicine and Surgery, University of Perugia, Perugia, Italy
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Meimetis N, Lauffenburger DA, Nilsson A. Inference of drug off-target effects on cellular signaling using interactome-based deep learning. iScience 2024; 27:109509. [PMID: 38591003 PMCID: PMC11000001 DOI: 10.1016/j.isci.2024.109509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 02/04/2024] [Accepted: 03/13/2024] [Indexed: 04/10/2024] Open
Abstract
Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.
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Affiliation(s)
- Nikolaos Meimetis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Douglas A. Lauffenburger
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Avlant Nilsson
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Cell and Molecular Biology, SciLifeLab, Karolinska Institutet, Stockholm, Sweden
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE 41296, Sweden
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Dong S, Tian Q, Hui M, Zhang S. Revealing the Antiperspirant Components of Floating Wheat and Their Mechanisms of Action through Metabolomics and Network Pharmacology. Molecules 2024; 29:553. [PMID: 38338298 PMCID: PMC10856516 DOI: 10.3390/molecules29030553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 02/12/2024] Open
Abstract
Floating wheat is a classical herbal with potential efficacy in the treatment of hyperhidrosis. Aiming at revealing the main components and potential mechanisms of floating wheat, a comprehensive and unique phytopharmacology profile study was carried out. First, common wheat was used as a control to look for chemical markers of floating wheat. In the screening analysis, a total of 180 shared compounds were characterized in common wheat and floating wheat, respectively. The results showed that floating wheat and common wheat contain similar types of compounds. In addition, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a difference between floating wheat and common wheat and 17 potential biomarkers for floating wheat. Meanwhile, a total of seven components targeted for hyperhidrosis were screened out based on network pharmacology. Seven key differential components were screened, among which kaempferol, asiatic acid, sclareol, enoxolone, and secoisolariciresinol had higher degree values than the others. The analysis of interacting genes revealed three key genes, namely, MAP2K1, ESR1, and ESR2. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that various signaling pathways were involved. Prolactin signaling, thyroid cancer, endocrine resistance, gonadotropin secretion, and estrogen signaling pathways were the main pathways of the intervention of floating wheat in excessive sweating, which was associated with the estrogenic response, hormone receptor binding, androgen metabolism, apoptosis, cancer, and many other biological processes. Molecular docking showed that the screened key components could form good bindings with the target proteins through intermolecular forces. This study reveals the active ingredients and potential molecular mechanism of floating wheat in the treatment of hyperhidrosis and provides a reference for subsequent basic research.
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Affiliation(s)
- Shengnan Dong
- College of Biological Engineering, Henan University of Technology, Zhengzhou 450001, China;
| | - Qing Tian
- Industrial Microorganism Preservation and Breeding Henan Engineering Laboratory, Zhengzhou 450001, China;
| | - Ming Hui
- College of Biological Engineering, Henan University of Technology, Zhengzhou 450001, China;
- Industrial Microorganism Preservation and Breeding Henan Engineering Laboratory, Zhengzhou 450001, China;
| | - Shouyu Zhang
- College of Smart Health, Henan Polytechnic, Zhengzhou 450046, China
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Yu Z, Wu Z, Wang Z, Wang Y, Zhou M, Li W, Liu G, Tang Y. Network-Based Methods and Their Applications in Drug Discovery. J Chem Inf Model 2024; 64:57-75. [PMID: 38150548 DOI: 10.1021/acs.jcim.3c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2023]
Abstract
Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.
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Affiliation(s)
- Zhuohang Yu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zengrui Wu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Ze Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yimeng Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Moran Zhou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Lu Y, Dong K, Yang M, Liu J. Network pharmacology-based strategy to investigate the bioactive ingredients and molecular mechanism of Evodia rutaecarpa in colorectal cancer. BMC Complement Med Ther 2023; 23:433. [PMID: 38041080 PMCID: PMC10691004 DOI: 10.1186/s12906-023-04254-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 11/10/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND Evodia rutaecarpa, a traditional herbal drug, is widely used as an analgesic and antiemetic. Many studies have confirmed that Evodia rutaecarpa has an anticancer effect. Here, our study explored the bioactive ingredients in Evodia rutaecarpa acting on colorectal cancer (CRC) by utilizing network pharmacology. METHODS We clarified the effective ingredients and corresponding targets of Evodia rutaecarpa. CRC-related genes were obtained from several public databases to extract candidate targets. Candidate targets were used to construct a protein-protein interaction (PPI) network for screening out core targets with topological analysis, and then we selected the core targets and corresponding ingredients for molecular docking. Cell proliferation experiments and enzyme-linked immunosorbent assays (ELISAs) verified the anticancer effect of the bioactive ingredients and the results of molecular docking. RESULTS Our study obtained a total of 24 bioactive ingredients and 100 candidate targets after intersecting ingredient-related targets and CRC-related genes, and finally, 10 genes-TNF, MAPK1, TP53, AKT1, RELA, RB1, ESR1, JUN, CCND1 and MYC-were screened out as core targets. In vitro experiments suggested that rutaecarpine excelled isorhamnetin, evodiamine and quercetin in the inhibition of CRC cells and the release of TNF-α was altered with the concentrations of rutaecarpine. Molecular docking showed that rutaecarpine could effectively bind with TNF-α. CONCLUSION The pairs of ingredients-targets in Evodia rutaecarpa acted on CRC were excavated. Rutaecarpine as a bioactive ingredient of Evodia rutaecarpamight effectively inhibit the proliferation of CRC cells by suppressing TNF-α.
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Affiliation(s)
- Yongqu Lu
- Department of Breast and Thyroid Surgery, China-Japan Friendship Hospital, Beijing, 100029, China
| | - Kangdi Dong
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China
| | - Meng Yang
- Department of Breast and Thyroid Surgery, China-Japan Friendship Hospital, Beijing, 100029, China.
| | - Jun Liu
- Department of Breast and Thyroid Surgery, China-Japan Friendship Hospital, Beijing, 100029, China.
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Aly Abdelkader G, Ngnamsie Njimbouom S, Oh TJ, Kim JD. ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction. Comput Biol Chem 2023; 107:107969. [PMID: 37866117 DOI: 10.1016/j.compbiolchem.2023.107969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 10/24/2023]
Abstract
Protein-ligand interaction plays a crucial role in drug discovery, facilitating efficient drug development and enabling drug repurposing. Several computational algorithms, such as Graph Neural Networks and Convolutional Neural Networks, have been proposed to predict the binding affinity using the three-dimensional structure of ligands and proteins. However, there are limitations due to the need for experimental characterization of the three-dimensional structure of protein sequences, which is still lacking for some proteins. Moreover, these models often suffer from unnecessary complexity, resulting in extraneous computations. This study presents ResBiGAAT, a novel deep learning model that combines a deep Residual Bidirectional Gated Recurrent Unit with two-sided self-attention mechanisms. ResBiGAAT leverages protein and ligand sequence-level features and their physicochemical properties to efficiently predict protein-ligand binding affinity. Through rigorous evaluation using 5-fold cross-validation, we demonstrate the performance of our proposed approach. The model exhibits competitive performance on an external dataset, highlighting its generalizability. Our publicly available web interface, located at resbigaat.streamlit.app, allows users to conveniently input protein and ligand sequences to estimate binding affinity.
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Affiliation(s)
- Gelany Aly Abdelkader
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea
| | - Soualihou Ngnamsie Njimbouom
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea
| | - Tae-Jin Oh
- Genome‑based BioIT Convergence Institute, Asan 31460, the Republic of Korea; Department of Pharmaceutical Engineering and Biotechnology, Sun Moon University, Asan 31460, the Republic of Korea
| | - Jeong-Dong Kim
- Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, the Republic of Korea; Division of Computer Science and Engineering, Sun Moon University, Asan 31460, the Republic of Korea.
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Cui Y, Hu J, Li Y, Au R, Fang Y, Cheng C, Xu F, Li W, Wu Y, Zhu L, Shen H. Integrated Network Pharmacology, Molecular Docking and Animal Experiment to Explore the Efficacy and Potential Mechanism of Baiyu Decoction Against Ulcerative Colitis by Enema. Drug Des Devel Ther 2023; 17:3453-3472. [PMID: 38024534 PMCID: PMC10680469 DOI: 10.2147/dddt.s432268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023] Open
Abstract
Background Baiyu Decoction (BYD), a clinical prescription of traditional Chinese medicine, has been proven to be valuable for treating ulcerative colitis (UC) by enema. However, the mechanism of BYD against UC remains unclear. Purpose A combination of bioinformatics methods including network pharmacology and molecular docking and animal experiments were utilized to investigate the potential mechanism of BYD in the treatment of UC. Materials and Methods Firstly, the representative compounds of each herb in BYD were detected by liquid chromatography-mass spectrometry. Subsequently, we predicted the core targets and potential pathways of BYD for treating UC through network pharmacology. And rat colitis model was established with dextran sodium sulfate. UC rats were subjected to BYD enema administration, during which we recorded body weight changes, disease activity index, and colon length to assess the effectiveness of BYD. Besides, quantitative real-time PCR, western blotting, ELISA and immunofluorescence were used to detect intestinal inflammatory factors, intestinal barrier biomarkers and TOLL-like receptor pathway in rats. Finally, the core components and targets of BYD were subjected to molecular docking so as to further validate the results of network pharmacology. Results A total of 41 active compositions and 203 targets related to BYD-UC were subjected to screening. The results of bioinformatics analysis showed that quercetin and kaempferol may be the main compounds. Additionally, AKT1, IL-6, TP53, TNF and IL-1β were regarded as potential therapeutic targets. KEGG results explained that TOLL-like receptor pathway might play a pivotal role in BYD protecting against UC. In addition, animal experiments and molecular docking validated the network pharmacology results. BYD enema treatment can reduce body weight loss, lower disease activity index score, reverse colon shortening, relieve intestinal inflammation, protect intestinal barrier, and inhibit TOLL-like receptor pathway in UC rats. Besides, molecular docking suggested that quercetin and kaempferol docked well with TLR4, AKT1, IL-6, TP53. Conclusion Utilizing network pharmacology, animal studies, and molecular docking, enema therapy with BYD was confirmed to have anti-UC efficacy by alleviating intestinal inflammation, protecting the intestinal barrier, and inhibiting the TOLL-like receptor pathway. Researchers should focus not only on oral medications but also on the rectal administration of medications in furtherance of the cure of ulcerative colitis.
