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Li S, Wang Y, Li C, Yang N, Yu H, Zhou W, Chen S, Yang S, Li Y. Study on Hepatotoxicity of Rhubarb Based on Metabolomics and Network Pharmacology. DRUG DESIGN DEVELOPMENT AND THERAPY 2021; 15:1883-1902. [PMID: 33976539 PMCID: PMC8106470 DOI: 10.2147/dddt.s301417] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 04/13/2021] [Indexed: 12/12/2022]
Abstract
Background Rhubarb, as a traditional Chinese medicine, is the preferred drug for the treatment of stagnation and constipation in clinical practice. It has been reported that rhubarb possesses hepatotoxicity, but its mechanism in vivo is still unclear. Methods In this study, the chemical components in rhubarb were identified based on UPLC-Q-TOF/MS combined with data postprocessing technology. The metabolic biomarkers obtained through metabolomics technology were related to rhubarb-induced hepatotoxicity. Furthermore, the potential targets of rhubarb-induced hepatotoxicity were obtained by network pharmacology involving the above components and metabolites. Meanwhile, GO gene enrichment analysis and KEGG pathway analysis were performed on the common targets. Results Twenty-eight components in rhubarb were identified based on UPLC-Q-TOF/MS, and 242 targets related to rhubarb ingredients were predicted. Nine metabolic biomarkers obtained through metabolomics technology were closely related to rhubarb-induced hepatotoxicity, and 282 targets of metabolites were predicted. Among them, the levels of 4 metabolites, namely dynorphin B (10–13), cervonoyl ethanolamide, lysoPE (18:2), and 3-hydroxyphenyl 2-hydroxybenzoate, significantly increased, while the levels of 5 metabolites, namely dopamine, biopterin, choline, coenzyme Q9 and P1, P4-bis (5ʹ-uridyl) tetraphosphate significantly decreased. In addition, 166 potential targets of rhubarb-induced hepatotoxicity were obtained by network pharmacology. The KEGG pathway analysis was performed on the common targets to obtain 46 associated signaling pathways. Conclusion These data suggested that rhubarb may cause liver toxicity due to its action on dopamine D1 receptor (DRD1), dopamine D2 receptor (DRD2), phosphodiesterase 4B (PDE4B), vanilloid receptor (TRPV1); transient receptor potential cation channel subfamily M member 8 (TRPM8), prostanoid EP2 receptor (PTGER2), acetylcholinesterase (ACHE), muscarinic acetylcholine receptor M3 (CHRM3) through the cAMP signaling pathway, cholinergic synapses, and inflammatory mediators to regulate TRP channels. Metabolomics technology and network pharmacology were integrated to explore rhubarb hepatotoxicity to promote the reasonable clinical application of rhubarb.
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Affiliation(s)
- Shanze Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Yuming Wang
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Chunyan Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Na Yang
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Hongxin Yu
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Wenjie Zhou
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Siyu Chen
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Shenshen Yang
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Yubo Li
- Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
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Farooq H, Niaz JS, Fakhar S, Naveed H. Leveraging digital media data for pharmacovigilance. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:442-451. [PMID: 33936417 PMCID: PMC8075481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The development of novel drugs in response to changing clinical requirements is a complex and costly method with uncertain outcomes. Postmarket pharmacovigilance is essential as drugs often have under-reported side effects. This study intends to use the power of digital media to discover the under-reported side effects of marketed drugs. We have collected tweets for 11 different Drugs (Alprazolam, Adderall, Fluoxetine, Venlafaxine, Adalimumab, Lamotrigine, Quetiapine, Trazodone, Paroxetine, Metronidazole and Miconazole). We have compiled a vast adverse drug reactions (ADRs) lexicon that is used to filter health related data. We constructed machine learning models for automatically annotating the huge amount of publicly available Twitter data. Our results show that on average 43 known ADRs are shared between Twitter and FAERS datasets. Moreover, we were able to recover on average 7 known side effects from Twitter data that are not reported on FAERS. Our results on Twitter dataset show a high concordance with FAERS, Medeffect and Drugs.com. Moreover, we manually validated some of the under-reported side effect predicted by our model using literature search. Common known and under-reported side effects can be found at https://github.com/cbrl-nuces/Leveraging-digital-media-data-for-pharmacovigilance.
