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Atz K, Cotos L, Isert C, Håkansson M, Focht D, Hilleke M, Nippa DF, Iff M, Ledergerber J, Schiebroek CCG, Romeo V, Hiss JA, Merk D, Schneider P, Kuhn B, Grether U, Schneider G. Prospective de novo drug design with deep interactome learning. Nat Commun 2024; 15:3408. [PMID: 38649351 PMCID: PMC11035696 DOI: 10.1038/s41467-024-47613-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: 09/13/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
De novo drug design aims to generate molecules from scratch that possess specific chemical and pharmacological properties. We present a computational approach utilizing interactome-based deep learning for ligand- and structure-based generation of drug-like molecules. This method capitalizes on the unique strengths of both graph neural networks and chemical language models, offering an alternative to the need for application-specific reinforcement, transfer, or few-shot learning. It enables the "zero-shot" construction of compound libraries tailored to possess specific bioactivity, synthesizability, and structural novelty. In order to proactively evaluate the deep interactome learning framework for protein structure-based drug design, potential new ligands targeting the binding site of the human peroxisome proliferator-activated receptor (PPAR) subtype gamma are generated. The top-ranking designs are chemically synthesized and computationally, biophysically, and biochemically characterized. Potent PPAR partial agonists are identified, demonstrating favorable activity and the desired selectivity profiles for both nuclear receptors and off-target interactions. Crystal structure determination of the ligand-receptor complex confirms the anticipated binding mode. This successful outcome positively advocates interactome-based de novo design for application in bioorganic and medicinal chemistry, enabling the creation of innovative bioactive molecules.
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Affiliation(s)
- Kenneth Atz
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Leandro Cotos
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Clemens Isert
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Maria Håkansson
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Dorota Focht
- SARomics Biostructures AB, Medicon Village, SE-223 81, Lund, Sweden
| | - Mattis Hilleke
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David F Nippa
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Michael Iff
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Jann Ledergerber
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Carl C G Schiebroek
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Valentina Romeo
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Jan A Hiss
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Daniel Merk
- Department of Pharmacy, Ludwig-Maximilians-Universität München, Butenandtstrasse 5, 81377, Munich, Germany
| | - Petra Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - Bernd Kuhn
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Uwe Grether
- Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland
| | - Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland.
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Kang H, Li J, Wu M, Shen L, Hou L. Building a Pharmacogenomics Knowledge Model Toward Precision Medicine: Case Study in Melanoma. JMIR Med Inform 2020; 8:e20291. [PMID: 33084582 PMCID: PMC7641779 DOI: 10.2196/20291] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 08/11/2020] [Accepted: 09/13/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Many drugs do not work the same way for everyone owing to distinctions in their genes. Pharmacogenomics (PGx) aims to understand how genetic variants influence drug efficacy and toxicity. It is often considered one of the most actionable areas of the personalized medicine paradigm. However, little prior work has included in-depth explorations and descriptions of drug usage, dosage adjustment, and so on. OBJECTIVE We present a pharmacogenomics knowledge model to discover the hidden relationships between PGx entities such as drugs, genes, and diseases, especially details in precise medication. METHODS PGx open data such as DrugBank and RxNorm were integrated in this study, as well as drug labels published by the US Food and Drug Administration. We annotated 190 drug labels manually for entities and relationships. Based on the annotation results, we trained 3 different natural language processing models to complete entity recognition. Finally, the pharmacogenomics knowledge model was described in detail. RESULTS In entity recognition tasks, the Bidirectional Encoder Representations from Transformers-conditional random field model achieved better performance with micro-F1 score of 85.12%. The pharmacogenomics knowledge model in our study included 5 semantic types: drug, gene, disease, precise medication (population, daily dose, dose form, frequency, etc), and adverse reaction. Meanwhile, 26 semantic relationships were defined in detail. Taking melanoma caused by a BRAF gene mutation into consideration, the pharmacogenomics knowledge model covered 7 related drugs and 4846 triples were established in this case. All the corpora, relationship definitions, and triples were made publically available. CONCLUSIONS We highlighted the pharmacogenomics knowledge model as a scalable framework for clinicians and clinical pharmacists to adjust drug dosage according to patient-specific genetic variation, and for pharmaceutical researchers to develop new drugs. In the future, a series of other antitumor drugs and automatic relation extractions will be taken into consideration to further enhance our framework with more PGx linked data.