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Affiliation(s)
- Yuan Cui
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Jingyi Hu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Yanan Li
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Ryan Au
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Yulai Fang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Cheng Cheng
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Feng Xu
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Weiyang Li
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Yuguang Wu
- Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Lei Zhu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
| | - Hong Shen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China
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Cingiz MÖ. k- Strong Inference Algorithm: A Hybrid Information Theory Based Gene Network Inference Algorithm. Mol Biotechnol 2023:10.1007/s12033-023-00929-2. [PMID: 37950851 DOI: 10.1007/s12033-023-00929-2] [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: 02/23/2023] [Accepted: 10/05/2023] [Indexed: 11/13/2023]
Abstract
Gene networks allow researchers to understand the underlying mechanisms between diseases and genes while reducing the need for wet lab experiments. Numerous gene network inference (GNI) algorithms have been presented in the literature to infer accurate gene networks. We proposed a hybrid GNI algorithm, k-Strong Inference Algorithm (ksia), to infer more reliable and robust gene networks from omics datasets. To increase reliability, ksia integrates Pearson correlation coefficient (PCC) and Spearman rank correlation coefficient (SCC) scores to determine mutual information scores between molecules to increase diversity of relation predictions. To infer a more robust gene network, ksia applies three different elimination steps to remove redundant and spurious relations between genes. The performance of ksia was evaluated on microbe microarrays database in the overlap analysis with other GNI algorithms, namely ARACNE, C3NET, CLR, and MRNET. Ksia inferred less number of relations due to its strict elimination steps. However, ksia generally performed better on Escherichia coli (E.coli) and Saccharomyces cerevisiae (yeast) gene expression datasets due to F- measure and precision values. The integration of association estimator scores and three elimination stages slightly increases the performance of ksia based gene networks. Users can access ksia R package and user manual of package via https://github.com/ozgurcingiz/ksia .
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Affiliation(s)
- Mustafa Özgür Cingiz
- Computer Engineering Department, Faculty of Engineering and Natural Sciences, Bursa Technical University, Mimar Sinan Campus, Yildirim, 16310, Bursa, Turkey.
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12
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Dong W, Yang Q, Wang J, Xu L, Li X, Luo G, Gao X. Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network. Brief Bioinform 2023; 24:7145903. [PMID: 37114624 DOI: 10.1093/bib/bbad161] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/19/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023] Open
Abstract
Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.
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Affiliation(s)
- Weihe Dong
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Qiang Yang
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Jian Wang
- College of information and Computer Engineering, Northeast Forestry University, Hexing Road, 150040, Harbin, China
| | - Long Xu
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Xiaokun Li
- School of Computer Science and Technology, Heilongjiang University, Xuefu Road, 150080, Harbin, China
- Postdoctoral Program of Heilongjiang Hengxun Technology Co., Ltd., Xuefu Road, 150080, Harbin, China
| | - Gongning Luo
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
- School of Computer Science and Technology, Harbin Institute of Technology, West Dazhi Street, 150001, Harbin, China
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology, 4700 KAUST, Thuwal 23955, Saudi Arabia
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13
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Mangione W, Falls Z, Samudrala R. Effective holistic characterization of small molecule effects using heterogeneous biological networks. Front Pharmacol 2023; 14:1113007. [PMID: 37180722 PMCID: PMC10169664 DOI: 10.3389/fphar.2023.1113007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/11/2023] [Indexed: 05/16/2023] Open
Abstract
The two most common reasons for attrition in therapeutic clinical trials are efficacy and safety. We integrated heterogeneous data to create a human interactome network to comprehensively describe drug behavior in biological systems, with the goal of accurate therapeutic candidate generation. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multiscale therapeutic discovery, repurposing, and design was enhanced by integrating drug side effects, protein pathways, protein-protein interactions, protein-disease associations, and the Gene Ontology, and complemented with its existing drug/compound, protein, and indication libraries. These integrated networks were reduced to a "multiscale interactomic signature" for each compound that describe its functional behavior as vectors of real values. These signatures are then used for relating compounds to each other with the hypothesis that similar signatures yield similar behavior. Our results indicated that there is significant biological information captured within our networks (particularly via side effects) which enhance the performance of our platform, as evaluated by performing all-against-all leave-one-out drug-indication association benchmarking as well as generating novel drug candidates for colon cancer and migraine disorders corroborated via literature search. Further, drug impacts on pathways derived from computed compound-protein interaction scores served as the features for a random forest machine learning model trained to predict drug-indication associations, with applications to mental disorders and cancer metastasis highlighted. This interactomic pipeline highlights the ability of Computational Analysis of Novel Drug Opportunities to accurately relate drugs in a multitarget and multiscale context, particularly for generating putative drug candidates using the information gleaned from indirect data such as side effect profiles and protein pathway information.
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Affiliation(s)
| | | | - Ram Samudrala
- Jacobs School of Medicine and Biomedical Sciences, Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, United States
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14
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Maghsoudi S, Taghavi Shahraki B, Rameh F, Nazarabi M, Fatahi Y, Akhavan O, Rabiee M, Mostafavi E, Lima EC, Saeb MR, Rabiee N. A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery. Chem Biol Drug Des 2022; 100:699-721. [PMID: 36002440 PMCID: PMC9539342 DOI: 10.1111/cbdd.14136] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/07/2022] [Accepted: 08/21/2022] [Indexed: 11/29/2022]
Abstract
Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.
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Affiliation(s)
- Saeid Maghsoudi
- Faculty of Medicine, Department of Physiology and PathophysiologyUniversity of ManitobaWinnipegManitobaCanada
- Biology of Breathing Group, Children's Hospital Research Institute of Manitoba (CHRIM), University of ManitobaWinnipegManitobaCanada
| | | | | | - Masoomeh Nazarabi
- Faculty of Organic Chemistry, Department of ChemistryUniversity of KashanKashanIran
| | - Yousef Fatahi
- Department of Pharmaceutical Nanotechnology, Faculty of PharmacyTehran University of Medical SciencesTehranIran
- Nanotechnology Research Center, Faculty of PharmacyTehran University of Medical SciencesTehranIran
| | - Omid Akhavan
- Department of PhysicsSharif University of TechnologyTehranIran
| | - Mohammad Rabiee
- Biomaterials Group, Department of Biomedical EngineeringAmirkabir University of TechnologyTehranIran
| | - Ebrahim Mostafavi
- Stanford Cardiovascular Institute, Stanford University School of MedicineStanfordCaliforniaUSA
- Department of MedicineStanford University School of MedicineStanfordCaliforniaUSA
| | - Eder C. Lima
- Institute of Chemistry, Federal University of Rio Grande Do Sul (UFRGS)Porto AlegreBrazil
| | - Mohammad Reza Saeb
- Department of Polymer Technology, Faculty of ChemistryGdańsk University of TechnologyGdańskPoland
| | - Navid Rabiee
- Department of PhysicsSharif University of TechnologyTehranIran
- School of EngineeringMacquarie UniversitySydneyNew South WalesAustralia
- Department of Materials Science and EngineeringPohang University of Science and Technology (POSTECH)PohangSouth Korea
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15
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Zhao Q, Bai J, Chen Y, Liu X, Zhao S, Ling G, Jia S, Zhai F, Xiang R. An optimized herbal combination for the treatment of liver fibrosis: Hub genes, bioactive ingredients, and molecular mechanisms. JOURNAL OF ETHNOPHARMACOLOGY 2022; 297:115567. [PMID: 35870684 DOI: 10.1016/j.jep.2022.115567] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/30/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Liver fibrosis is a chronic liver disease that can lead to cirrhosis, liver failure, and hepatocellular carcinoma, and it is associated with long-term adverse outcomes and mortality. As a primary resource for complementary and alternative medicine, traditional Chinese medicine (TCM) has accumulated a large number of effective formulas for the treatment of liver fibrosis in clinical practice. However, studies on how to systematically optimize TCM formulas are still lacking. AIM OF THE REVIEW To provide a methodological reference for the systematic optimization of TCM formulae against liver fibrosis and explored the underlying molecular mechanisms; To provide an efficient method for searching for lead compounds from natural sources and developing from herbal medicines; To enable clinicians and patients to make more reasonable choices and promote the effective treatment toward those patients with liver fibrosis. MATERIALS AND METHODS TCM formulas related to treating liver fibrosis were collected from the Web of Science, PubMed, the China National Knowledge Infrastructure (CNKI), Wan Fang, and the Chinese Scientific Journals Database (VIP). Furthermore, the TCM compatibility patterns were mined using association analysis. The core TCM combinations were found by designing an optimized formulas algorithm. Finally, the hub target proteins, potential molecular mechanisms, and active compounds were explored through integrative pharmacology and docking-based inverse virtual screening (IVS) approaches. RESULTS We found that the herbs for reinforcing deficiency, activating blood, removing blood stasis, and clearing heat were the basis of TCM formulae patterns. Furthermore, the combination of Salviae Miltiorrhizae (Salvia miltiorrhiza Bunge; Chinese salvia/Danshen), Astragali Radix (Astragalus membranaceus (Fisch.) Bunge; Astragalus/Huangqi), and Radix Bupleuri (Bupleurum chinense DC.; Bupleurum/Chaihu) was identified as core groups. A total of six targets (TNF, STAT3, EGFR, IL2, ICAM1, PTGS2) play a pivotal role in TCM-mediated liver fibrosis inhibition. (-)-Cryptotanshinone, Tanshinaldehyde, Ononin, Thymol, Daidzein, and Formononetin were identified as active compounds in TCM. And mechanistically, TCM could affect the development of liver fibrosis by regulating inflammation, immunity, angiogenesis, antioxidants, and involvement in TNF, MicroRNAs, Jak-STAT, NF-kappa B, and C-type lectin receptors (CLRs) signaling pathways. Molecular docking results showed that key components had good potential to bind to the target genes. CONCLUSION In summary, this study provides a methodological reference for the systematic optimization of TCM formulae and exploration of underlying molecular mechanisms.
