1
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Wang Y, Yin Z. Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology. Sci Rep 2024; 14:18104. [PMID: 39103483 PMCID: PMC11300600 DOI: 10.1038/s41598-024-69186-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 08/01/2024] [Indexed: 08/07/2024] Open
Abstract
The study of drug-target interaction plays an important role in the process of drug development. The subject of DTI forecasting has advanced significantly in the last several years, yielding numerous significant research findings and methodologies. Heterogeneous data sources provide richer information and comprehensive perspectives for drug-target interaction prediction, so many existing methods rely on heterogeneous networks, and graph embedding technology becomes an important technology to extract information from heterogeneous networks. These approaches, however, are less concerned with potential noisy information in heterogeneous networks and more focused on the extent of information extraction in those networks. Based on this, a potential DTI predictive network model called FBRWPC is proposed in this paper. It uses a fine-grained similarity selection program to first integrate similarity on similar networks and then a bidirectional random walk graph embedding learning method with restart to obtain an updated drug target interaction matrix. Through the use of similarity selection and fine-grained selection similarity integration, the framework can effectively filter out the noise present in heterogeneous networks and enhance the model's prediction performance. The experimental findings demonstrate that, even after being split up into four distinct types of data sets, FBRWPC can still retain great prediction performance, a sign of the model's resilience and good generalization.
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Affiliation(s)
- YaPing Wang
- School of Mathematics, Physics and Statistics, Institute for Frontier Medical Technology, Center of Intelligent Computing and Applied Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China
| | - ZhiXiang Yin
- School of Mathematics, Physics and Statistics, Institute for Frontier Medical Technology, Center of Intelligent Computing and Applied Statistics, Shanghai University of Engineering Science, Shanghai, 201620, China.
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2
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Zheng S, Tsao PS, Pan C. Abdominal aortic aneurysm and cardiometabolic traits share strong genetic susceptibility to lipid metabolism and inflammation. Nat Commun 2024; 15:5652. [PMID: 38969659 PMCID: PMC11226445 DOI: 10.1038/s41467-024-49921-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 06/25/2024] [Indexed: 07/07/2024] Open
Abstract
Abdominal aortic aneurysm has a high heritability and often co-occurs with other cardiometabolic disorders, suggesting shared genetic susceptibility. We investigate this commonality leveraging recent GWAS studies of abdominal aortic aneurysm and 32 cardiometabolic traits. We find significant genetic correlations between abdominal aortic aneurysm and 21 of the cardiometabolic traits investigated, including causal relationships with coronary artery disease, hypertension, lipid traits, and blood pressure. For each trait pair, we identify shared causal variants, genes, and pathways, revealing that cholesterol metabolism and inflammation are shared most prominently. Additionally, we show the tissue and cell type specificity in the shared signals, with strong enrichment across traits in the liver, arteries, adipose tissues, macrophages, adipocytes, and fibroblasts. Finally, we leverage drug-gene databases to identify several lipid-lowering drugs and antioxidants with high potential to treat abdominal aortic aneurysm with comorbidities. Our study provides insight into the shared genetic mechanism between abdominal aortic aneurysm and cardiometabolic traits, and identifies potential targets for pharmacological intervention.
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Affiliation(s)
- Shufen Zheng
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Guangzhou, China
- Center for Evolutionary Biology, Intelligent Medicine Institute, School of Life Sciences, Fudan University, Shanghai, China
| | - Philip S Tsao
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California, USA.
- Stanford Cardiovascular Institute, Stanford University, California, USA.
- VA Palo Alto Health Care System, Palo Alto, California, USA.
| | - Cuiping Pan
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), Guangzhou, China.
- Center for Evolutionary Biology, Intelligent Medicine Institute, School of Life Sciences, Fudan University, Shanghai, China.
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3
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Wu H, Liu J, Zhang R, Lu Y, Cui G, Cui Z, Ding Y. A review of deep learning methods for ligand based drug virtual screening. FUNDAMENTAL RESEARCH 2024; 4:715-737. [PMID: 39156568 PMCID: PMC11330120 DOI: 10.1016/j.fmre.2024.02.011] [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: 10/30/2023] [Revised: 01/10/2024] [Accepted: 02/18/2024] [Indexed: 08/20/2024] Open
Abstract
Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.
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Affiliation(s)
- Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Junkai Liu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Runhua Zhang
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yaoyao Lu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Guozeng Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Zhiming Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China
| | - Yijie Ding
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
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4
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Feng X, Ma Z, Yu C, Xin R. MRNDR: Multihead Attention-Based Recommendation Network for Drug Repurposing. J Chem Inf Model 2024; 64:2654-2669. [PMID: 38373300 DOI: 10.1021/acs.jcim.3c01726] [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: 02/21/2024]
Abstract
As is well-known, the process of developing new drugs is extremely expensive, whereas drug repurposing represents a promising approach to augment the efficiency of new drug development. While this method can indeed spare us from expensive drug toxicity and safety experiments, it still demands a substantial amount of time to carry out precise efficacy experiments for specific diseases, thereby consuming a significant quantity of resources. Therefore, if we can prescreen potential other indications for selected drugs, it could result in substantial cost savings. In light of this, this paper introduces a drug repurposing recommendation model called MRNDR, which stands for Multi-head attention-based Recommendation Network for Drug Repurposing. This model serves as a prediction tool for drug-disease relationships, leveraging the multihead self-attention mechanism that demonstrates robust generalization capabilities. These capabilities stem not only from our extensive million-level training data set, BioRE (Biology Recommended Entity data), but also from the utilization of the WRDS (Weighted Representation Distance Score) algorithm proposed by us. The MRNDR model has achieved new state-of-the-art results on the GP-KG public data set, with an MRR (Mean Reciprocal Rank) score of 0.308 and a Hits@10 score of 0.628. This represents significant improvements of 4.7% (MRR) and 18.1% (Hits@10) over the current best-performing models. Additionally, to further validate the practical utility of the model, we examined results recommended by MRNDR that were not present in the training data set. Some of these recommendations have undergone clinical trials, as evidenced by their presence on ClinicalTrials.gov and the China Clinical Trials Center, indirectly confirming the applicability of MRNDR. The MRNDR model can predict the reusability of candidate drugs, reducing the need for manual expert assessments and enabling efficient drug repurposing.
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Affiliation(s)
- Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130012, P.R. China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, P.R. China
| | - Zhansen Ma
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
| | - Cuinan Yu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
| | - Ruihao Xin
- College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 130000, P.R. China
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P.R. China
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5
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Oprea TI, Bologa C, Holmes J, Mathias S, Metzger VT, Waller A, Yang JJ, Leach AR, Jensen LJ, Kelleher KJ, Sheils TK, Mathé E, Avram S, Edwards JS. Overview of the Knowledge Management Center for Illuminating the Druggable Genome. Drug Discov Today 2024; 29:103882. [PMID: 38218214 PMCID: PMC10939799 DOI: 10.1016/j.drudis.2024.103882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 12/22/2023] [Accepted: 01/09/2024] [Indexed: 01/15/2024]
Abstract
The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.
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Affiliation(s)
- Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Stephen Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Anna Waller
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Andrew R Leach
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, UK
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Keith J Kelleher
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Ewy Mathé
- National Center for Advancing Translational Sciences (NCATS), NIH, Bethesda, MD, USA
| | - Sorin Avram
- Coriolan Dragulescu Institute of Chemistry, Timisoara, Romania
| | - Jeremy S Edwards
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA; Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, NM, USA.
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6
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Wei J, Lu L, Shen T. Predicting drug-protein interactions by preserving the graph information of multi source data. BMC Bioinformatics 2024; 25:10. [PMID: 38177981 PMCID: PMC10768380 DOI: 10.1186/s12859-023-05620-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 12/15/2023] [Indexed: 01/06/2024] Open
Abstract
Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.
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Affiliation(s)
- Jiahao Wei
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China
| | - Linzhang Lu
- School of Mathematical Sciences, Guizhou Normal University, Guiyang, 550025, China.
- School of Mathematical Sciences, Xiamen University, Xiamen, 361005, China.
| | - Tie Shen
- Key Laboratory of Information and Computing Science Guizhou Province, Guizhou Normal University, Guizhou, 550001, China.
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7
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Halip L, Avram S, Curpan R, Borota A, Bora A, Bologa C, Oprea TI. Exploring DrugCentral: from molecular structures to clinical effects. J Comput Aided Mol Des 2023; 37:681-694. [PMID: 37707619 PMCID: PMC10692006 DOI: 10.1007/s10822-023-00529-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/14/2023] [Indexed: 09/15/2023]
Abstract
DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.
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Affiliation(s)
- Liliana Halip
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Sorin Avram
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ramona Curpan
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Ana Borota
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Alina Bora
- Department of Computational Chemistry, "Coriolan Dragulescu" Institute of Chemistry, Timisoara, Romania
| | - Cristian Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.
- Expert Systems Inc, San Diego, CA, USA.