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Affiliation(s)
- Hongyu Kang
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jiao Li
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Meng Wu
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Liu Shen
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
| | - Li Hou
- Institute of Medical Information &Library, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing, China
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Autophagic Network Analysis of the Dual Effect of Sevoflurane on Neurons Associated with GABARAPL1 and 2. BIOMED RESEARCH INTERNATIONAL 2020; 2020:1587214. [PMID: 32685442 PMCID: PMC7335402 DOI: 10.1155/2020/1587214] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 05/07/2020] [Accepted: 05/18/2020] [Indexed: 12/22/2022]
Abstract
Background Sevoflurane is commonly used as a general anesthetic in neonates to aged patients. Preconditioning or postconditioning with sevoflurane protects neurons from excitotoxic injury. Conversely, sevoflurane exposure induces neurotoxicity during early or late life. However, little is known about the underlying mechanism of the dual effect of sevoflurane on neurons. Autophagy is believed to control neuronal homeostasis. We hypothesized that autophagy determined the dual effect of sevoflurane on neurons. Methods DTome was used to identify the direct protein target (DPT) of sevoflurane. The STRING database was employed to investigate the proteins associated with the DPTs. Protein-protein interaction was assessed using Cytoscape. WebGestalt was used to analyze gene set enrichment. The linkage between candidate genes and autophagy was identified using GeneCards. Results This study found that 23 essential DPTs of sevoflurane interacted with 77 proteins from the STRING database. GABARAPL1 and 2, both of which are DPT- and autophagy-associated proteins, were significantly expressed in the brain and enriched in GABAergic synapses. Conclusions Taken together, our findings showed that the network of sevoflurane-DPT-GABARAPL1 and 2 is related to the dual effect of sevoflurane on neurons.
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Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases. Nat Rev Rheumatol 2019; 16:32-52. [PMID: 31831878 DOI: 10.1038/s41584-019-0337-0] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2019] [Indexed: 02/08/2023]
Abstract
The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.
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Wang W, Zhang L, Wang X, Lin D, Pan Q, Guo L. Functional network analysis of gene-phenotype connectivity based on pioglitazone. Exp Ther Med 2019; 18:4790-4798. [PMID: 31798704 PMCID: PMC6880387 DOI: 10.3892/etm.2019.8162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 09/04/2019] [Indexed: 12/15/2022] Open
Abstract
Pioglitazone, a type of insulin sensitizer, serves as an effective anti-hyperglycemic drug. The mechanism of action of pioglitazone is through the activation of the peroxisome proliferator-activated receptor (PPAR), which results in enhanced insulin sensitivity of peripheral tissues and the liver, causing a reduction in the production and output of liver sugar. It has been reported that pioglitazone increases the risk of bladder cancer, but the underlying mechanisms have remained elusive. It was hypothesized that modulation of pioglitazone activity may be predicted by systematically analyzing data published on drugs. This hypothesis was tested by querying the Drug-Target Interactome (DTome), a web-based tool that provides open-source data from three databases (DrugBank, PharmGSK and Protein Interaction Network analysis). A total of 4 direct target proteins (DTPs) and further DTP-associated genes were identified for pioglitazone. Subsequently, an enrichment analysis was performed for all DTP-associated genes using Cytoscape software. A total of 12 Kyoto Encyclopedia of Genes and Genomes pathways were identified, including the 'PPAR signaling pathway' as well as 'pathways in cancer' as relevant pathways. Functional network analysis was able to identify direct and indirect target genes of pioglitazone, resulting in a list of possible biological functions based on published databases. Furthermore, Kaplan-Meier analysis indicated that pioglitazone may affect the survival rate of patients with bladder cancer through genetic alterations (missense mutation, truncating mutation, amplification, deep deletion and fusion) of target genes. Therefore, it should be used with caution.