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Affiliation(s)
- Qianqian Zhao
- Faculty of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Jinwei Bai
- School of Medical Equipment, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Yiwei Chen
- Faculty of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Xin Liu
- Faculty of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Shangfeng Zhao
- Faculty of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Guixia Ling
- School of Medical Equipment, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Shubing Jia
- Faculty of Life Science and Biopharmaceutics, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Fei Zhai
- School of Medical Equipment, Shenyang Pharmaceutical University, Shenyang, 110016, China.
| | - Rongwu Xiang
- School of Medical Equipment, Shenyang Pharmaceutical University, Shenyang, 110016, China; Liaoning Professional Technology Innovation Center on Medical Big Data and Artificial Intelligence, Shenyang, 110016, China.
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16
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Lu Y, Bi J, Li F, Wang G, Zhu J, Jin J, Liu Y. Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images. Front Physiol 2022; 13:942049. [PMID: 35874525 PMCID: PMC9304584 DOI: 10.3389/fphys.2022.942049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
The purpose of this study was to use network pharmacology, biomedical images and molecular docking technology in the treatment of breast cancer to investigate the feasible therapeutic targets and mechanisms of trastuzumab. In the first place, we applied pubchem swisstarget (http://www.swisstargetprediction.ch/), (https://pubchem.ncbi.nlm.nih.gov/) pharmmapper (http://lilab-ecust.cn/pharmmapper/), and the batman-tcm (http://bionet.ncpsb.org.cn/batman-tcm/) database to collect the trastuzumab targets. Then, in NCBI-GEO, breast cancer target genes were chosen (https://www.ncbi.nlm.nih.gov/geo/). The intersection regions of drug and disease target genes were used to draw a Venn diagram. Through Cytoscape 3.7.2 software, and the STRING database, we then formed a protein-protein interaction (PPI) network. Besides, we concluded KEGG pathway analysis and Geen Ontology analysis by using ClueGO in Cytospace. Finally, the top 5 target proteins in the PPI network to dock with trastuzumab were selected. After screening trastuzumab and breast cancer in databases separately, we got 521 target genes of the drug and 1,464 target genes of breast cancer. The number of overlapping genes was 54. PPI network core genes include GAPDH, MMP9, CCNA2, RRM2, CHEK1, etc. GO analysis indicated that trastuzumab treats breast cancer through abundant biological processes, especially positive regulation of phospholipase activity, linoleic acid metabolic process, and negative regulation of endothelial cell proliferation. The molecular function is NADP binding and the cellular component is tertiary granule lumen. The results of KEGG enrichment analysis exhibited four pathways related to the formation and cure of breast cancer, containing Drug metabolism, Glutathione metabolism, Pyrimidine metabolism and PPAR signaling pathway. Molecular docking showed that trastuzumab has good binding abilities with five core target proteins (GAPDH, MMP9, CCNA2, RRM2, CHEK1). This study, through network pharmacology and molecular docking, provides new pieces of evidence and ideas to understand how trastuzumab treats breast cancer at the gene level.
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Affiliation(s)
- Yuan Lu
- Shanghai Eighth People’s Hospital, Shanghai, China
| | - Juan Bi
- Department of Pharmacy, First Affiliated Hospital, Naval Medical University, Shanghai, China
| | - Fei Li
- Shanghai Eighth People’s Hospital, Shanghai, China
| | - Gang Wang
- Shanghai Eighth People’s Hospital, Shanghai, China
| | - Junjie Zhu
- Shanghai Eighth People’s Hospital, Shanghai, China
| | - Jiqing Jin
- Shanghai Eighth People’s Hospital, Shanghai, China
| | - Yueyun Liu
- Shanghai Eighth People’s Hospital, Shanghai, China
- *Correspondence: Yueyun Liu,
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17
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Li A, Chen JY, Hsu CL, Oyang YJ, Huang HC, Juan HF. A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis. Pharmaceutics 2022; 14:pharmaceutics14050971. [PMID: 35631558 PMCID: PMC9147547 DOI: 10.3390/pharmaceutics14050971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/22/2022] [Accepted: 04/29/2022] [Indexed: 12/04/2022] Open
Abstract
Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.
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Affiliation(s)
- Albert Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Jhih-Yu Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Chia-Lang Hsu
- Department of Medical Research, National Taiwan University Hospital, Taipei 106, Taiwan;
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 106, Taiwan
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
| | - Hsueh-Fen Juan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan
- Center for Computational and Systems Biology, National Taiwan University, Taipei 106, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
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18
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Wilson JL, Gravina A, Grimes K. From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways. Integr Biol (Camb) 2022; 14:13-24. [PMID: 35293584 DOI: 10.1093/intbio/zyac002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 12/20/2022]
Abstract
With high drug attrition, protein-protein interaction (PPI) network models are attractive as efficient methods for predicting drug outcomes by analyzing proteins downstream of drug targets. Unfortunately, these methods tend to overpredict associations and they have low precision and prediction performance; performance is often no better than random (AUROC ~0.5). Typically, PPI models identify ranked phenotypes associated with downstream proteins, yet methods differ in prioritization of downstream proteins. Most methods apply global approaches for assessing all phenotypes. We hypothesized that a per-phenotype analysis could improve prediction performance. We compared two global approaches-statistical and distance-based-and our novel per-phenotype approach, 'context-specific interaction' (CSI) analysis, on severe side effect prediction. We used a novel dataset of adverse events (or designated medical events, DMEs) and discovered that CSI had a 50% improvement over global approaches (AUROC 0.77 compared to 0.51), and a 76-95% improvement in average precision (0.499 compared to 0.284, 0.256). Our results provide a quantitative rationale for considering downstream proteins on a per-phenotype basis when using PPI network methods to predict drug phenotypes.
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Affiliation(s)
- Jennifer L Wilson
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Alessio Gravina
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Kevin Grimes
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
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Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis. Comput Biol Med 2022; 144:105389. [PMID: 35303581 DOI: 10.1016/j.compbiomed.2022.105389] [Citation(s) in RCA: 138] [Impact Index Per Article: 69.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/05/2022] [Accepted: 03/07/2022] [Indexed: 12/13/2022]
Abstract
BACKGROUND Huanglian Jiedu Decoction (HLJDD) is a classical herbal formula with potential efficacy in the treatment of sepsis. However, the main components and potential mechanisms of HLJDD remain unclear. This study aims to initially clarify the potential mechanism of HLJDD in the treatment of sepsis based on network pharmacology and molecular docking techniques. METHODS The principal components and corresponding protein targets of HLJDD were searched on TCMSP, BATMAN-TCM and ETCM and the compound-target network was constructed by Cytoscape3.8.2. Sepsis targets were searched on OMIM and DisGeNET databases. The intersection of compound target and disease target was obtained and the coincidence target was imported into STRING database to construct a PPI network. We further performed GO and KEGG enrichment analysis on the targets. Finally, molecular docking study was approved for the core target and the active compound. RESULTS There are 257 nodes and 792 edges in the component target network. The compounds with a higher degree value are quercetin, kaempferol, and wogonin. The protein with a higher degree in the PPI network is JUN, RELA, TNF. GO and KEGG analysis showed that HLJDD treatment of sepsis mainly involves positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptosis process, response to hypoxia and other biological processes. The signaling pathways mainly include PI3K-AKT, MAPK, TNF signaling pathway. The molecular docking results showed that quercetin, kaempferol and wogonin have higher affinity with JUN, RELA and TNF. CONCLUSION This study reveals the active ingredients and potential molecular mechanism of HLJDD in the treatment of sepsis, and provides a reference for subsequent basic research.
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20
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Alarcon-Barrera JC, Kostidis S, Ondo-Mendez A, Giera M. Recent advances in metabolomics analysis for early drug development. Drug Discov Today 2022; 27:1763-1773. [PMID: 35218927 DOI: 10.1016/j.drudis.2022.02.018] [Citation(s) in RCA: 52] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/25/2022] [Accepted: 02/21/2022] [Indexed: 12/25/2022]
Abstract
The pharmaceutical industry adapted proteomics and other 'omics technologies for drug research early following their initial introduction. Although metabolomics lacked behind in this development, it has now become an accepted and widely applied approach in early drug development. Over the past few decades, metabolomics has evolved from a pure exploratory tool to a more mature and quantitative biochemical technology. Several metabolomics-based platforms are now applied during the early phases of drug discovery. Metabolomics analysis assists in the definition of the physiological response and target engagement (TE) markers as well as elucidation of the mode of action (MoA) of drug candidates under investigation. In this review, we highlight recent examples and novel developments of metabolomics analyses applied during early drug development.