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8
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Tang R, Sun C, Huang J, Li M, Wei J, Liu J. Predicting Drug-Protein Interactions by Self-Adaptively Adjusting the Topological Structure of the Heterogeneous Network. IEEE J Biomed Health Inform 2023; 27:5675-5684. [PMID: 37672364 DOI: 10.1109/jbhi.2023.3312374] [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: 09/08/2023]
Abstract
Many powerful computational methods based on graph neural networks (GNNs) have been proposed to predict drug-protein interactions (DPIs). It can effectively reduce laboratory workload and the cost of drug discovery and drug repurposing. However, many clinical functions of drugs and proteins are unknown due to their unobserved indications. Therefore, it is difficult to establish a reliable drug-protein heterogeneous network that can describe the relationships between drugs and proteins based on the available information. To solve this problem, we propose a DPI prediction method that can self-adaptively adjust the topological structure of the heterogeneous networks, and name it SATS. SATS establishes a representation learning module based on graph attention network to carry out the drug-protein heterogeneous network. It can self-adaptively learn the relationships among the nodes based on their attributes and adjust the topological structure of the network according to the training loss of the model. Finally, SATS predicts the interaction propensity between drugs and proteins based on their embeddings. The experimental results show that SATS can effectively improve the topological structure of the network. The performance of SATS outperforms several state-of-the-art DPI prediction methods under various evaluation metrics. These prove that SATS is useful to deal with incomplete data and unreliable networks. The case studies on the top section of the prediction results further demonstrate that SATS is powerful for discovering novel DPIs.
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9
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Adamowicz K, Arend L, Maier A, Schmidt JR, Kuster B, Tsoy O, Zolotareva O, Baumbach J, Laske T. Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD. NPJ Syst Biol Appl 2023; 9:49. [PMID: 37816770 PMCID: PMC10564802 DOI: 10.1038/s41540-023-00311-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/25/2023] [Indexed: 10/12/2023] Open
Abstract
Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .
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Affiliation(s)
- Klaudia Adamowicz
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
| | - Lis Arend
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
| | - Andreas Maier
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
| | - Johannes R Schmidt
- Department of Preclinical Development and Validation, Fraunhofer Institute for Cell Therapy and Immunology IZI, Leipzig, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Olga Tsoy
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
| | - Olga Zolotareva
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
- Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, 5230, Denmark
| | - Tanja Laske
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, 22607, Germany.
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10
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Gonzalez-Cavazos AC, Tanska A, Mayers M, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. Sci Data 2023; 10:632. [PMID: 37717042 PMCID: PMC10505144 DOI: 10.1038/s41597-023-02534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
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Affiliation(s)
- Adriana Carolina Gonzalez-Cavazos
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Michael Mayers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Patrick A Rewers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Andrew I Su
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
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11
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Sutherland JJ, Yonchev D, Fekete A, Urban L. A preclinical secondary pharmacology resource illuminates target-adverse drug reaction associations of marketed drugs. Nat Commun 2023; 14:4323. [PMID: 37468498 DOI: 10.1038/s41467-023-40064-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 07/11/2023] [Indexed: 07/21/2023] Open
Abstract
In vitro secondary pharmacology assays are an important tool for predicting clinical adverse drug reactions (ADRs) of investigational drugs. We created the Secondary Pharmacology Database (SPD) by testing 1958 drugs using 200 assays to validate target-ADR associations. Compared to public and subscription resources, 95% of all and 36% of active (AC50 < 1 µM) results are unique to SPD, with bias towards higher activity in public resources. Annotating drugs with free maximal plasma concentrations, we find 684 physiologically relevant unpublished off-target activities. Furthermore, 64% of putative ADRs linked to target activity in key literature reviews are not statistically significant in SPD. Systematic analysis of all target-ADR pairs identifies several putative associations supported by publications. Finally, candidate mechanisms for known ADRs are proposed based on SPD off-target activities. Here we present a freely-available resource for benchmarking ADR predictions, explaining phenotypic activity and investigating clinical properties of marketed drugs.
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Affiliation(s)
| | - Dimitar Yonchev
- Novartis Institutes for Biomedical Research, Basel, Switzerland
| | | | - Laszlo Urban
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA.
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12
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Danis D, Jacobsen JOB, Wagner AH, Groza T, Beckwith MA, Rekerle L, Carmody LC, Reese J, Hegde H, Ladewig MS, Seitz B, Munoz-Torres M, Harris NL, Rambla J, Baudis M, Mungall CJ, Haendel MA, Robinson PN. Phenopacket-tools: Building and validating GA4GH Phenopackets. PLoS One 2023; 18:e0285433. [PMID: 37196000 PMCID: PMC10191354 DOI: 10.1371/journal.pone.0285433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/21/2023] [Indexed: 05/19/2023] Open
Abstract
The Global Alliance for Genomics and Health (GA4GH) is a standards-setting organization that is developing a suite of coordinated standards for genomics. The GA4GH Phenopacket Schema is a standard for sharing disease and phenotype information that characterizes an individual person or biosample. The Phenopacket Schema is flexible and can represent clinical data for any kind of human disease including rare disease, complex disease, and cancer. It also allows consortia or databases to apply additional constraints to ensure uniform data collection for specific goals. We present phenopacket-tools, an open-source Java library and command-line application for construction, conversion, and validation of phenopackets. Phenopacket-tools simplifies construction of phenopackets by providing concise builders, programmatic shortcuts, and predefined building blocks (ontology classes) for concepts such as anatomical organs, age of onset, biospecimen type, and clinical modifiers. Phenopacket-tools can be used to validate the syntax and semantics of phenopackets as well as to assess adherence to additional user-defined requirements. The documentation includes examples showing how to use the Java library and the command-line tool to create and validate phenopackets. We demonstrate how to create, convert, and validate phenopackets using the library or the command-line application. Source code, API documentation, comprehensive user guide and a tutorial can be found at https://github.com/phenopackets/phenopacket-tools. The library can be installed from the public Maven Central artifact repository and the application is available as a standalone archive. The phenopacket-tools library helps developers implement and standardize the collection and exchange of phenotypic and other clinical data for use in phenotype-driven genomic diagnostics, translational research, and precision medicine applications.
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Affiliation(s)
- Daniel Danis
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Julius O. B. Jacobsen
- William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Alex H. Wagner
- Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, United States of America
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, United States of America
| | | | - Martha A. Beckwith
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Lauren Rekerle
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Leigh C. Carmody
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
| | - Justin Reese
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Harshad Hegde
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Markus S. Ladewig
- Department of Ophthalmology, Klinikum Saarbrücken, Saarbrücken, Germany
| | - Berthold Seitz
- Department of Ophthalmology, Saarland University Medical Center, Homburg/Saar, Germany
| | - Monica Munoz-Torres
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Nomi L. Harris
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Jordi Rambla
- European Genome-Phenome Archive (EGA) in the Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Michael Baudis
- University of Zurich and Swiss Institute of Bioinformatics, Zurich, Switzerland
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - Melissa A. Haendel
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Peter N. Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, United States of America
- Institute for Systems Genomics, University of Connecticut, Farmington, CT, United States of America
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13
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Isomeric Activity Cliffs-A Case Study for Fluorine Substitution of Aminergic G Protein-Coupled Receptor Ligands. Molecules 2023; 28:molecules28020490. [PMID: 36677547 PMCID: PMC9863698 DOI: 10.3390/molecules28020490] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/30/2022] [Accepted: 01/01/2023] [Indexed: 01/06/2023] Open
Abstract
Currently, G protein-coupled receptors (GPCRs) constitute a significant group of membrane-bound receptors representing more than 30% of therapeutic targets. Fluorine is commonly used in designing highly active biological compounds, as evidenced by the steadily increasing number of drugs by the Food and Drug Administration (FDA). Herein, we identified and analyzed 898 target-based F-containing isomeric analog sets for SAR analysis in the ChEMBL database-FiSAR sets active against 33 different aminergic GPCRs comprising a total of 2163 fluorinated (1201 unique) compounds. We found 30 FiSAR sets contain activity cliffs (ACs), defined as pairs of structurally similar compounds showing significant differences in affinity (≥50-fold change), where the change of fluorine position may lead up to a 1300-fold change in potency. The analysis of matched molecular pair (MMP) networks indicated that the fluorination of aromatic rings showed no clear trend toward a positive or negative effect on affinity. Additionally, we propose an in silico workflow (including induced-fit docking, molecular dynamics, quantum polarized ligand docking, and binding free energy calculations based on the Generalized-Born Surface-Area (GBSA) model) to score the fluorine positions in the molecule.