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Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
| | - Lina Zhang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
| | - Xiaoxia Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
| | - Dong Lin
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Beijing 100010, P.R. China
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Zhou L, Li Z, Yang J, Tian G, Liu F, Wen H, Peng L, Chen M, Xiang J, Peng L. Revealing Drug-Target Interactions with Computational Models and Algorithms. Molecules 2019; 24:molecules24091714. [PMID: 31052598 PMCID: PMC6540161 DOI: 10.3390/molecules24091714] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 04/24/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022] Open
Abstract
Background: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. Results: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | | | - Geng Tian
- Geneis (Beijing) Co. Ltd., Beijing 100102, China.
| | - Fuxing Liu
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Hong Wen
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
| | - Li Peng
- School of Computer Science, University of Science and Technology of Hunan, Xiangtan 411201, Hunan, China.
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Henyang 421002, Hunan, China.
| | - Ju Xiang
- School of Computer Science and Engineering, Central South University, Changsha 410083, China.
- Neuroscience Research Center, Department of Basic Medical Sciences, Changsha Medical University, Changsha 410219, Hunan, China.
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou 412007, Hunan, China.
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Long S, Yuan C, Wang Y, Zhang J, Li G. Network Pharmacology Analysis of Damnacanthus indicus C.F.Gaertn in Gene-Phenotype. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE : ECAM 2019; 2019:1368371. [PMID: 30906409 PMCID: PMC6398045 DOI: 10.1155/2019/1368371] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2018] [Revised: 01/21/2019] [Accepted: 02/03/2019] [Indexed: 12/11/2022]
Abstract
Damnacanthus indicus C.F.Gaertn is known as Huci in traditional Chinese medicine. It contains a component having anthraquinone-like structure which is a part of the many used anticancer drugs. This study was to collect the evidence of disease-modulatory activities of Huci by analyzing the published literature on the chemicals and drugs. A list of its compounds and direct protein targets is predicted by using Bioinformatics Analysis Tool for Molecular Mechanism of TCM. A protein-protein interaction network using links between its directed targets and the other known targets was constructed. The DPT-associated genes in net were scrutinized by WebGestalt. Exploring the cancer genomics data related to Huci through cBio Portal. Survival analysis for the overlap genes is done by using UALCAN. We got 16 compounds and it predicts 62 direct protein targets and 100 DPTs and they were identified for these compounds. DPT-associated genes were analyzed by WebGestalt. Through the enrichment analysis, we got top 10 identified KEGG pathways. Refined analysis of KEGG pathways showed that one of these ten pathways is linked to Rap1 signaling pathway and another one is related to breast cancer. The survival analysis for the overlap genes shows the significant negative effect of these genes on the breast cancer patients. Through the research results of Damnacanthus indicus C.F.Gaertn, it is shown that medicine network pharmacology may be regarded as a new paradigm for guiding the future studies of the traditional Chinese medicine in different fields.