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Affiliation(s)
- Juan Carlos Alarcon-Barrera
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands; Clinical Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 # 63C-69, Bogotá, Colombia
| | - Sarantos Kostidis
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands
| | - Alejandro Ondo-Mendez
- Clinical Research Group, School of Medicine and Health Sciences, Universidad del Rosario, Carrera 24 # 63C-69, Bogotá, Colombia
| | - Martin Giera
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Albinusdreef 2, 2333 ZA Leiden, the Netherlands.
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21
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Zareifi DS, Chaliotis O, Chala N, Meimetis N, Sofotasiou M, Zeakis K, Pantiora E, Vezakis A, Matsopoulos GK, Fragulidis G, Alexopoulos LG. A network-based computational and experimental framework for repurposing compounds towards the treatment of Non-Alcoholic Fatty Liver Disease. iScience 2022; 25:103890. [PMID: 35252807 PMCID: PMC8889147 DOI: 10.1016/j.isci.2022.103890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/11/2022] [Accepted: 02/04/2022] [Indexed: 11/29/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is among the most common liver pathologies, however, none approved condition-specific therapy yet exists. The present study introduces a drug repositioning (DR) approach that combines in vitro steatosis models with a network-based computational platform, constructed upon genomic data from diseased liver biopsies and compound-treated cell lines, to propose effectively repositioned therapeutic compounds. The introduced in silico approach screened 20′000 compounds, while complementary in vitro and proteomic assays were developed to test the efficacy of the 46 in silico predictions. This approach successfully identified six compounds, including the known anti-steatogenic drugs resveratrol and sirolimus. In short, gallamine triethiotide, diflorasone, fenoterol, and pralidoxime ameliorate steatosis similarly to resveratrol/sirolimus. The implementation holds great potential in reducing screening time in the early drug discovery stages and in delivering promising compounds for in vivo testing. A computational and experimental drug-screening platform for NAFLD was created This framework evaluates in silico and validates in vitro a great number of compounds 20′000 compounds were screened in silico and 21 were selected for validation Six compounds reversed fully or partially the steatotic phenotype
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Affiliation(s)
- Danae Stella Zareifi
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
| | - Odysseas Chaliotis
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
| | - Nafsika Chala
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
| | - Nikos Meimetis
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
| | - Maria Sofotasiou
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
| | - Konstantinos Zeakis
- School of Electrical Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Eirini Pantiora
- 2nd Department of Surgery, Aretaieio Hospital, University of Athens, School of Medicine, 11528, Athens, Greece
| | - Antonis Vezakis
- 2nd Department of Surgery, Aretaieio Hospital, University of Athens, School of Medicine, 11528, Athens, Greece
| | - George K. Matsopoulos
- School of Electrical Engineering, National Technical University of Athens, 15780 Athens, Greece
| | - Georgios Fragulidis
- 2nd Department of Surgery, Aretaieio Hospital, University of Athens, School of Medicine, 11528, Athens, Greece
| | - Leonidas G. Alexopoulos
- School of Mechanical Engineering, National Technical University of Athens, Iroon Polytechneiou 9, Zografou, 15780 Athens, Greece
- ProtATonce Ltd, Patriarchou Grigoriou & Neapoleos Demokritos Science Park, Building#27, Agia Paraskevi GR15343, Greece
- Corresponding author
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22
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Domingo-Fernández D, Gadiya Y, Patel A, Mubeen S, Rivas-Barragan D, Diana CW, Misra BB, Healey D, Rokicki J, Colluru V. Causal reasoning over knowledge graphs leveraging drug-perturbed and disease-specific transcriptomic signatures for drug discovery. PLoS Comput Biol 2022; 18:e1009909. [PMID: 35213534 PMCID: PMC8906585 DOI: 10.1371/journal.pcbi.1009909] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/09/2022] [Accepted: 02/09/2022] [Indexed: 12/29/2022] Open
Abstract
Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.
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Affiliation(s)
| | - Yojana Gadiya
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Abhishek Patel
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Sarah Mubeen
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | | | - Chris W. Diana
- Enveda Biosciences, Boulder, Colorado, United States of America
| | | | - David Healey
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Joe Rokicki
- Enveda Biosciences, Boulder, Colorado, United States of America
| | - Viswa Colluru
- Enveda Biosciences, Boulder, Colorado, United States of America
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23
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Zhou S, Bi J, Zhao W, Zhao J, Wan H, Wang S. Study on the Mechanism of Fentanyl in Pain Treatment Based on Network Pharmacology. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:4139200. [PMID: 35083024 PMCID: PMC8786484 DOI: 10.1155/2022/4139200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/08/2021] [Accepted: 12/13/2021] [Indexed: 11/17/2022]
Abstract
This is a study that is based on network pharmacology, focusing on the pharmacological mechanism of fentanyl in easing pain. Through PPI, GO, and KEGG network pharmacology, the potential pharmacological mechanism of fentanyl was studied. This study compared and analyzed the overlap between the target genes of the active ingredient of fentanyl and the pain treatment target genes. After constructing PPI based on fentanyl, the GO and KEDD pathways were analyzed for enrichment. On the basis of overlapping genes, we constructed the PPI, GO, and KEGG, and analysis showed that the mechanism was likely to be related to some biological process. This study preliminarily identified the important proteins and metabolic pathways related to fentanyl in pain treatment and expected to provide new evidence and research ideas for the use of fentanyl, enhancing effects and alleviating adverse drug reactions.
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Affiliation(s)
- Shuqin Zhou
- Department of Emergency, Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China
| | - Juan Bi
- Department of Pharmacy, First Affiliated Hospital, Naval Medical University, Shanghai 200000, China
| | - Wei Zhao
- Department of Emergency, Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China
| | - Jian Zhao
- Department of Emergency, Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China
| | - Honghong Wan
- Department of Emergency, Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China
| | - Sheng Wang
- Department of Critical Care Medicine, Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China
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24
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Bima AIH, Elsamanoudy AZ, Albaqami WF, Khan Z, Parambath SV, Al-Rayes N, Kaipa PR, Elango R, Banaganapalli B, Shaik NA. Integrative system biology and mathematical modeling of genetic networks identifies shared biomarkers for obesity and diabetes. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:2310-2329. [PMID: 35240786 DOI: 10.3934/mbe.2022107] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Obesity and type 2 and diabetes mellitus (T2D) are two dual epidemics whose shared genetic pathological mechanisms are still far from being fully understood. Therefore, this study is aimed at discovering key genes, molecular mechanisms, and new drug targets for obesity and T2D by analyzing the genome wide gene expression data with different computational biology approaches. In this study, the RNA-sequencing data of isolated primary human adipocytes from individuals who are lean, obese, and T2D was analyzed by an integrated framework consisting of gene expression, protein interaction network (PIN), tissue specificity, and druggability approaches. Our findings show a total of 1932 unique differentially expressed genes (DEGs) across the diabetes versus obese group comparison (p≤0.05). The PIN analysis of these 1932 DEGs identified 190 high centrality network (HCN) genes, which were annotated against 3367 GO terms and functional pathways, like response to insulin signaling, phosphorylation, lipid metabolism, glucose metabolism, etc. (p≤0.05). By applying additional PIN and topological parameters to 190 HCN genes, we further mapped 25 high confidence genes, functionally connected with diabetes and obesity traits. Interestingly, ERBB2, FN1, FYN, HSPA1A, HBA1, and ITGB1 genes were found to be tractable by small chemicals, antibodies, and/or enzyme molecules. In conclusion, our study highlights the potential of computational biology methods in correlating expression data to topological parameters, functional relationships, and druggability characteristics of the candidate genes involved in complex metabolic disorders with a common etiological basis.
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Affiliation(s)
- Abdulhadi Ibrahim H Bima
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ayman Zaky Elsamanoudy
- Department of Clinical Biochemistry, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Walaa F Albaqami
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | - Zeenath Khan
- Department of Science, Prince Sultan Military College of Health Sciences, Dhahran, Saudi Arabia
| | | | - Nuha Al-Rayes
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Prabhakar Rao Kaipa
- Department of Genetics, College of Science, Osmania University, Hyderabad, India
| | - Ramu Elango
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Babajan Banaganapalli
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Noor A Shaik
- Princess Al-Jawhara Al-Brahim Center of Excellence in Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
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25
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Nayeri Rad A, Shams G, Avelar RA, Morowvat MH, Ghasemi Y. Potential senotherapeutic candidates and their combinations derived from transcriptional connectivity and network measures. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100920] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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26
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Benchoua A, Lasbareilles M, Tournois J. Contribution of Human Pluripotent Stem Cell-Based Models to Drug Discovery for Neurological Disorders. Cells 2021; 10:cells10123290. [PMID: 34943799 PMCID: PMC8699352 DOI: 10.3390/cells10123290] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 11/19/2021] [Accepted: 11/23/2021] [Indexed: 02/07/2023] Open
Abstract
One of the major obstacles to the identification of therapeutic interventions for central nervous system disorders has been the difficulty in studying the step-by-step progression of diseases in neuronal networks that are amenable to drug screening. Recent advances in the field of human pluripotent stem cell (PSC) biology offers the capability to create patient-specific human neurons with defined clinical profiles using reprogramming technology, which provides unprecedented opportunities for both the investigation of pathogenic mechanisms of brain disorders and the discovery of novel therapeutic strategies via drug screening. Many examples not only of the creation of human pluripotent stem cells as models of monogenic neurological disorders, but also of more challenging cases of complex multifactorial disorders now exist. Here, we review the state-of-the art brain cell types obtainable from PSCs and amenable to compound-screening formats. We then provide examples illustrating how these models contribute to the definition of new molecular or functional targets for drug discovery and to the design of novel pharmacological approaches for rare genetic disorders, as well as frequent neurodegenerative diseases and psychiatric disorders.