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14
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Lichtenberg FR. Effect on mortality of inclusion of drugs in Thailand's National List of Essential Medicines, 2005-2016. HEALTH POLICY AND TECHNOLOGY 2023. [DOI: 10.1016/j.hlpt.2023.100726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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15
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Brock S, Soldatos TG, Jackson DB, Diella F, Hornischer K, Schäfer A, Hoerstrup SP, Emmert MY. The COVID-19 explorer-An integrated, whole patient knowledge model of COVID-19 disease. FRONTIERS IN MOLECULAR MEDICINE 2022; 2:1035215. [PMID: 39086977 PMCID: PMC11285624 DOI: 10.3389/fmmed.2022.1035215] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/07/2022] [Indexed: 08/02/2024]
Abstract
Since early 2020 the COVID-19 pandemic has paralyzed the world, resulting in more than half a billion infections and over 6 million deaths within a 28-month period. Knowledge about the disease remains largely disjointed, especially when considering the molecular mechanisms driving the diversity of clinical manifestations and symptoms. Despite the recent availability of vaccines, there remains an urgent need to develop effective treatments for cases of severe disease, especially in the face of novel virus variants. The complexity of the situation is exacerbated by the emergence of COVID-19 as a complex and multifaceted systemic disease affecting independent tissues and organs throughout the body. The development of effective treatment strategies is therefore predicated on an integrated understanding of the underlying disease mechanisms and their potentially causative link to the diversity of observed clinical phenotypes. To address this need, we utilized a computational technology (the Dataome platform) to build an integrated clinico-molecular view on the most important COVID-19 clinical phenotypes. Our results provide the first integrated, whole-patient model of COVID-19 symptomatology that connects the molecular lifecycle of SARS-CoV-2 with microvesicle-mediated intercellular communication and the contact activation and kallikrein-kinin systems. The model not only explains the clinical pleiotropy of COVID-19, but also provides an evidence-driven framework for drug development/repurposing and the identification of critical risk factors. The associated knowledge is provided in the form of the open source COVID-19 Explorer (https://covid19.molecularhealth.com), enabling the global community to explore and analyze the key molecular features of systemic COVID-19 and associated implications for research priorities and therapeutic strategies. Our work suggests that knowledge modeling solutions may offer important utility in expediting the global response to future health emergencies.
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Affiliation(s)
| | - Theodoros G. Soldatos
- Molecular Health GmbH, Heidelberg, Germany
- SRH Hochscule, University of Applied Science, Heidelberg, Germany
| | | | | | | | | | - Simon P. Hoerstrup
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Wyss Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Maximilian Y. Emmert
- Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland
- Wyss Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Cardiothoracic and Vascular Surgery, German Heart Institute Berlin, Berlin, Germany
- Department of Cardiovascular Surgery, Charité Universitätsmedizin Berlin, Berlin, Germany
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16
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Avram S, Wilson TB, Curpan R, Halip L, Borota A, Bora A, Bologa C, Holmes J, Knockel J, Yang J, Oprea T. DrugCentral 2023 extends human clinical data and integrates veterinary drugs. Nucleic Acids Res 2022; 51:D1276-D1287. [PMID: 36484092 PMCID: PMC9825566 DOI: 10.1093/nar/gkac1085] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/20/2022] [Accepted: 12/02/2022] [Indexed: 12/14/2022] Open
Abstract
DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.
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Affiliation(s)
| | | | - Ramona Curpan
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Liliana Halip
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Ana Borota
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Alina Bora
- Department of Computational Chemistry, “Coriolan Dragulescu” Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş 300223, Romania
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, 1901 Redondo S Dr, Albuquerque, NM 87106, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, 700 Camino de Salud NE, Albuquerque, NM 87106, USA
| | - Tudor I Oprea
- To whom correspondence should be addressed. Tel: +1 505 925 7529; Fax: +1 505 925 7625;
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17
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Kelleher KJ, Sheils TK, Mathias SL, Yang JJ, Metzger V, Siramshetty V, Nguyen DT, Jensen LJ, Vidović D, Schürer S, Holmes J, Sharma K, Pillai A, Bologa C, Edwards J, Mathé E, Oprea T. Pharos 2023: an integrated resource for the understudied human proteome. Nucleic Acids Res 2022; 51:D1405-D1416. [PMID: 36624666 PMCID: PMC9825581 DOI: 10.1093/nar/gkac1033] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/12/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.
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Affiliation(s)
- Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Timothy K Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vincent T Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Vishal B Siramshetty
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen 2200, Copenhagen, Denmark
| | - Dušica Vidović
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Stephan C Schürer
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA,Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA,Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Karlie R Sharma
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ajay Pillai
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy S Edwards
- Correspondence may also be addressed to Jeremy Edwards. Tel: +1 505 277 6655;
| | - Ewy A Mathé
- To whom correspondence should be addressed. Tel: +1 301 402 8953;
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
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18
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Li Y, Sun C, Wei JM, Liu J. Drug-Protein interaction prediction by correcting the effect of incomplete information in heterogeneous information. Bioinformatics 2022; 38:5073-5080. [PMID: 36111859 DOI: 10.1093/bioinformatics/btac629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/30/2022] [Accepted: 09/15/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Large-scale heterogeneous data provide diverse perspectives for predicting drug-protein interactions (DPIs). However, the available information on molecular interactions and clinical associations related to drugs or proteins is incomplete because there may be unproven interactions and associations. This incomplete information in the available data is presented in the form of non-interaction and non-correlation, which may mislead the prediction model. Existing methods fuse incomplete and complete information without considering their integrity, so the negative effects of incomplete information still exist. RESULTS We develop a network-based DPI prediction method named BRWCP, which uses the complete information network to correct the prediction results acquired by the incomplete information network. By integrating relevant heterogeneous information that may be incomplete, the feature similarities of drugs and proteins are obtained. Combining the feature similarities and known DPIs, an incomplete information-based drug-protein heterogeneous network is constructed. Then, a bidirectional random walk with pruning algorithm is adopted in this heterogeneous network to predict potential DPIs. Next, the predicted DPIs are combined with the chemical fingerprint similarity of drugs and amino acid sequence similarity of proteins to construct the complete information network. The bidirectional random walk with pruning algorithm is applied in the new network to obtain the final prediction results until it converges. Experimental results show that BRWCP is superior to several state-of-the-art DPI prediction methods, and case studies further confirm its ability to tap potential DPIs. AVAILABILITY AND IMPLEMENTATION The code and data used in BRWCP are available at https://github.com/lyfdomain/BRWCP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yanfei Li
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Chang Sun
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Jin-Mao Wei
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
| | - Jian Liu
- College of Computer Science, Nankai University, Tianjin 300071, China.,Institute of Big Data, Nankai University, Tianjin 300071, China
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19
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Wang YX, Yang Z, Wang WX, Huang YX, Zhang Q, Li JJ, Tang YP, Yue SJ. Methodology of network pharmacology for research on Chinese herbal medicine against COVID-19: A review. JOURNAL OF INTEGRATIVE MEDICINE 2022; 20:477-487. [PMID: 36182651 PMCID: PMC9508683 DOI: 10.1016/j.joim.2022.09.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 08/15/2022] [Indexed: 12/09/2022]
Abstract
Traditional Chinese medicine, as a complementary and alternative medicine, has been practiced for thousands of years in China and possesses remarkable clinical efficacy. Thus, systematic analysis and examination of the mechanistic links between Chinese herbal medicine (CHM) and the complex human body can benefit contemporary understandings by carrying out qualitative and quantitative analysis. With increasing attention, the approach of network pharmacology has begun to unveil the mystery of CHM by constructing the heterogeneous network relationship of "herb-compound-target-pathway," which corresponds to the holistic mechanisms of CHM. By integrating computational techniques into network pharmacology, the efficiency and accuracy of active compound screening and target fishing have been improved at an unprecedented pace. This review dissects the core innovations to the network pharmacology approach that were developed in the years since 2015 and highlights how this tool has been applied to understanding the coronavirus disease 2019 and refining the clinical use of CHM to combat it.
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Affiliation(s)
- Yi-xuan Wang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China,Department of Scientific Research, Shaanxi Provincial People’s Hospital, Xi’an 710068, Shaanxi Province, China
| | - Zhen Yang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Wen-xiao Wang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Yu-xi Huang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Qiao Zhang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Jia-jia Li
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Yu-ping Tang
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China
| | - Shi-jun Yue
- Key Laboratory of Shaanxi Administration of Traditional Chinese Medicine for TCM Compatibility, State Key Laboratory of Research & Development of Characteristic Qin Medicine Resources (Cultivation), and Shaanxi Collaborative Innovation Center of Chinese Medicinal Resources Industrialization, Shaanxi University of Chinese Medicine, Xi’an 712046, Shaanxi Province, China,Corresponding author
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20
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Towards systematic exploration of chemical space: building the fragment library module in molecular property diagnostic suite. Mol Divers 2022:10.1007/s11030-022-10506-5. [PMID: 35925528 PMCID: PMC9362107 DOI: 10.1007/s11030-022-10506-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 07/23/2022] [Indexed: 11/04/2022]
Abstract
A fragment-based drug discovery (FBDD) approach has traditionally been of utmost significance in drug design studies. It allows the exploration of large chemical space to find novel scaffolds and chemotypes which can be improved into selective inhibitors with good affinity. In the current work, several public domain chemical libraries (ChEMBL, DrugCentral, PDB ligands, COCONUT, and SAVI) comprising bioactive and virtual molecules were retrieved to develop a fragment library. A systematic fragmentation method that breaks a given molecule into rings, linkers, and substituents was used to cleave the molecules and the fragments were analyzed. Further, only the ring framework was taken into the consideration to develop a fragment library that consists of a total number of 107,614 unique fragments. This set represents a rich diverse structure framework that covers a wide variety of yet-to-be-explored fragments for a wide range of small molecule-based applications. This fragment library is an integral part of the molecular property diagnostic suite (MPDS) suite that can be used with other modeling and informatics methods for FBDD approaches. The fragment library module of MPDS can be accessed at http://mpds.neist.res.in:8085.