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Affiliation(s)
- Hammad Farooq
- Computational Biology Research Lab, Department of Computer Science National University of Computer and Emerging Sciences
| | - Junaid Suhail Niaz
- Computational Biology Research Lab, Department of Computer Science National University of Computer and Emerging Sciences
| | - Saira Fakhar
- Computational Biology Research Lab, Department of Computer Science National University of Computer and Emerging Sciences
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He S, Wen Y, Yang X, Liu Z, Song X, Huang X, Bo X. PIMD: An Integrative Approach for Drug Repositioning Using Multiple Characterization Fusion. GENOMICS PROTEOMICS & BIOINFORMATICS 2020; 18:565-581. [PMID: 33075523 PMCID: PMC8377380 DOI: 10.1016/j.gpb.2018.10.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 09/21/2018] [Accepted: 10/10/2018] [Indexed: 11/28/2022]
Abstract
The accumulation of various types of drug informatics data and computational approaches for drug repositioning can accelerate pharmaceutical research and development. However, the integration of multi-dimensional drug data for precision repositioning remains a pressing challenge. Here, we propose a systematic framework named PIMD to predict drug therapeutic properties by integrating multi-dimensional data for drug repositioning. In PIMD, drug similarity networks (DSNs) based on chemical, pharmacological, and clinical data are fused into an integrated DSN (iDSN) composed of many clusters. Rather than simple fusion, PIMD offers a systematic way to annotate clusters. Unexpected drugs within clusters and drug pairs with a high iDSN similarity score are therefore identified to predict novel therapeutic uses. PIMD provides new insights into the universality, individuality, and complementarity of different drug properties by evaluating the contribution of each property data. To test the performance of PIMD, we use chemical, pharmacological, and clinical properties to generate an iDSN. Analyses of the contributions of each drug property indicate that this iDSN was driven by all data types and performs better than other DSNs. Within the top 20 recommended drug pairs, 7 drugs have been reported to be repurposed. The source code for PIMD is available at https://github.com/Sepstar/PIMD/.
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Affiliation(s)
- Song He
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaoxi Yang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Zhen Liu
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xinyu Song
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xin Huang
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing 100850, China.
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Iftikhar H, Ali HN, Farooq S, Naveed H, Shahzad-Ul-Hussan S. Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach. Comput Biol Med 2020; 122:103848. [PMID: 32658735 PMCID: PMC7282781 DOI: 10.1016/j.compbiomed.2020.103848] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 06/04/2020] [Accepted: 06/04/2020] [Indexed: 12/18/2022]
Abstract
The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the identification of small molecules that specifically bind to any of the three essential proteins (RdRp, 3CL-protease and helicase) of SARS-CoV-2. By applying computational approaches we screened a library of 4574 compounds also containing FDA-approved drugs against these viral proteins. Shortlisted hits from initial screening were subjected to iterative docking with the respective proteins. Ranking score on the basis of binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify the binding compounds. Finally, to minimize chances of false positives, we performed docking of the identified molecules with 100 irrelevant proteins of diverse classes thereby ruling out the non-specific binding. Three FDA-approved drugs showed binding to 3CL-protease either at the catalytic pocket or at an allosteric site related to functionally important dimer formation. A drug-like molecule showed binding to RdRp in its catalytic pocket blocking the key catalytic residues. Two other drug-like molecules showed specific interactions with helicase at a key domain involved in catalysis. This study provides lead drugs or drug-like molecules for further in vitro and clinical investigation for drug repurposing and new drug development prospects.
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Affiliation(s)
- Hafsa Iftikhar
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
| | - Hafiza Nayyer Ali
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan
| | - Sadia Farooq
- Department of Computer Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan
| | - Hammad Naveed
- Department of Computer Science, National University of Computer & Emerging Sciences, Islamabad, Pakistan.
| | - Syed Shahzad-Ul-Hussan
- Department of Biology, Syed Babar Ali School of Science and Engineering, Lahore University of Management Sciences, Lahore, Pakistan.
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Thafar MA, Olayan RS, Ashoor H, Albaradei S, Bajic VB, Gao X, Gojobori T, Essack M. DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform 2020; 12:44. [PMID: 33431036 PMCID: PMC7325230 DOI: 10.1186/s13321-020-00447-2] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 06/16/2020] [Indexed: 12/14/2022] Open
Abstract
In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug–Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.
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Affiliation(s)
- Maha A Thafar
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Collage of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia
| | - Rawan S Olayan
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Haitham Ashoor
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Somayah Albaradei
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia
| | - Vladimir B Bajic
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Takashi Gojobori
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.,Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
| | - Magbubah Essack
- Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
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Abstract
Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.
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Affiliation(s)
- Xuhua Xia
- Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
- Ottawa Institute of Systems Biology, Ottawa K1H 8M5, Canada
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