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Affiliation(s)
- Alexandra Benchoua
- Neuroplasticity and Therapeutics, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- High Throughput Screening Platform, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- Correspondence:
| | - Marie Lasbareilles
- Neuroplasticity and Therapeutics, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
- UEVE UMR 861, I-STEM, AFM, 91100 Corbeil-Essonnes, France
| | - Johana Tournois
- High Throughput Screening Platform, CECS, I-STEM, AFM, 91100 Corbeil-Essonnes, France;
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27
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Wei YP, Yao LY, Wu YY, Liu X, Peng LH, Tian YL, Ding JH, Li KH, He QG. Critical Review of Synthesis, Toxicology and Detection of Acyclovir. Molecules 2021; 26:molecules26216566. [PMID: 34770975 PMCID: PMC8587948 DOI: 10.3390/molecules26216566] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/25/2021] [Accepted: 10/27/2021] [Indexed: 02/02/2023] Open
Abstract
Acyclovir (ACV) is an effective and selective antiviral drug, and the study of its toxicology and the use of appropriate detection techniques to control its toxicity at safe levels are extremely important for medicine efforts and human health. This review discusses the mechanism driving ACV’s ability to inhibit viral coding, starting from its development and pharmacology. A comprehensive summary of the existing preparation methods and synthetic materials, such as 5-aminoimidazole-4-carboxamide, guanine and its derivatives, and other purine derivatives, is presented to elucidate the preparation of ACV in detail. In addition, it presents valuable analytical procedures for the toxicological studies of ACV, which are essential for human use and dosing. Analytical methods, including spectrophotometry, high performance liquid chromatography (HPLC), liquid chromatography/tandem mass spectrometry (LC-MS/MS), electrochemical sensors, molecularly imprinted polymers (MIPs), and flow injection–chemiluminescence (FI-CL) are also highlighted. A brief description of the characteristics of each of these methods is also presented. Finally, insight is provided for the development of ACV to drive further innovation of ACV in pharmaceutical applications. This review provides a comprehensive summary of the past life and future challenges of ACV.
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Affiliation(s)
- Yan-Ping Wei
- School of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China; (Y.-P.W.); (Y.-Y.W.); (L.-H.P.); (Y.-L.T.)
- Zhuzhou People’s Hospital, Zhuzhou 412001, China; (X.L.); (J.-H.D.)
- Hunan Qianjin Xiangjiang Pharmaceutical Joint Stock Co., Ltd., Zhuzhou 412001, China;
| | - Liang-Yuan Yao
- Hunan Qianjin Xiangjiang Pharmaceutical Joint Stock Co., Ltd., Zhuzhou 412001, China;
| | - Yi-Yong Wu
- School of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China; (Y.-P.W.); (Y.-Y.W.); (L.-H.P.); (Y.-L.T.)
| | - Xia Liu
- Zhuzhou People’s Hospital, Zhuzhou 412001, China; (X.L.); (J.-H.D.)
| | - Li-Hong Peng
- School of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China; (Y.-P.W.); (Y.-Y.W.); (L.-H.P.); (Y.-L.T.)
| | - Ya-Ling Tian
- School of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China; (Y.-P.W.); (Y.-Y.W.); (L.-H.P.); (Y.-L.T.)
| | - Jian-Hua Ding
- Zhuzhou People’s Hospital, Zhuzhou 412001, China; (X.L.); (J.-H.D.)
| | - Kang-Hua Li
- Zhuzhou People’s Hospital, Zhuzhou 412001, China; (X.L.); (J.-H.D.)
- Correspondence: (K.-H.L.); (Q.-G.H.); Tel./Fax: +86-731-2218-3426 (Q.-G.H.)
| | - Quan-Guo He
- School of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China; (Y.-P.W.); (Y.-Y.W.); (L.-H.P.); (Y.-L.T.)
- Zhuzhou People’s Hospital, Zhuzhou 412001, China; (X.L.); (J.-H.D.)
- Hunan Qianjin Xiangjiang Pharmaceutical Joint Stock Co., Ltd., Zhuzhou 412001, China;
- Correspondence: (K.-H.L.); (Q.-G.H.); Tel./Fax: +86-731-2218-3426 (Q.-G.H.)
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28
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Wang J, Luo L, Ding Q, Wu Z, Peng Y, Li J, Wang X, Li W, Liu G, Zhang B, Tang Y. Development of a Multi-Target Strategy for the Treatment of Vitiligo via Machine Learning and Network Analysis Methods. Front Pharmacol 2021; 12:754175. [PMID: 34603063 PMCID: PMC8479195 DOI: 10.3389/fphar.2021.754175] [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: 08/06/2021] [Accepted: 09/03/2021] [Indexed: 01/14/2023] Open
Abstract
Vitiligo is a complex disorder characterized by the loss of pigment in the skin. The current therapeutic strategies are limited. The identification of novel drug targets and candidates is highly challenging for vitiligo. Here we proposed a systematic framework to discover potential therapeutic targets, and further explore the underlying mechanism of kaempferide, one of major ingredients from Vernonia anthelmintica (L.) willd, for vitiligo. By collecting transcriptome and protein-protein interactome data, the combination of random forest (RF) and greedy articulation points removal (GAPR) methods was used to discover potential therapeutic targets for vitiligo. The results showed that the RF model performed well with AUC (area under the receiver operating characteristic curve) = 0.926, and led to prioritization of 722 important transcriptomic features. Then, network analysis revealed that 44 articulation proteins in vitiligo network were considered as potential therapeutic targets by the GAPR method. Finally, through integrating the above results and proteomic profiling of kaempferide, the multi-target strategy for vitiligo was dissected, including 1) the suppression of the p38 MAPK signaling pathway by inhibiting CDK1 and PBK, and 2) the modulation of cellular redox homeostasis, especially the TXN and GSH antioxidant systems, for the purpose of melanogenesis. Meanwhile, this strategy may offer a novel perspective to discover drug candidates for vitiligo. Thus, the framework would be a useful tool to discover potential therapeutic strategies and drug candidates for complex diseases.
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Affiliation(s)
- Jiye Wang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Lin Luo
- Key Laboratory of Xinjiang Phytomedicine Resources of Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China
| | - Qiong Ding
- Key Laboratory of Xinjiang Phytomedicine Resources of Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Yayuan Peng
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Xiaoqin Wang
- Key Laboratory of Xinjiang Phytomedicine Resources of Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China.,Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, Sichuan Industrial Institute of Antibiotics, School of Pharmacy, Chengdu University, Chengdu, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Bo Zhang
- Key Laboratory of Xinjiang Phytomedicine Resources of Ministry of Education, School of Pharmacy, Shihezi University, Shihezi, China.,Key Laboratory of Medicinal and Edible Plants Resources Development of Sichuan Education Department, Sichuan Industrial Institute of Antibiotics, School of Pharmacy, Chengdu University, Chengdu, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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29
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Prospective adverse event risk evaluation in clinical trials. Health Care Manag Sci 2021; 25:89-99. [PMID: 34559339 DOI: 10.1007/s10729-021-09584-y] [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: 09/30/2020] [Accepted: 09/10/2021] [Indexed: 10/20/2022]
Abstract
Proactive and objective regulatory risk management of ongoing clinical trials is limited, especially when it involves the safety of the trial. We seek to prospectively evaluate the risk of facing adverse outcomes from standardized and routinely collected protocol data. We conducted a retrospective cohort study of 2860 Phase 2 and Phase 3 trials that were started and completed between 1993 and 2017 and documented in ClinicalTrials.gov. Adverse outcomes considered in our work include Serious or Non-Serious as per the ClinicalTrials.gov definition. Random-forest-based prediction models were created to determine a trial's risk of adverse outcomes based on protocol data that is available before the start of a trial enrollment. A trial's risk is defined by dichotomic (classification) and continuous (log-odds) risk scores. The classification-based prediction models had an area under the curve (AUC) ranging from 0.865 to 0.971 and the continuous-score based models indicate a rank correlation of 0.6-0.66 (with p-values < 0.001), thereby demonstrating improved identification of risk of adverse outcomes. Whereas related frameworks highlight the prediction benefits of incorporating data that is highly context-specific, our results indicate that Adverse Event (AE) risks can be reliably predicted through a framework of mild data requirements. We propose three potential applications in leading regulatory remits, highlighting opportunities to support regulatory oversight and informed consent decisions.
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30
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Gayathri S, Chandrashekar H R, Fayaz S M. Phytotherapeutics Against Alzheimer's Disease: Mechanism, Molecular Targets and Challenges for Drug Development. CNS & NEUROLOGICAL DISORDERS-DRUG TARGETS 2021; 21:409-426. [PMID: 34544351 DOI: 10.2174/1871527320666210920120612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/24/2021] [Accepted: 08/30/2021] [Indexed: 11/22/2022]
Abstract
Alzheimer's disease is inflating worldwide and is combatted by only a few approved drugs. At best, these drugs treat symptomatic conditions by targeting cholinesterase and N-methyl-D-aspartate receptors. Most of the clinical trials in progress are focused to develop disease-modifying agents that aim single targets. The 'one drug-one target' approach is failing in the case of Alzheimer's disease due to its labyrinth etiopathogenesis. Traditional medicinal systems like ayurveda uses a holistic approach encompassing legion of medicinal plants exhibiting multimodal activity. Recent advances in high-throughput technologies have catapulted the research in the arena of ayurveda, specifically in identifying plants with potent anti-Alzheimer's disease properties and their phytochemical characterization. Nonetheless, clinical trials of very few herbal medicines are in progress. This review is a compendium of Indian plants and ayurvedic medicines against Alzheimer's disease and their paraphernalia. A record of 230 plants that are found in India with anti-Alzheimer's disease potential and about 500 phytochemicals from medicinal plants has been solicited with the hope of exploring the unexplored. Further, the molecular targets of phytochemicals isolated from commonly used medicinal plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifolia and Withania somnifera have been reviewed with respect to their multidimensional property such as antioxidant, anti-inflammation, anti-aggregation, synaptic plasticity modulation, cognition and memory enhancing activity. In addition, the strengths, and challenges in ayurvedic medicine that limit its use as mainstream therapy is discussed and a framework for the development of herbal medicine has been proposed.