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21
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Pan X, Lin X, Cao D, Zeng X, Yu PS, He L, Nussinov R, Cheng F. Deep learning for drug repurposing: Methods, databases, and applications. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1597] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Xiaoqin Pan
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Xuan Lin
- School of Computer Science Xiangtan University Xiangtan China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Xiangtan China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences Central South University Changsha China
| | - Xiangxiang Zeng
- School of Computer Science and Engineering Hunan University Changsha Hunan China
| | - Philip S. Yu
- Department of Computer Science University of Illinois at Chicago Chicago Illinois USA
| | - Lifang He
- Department of Computer Science and Engineering Lehigh University Bethlehem Pennsylvania USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick Frederick Maryland USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine Tel Aviv University Tel Aviv Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic Cleveland Ohio USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine Case Western Reserve University Cleveland Ohio USA
- Case Comprehensive Cancer Center Case Western Reserve University School of Medicine Cleveland Ohio USA
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22
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Ianevski A, Simonsen RM, Myhre V, Tenson T, Oksenych V, Bjørås M, Kainov DE. DrugVirus.info 2.0: an integrative data portal for broad-spectrum antivirals (BSA) and BSA-containing drug combinations (BCCs). Nucleic Acids Res 2022; 50:W272-W275. [PMID: 35610052 PMCID: PMC9252782 DOI: 10.1093/nar/gkac348] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/12/2022] [Accepted: 05/09/2022] [Indexed: 12/18/2022] Open
Abstract
Viruses can cross species barriers and cause unpredictable outbreaks in man with substantial economic and public health burdens. Broad-spectrum antivirals, (BSAs, compounds inhibiting several human viruses), and BSA-containing drug combinations (BCCs) are deemed as immediate therapeutic options that fill the void between virus identification and vaccine development. Here, we present DrugVirus.info 2.0 (https://drugvirus.info), an integrative interactive portal for exploration and analysis of BSAs and BCCs, that greatly expands the database and functionality of DrugVirus.info 1.0 webserver. Through the data portal that now expands the spectrum of BSAs and provides information on BCCs, we developed two modules for (i) interactive analysis of users' own antiviral drug and combination screening data and their comparison with published datasets, and (ii) exploration of the structure-activity relationship between various BSAs. The updated portal provides an essential toolbox for antiviral drug development and repurposing applications aiming to identify existing and novel treatments of emerging and re-emerging viral threats.
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Affiliation(s)
- Aleksandr Ianevski
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway
| | - Ronja M Simonsen
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway
| | - Vegard Myhre
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway
| | - Tanel Tenson
- Institute of Technology, University of Tartu, 50411 Tartu, Estonia
| | - Valentyn Oksenych
- Institute of Clinical Medicine, University of Oslo, 0318 Oslo, Norway
| | - Magnar Bjørås
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway.,Department of Microbiology, Oslo University Hospital and University of Oslo, 0424 Oslo, Norway
| | - Denis E Kainov
- Department of Clinical and Molecular Medicine (IKOM), Norwegian University of Science and Technology (NTNU), 7028 Trondheim, Norway.,Institute of Technology, University of Tartu, 50411 Tartu, Estonia.,Institute for Molecular Medicine Finland, University of Helsinki, 00014 Helsinki, Finland
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23
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Abstract
Broadly effective antiviral therapies must be developed to be ready for clinical trials, which should begin soon after the emergence of new life-threatening viruses. Here, we pave the way towards this goal by reviewing conserved druggable virus-host interactions, mechanisms of action, immunomodulatory properties of available broad-spectrum antivirals (BSAs), routes of BSA delivery, and interactions of BSAs with other antivirals. Based on the review, we concluded that the range of indications of BSAs can be expanded, and new pan- and cross-viral mono- and combinational therapies can be developed. We have also developed a new scoring algorithm that can help identify the most promising few of the thousands of potential BSAs and BSA-containing drug cocktails (BCCs) to prioritize their development during the critical period between the identification of a new virus and the development of virus-specific vaccines, drugs, and therapeutic antibodies.
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24
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Zahoránszky-Kőhalmi G, Siramshetty VB, Kumar P, Gurumurthy M, Grillo B, Mathew B, Metaxatos D, Backus M, Mierzwa T, Simon R, Grishagin I, Brovold L, Mathé EA, Hall MD, Michael SG, Godfrey AG, Mestres J, Jensen LJ, Oprea TI. A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research. J Chem Inf Model 2022; 62:718-729. [PMID: 35057621 PMCID: PMC10790216 DOI: 10.1021/acs.jcim.1c00431] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, host-pathogen, and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy. Here, we describe a workflow we designed for a semiautomated integration of rapidly emerging data sets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 63 278 host-host protein, and 1221 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is made publicly accessible via a web interface and via API calls based on the Bolt protocol. Details for accessing the database are provided on a landing page (https://neo4covid19.ncats.io/). We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.
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Affiliation(s)
| | - Vishal B. Siramshetty
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Praveen Kumar
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Department of Computer Science, University of New Mexico, 1 University of New Mexico Albuquerque, NM 87131, USA
| | - Manideep Gurumurthy
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Busola Grillo
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Biju Mathew
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Dimitrios Metaxatos
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Mark Backus
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Tim Mierzwa
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Reid Simon
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Ivan Grishagin
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Laura Brovold
- Rancho BioSciences LLC., 16955 Via Del Campo Suite 200, San Diego, CA 92127, USA
| | - Ewy A. Mathé
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Matthew D. Hall
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Samuel G. Michael
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Alexander G. Godfrey
- National Center for Advancing Translational Sciences, Rockville, 9800 Medical Center Dr., MD 20850, USA
| | - Jordi Mestres
- Research Group on Systems Pharmacology, Research Program on Biomedical Informatics (GRIB), IMIM Hospital del Mar Medical Research Institute and University Pompeu Fabra, Doctor Aiguader 88, 08003 Barcelona, Catalonia, Spain
| | - Lars J. Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
| | - Tudor I. Oprea
- Department of Internal Medicine, University of New Mexico School of Medicine, 1 University of New Mexico, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences,University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen N, Denmark
- UNM Comprehensive Cancer Center, 1201 Camino de Salud NE, Albuquerque, NM 87102, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, Box 480, 40530 Gothenburg, Sweden
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25
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Pietruś W, Kafel R, Bojarski AJ, Kurczab R. Hydrogen Bonds with Fluorine in Ligand-Protein Complexes-the PDB Analysis and Energy Calculations. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27031005. [PMID: 35164270 PMCID: PMC8838457 DOI: 10.3390/molecules27031005] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/25/2022] [Accepted: 01/29/2022] [Indexed: 12/30/2022]
Abstract
Fluorine is a common substituent in medicinal chemistry and is found in up to 50% of the most profitable drugs. In this study, a statistical analysis of the nature, geometry, and frequency of hydrogen bonds (HBs) formed between the aromatic and aliphatic C-F groups of small molecules and biological targets found in the Protein Data Bank (PDB) repository was presented. Interaction energies were calculated for those complexes using three different approaches. The obtained results indicated that the interaction energy of F-containing HBs is determined by the donor-acceptor distance and not by the angles. Moreover, no significant relationship between the energies of HBs with fluorine and the donor type was found, implying that fluorine is a weak HB acceptor for all types of HB donors. However, the statistical analysis of the PDB repository revealed that the most populated geometric parameters of HBs did not match the calculated energetic optima. In a nutshell, HBs containing fluorine are forced to form due to the stronger ligand-receptor neighboring interactions, which make fluorine the "donor's last resort".