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Affiliation(s)
- Shengrong Long
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Caihong Yuan
- Department of Chinese Medicine, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Yue Wang
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Jie Zhang
- Department of Chinese Medicine, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
| | - Guangyu Li
- Department of Neurosurgery, The First Affiliated Hospital of China Medical University, NanJing Bei Road, Heping District, Shenyang, 110001, LiaoNing Province, China
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Hsieh TC, Wu ST, Bennett DJ, Doonan BB, Wu E, Wu JM. Functional/activity network (FAN) analysis of gene-phenotype connectivity liaised by grape polyphenol resveratrol. Oncotarget 2018; 7:38670-38680. [PMID: 27232943 PMCID: PMC5122419 DOI: 10.18632/oncotarget.9578] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 05/08/2016] [Indexed: 01/09/2023] Open
Abstract
Resveratrol is a polyphenol that has witnessed an unprecedented yearly growth in PubMed citations since the late 1990s. Based on the diversity of cellular processes and diseases resveratrol reportedly affects and benefits, it is likely that the interest in resveratrol will continue, although uncertainty regarding its mechanism in different biological systems remains. We hypothesize that insights on disease-modulatory activities of resveratrol might be gleaned by systematically dissecting the publicly available published data on chemicals and drugs. In this study, we tested our hypothesis by querying DTome (Drug-Target Interactome), a web-based tool containing data compiled from open-source databases including DrugBank, PharmGSK, and Protein Interaction Network Analysis (PINA). Four direct protein targets (DPT) and 219 DPT-associated genes were identified for resveratrol. The DPT-associated genes were scrutinized by WebGestalt (WEB-based Gene SeT Analysis Toolkit). This enrichment analysis resulted in 10 identified KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Refined analysis of KEGG pathways showed that 2 — one linked to p53 and a second to prostate cancer — have functional connectivity to resveratrol and its four direct protein targets. These results suggest that a functional activity network (FAN) approach may be considered as a new paradigm for guiding future studies of resveratrol. FAN analysis resembles a BioGPS, with capability for mapping a Web-based scientific track that can productively and cost effectively connect resveratrol to its primary and secondary target proteins and to its biological functions.
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Affiliation(s)
- Tze-Chen Hsieh
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, New York 10595, U.S.A
| | - Sheng-Tang Wu
- Division of Urology, Department of Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Dylan John Bennett
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, New York 10595, U.S.A
| | - Barbara B Doonan
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, New York 10595, U.S.A
| | - Erxi Wu
- Department of Neurosurgery, Baylor Scott and White Health, Temple, Texas, 76508, U.S.A.,Department of Surgery, Texas A&M College of Medicine, Temple, Texas 76504, U.S.A.,Department of Pharmaceutical Sciences, Texas A&M Health Science Center, College Station, Texas 77843, U.S.A
| | - Joseph M Wu
- Department of Biochemistry and Molecular Biology, New York Medical College, Valhalla, New York 10595, U.S.A
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Zhu Y, Elemento O, Pathak J, Wang F. Drug knowledge bases and their applications in biomedical informatics research. Brief Bioinform 2018; 20:1308-1321. [DOI: 10.1093/bib/bbx169] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 11/15/2017] [Indexed: 11/14/2022] Open
Abstract
Abstract
Recent advances in biomedical research have generated a large volume of drug-related data. To effectively handle this flood of data, many initiatives have been taken to help researchers make good use of them. As the results of these initiatives, many drug knowledge bases have been constructed. They range from simple ones with specific focuses to comprehensive ones that contain information on almost every aspect of a drug. These curated drug knowledge bases have made significant contributions to the development of efficient and effective health information technologies for better health-care service delivery. Understanding and comparing existing drug knowledge bases and how they are applied in various biomedical studies will help us recognize the state of the art and design better knowledge bases in the future. In addition, researchers can get insights on novel applications of the drug knowledge bases through a review of successful use cases. In this study, we provide a review of existing popular drug knowledge bases and their applications in drug-related studies. We discuss challenges in constructing and using drug knowledge bases as well as future research directions toward a better ecosystem of drug knowledge bases.