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Affiliation(s)
- Gayathri S
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka - 576104. India
| | - Raghu Chandrashekar H
- Department of Pharmaceutical Biotechnology, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka - 576104. India
| | - Fayaz S M
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka - 576104. India
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31
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Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB. Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review. Int J Mol Sci 2021; 22:8962. [PMID: 34445667 PMCID: PMC8396480 DOI: 10.3390/ijms22168962] [Citation(s) in RCA: 3] [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: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023] Open
Abstract
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Gayatri Gandhi
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Jian Ming Lee
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Wendy Wai Yeng Yeo
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Sy-Bing Choi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
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32
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Veit-Acosta M, de Azevedo Junior WF. Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery. Curr Med Chem 2021; 29:2438-2455. [PMID: 34365938 DOI: 10.2174/0929867328666210806105810] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/15/2021] [Accepted: 06/22/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND CDK2 participates in the control of eukaryotic cell-cycle progression. Due to the great interest in CDK2 for drug development and the relative easiness in crystallizing this enzyme, we have over 400 structural studies focused on this protein target. This structural data is the basis for the development of computational models to estimate CDK2-ligand binding affinity. OBJECTIVE This work focuses on the recent developments in the application of supervised machine learning modeling to develop scoring functions to predict the binding affinity of CDK2. METHOD We employed the structures available at the protein data bank and the ligand information accessed from the BindingDB, Binding MOAD, and PDBbind to evaluate the predictive performance of machine learning techniques combined with physical modeling used to calculate binding affinity. We compared this hybrid methodology with classical scoring functions available in docking programs. RESULTS Our comparative analysis of previously published models indicated that a model created using a combination of a mass-spring system and cross-validated Elastic Net to predict the binding affinity of CDK2-inhibitor complexes outperformed classical scoring functions available in AutoDock4 and AutoDock Vina. CONCLUSION All studies reviewed here suggest that targeted machine learning models are superior to classical scoring functions to calculate binding affinities. Specifically for CDK2, we see that the combination of physical modeling with supervised machine learning techniques exhibits improved predictive performance to calculate the protein-ligand binding affinity. These results find theoretical support in the application of the concept of scoring function space.
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Affiliation(s)
- Martina Veit-Acosta
- Western Michigan University, 1903 Western, Michigan Ave, Kalamazoo, MI 49008. United States
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Li A, Huang HT, Huang HC, Juan HF. LncTx: A network-based method to repurpose drugs acting on the survival-related lncRNAs in lung cancer. Comput Struct Biotechnol J 2021; 19:3990-4002. [PMID: 34377365 PMCID: PMC8319574 DOI: 10.1016/j.csbj.2021.07.007] [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: 02/25/2021] [Revised: 07/06/2021] [Accepted: 07/07/2021] [Indexed: 12/13/2022] Open
Abstract
Despite the fact that an increased amount of survival-related lncRNAs have been found in cancer, few drugs that target lncRNAs are approved for treatment. Here, we developed a network-based algorithm, LncTx, to repurpose the medications that potentially act on survival-related lncRNAs in lung cancer. We used eight survival-related lncRNAs derived from our previous study to test the efficacy of this method. LncTx calculates the shortest path length (proximity) between the drug targets and the lncRNA-correlated proteins in the protein-protein interaction network (interactome). LncTx contains seven different proximity measures, which are calculated in the unweighted or weighted interactome. First, to test the performance of LncTx in predicting correct indication of drugs, we benchmarked the proximity measures based on the accuracy of differentiating anticancer drugs from non-anticancer drugs. The closest proximity weighted by clustering coefficient (closestCC) has the best performance (AUC around 0.8) compared to other proximity measures across all survival-related lncRNAs. The majority of the other six proximity measures have decent performance as well, with AUC greater than 0.7. Second, to evaluate whether LncTx can repurpose the drugs effectively acting on the lncRNAs, we clustered the drugs according to their proximities by hierarchical clustering. The drugs with smaller proximity (proximal drugs) were proved to be more effective than the drugs with larger proximity (distal drugs). In conclusion, LncTx enables us to accurately identify anticancer drugs and can potentially be an index to repurpose effective agents acting on survival-related lncRNAs in lung cancer.
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Affiliation(s)
- Albert Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
| | | | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Hsueh-Fen Juan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan
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34
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Zhang M, Yang J, Zhao X, Zhao Y, Zhu S. Network pharmacology and molecular docking study on the active ingredients of qidengmingmu capsule for the treatment of diabetic retinopathy. Sci Rep 2021; 11:7382. [PMID: 33795817 PMCID: PMC8016862 DOI: 10.1038/s41598-021-86914-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Accepted: 03/22/2021] [Indexed: 02/06/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Qidengmingmu Capsule (QC) is a Chinese patent medicine used to treat DR, but the molecular mechanism of the treatment remains unknown. In this study, we identified and validated potential molecular mechanisms involved in the treatment of DR with QC via network pharmacology and molecular docking methods. The results of Ingredient-DR Target Network showed that 134 common targets and 20 active ingredients of QC were involved. According to the results of enrichment analysis, 2307 biological processes and 40 pathways were related to the treatment effects. Most of these processes and pathways were important for cell survival and were associated with many key factors in DR, such as vascular endothelial growth factor-A (VEGFA), hypoxia-inducible factor-1A (HIF-1Α), and tumor necrosis factor-α (TNFα). Based on the results of the PPI network and KEGG enrichment analyses, we selected AKT1, HIF-1α, VEGFA, TNFα and their corresponding active ingredients for molecular docking. According to the molecular docking results, several key targets of DR (including AKT1, HIF-1α, VEGFA, and TNFα) can form stable bonds with the corresponding active ingredients of QC. In conclusion, through network pharmacology methods, we found that potential biological mechanisms involved in the alleviation of DR by QC are related to multiple biological processes and signaling pathways. The molecular docking results also provide us with sound directions for further experiments.
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Affiliation(s)
- Mingxu Zhang
- Eye School, Chengdu University of Traditional Chinese Medicine, 37 Shi Er Qiao Road, Jinniu District, Chengdu, 610036, China
| | - Jiawei Yang
- Eye School, Chengdu University of Traditional Chinese Medicine, 37 Shi Er Qiao Road, Jinniu District, Chengdu, 610036, China.,National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Lvyuan Road, Haidin District, Beijing, 100089, China
| | - Xiulan Zhao
- Eye School, Chengdu University of Traditional Chinese Medicine, 37 Shi Er Qiao Road, Jinniu District, Chengdu, 610036, China
| | - Ying Zhao
- Eye School, Chengdu University of Traditional Chinese Medicine, 37 Shi Er Qiao Road, Jinniu District, Chengdu, 610036, China
| | - Siquan Zhu
- Eye School, Chengdu University of Traditional Chinese Medicine, 37 Shi Er Qiao Road, Jinniu District, Chengdu, 610036, China. .,Department of Ophthalmology, Beijing Anzhen Hospital of Capital Medical University, 2 Anzhen Road, Chaoyang District, Beijing, 100020, China.
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35
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Yang J, Li H, Wang F, Xiao F, Yan W, Hu G. Network-Based Target Prioritization and Drug Candidate Identification for Multiple Sclerosis: From Analyzing "Omics Data" to Druggability Simulations. ACS Chem Neurosci 2021; 12:917-929. [PMID: 33565875 DOI: 10.1021/acschemneuro.1c00011] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Multiple sclerosis (MS) is the most common chronic inflammatory demyelinating disease of the central nervous system. While the drugs currently available for MS provide symptomatic benefit, there is no curative treatment. The emergence of large-scale multiomics data and network theory provide new opportunities for drug discovery in MS, as these are promising strategies for developing novel drugs. In this study, we proposed a computational framework that combined biomolecular network modeling and structural dynamics analysis to facilitate the discovery of new drugs with potential activity in MS. First, we developed a new shortest path-based algorithm that prioritized differentially expressed genes using a newly topological and functional exploration of protein-protein interaction network. Then, pathway enrichment analysis and an assessment of target druggability suggested that TNF-α-induced protein 3 (TNFAIP3), which is involved in NF-κ B signaling, could be a potential therapeutic target for MS. Finally, druggability simulations and mutation enrichment analysis of the TNFAIP3 dimer presented two druggable sites. Follow-up pharmacophore model-based virtual screening of the two sites yielded 30 hit compounds with low energy scores. In summary, this novel method based on analyzing "omics data" and performing druggability simulations, is a systematic approach that unravels disease mechanisms and links them to the chemical space to develop treatments and can be applied to other complex diseases.
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Affiliation(s)
- Ji Yang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Hongchun Li
- Research Center for Computer-Aided Drug Discovery, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Fan Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Wenying Yan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
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36
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Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism. FUTURE INTERNET 2021. [DOI: 10.3390/fi13010013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.