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26
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Medina-Franco JL, López-López E, Andrade E, Ruiz-Azuara L, Frei A, Guan D, Zuegg J, Blaskovich MA. Bridging informatics and medicinal inorganic chemistry: toward a database of metallodrugs and metallodrug candidates. Drug Discov Today 2022; 27:1420-1430. [DOI: 10.1016/j.drudis.2022.02.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 11/04/2021] [Accepted: 02/22/2022] [Indexed: 12/11/2022]
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27
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Yang JJ, Gessner CR, Duerksen JL, Biber D, Binder JL, Ozturk M, Foote B, McEntire R, Stirling K, Ding Y, Wild DJ. Knowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination. BMC Bioinformatics 2022; 23:37. [PMID: 35021991 PMCID: PMC8756622 DOI: 10.1186/s12859-021-04530-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 12/13/2021] [Indexed: 11/12/2022] Open
Abstract
Background LINCS, "Library of Integrated Network-based Cellular Signatures", and IDG, "Illuminating the Druggable Genome", are both NIH projects and consortia that have generated rich datasets for the study of the molecular basis of human health and disease. LINCS L1000 expression signatures provide unbiased systems/omics experimental evidence. IDG provides compiled and curated knowledge for illumination and prioritization of novel drug target hypotheses. Together, these resources can support a powerful new approach to identifying novel drug targets for complex diseases, such as Parkinson's disease (PD), which continues to inflict severe harm on human health, and resist traditional research approaches. Results Integrating LINCS and IDG, we built the Knowledge Graph Analytics Platform (KGAP) to support an important use case: identification and prioritization of drug target hypotheses for associated diseases. The KGAP approach includes strong semantics interpretable by domain scientists and a robust, high performance implementation of a graph database and related analytical methods. Illustrating the value of our approach, we investigated results from queries relevant to PD. Approved PD drug indications from IDG’s resource DrugCentral were used as starting points for evidence paths exploring chemogenomic space via LINCS expression signatures for associated genes, evaluated as target hypotheses by integration with IDG. The KG-analytic scoring function was validated against a gold standard dataset of genes associated with PD as elucidated, published mechanism-of-action drug targets, also from DrugCentral. IDG's resource TIN-X was used to rank and filter KGAP results for novel PD targets, and one, SYNGR3 (Synaptogyrin-3), was manually investigated further as a case study and plausible new drug target for PD. Conclusions The synergy of LINCS and IDG, via KG methods, empowers graph analytics methods for the investigation of the molecular basis of complex diseases, and specifically for identification and prioritization of novel drug targets. The KGAP approach enables downstream applications via integration with resources similarly aligned with modern KG methodology. The generality of the approach indicates that KGAP is applicable to many disease areas, in addition to PD, the focus of this paper. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04530-9.
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28
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Kropiwnicki E, Binder J, Yang J, Holmes J, Lachmann A, Clarke DJB, Sheils T, Kelleher K, Metzger V, Bologa CG, Oprea TI, Ma’ayan A. Getting Started with the IDG KMC Datasets and Tools. Curr Protoc 2022; 2:e355. [PMID: 35085427 PMCID: PMC10789444 DOI: 10.1002/cpz1.355] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.
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Affiliation(s)
- Eryk Kropiwnicki
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Jessica Binder
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jeremy Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Alexander Lachmann
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Daniel J. B. Clarke
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
| | - Timothy Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Keith Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Vincent Metzger
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Cristian G. Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Tudor I. Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico, Albuquerque, NM, USA
| | - Avi Ma’ayan
- Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, Box 1603, New York, NY 10029, USA
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29
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Ilter M, Kasmer R, Jalalypour F, Atilgan C, Topcu O, Karakas N, Sensoy O. Inhibition of mutant RAS-RAF interaction by mimicking structural and dynamic properties of phosphorylated RAS. eLife 2022; 11:79747. [PMID: 36458814 PMCID: PMC9762712 DOI: 10.7554/elife.79747] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
Undruggability of RAS proteins has necessitated alternative strategies for the development of effective inhibitors. In this respect, phosphorylation has recently come into prominence as this reversible post-translational modification attenuates sensitivity of RAS towards RAF. As such, in this study, we set out to unveil the impact of phosphorylation on dynamics of HRASWT and aim to invoke similar behavior in HRASG12D mutant by means of small therapeutic molecules. To this end, we performed molecular dynamics (MD) simulations using phosphorylated HRAS and showed that phosphorylation of Y32 distorted Switch I, hence the RAS/RAF interface. Consequently, we targeted Switch I in HRASG12D by means of approved therapeutic molecules and showed that the ligands enabled detachment of Switch I from the nucleotide-binding pocket. Moreover, we demonstrated that displacement of Switch I from the nucleotide-binding pocket was energetically more favorable in the presence of the ligand. Importantly, we verified computational findings in vitro where HRASG12D/RAF interaction was prevented by the ligand in HEK293T cells that expressed HRASG12D mutant protein. Therefore, these findings suggest that targeting Switch I, hence making Y32 accessible might open up new avenues in future drug discovery strategies that target mutant RAS proteins.
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Affiliation(s)
- Metehan Ilter
- Graduate School of Engineering and Natural Sciences, Istanbul Medipol UniversityIstanbulTurkey
| | - Ramazan Kasmer
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey,Cancer Research Center, Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol UniversityIstanbulTurkey
| | - Farzaneh Jalalypour
- Faculty of Engineering and Natural Sciences, Sabanci UniversityIstanbulTurkey
| | - Canan Atilgan
- Faculty of Engineering and Natural Sciences, Sabanci UniversityIstanbulTurkey
| | - Ozan Topcu
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey
| | - Nihal Karakas
- Medical Biology and Genetics Program, Graduate School for Health Sciences, Istanbul Medipol UniversityIstanbulTurkey,Department of Medical Biology, International School of Medicine, Istanbul Medipol UniversityIstanbulTurkey
| | - Ozge Sensoy
- Department of Computer Engineering, School of Engineering and Natural Sciences, Istanbul Medipol UniversityIstanbulTurkey,Regenerative and Restorative Medicine Research Center (REMER), Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol UniversityIstanbulTurkey
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30
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Wu Q, Bagdad Y, Taboureau O, Audouze K. Capturing a Comprehensive Picture of Biological Events From Adverse Outcome Pathways in the Drug Exposome. Front Public Health 2021; 9:763962. [PMID: 34976924 PMCID: PMC8718398 DOI: 10.3389/fpubh.2021.763962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background: The chemical part of the exposome, including drugs, may explain the increase of health effects with outcomes such as infertility, allergies, metabolic disorders, which cannot be only explained by the genetic changes. To better understand how drug exposure can impact human health, the concepts of adverse outcome pathways (AOPs) and AOP networks (AONs), which are representations of causally linked events at different biological levels leading to adverse health, could be used for drug safety assessment.Methods: To explore the action of drugs across multiple scales of the biological organization, we investigated the use of a network-based approach in the known AOP space. Considering the drugs and their associations to biological events, such as molecular initiating event and key event, a bipartite network was developed. This bipartite network was projected into a monopartite network capturing the event–event linkages. Nevertheless, such transformation of a bipartite network to a monopartite network had a huge risk of information loss. A way to solve this problem is to quantify the network reduction. We calculated two scoring systems, one measuring the uncertainty and a second one describing the loss of coverage on the developed event–event network to better investigate events from AOPs linked to drugs.Results: This AON analysis allowed us to identify biological events that are highly connected to drugs, such as events involving nuclear receptors (ER, AR, and PXR/SXR). Furthermore, we observed that the number of events involved in a linkage pattern with drugs is a key factor that influences information loss during monopartite network projection. Such scores have the potential to quantify the uncertainty of an event involved in an AON, and could be valuable for the weight of evidence assessment of AOPs. A case study related to infertility, more specifically to “decrease, male agenital distance” is presented.Conclusion: This study highlights that computational approaches based on network science may help to understand the complexity of drug health effects, with the aim to support drug safety assessment.
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Affiliation(s)
- Qier Wu
- INSERM U1124, CNRS ERL3649, Université de Paris, Paris, France
| | - Youcef Bagdad
- INSERM U1124, CNRS ERL3649, Université de Paris, Paris, France
| | | | - Karine Audouze
- INSERM U1124, CNRS ERL3649, Université de Paris, Paris, France
- *Correspondence: Karine Audouze
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31
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Network medicine for disease module identification and drug repurposing with the NeDRex platform. Nat Commun 2021; 12:6848. [PMID: 34824199 PMCID: PMC8617287 DOI: 10.1038/s41467-021-27138-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/04/2021] [Indexed: 12/17/2022] Open
Abstract
Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.
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32
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Dafniet B, Cerisier N, Boezio B, Clary A, Ducrot P, Dorval T, Gohier A, Brown D, Audouze K, Taboureau O. Development of a chemogenomics library for phenotypic screening. J Cheminform 2021; 13:91. [PMID: 34819133 PMCID: PMC8611952 DOI: 10.1186/s13321-021-00569-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 11/06/2021] [Indexed: 12/03/2022] Open
Abstract
With the development of advanced technologies in cell-based phenotypic screening, phenotypic drug discovery (PDD) strategies have re-emerged as promising approaches in the identification and development of novel and safe drugs. However, phenotypic screening does not rely on knowledge of specific drug targets and needs to be combined with chemical biology approaches to identify therapeutic targets and mechanisms of actions induced by drugs and associated with an observable phenotype. In this study, we developed a system pharmacology network integrating drug-target-pathway-disease relationships as well as morphological profile from an existing high content imaging-based high-throughput phenotypic profiling assay known as “Cell Painting”. Furthermore, from this network, a chemogenomic library of 5000 small molecules that represent a large and diverse panel of drug targets involved in diverse biological effects and diseases has been developed. Such a platform and a chemogenomic library could assist in the target identification and mechanism deconvolution of some phenotypic assays. The usefulness of the platform is illustrated through examples.