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Sam E, Athri P. Web-based drug repurposing tools: a survey. Brief Bioinform 2017; 20:299-316. [DOI: 10.1093/bib/bbx125] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Indexed: 12/15/2022] Open
Affiliation(s)
- Elizabeth Sam
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
| | - Prashanth Athri
- Department of Computer Science & Engineering Amrita, University Bengaluru, India
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Grammer AC, Lipsky PE. Drug Repositioning Strategies for the Identification of Novel Therapies for Rheumatic Autoimmune Inflammatory Diseases. Rheum Dis Clin North Am 2017; 43:467-480. [DOI: 10.1016/j.rdc.2017.04.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Zhou W, Yuan WF, Chen C, Wang SM, Liang SW. Study on material base and action mechanism of compound Danshen dripping pills for treatment of atherosclerosis based on modularity analysis. JOURNAL OF ETHNOPHARMACOLOGY 2016; 193:36-44. [PMID: 27396350 DOI: 10.1016/j.jep.2016.07.014] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Revised: 06/20/2016] [Accepted: 07/07/2016] [Indexed: 06/06/2023]
Abstract
ETHNOPHARMACOLOGICAL RELEVANCE Traditional Chinese medicine (TCM) has been widely used in China and its surrounding countries in clinical treatments for centuries-long time. However, due to the complexity of TCM constituents, both action mechanism and material base of TCM remain nearly unknown. AIM OF THE STUDY The present study was designed to uncover the action mechanism and material base of TCM in a low-cost manner. MATERIALS AND METHODS Compound Danshen dripping pills (DSP) is a widely used TCM for treatment of atherosclerosis, and was researched here to demonstrate the effectiveness of our method. We constructed a heterogeneous network for DSP, identified the significant network module, and analyzed the primary pharmacological units by performing GO and pathways enrichment analysis. RESULTS Two significant network modules were identified from the heterogeneous network of DSP, and three compounds out of four hub nodes in the network were found to intervene in the process of atherosclerosis. Moreover, 13 out of 20 enriched pathways that were ranked in top 10 corresponding to both the two pharmacological units were found to be involved in the process of atherosclerosis. CONCLUSIONS Quercetin, luteolin and apigenin may be the main active compounds which modulate the signaling pathways, such as metabolism of xenobiotics by cytochrome P450, retinol metabolism, etc. The present method helps reveal the action mechanism and material base of DSP for treatment of atherosclerosis.
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Affiliation(s)
- Wei Zhou
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Wen-Feng Yuan
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Chao Chen
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China.
| | - Shu-Mei Wang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
| | - Sheng-Wang Liang
- School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, PR China; The Key Unit of Chinese Medicine Digitalization Quality Evaluation of SATCM, Guangzhou 510006, PR China; The Research Center for Quality Engineering Technology of Traditional Chinese Medicine in Guangdong Universities, Guangzhou 510006, PR China
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Handen A, Ganapathiraju MK. LENS: web-based lens for enrichment and network studies of human proteins. BMC Med Genomics 2015; 8 Suppl 4:S2. [PMID: 26680011 PMCID: PMC4682415 DOI: 10.1186/1755-8794-8-s4-s2] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Background Network analysis is a common approach for the study of genetic view of diseases and biological pathways. Typically, when a set of genes are identified to be of interest in relation to a disease, say through a genome wide association study (GWAS) or a different gene expression study, these genes are typically analyzed in the context of their protein-protein interaction (PPI) networks. Further analysis is carried out to compute the enrichment of known pathways and disease-associations in the network. Having tools for such analysis at the fingertips of biologists without the requirement for computer programming or curation of data would accelerate the characterization of genes of interest. Currently available tools do not integrate network and enrichment analysis and their visualizations, and most of them present results in formats not most conducive to human cognition. Results We developed the tool Lens for Enrichment and Network Studies of human proteins (LENS) that performs network and pathway and diseases enrichment analyses on genes of interest to users. The tool creates a visualization of the network, provides easy to read statistics on network connectivity, and displays Venn diagrams with statistical significance values of the network's association with drugs, diseases, pathways, and GWASs. We used the tool to analyze gene sets related to craniofacial development, autism, and schizophrenia. Conclusion LENS is a web-based tool that does not require and download or plugins to use. The tool is free and does not require login for use, and is available at http://severus.dbmi.pitt.edu/LENS.