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37
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Fang T, Liu L, Liu W. Network pharmacology-based strategy for predicting therapy targets of Tripterygium wilfordii on acute myeloid leukemia. Medicine (Baltimore) 2020; 99:e23546. [PMID: 33327305 PMCID: PMC7738111 DOI: 10.1097/md.0000000000023546] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
This is a study on the potential therapeutic targets and pharmacological mechanism of Tripterygium wilfordii (TW) in acute myeloid leukemia (AML) based on network pharmacology.Active components of TW were obtained by network pharmacology through oral bioavailability, drug-likeness filtration. Comparative analysis was used to investigate the overlapping genes between active ingredient's targets and AML treatment-related targets. Using STRING database to analyze interactions among overlapping genes. Both KEGG pathway analysis and Gene Ontology enrichment analysis were conducted in DAVID. These genes were analyzed for survival in OncoLnc database.We screened 53 active ingredients; the results of comparative analysis showed that 8 active ingredients had an effect on AML treatment. On the basis of the active ingredients and overlapping genes, we constructed the Drug-Compounds-Genes-Disease Network. Survival analysis of overlapping genes indicated that some targets possessed a significant influence on patients' survival and prognosis. The enrichment analysis showed that the main pathways of targets were Toll-like receptor signaling pathway, NF-kappa B signaling pathway, and HIF-1 signaling pathway.This study, using a network pharmacologic approach, provides another strategy that can help us to understand the mechanisms by which TW treats AML comprehensively.
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Affiliation(s)
- Tingting Fang
- Department of Pediatric Hematology, The Affiliated Hospital of Southwest Medical University
| | - Lanqin Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Southwest Medical University
| | - Wenjun Liu
- Department of Pediatric Hematology, The Affiliated Hospital of Southwest Medical University
- Birth Defects Clinical Research Center of Sichuan Province, Luzhou, Sichuan, China
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38
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Fotis C, Meimetis N, Sardis A, Alexopoulos LG. DeepSIBA: chemical structure-based inference of biological alterations using deep learning. Mol Omics 2020; 17:108-120. [PMID: 33188379 DOI: 10.1039/d0mo00129e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. To make this complex problem computationally tractable, compound differences were mapped to biological effect alterations using Siamese Graph Convolutional Neural Networks. The proposed model was able to encode molecular graph pairs and identify structurally dissimilar compounds that affect similar biological processes with high precision. Additionally, by utilizing deep ensembles to estimate uncertainty, we were able to provide reliable and accurate predictions for chemical structures that are very different from the ones used during training. Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathway signature of a compound perturbation, using only its chemical structure as input, and subsequently identify which substructures influenced the predicted pathways. As a use case, this approach was used to infer important substructures and affected signaling pathways of FDA-approved anticancer drugs.
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Affiliation(s)
- C Fotis
- Biomedical Systems Laboratory, National Technical University of Athens, Athens, Greece.
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39
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Amaral MD. How to determine the mechanism of action of CFTR modulator compounds: A gateway to theranostics. Eur J Med Chem 2020; 210:112989. [PMID: 33190956 DOI: 10.1016/j.ejmech.2020.112989] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/02/2020] [Accepted: 11/02/2020] [Indexed: 12/12/2022]
Abstract
The greatest challenge of 21st century biology is to fully understand mechanisms of disease to drive new approaches and medical innovation. Parallel to this is the huge biomedical endeavour of treating people through personalized medicine. Until now all CFTR modulator drugs that have entered clinical trials have been genotype-dependent. An emerging alternative is personalized/precision medicine in CF, i.e., to determine whether rare CFTR mutations respond to existing (or novel) CFTR modulator drugs by pre-assessing them directly on patient's tissues ex vivo, an approach also now termed theranostics. To administer the right drug to the right person it is essential to understand how drugs work, i.e., to know their mechanism of action (MoA), so as to predict their applicability, not just in certain mutations but also possibly in other diseases that share the same defect/defective pathway. Moreover, an understanding the MoA of a drug before it is tested in clinical trials is the logical path to drug discovery and can increase its chance for success and hence also approval. In conclusion, the most powerful approach to determine the MoA of a compound is to understand the underlying biology. Novel large datasets of intervenients in most biological processes, namely those emerging from the post-genomic era tools, are available and should be used to help in this task.
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Affiliation(s)
- Margarida D Amaral
- BioISI - Biosystems & Integrative Sciences Institute, Lisboa, Faculty of Sciences, University of Lisboa, Portugal.
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40
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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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41
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Wang X, Liu M, Zhang L, Wang Y, Li Y, Lu T. Optimizing Pharmacokinetic Property Prediction Based on Integrated Datasets and a Deep Learning Approach. J Chem Inf Model 2020; 60:4603-4613. [PMID: 32804486 DOI: 10.1021/acs.jcim.0c00568] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Oral bioavailability (OBA)-related pharmacokinetic properties, such as aqueous solubility, lipophilicity, and intestinal membrane permeability, play a significant role in drug discovery. However, their measurement is usually costly and time-consuming. Therefore, prediction models based on diverse approaches have been established in recent decades. Computational prediction of molecular properties has become an important step in drug discovery, aiming to identify potential drug-like candidates and reduce costs. However, limitations related to dataset capacity and algorithm adaptation still place restrictions on the applicability of the related models. In this study, we considered both dataset and algorithm optimization to address the challenge of predicting OBA-related molecular properties. Benchmark datasets of aqueous solubility (log S), lipophilicity (log D), and membrane permeability measured using the Caco-2 cell line (log Papp) were constructed by merging and calibrating experimental data from diverse articles and databases. Then, a novel molecular property prediction model, called a multiembedding-based synthetic network (MESN), was generated by applying a deep learning algorithm based on the synthesis of multiple types of molecular embeddings. MESN achieves performance improvements over other state-of-the-art methods for the prediction of aqueous solubility, lipophilicity, and membrane permeability. Results were also obtained using several other algorithms and independent validation datasets as a control study. Moreover, a dimension reduction analysis (based on t-distributed stochastic neighbor embedding, t-SNE) and an atomic feature similarity analysis showed that the molecular embeddings extracted from the MESN model exhibit good clustering and diversity. Overall, considering the fundamental role of the data and the superior prediction performance of the model, we highlight the applicability of MESN on benchmark datasets for further utility in drug discovery-related molecular property prediction.
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Affiliation(s)
- Xiting Wang
- Life Science School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Meng Liu
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Lan Zhang
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yun Wang
- School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yu Li
- Chinese Medicine School, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Tao Lu
- Life Science School, Beijing University of Chinese Medicine, Beijing 100029, China
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42
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Relieving Sore Throat Formula Exerts a Therapeutic Effect on Pharyngitis through Immunoregulation and NF- κB Pathway. Mediators Inflamm 2020; 2020:2929163. [PMID: 32508523 PMCID: PMC7245656 DOI: 10.1155/2020/2929163] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 10/13/2019] [Accepted: 01/25/2020] [Indexed: 12/17/2022] Open
Abstract
Relieving Sore Throat Formula (RSTF) is a formula approved by the China Food and Drug Administration and has been used for the treatment of pharyngitis in clinic for many years. However, the potential pharmacological mechanism still remains unknown. We combined multiple methods including bioinformatics data digging, network pharmacology analysis, and pathway analysis to predict the potential target of RSTF. We verified our in silico prediction results with an in vivo/vitro antibacterial effect test, mouse phagocytic index test, proliferation, transformation, and migration of mouse spleen lymphocytes. Alteration of NF-κB pathway was determined by Western blotting, immunofluorescence, and PCR. The in vivo experiments demonstrated that the RSTF could significantly relieve the symptoms of pharyngitis. A rat saliva secretion test showed that RSTF can effectively relieve the xerostomia symptom. A phenol red excretion test showed that RSTF has an eliminating phlegm effect. A hot plate method and granuloma experiment proved that RSTF also have analgesic and anti-inflammatory effects. In silico prediction demonstrates that 70 active compounds of RSTF were filtered out through ADME screening and 84 putative targets correlated with different diseases. Pathway enrichment analysis showed that the candidate targets were mostly related to the response to bacteria and immunity signalling pathways, which are known contributors to pharyngitis. Experimental results confirmed that RSTF exerted therapeutic effects on pharyngitis mainly by antibacterial effect and downregulation of NF-κB activities. It is demonstrated both in silico and in vivo/vitro that RSTF exerted therapeutic effects on pharyngitis mainly through an antibiotic effect and downregulation of NF-κB signalling pathway.
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43
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Zhao T, Hu Y, Valsdottir LR, Zang T, Peng J. Identifying drug-target interactions based on graph convolutional network and deep neural network. Brief Bioinform 2020; 22:2141-2150. [PMID: 32367110 DOI: 10.1093/bib/bbaa044] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 03/05/2020] [Accepted: 03/06/2020] [Indexed: 12/21/2022] Open
Abstract
Identification of new drug-target interactions (DTIs) is an important but a time-consuming and costly step in drug discovery. In recent years, to mitigate these drawbacks, researchers have sought to identify DTIs using computational approaches. However, most existing methods construct drug networks and target networks separately, and then predict novel DTIs based on known associations between the drugs and targets without accounting for associations between drug-protein pairs (DPPs). To incorporate the associations between DPPs into DTI modeling, we built a DPP network based on multiple drugs and proteins in which DPPs are the nodes and the associations between DPPs are the edges of the network. We then propose a novel learning-based framework, 'graph convolutional network (GCN)-DTI', for DTI identification. The model first uses a graph convolutional network to learn the features for each DPP. Second, using the feature representation as an input, it uses a deep neural network to predict the final label. The results of our analysis show that the proposed framework outperforms some state-of-the-art approaches by a large margin.