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Affiliation(s)
- Bryan Dafniet
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Natacha Cerisier
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Batiste Boezio
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France
| | - Anaelle Clary
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Pierre Ducrot
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Thierry Dorval
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Arnaud Gohier
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - David Brown
- Institut de Recherche Servier, 125 Chemin de Ronde, 78290, Croissy-sur-Seine, France
| | - Karine Audouze
- Université de Paris, INSERM UMR S-1124, 75006, Paris, France
| | - Olivier Taboureau
- Université de Paris, INSERM U1133, CNRS UMR8251, 75006, Paris, France.
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33
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Pietruś W, Kurczab R, Stumpfe D, Bojarski AJ, Bajorath J. Data-Driven Analysis of Fluorination of Ligands of Aminergic G Protein Coupled Receptors. Biomolecules 2021; 11:1647. [PMID: 34827645 PMCID: PMC8615825 DOI: 10.3390/biom11111647] [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: 10/13/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 11/16/2022] Open
Abstract
Currently, G protein-coupled receptors are the targets with the highest number of drugs in many therapeutic areas. Fluorination has become a common strategy in designing highly active biological compounds, as evidenced by the steadily increasing number of newly approved fluorine-containing drugs. Herein, we identified in the ChEMBL database and analysed 1554 target-based FSAR sets (non-fluorinated compounds and their fluorinated analogues) comprising 966 unique non-fluorinated and 2457 unique fluorinated compounds active against 33 different aminergic GPCRs. Although a relatively small number of activity cliffs (defined as a pair of structurally similar compounds showing significant differences of activity -ΔpPot > 1.7) was found in FSAR sets, it is clear that appropriately introduced fluorine can increase ligand potency more than 50-fold. The analysis of matched molecular pairs (MMPs) networks indicated that the fluorination of the aromatic ring showed no clear trend towards a positive or negative effect on affinity; however, a favourable site for a positive potency effect of fluorination was the ortho position. Fluorination of aliphatic fragments more often led to a decrease in biological activity. The results may constitute the rules of thumb for fluorination of aminergic receptor ligands and provide insights into the role of fluorine substitutions in medicinal chemistry.
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Affiliation(s)
- Wojciech Pietruś
- Department of Medicinal Chemistry, Maj Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland; (W.P.); (A.J.B.)
- Department of Life Science Informatics, LIMES Program Unit Chemical Biology and Medicinal Chemistry, B-IT, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany;
| | - Rafał Kurczab
- Department of Medicinal Chemistry, Maj Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland; (W.P.); (A.J.B.)
| | - Dagmar Stumpfe
- Department of Life Science Informatics, LIMES Program Unit Chemical Biology and Medicinal Chemistry, B-IT, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany;
| | - Andrzej J. Bojarski
- Department of Medicinal Chemistry, Maj Institute of Pharmacology, Polish Academy of Sciences, Smetna 12, 31-343 Krakow, Poland; (W.P.); (A.J.B.)
| | - Jürgen Bajorath
- Department of Life Science Informatics, LIMES Program Unit Chemical Biology and Medicinal Chemistry, B-IT, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115 Bonn, Germany;
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34
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The Complex Structure of the Pharmacological Drug-Disease Network. ENTROPY 2021; 23:e23091139. [PMID: 34573762 PMCID: PMC8466955 DOI: 10.3390/e23091139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/29/2022]
Abstract
The complexity of drug–disease interactions is a process that has been explained in terms of the need for new drugs and the increasing cost of drug development, among other factors. Over the last years, diverse approaches have been explored to understand drug–disease relationships. Here, we construct a bipartite graph in terms of active ingredients and diseases based on thoroughly classified data from a recognized pharmacological website. We find that the connectivities between drugs (outgoing links) and diseases (incoming links) follow approximately a stretched-exponential function with different fitting parameters; for drugs, it is between exponential and power law functions, while for diseases, the behavior is purely exponential. The network projections, onto either drugs or diseases, reveal that the co-ocurrence of drugs (diseases) in common target diseases (drugs) lead to the appearance of connected components, which varies as the threshold number of common target diseases (drugs) is increased. The corresponding projections built from randomized versions of the original bipartite networks are considered to evaluate the differences. The heterogeneity of association at group level between active ingredients and diseases is evaluated in terms of the Shannon entropy and algorithmic complexity, revealing that higher levels of diversity are present for diseases compared to drugs. Finally, the robustness of the original bipartite network is evaluated in terms of most-connected nodes removal (direct attack) and random removal (random failures).
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Yao R, Ianevski A, Kainov D. Safe-in-Man Broad Spectrum Antiviral Agents. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1322:313-337. [PMID: 34258746 DOI: 10.1007/978-981-16-0267-2_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Emerging and re-emerging viral diseases occur with regularity within the human population. The conventional 'one drug, one virus' paradigm for antivirals does not adequately allow for proper preparedness in the face of unknown future epidemics. In addition, drug developers lack the financial incentives to work on antiviral drug discovery, with most pharmaceutical companies choosing to focus on more profitable disease areas. Safe-in-man broad spectrum antiviral agents (BSAAs) can help meet the need for antiviral development by already having passed phase I clinical trials, requiring less time and money to develop, and having the capacity to work against many viruses, allowing for a speedy response when unforeseen epidemics arise. In this chapter, we discuss the benefits of repurposing existing drugs as BSAAs, describe the major steps in safe-in-man BSAA drug development from discovery through clinical trials, and list several database resources that are useful tools for antiviral drug repositioning.
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Affiliation(s)
- Rouan Yao
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Aleksandr Ianevski
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Denis Kainov
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
- Institute of Technology, University of Tartu, Tartu, Estonia.
- Institute for Molecule Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland.
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Chen T, Philip M, Lê Cao KA, Tyagi S. A multi-modal data harmonisation approach for discovery of COVID-19 drug targets. Brief Bioinform 2021; 22:6279836. [PMID: 34036326 PMCID: PMC8194516 DOI: 10.1093/bib/bbab185] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 03/09/2021] [Accepted: 04/22/2021] [Indexed: 12/27/2022] Open
Abstract
Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.
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Affiliation(s)
- Tyrone Chen
- School of Biological Sciences, Monash University, 25 Rainforest Walk, 3800, VIC, Australia
| | - Melcy Philip
- School of Biological Sciences, Monash University, 25 Rainforest Walk, 3800, VIC, Australia
| | - Kim-Anh Lê Cao
- Melbourne Integrative Genomics, University of Melbourne, Building 184, Royal Parade, 3010, VIC, Australia.,School of Mathematics and Statistics, University of Melbourne, 813 Swanston Street, 3010, VIC, Australia
| | - Sonika Tyagi
- School of Biological Sciences, Monash University, 25 Rainforest Walk, 3800, VIC, Australia.,Monash eResearch Centre, Monash University, 15 Innovation Walk, 3800, VIC, Australia.,Department of Infectious Disease, Monash University, 85 Commercial Road, 3004, VIC, Australia
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KC GB, Bocci G, Verma S, Hassan MM, Holmes J, Yang JJ, Sirimulla S, Oprea TI. A machine learning platform to estimate anti-SARS-CoV-2 activities. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00335-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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38
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Karatzas E, Kakouri AC, Kolios G, Delis A, Spyrou GM. Fibrotic expression profile analysis reveals repurposed drugs with potential anti-fibrotic mode of action. PLoS One 2021; 16:e0249687. [PMID: 33826640 PMCID: PMC8026018 DOI: 10.1371/journal.pone.0249687] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
Fibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues. Idiopathic Pulmonary Fibrosis, an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target. In this study we highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among 9 fibrotic diseases. We identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our Drug Repurposing results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven't been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis. We suggest digoxin to be tested against Oral Submucous Fibrosis, since we observe cardiomyopathy-related pathways implicated in Oral Submucous Fibrosis and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that Idiopathic Pulmonary Fibrosis shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren's Disease, IgG4-related Disease, Systemic Sclerosis and Cystic Fibrosis. We propose that treatments for these fibrotic diseases should be jointly pursued.
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Affiliation(s)
- Evangelos Karatzas
- Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - Andrea C. Kakouri
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- Department of Neurogenetics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
| | - George Kolios
- Department of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Alex Delis
- Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - George M. Spyrou
- Department of Bioinformatics, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus School of Molecular Medicine, Nicosia, Cyprus
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Tanoli Z, Seemab U, Scherer A, Wennerberg K, Tang J, Vähä-Koskela M. Exploration of databases and methods supporting drug repurposing: a comprehensive survey. Brief Bioinform 2021; 22:1656-1678. [PMID: 32055842 PMCID: PMC7986597 DOI: 10.1093/bib/bbaa003] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/09/2019] [Indexed: 02/07/2023] Open
Abstract
Drug development involves a deep understanding of the mechanisms of action and possible side effects of each drug, and sometimes results in the identification of new and unexpected uses for drugs, termed as drug repurposing. Both in case of serendipitous observations and systematic mechanistic explorations, confirmation of new indications for a drug requires hypothesis building around relevant drug-related data, such as molecular targets involved, and patient and cellular responses. These datasets are available in public repositories, but apart from sifting through the sheer amount of data imposing computational bottleneck, a major challenge is the difficulty in selecting which databases to use from an increasingly large number of available databases. The database selection is made harder by the lack of an overview of the types of data offered in each database. In order to alleviate these problems and to guide the end user through the drug repurposing efforts, we provide here a survey of 102 of the most promising and drug-relevant databases reported to date. We summarize the target coverage and types of data available in each database and provide several examples of how multi-database exploration can facilitate drug repurposing.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Umair Seemab
- Haartman Institute, University of Helsinki, Finland
| | - Andreas Scherer
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
| | - Krister Wennerberg
- Biotech Research & Innovation Centre (BRIC), University of Copenhagen, Denmark
| | - Jing Tang
- Faculty of medicine, University of Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland
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40
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Hufsky F, Lamkiewicz K, Almeida A, Aouacheria A, Arighi C, Bateman A, Baumbach J, Beerenwinkel N, Brandt C, Cacciabue M, Chuguransky S, Drechsel O, Finn RD, Fritz A, Fuchs S, Hattab G, Hauschild AC, Heider D, Hoffmann M, Hölzer M, Hoops S, Kaderali L, Kalvari I, von Kleist M, Kmiecinski R, Kühnert D, Lasso G, Libin P, List M, Löchel HF, Martin MJ, Martin R, Matschinske J, McHardy AC, Mendes P, Mistry J, Navratil V, Nawrocki EP, O’Toole ÁN, Ontiveros-Palacios N, Petrov AI, Rangel-Pineros G, Redaschi N, Reimering S, Reinert K, Reyes A, Richardson L, Robertson DL, Sadegh S, Singer JB, Theys K, Upton C, Welzel M, Williams L, Marz M. Computational strategies to combat COVID-19: useful tools to accelerate SARS-CoV-2 and coronavirus research. Brief Bioinform 2021; 22:642-663. [PMID: 33147627 PMCID: PMC7665365 DOI: 10.1093/bib/bbaa232] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 07/28/2020] [Accepted: 08/26/2020] [Indexed: 12/16/2022] Open
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) is a novel virus of the family Coronaviridae. The virus causes the infectious disease COVID-19. The biology of coronaviruses has been studied for many years. However, bioinformatics tools designed explicitly for SARS-CoV-2 have only recently been developed as a rapid reaction to the need for fast detection, understanding and treatment of COVID-19. To control the ongoing COVID-19 pandemic, it is of utmost importance to get insight into the evolution and pathogenesis of the virus. In this review, we cover bioinformatics workflows and tools for the routine detection of SARS-CoV-2 infection, the reliable analysis of sequencing data, the tracking of the COVID-19 pandemic and evaluation of containment measures, the study of coronavirus evolution, the discovery of potential drug targets and development of therapeutic strategies. For each tool, we briefly describe its use case and how it advances research specifically for SARS-CoV-2. All tools are free to use and available online, either through web applications or public code repositories. Contact:evbc@unj-jena.de.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Christian Brandt
- Institute of Infectious Disease and Infection Control at Jena University Hospital, Germany
| | - Marco Cacciabue
- Consejo Nacional de Investigaciones Científicas y Tócnicas (CONICET) working on FMDV virology at the Instituto de Agrobiotecnología y Biología Molecular (IABiMo, INTA-CONICET) and at the Departamento de Ciencias Básicas, Universidad Nacional de Luján (UNLu), Argentina
| | | | - Oliver Drechsel
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Adrian Fritz
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research, Germany
| | - Stephan Fuchs
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Georges Hattab
- Bioinformatics Division at Philipps-University Marburg, Germany
| | | | - Dominik Heider
- Data Science in Biomedicine at the Philipps-University of Marburg, Germany
| | | | | | - Stefan Hoops
- Biocomplexity Institute and Initiative at the University of Virginia, USA
| | - Lars Kaderali
- Bioinformatics and head of the Institute of Bioinformatics at University Medicine Greifswald, Germany
| | | | - Max von Kleist
- bioinformatics department at the Robert Koch-Institute, Germany
| | - Renó Kmiecinski
- bioinformatics department at the Robert Koch-Institute, Germany
| | | | - Gorka Lasso
- Chandran Lab, Albert Einstein College of Medicine, USA
| | | | | | | | | | | | | | - Alice C McHardy
- Computational Biology of Infection Research Lab at the Helmholtz Centre for Infection Research in Braunschweig, Germany
| | - Pedro Mendes
- Center for Quantitative Medicine of the University of Connecticut School of Medicine, USA
| | | | - Vincent Navratil
- Bioinformatics and Systems Biology at the Rhône Alpes Bioinformatics core facility, Universitó de Lyon, France
| | | | | | | | | | | | - Nicole Redaschi
- Development of the Swiss-Prot group at the SIB for UniProt and SIB resources that cover viral biology (ViralZone)
| | - Susanne Reimering
- Computational Biology of Infection Research group of Alice C. McHardy at the Helmholtz Centre for Infection Research
| | | | | | | | | | - Sepideh Sadegh
- Chair of Experimental Bioinformatics at Technical University of Munich, Germany
| | - Joshua B Singer
- MRC-University of Glasgow Centre for Virus Research, Glasgow, Scotland, UK
| | | | - Chris Upton
- Department of Biochemistry and Microbiology, University of Victoria, Canada
| | | | | | - Manja Marz
- Friedrich Schiller University Jena, Germany
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41
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Li Z, Yao Y, Cheng X, Chen Q, Zhao W, Ma S, Li Z, Zhou H, Li W, Fei T. A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses. iScience 2021; 24:102148. [PMID: 33665567 PMCID: PMC7900436 DOI: 10.1016/j.isci.2021.102148] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 01/11/2021] [Accepted: 02/01/2021] [Indexed: 12/18/2022] Open
Abstract
RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.
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Affiliation(s)
- Zexu Li
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Yingjia Yao
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Xiaolong Cheng
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Qing Chen
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Wenchang Zhao
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Shixin Ma
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Zihan Li
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
| | - Hu Zhou
- School of Pharmaceutical Sciences, Fujian Provincial Key Laboratory of Innovative Drug Target Research, Xiamen University, Xiamen, Fujian 361102, China
- High Throughput Drug Screening Platform, Xiamen University, Xiamen, Fujian 361102, China
| | - Wei Li
- Center for Genetic Medicine Research, Children's National Hospital, 111 Michigan Avenue NW, Washington, DC 20010, USA
- Department of Genomics and Precision Medicine, George Washington University, 111 Michigan Avenue NW, Washington, DC 20010, USA
| | - Teng Fei
- College of Life and Health Sciences, Northeastern University, Shenyang 110819, People's Republic of China
- Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University), Ministry of Education, Shenyang 110819, People's Republic of China
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Debnath A. Drug discovery for primary amebic meningoencephalitis: from screen to identification of leads. Expert Rev Anti Infect Ther 2021; 19:1099-1106. [PMID: 33496193 DOI: 10.1080/14787210.2021.1882302] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Introduction: Naegleria fowleri is responsible for primary amebic meningoencephalitis (PAM) which has a fatality rate of >97%. Because of the rarity of the disease, pharmaceutical companies do not pursue new drug discovery for PAM. Yet, it is possible that the infection is underreported and finding a better drug would have an impact on people suffering from this deadly infection.Areas covered: This paper reports the efforts undertaken by different academic groups over the last 20 years to test different compounds against N. fowleri. The drug discovery research encompassed synthesis of new compounds, development and use of high-throughput screening methods and attempts to repurpose clinically developed or FDA-approved compounds for the treatment of PAM.Expert opinion: In absence of economic investment to develop new drugs for PAM, repurposing the FDA-approved drugs has been the best strategy so far to identify new leads against N. fowleri. Increasing use of high-throughput phenotypic screening has the potential to accelerate the identification of new leads, either in monotherapy or in combination treatment. Since phase II clinical trial is not possible for PAM, it is critical to demonstrate in vivo efficacy of a clinically safe compound to translate the discovery from lab to the clinic.