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Affiliation(s)
- Tianyi Zhao
- Department of Computer Science at Harbin Institute of Technology. He currently works as a bioinformatician in Beth Israel Deaconess Medical Center
| | - Yang Hu
- Department of Life Science at Harbin Institute of Technology. His expertise is bioinformatics
| | - Linda R Valsdottir
- MS in Biology and works as a scientific writer at the Smith Center for Outcomes Research in Cardiology at Beth Israel Deaconess Medical Center in Boston, MA. Her work is focused on helping researchers communicate their findings in an effort to translate novel analytical approaches and clinical expertise into improved outcomes for patients
| | - Tianyi Zang
- School of Computer Science and Technology at Harbin Institute of Technology (HIT), China. Before joining HIT in 2009, he was a research fellow at the Department of Computer Science at University of Oxford, UK. His current research is concerned with biomedical bigdata computing and algorithms, deep-learning algorithms for network data, intelligent recommendation algorithms, and modeling and analysis methods for complex systems
| | - Jiajie Peng
- School of Computer Science at Northwestern Polytechnical University. His expertise is computational biology and machine learning. Availability and implementation: https://github.com/zty2009/GCN-DNN/
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44
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Yan W, Liu X, Wang Y, Han S, Wang F, Liu X, Xiao F, Hu G. Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling. Front Pharmacol 2020; 11:534. [PMID: 32425783 PMCID: PMC7204992 DOI: 10.3389/fphar.2020.00534] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 04/06/2020] [Indexed: 12/16/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer-related death and has an extremely poor prognosis. Thus, identifying new disease-associated genes and targets for PDAC diagnosis and therapy is urgently needed. This requires investigations into the underlying molecular mechanisms of PDAC at both the systems and molecular levels. Herein, we developed a computational method of predicting cancer genes and anticancer drug targets that combined three independent expression microarray datasets of PDAC patients and protein-protein interaction data. First, Support Vector Machine–Recursive Feature Elimination was applied to the gene expression data to rank the differentially expressed genes (DEGs) between PDAC patients and controls. Then, protein-protein interaction networks were constructed based on the DEGs, and a new score comprising gene expression and network topological information was proposed to identify cancer genes. Finally, these genes were validated by “druggability” prediction, survival and common network analysis, and functional enrichment analysis. Furthermore, two integrins were screened to investigate their structures and dynamics as potential drug targets for PDAC. Collectively, 17 disease genes and some stroma-related pathways including extracellular matrix-receptor interactions were predicted to be potential drug targets and important pathways for treating PDAC. The protein-drug interactions and hinge sites predication of ITGAV and ITGA2 suggest potential drug binding residues in the Thigh domain. These findings provide new possibilities for targeted therapeutic interventions in PDAC, which may have further applications in other cancer types.
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Affiliation(s)
- Wenying Yan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Xingyi Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Yibo Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Shuqing Han
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Fan Wang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Xin Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Fei Xiao
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, China.,State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
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45
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Hou X, Li M, Jia C, Zhang X, Wang Y. Attractor - a new turning point in drug discovery. DRUG DESIGN DEVELOPMENT AND THERAPY 2019; 13:2957-2968. [PMID: 31686779 PMCID: PMC6709805 DOI: 10.2147/dddt.s216397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 07/28/2019] [Indexed: 11/23/2022]
Abstract
Drug discovery for complex diseases can be viewed as a challenging problem in which the influence of compounds on dynamic features of disease system should be considered, especially the strategies escaping from the disease attractors. Moreover, escaping from the disease-related attractors has been proved to be a cue for the treatment of the complex diseases. The drug discovery methodology based on the attractor theory indicates new solutions for target identification, drug discovery and drug combination design. The methodology is based on the holism level of the organism and the features of system dynamics, so it has advantages for the classification of complex diseases and drug discovery. Currently, research results of this method have increased, which expand the insight scope for drug discovery. This article introduces the major drug discovery methods in the history of pharmacy development and their characteristics, so as to illustrate the reasons and inevitability of the appearance of attractor method, its position in the history of pharmacy development, and its advantages for drug discovery and design, thereby to prove that the attractor method can indeed become the next major drug development method. In addition, it provides a comprehensive description about the concept of attractor, the pipeline of attractor analysis, the common methods of each process and its research progress, so as to provide a macroscopic framework and optional methods and tools for the follow-up researchers.
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Affiliation(s)
- Xucan Hou
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Meng Li
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Congmin Jia
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Xianbao Zhang
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
| | - Yun Wang
- Department of Traditional Chinese Medicine Information Fusion and Utilization, Beijing University of Chinese Medicine, Beijing, People's Republic of China
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46
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Neidlin M, Dimitrakopoulou S, Alexopoulos LG. Multi-tissue network analysis for drug prioritization in knee osteoarthritis. Sci Rep 2019; 9:15176. [PMID: 31645614 PMCID: PMC6811565 DOI: 10.1038/s41598-019-51627-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Accepted: 10/02/2019] [Indexed: 01/17/2023] Open
Abstract
Knee osteoarthritis (OA) is a joint disease that affects several tissues: cartilage, synovium, meniscus and subchondral bone. The pathophysiology of this complex disease is still not completely understood and existing pharmaceutical strategies are limited to pain relief treatments. Therefore, a computational method was developed considering the diverse mechanisms and the multi-tissue nature of OA in order to suggest pharmaceutical compounds. Specifically, weighted gene co-expression network analysis (WGCNA) was utilized to identify gene modules that were preserved across four joint tissues. The driver genes of these modules were selected as an input for a network-based drug discovery approach. WGCNA identified two preserved modules that described functions related to extracellular matrix physiology and immune system responses. Compounds that affected various anti-inflammatory pathways and drugs targeted at coagulation pathways were suggested. 9 out of the top 10 compounds had a proven association with OA and significantly outperformed randomized approaches not including WGCNA. The method presented herein is a viable strategy to identify overlapping molecular mechanisms in multi-tissue diseases such as OA and employ this information for drug discovery and compound prioritization.
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Affiliation(s)
- Michael Neidlin
- Department of Mechanical Engineering, National Technical University of Athens, Athens, Greece
| | | | - Leonidas G Alexopoulos
- Department of Mechanical Engineering, National Technical University of Athens, Athens, Greece.
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47
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Wang R, Liu H, Shao Y, Wang K, Yin S, Qiu Y, Wu H, Liu E, Wang T, Gao X, Yu H. Sophoridine Inhibits Human Colorectal Cancer Progression via Targeting MAPKAPK2. Mol Cancer Res 2019; 17:2469-2479. [DOI: 10.1158/1541-7786.mcr-19-0553] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 08/23/2019] [Accepted: 09/27/2019] [Indexed: 12/24/2022]
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48
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In silico drug repositioning: from large-scale transcriptome data to therapeutics. Arch Pharm Res 2019; 42:879-889. [PMID: 31482491 DOI: 10.1007/s12272-019-01176-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Accepted: 07/26/2019] [Indexed: 02/06/2023]
Abstract
Drug repositioning is an attractive alternative to conventional drug development when new beneficial effects of old drugs are clinically validated because pharmacokinetic and safety profiles are generally already available. Since ~ 30% of drugs newly approved by the US food and drug administration (FDA) are developed through drug repositioning, identifying novel usage for existing drugs is an emerging strategy for developing disease treatments. With advances in next-generation sequencing technologies, available transcriptome data related to diseases have expanded rapidly. Harnessing these resources enables a better understanding of disease mechanisms and drug mode of action (MOA), and moves toward personalized pharmacotherapy. In this review, we briefly outline publicly available large-scale transcriptome databases and tools for drug repositioning. We also highlight recent approaches leading to the discovery of novel drug targets, drug response biomarkers, drug indications, and drug MOA.
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Gomez-Verjan JC, Ramírez-Aldana R, Pérez-Zepeda MU, Quiroz-Baez R, Luna-López A, Gutierrez Robledo LM. Systems biology and network pharmacology of frailty reveal novel epigenetic targets and mechanisms. Sci Rep 2019; 9:10593. [PMID: 31332237 PMCID: PMC6646318 DOI: 10.1038/s41598-019-47087-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 07/10/2019] [Indexed: 12/20/2022] Open
Abstract
Frailty is an age-associated condition, characterized by an inappropriate response to stress that results in a higher frequency of adverse outcomes (e.g., mortality, institutionalization and disability). Some light has been shed over its genetic background, but this is still a matter of debate. In the present study, we used network biology to analyze the interactome of frailty-related genes at different levels to relate them with pathways, clinical deficits and drugs with potential therapeutic implications. Significant pathways involved in frailty: apoptosis, proteolysis, muscle proliferation, and inflammation; genes as FN1, APP, CREBBP, EGFR playing a role as hubs and bottlenecks in the interactome network and epigenetic factors as HIST1H3 cluster and miR200 family were also involved. When connecting clinical deficits and genes, we identified five clusters that give insights into the biology of frailty: cancer, glucocorticoid receptor, TNF-α, myostatin, angiotensin converter enzyme, ApoE, interleukine-12 and −18. Finally, when performing network pharmacology analysis of the target nodes, some compounds were identified as potentially therapeutic (e.g., epigallocatechin gallate and antirheumatic agents); while some other substances appeared to be toxicants that may be involved in the development of this condition.
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Affiliation(s)
| | | | - M U Pérez-Zepeda
- Instituto Nacional de Geriatría (INGER), Mexico City, Mexico.,Geriatric Medicine Research, Dalhousie University and Nova Scotia Health Authority, Halifax, NS, Canada
| | - R Quiroz-Baez
- Instituto Nacional de Geriatría (INGER), Mexico City, Mexico
| | - A Luna-López
- Instituto Nacional de Geriatría (INGER), Mexico City, Mexico
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Pawar G, Madden JC, Ebbrell D, Firman JW, Cronin MTD. In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Front Pharmacol 2019; 10:561. [PMID: 31244651 PMCID: PMC6580867 DOI: 10.3389/fphar.2019.00561] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Accepted: 05/03/2019] [Indexed: 12/14/2022] Open
Abstract
A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.
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Affiliation(s)
| | | | | | | | - Mark T. D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom
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