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Affiliation(s)
- Anjan Debnath
- Center for Discovery and Innovation in Parasitic Diseases, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
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43
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Lim S, Lu Y, Cho CY, Sung I, Kim J, Kim Y, Park S, Kim S. A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput Struct Biotechnol J 2021; 19:1541-1556. [PMID: 33841755 PMCID: PMC8008185 DOI: 10.1016/j.csbj.2021.03.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 01/27/2023] Open
Abstract
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
| | - Yijingxiu Lu
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Chang Yun Cho
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Inyoung Sung
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Jungwoo Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Youngkuk Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
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Bhanot A, Sundriyal S. Physicochemical Profiling and Comparison of Research Antiplasmodials and Advanced Stage Antimalarials with Oral Drugs. ACS OMEGA 2021; 6:6424-6437. [PMID: 33718733 PMCID: PMC7948433 DOI: 10.1021/acsomega.1c00104] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 02/18/2021] [Indexed: 06/12/2023]
Abstract
To understand the property space of antimalarials, we collated a large dataset of research antiplasmodial (RAP) molecules with known in vitro potencies and advanced stage antimalarials (ASAMs) with established oral bioavailability. While RAP molecules are "non-druglike", ASAM molecules display properties closer to Lipinski's and Veber's thresholds. Comparison within the different potency groups of RAP molecules indicates that the in vitro potency is positively correlated to the molecular weight, the calculated octanol-water partition coefficient (clog P), aromatic ring counts (#Ar), and hydrogen bond acceptors. Despite both categories being bioavailable, the ASAM molecules are relatively larger and more lipophilic, have a lower polar surface area, and possess a higher count of heteroaromatic rings than oral drugs. Also, antimalarials are found to have a higher proportion of aromatic (#ArN) and basic nitrogen (#BaN) counts, features implicitly used in the design of antimalarial molecules but not well studied hitherto. We also propose using descriptors scaled by the sum of #ArN and #BaN (SBAN) to define an antimalarial property space. Together, these results may have important applications in the identification and optimization of future antimalarials.
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Affiliation(s)
- Amritansh Bhanot
- Department of Pharmacy, Birla
Institute of Technology and Science Pilani, Pilani Campus,
Vidya Vihar, Pilani, Rajasthan 333 031, India
| | - Sandeep Sundriyal
- Department of Pharmacy, Birla
Institute of Technology and Science Pilani, Pilani Campus,
Vidya Vihar, Pilani, Rajasthan 333 031, India
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45
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Sadeghi SS, Keyvanpour MR. An Analytical Review of Computational Drug Repurposing. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:472-488. [PMID: 31403439 DOI: 10.1109/tcbb.2019.2933825] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or failed drugs. It is cost-effective and reliable in contrast to experimental drug discovery, which is a costly, time-consuming, and risky process and limited to a relatively small number of targets. Accordingly, a plethora of computational methodologies have been propounded to repurpose drugs on a large scale by utilizing available high throughput data. The available literature, however, lacks a contemporary and comprehensive analysis of the current computational drug repurposing methodologies. In this paper, we presented a systematic analysis of computational drug repurposing which consists of three main sections: Initially, we categorize the computational drug repurposing methods based on their technical approach and artificial intelligence perspective and discuss the strengths and weaknesses of various methods. Secondly, some general criteria are recommended to analyze our proposed categorization. In the third and final section, a qualitative comparison is made between each approach which is a guide to understanding their preference to one another. Further, this systematic analysis can help in the efficient selection and improvement of drug repurposing techniques based on the nature of computational methods implemented on biological resources.
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Belyaeva A, Cammarata L, Radhakrishnan A, Squires C, Yang KD, Shivashankar GV, Uhler C. Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing. Nat Commun 2021; 12:1024. [PMID: 33589624 PMCID: PMC7884845 DOI: 10.1038/s41467-021-21056-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 01/05/2021] [Indexed: 12/21/2022] Open
Abstract
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical interventions. While a number of data-driven and experimental approaches have been suggested in the context of drug repurposing, a platform that systematically integrates available transcriptomic, proteomic and structural data is missing. More importantly, given that SARS-CoV-2 pathogenicity is highly age-dependent, it is critical to integrate aging signatures into drug discovery platforms. We here take advantage of large-scale transcriptional drug screens combined with RNA-seq data of the lung epithelium with SARS-CoV-2 infection as well as the aging lung. To identify robust druggable protein targets, we propose a principled causal framework that makes use of multiple data modalities. Our analysis highlights the importance of serine/threonine and tyrosine kinases as potential targets that intersect the SARS-CoV-2 and aging pathways. By integrating transcriptomic, proteomic and structural data that is available for many diseases, our drug discovery platform is broadly applicable. Rigorous in vitro experiments as well as clinical trials are needed to validate the identified candidate drugs.
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Affiliation(s)
| | | | | | | | | | - G V Shivashankar
- ETH Zurich, Zurich, Switzerland
- Paul Scherrer Institute, Villigen, Switzerland
| | - Caroline Uhler
- Massachusetts Institute of Technology, Cambridge, MA, USA.
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47
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Tanoli Z, Vähä-Koskela M, Aittokallio T. Artificial intelligence, machine learning, and drug repurposing in cancer. Expert Opin Drug Discov 2021; 16:977-989. [PMID: 33543671 DOI: 10.1080/17460441.2021.1883585] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.
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Affiliation(s)
- Ziaurrehman Tanoli
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Markus Vähä-Koskela
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland
| | - Tero Aittokallio
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLife, University of Helsinki, Helsinki, Finland.,Institute for Cancer Research, Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway.,Centre for Biostatistics and Epidemiology (OCBE), Faculty of Medicine, University of Oslo, Oslo, Norway
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48
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Drugs, Active Ingredients and Diseases Database in Spanish. Augmenting the Resources for Analyses on Drug–Illness Interactions. DATA 2021. [DOI: 10.3390/data6010003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Quantitative and qualitative data on active-ingredient drug composition are essential information for characterizing near-field exposure of consumers to product-related chemicals, among other things. Equally as important is the characterization of the relationship between one or many active ingredients in terms of the diseases they are prescribed for. Such evaluations, however, require quantitative information at different anatomical levels. To complement the available sources of information on active substances and diseases, we have designed a database with enough versatility to potentially be used in a variety of analyzes. By using information provided by a well-established online pharmacological dictionary, we present a database with 11 tables which are easy to access and manipulate. Specifically, we present datasets containing the details of 12,827 marketed drug products, 40,164 diseases, 6231 active pharmaceutical ingredients and 4093 side effects. We exemplify the usefulness of our database with three simple visualizations, which confirm the importance of the data for quantifying the complexity in the associations among active substances, diseases and side effects. Although there are databases with detailed information on active substances and diseases, none of them can be found in Spanish. Our work presents an option that contributes substantially to obtaining well classified information in order to evaluate the roles of active pharmaceutical ingredients, diseases and side effects. These datasets also provide information about clinical and pharmacological groupings which may be useful for clinical and academic researchers. The database will be regularly updated and extended with the newly available Virtual Medicinal Products.
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49
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Avram S, Bologa CG, Holmes J, Bocci G, Wilson TB, Nguyen DT, Curpan R, Halip L, Bora A, Yang JJ, Knockel J, Sirimulla S, Ursu O, Oprea TI. DrugCentral 2021 supports drug discovery and repositioning. Nucleic Acids Res 2021; 49:D1160-D1169. [PMID: 33151287 PMCID: PMC7779058 DOI: 10.1093/nar/gkaa997] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/18/2022] Open
Abstract
DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.
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Affiliation(s)
- Sorin Avram
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- UNM Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Thomas B Wilson
- College of Pharmacy, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ramona Curpan
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Liliana Halip
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Alina Bora
- Department of Computational Chemistry, “Coriolan Dragulescu’’ Institute of Chemistry, 24 Mihai Viteazu Blvd, Timişoara, Timiş, 300223, România
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeffrey Knockel
- Department of Computer Science, University of New Mexico, Albuquerque, NM 87131, USA
| | - Suman Sirimulla
- Department of Pharmaceutical Sciences, School of Pharmacy, The University of Texas at El Paso, TX 79902, USA
| | - Oleg Ursu
- Computational and Structural Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Computational and Structural Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, NJ 07033, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 40530 Gothenburg, Sweden
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
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50
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Sheils TK, Mathias SL, Kelleher KJ, Siramshetty VB, Nguyen DT, Bologa CG, Jensen LJ, Vidović D, Koleti A, Schürer SC, Waller A, Yang JJ, Holmes J, Bocci G, Southall N, Dharkar P, Mathé E, Simeonov A, Oprea TI. TCRD and Pharos 2021: mining the human proteome for disease biology. Nucleic Acids Res 2021; 49:D1334-D1346. [PMID: 33156327 PMCID: PMC7778974 DOI: 10.1093/nar/gkaa993] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 12/13/2022] Open
Abstract
In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.
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Affiliation(s)
- Timothy K Sheils
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Stephen L Mathias
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Keith J Kelleher
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Vishal B Siramshetty
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Cristian G Bologa
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Dušica Vidović
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Amar Koleti
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
| | - Stephan C Schürer
- Institute for Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Anna Waller
- UNM Center for Molecular Discovery, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jeremy J Yang
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Jayme Holmes
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Giovanni Bocci
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
| | - Noel Southall
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Poorva Dharkar
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Ewy Mathé
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Anton Simeonov
- National Center for Advancing Translational Science, 9800 Medical Center Drive, Rockville, MD 20850, USA
| | - Tudor I Oprea
- Translational Informatics Division, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 Copenhagen, Denmark
- UNM Comprehensive Cancer Center, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy at University of Gothenburg, 40530 Gothenburg, Sweden
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