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AHMED MOHAMMADU, BENNETT DYLANJ, HSIEH TZECHEN, DOONAN BARBARAB, AHMED SABA, WU JOSEPHM. Repositioning of drugs using open-access data portal DTome: A test case with probenecid (Review). Int J Mol Med 2015; 37:3-10. [DOI: 10.3892/ijmm.2015.2411] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 10/12/2015] [Indexed: 11/05/2022] Open
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15
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Chu LH, Annex BH, Popel AS. Computational drug repositioning for peripheral arterial disease: prediction of anti-inflammatory and pro-angiogenic therapeutics. Front Pharmacol 2015; 6:179. [PMID: 26379552 PMCID: PMC4548203 DOI: 10.3389/fphar.2015.00179] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 08/10/2015] [Indexed: 12/17/2022] Open
Abstract
Peripheral arterial disease (PAD) results from atherosclerosis that leads to blocked arteries and reduced blood flow, most commonly in the arteries of the legs. PAD clinical trials to induce angiogenesis to improve blood flow conducted in the last decade have not succeeded. We have recently constructed PADPIN, protein-protein interaction network (PIN) of PAD, and here we combine it with the drug-target relations to identify potential drug targets for PAD. Specifically, the proteins in the PADPIN were classified as belonging to the angiome, immunome, and arteriome, characterizing the processes of angiogenesis, immune response/inflammation, and arteriogenesis, respectively. Using the network-based approach we predict the candidate drugs for repositioning that have potential applications to PAD. By compiling the drug information in two drug databases DrugBank and PharmGKB, we predict FDA-approved drugs whose targets are the proteins annotated as anti-angiogenic and pro-inflammatory, respectively. Examples of pro-angiogenic drugs are carvedilol and urokinase. Examples of anti-inflammatory drugs are ACE inhibitors and maraviroc. This is the first computational drug repositioning study for PAD.
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Affiliation(s)
- Liang-Hui Chu
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University Baltimore, MD, USA
| | - Brian H Annex
- Division of Cardiovascular Medicine, Department of Medicine and Robert M. Berne Cardiovascular Research Center, University of Virginia School of Medicine Charlottesville, VA, USA
| | - Aleksander S Popel
- Department of Biomedical Engineering, School of Medicine, Johns Hopkins University Baltimore, MD, USA
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16
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Huang LC, Soysal E, Zheng W, Zhao Z, Xu H, Sun J. A weighted and integrated drug-target interactome: drug repurposing for schizophrenia as a use case. BMC SYSTEMS BIOLOGY 2015; 9 Suppl 4:S2. [PMID: 26100720 PMCID: PMC4474536 DOI: 10.1186/1752-0509-9-s4-s2] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Computational pharmacology can uniquely address some issues in the process of drug development by providing a macroscopic view and a deeper understanding of drug action. Specifically, network-assisted approach is promising for the inference of drug repurposing. However, the drug-target associations coming from different sources and various assays have much noise, leading to an inflation of the inference errors. To reduce the inference errors, it is necessary and critical to create a comprehensive and weighted data set of drug-target associations. RESULTS In this study, we created a weighted and integrated drug-target interactome (WinDTome) to provide a comprehensive resource of drug-target associations for computational pharmacology. We first collected drug-target interactions from six commonly used drug-target centered data sources including DrugBank, KEGG, TTD, MATADOR, PDSP K(i) Database, and BindingDB. Then, we employed the record linkage method to normalize drugs and targets to the unique identifiers by utilizing the public data sources including PubChem, Entrez Gene, and UniProt. To assess the reliability of the drug-target associations, we assigned two scores (Score_S and Score_R) to each drug-target association based on their data sources and publication references. Consequently, the WinDTome contains 546,196 drug-target associations among 303,018 compounds and 4,113 genes. To assess the application of the WinDTome, we designed a network-based approach for drug repurposing using mental disorder schizophrenia (SCZ) as a case. Starting from 41 known SCZ drugs and their targets, we inferred a total of 264 potential SCZ drugs through the associations of drug-target with Score_S higher than two in WinDTome and human protein-protein interactions. Among the 264 SCZ-related drugs, 39 drugs have been investigated in clinical trials for SCZ treatment and 74 drugs for the treatment of other mental disorders, respectively. Compared with the results using other Score_S cutoff values, single data source, or the data from STITCH, the inference of 264 SCZ-related drugs had the highest performance. CONCLUSIONS The WinDTome generated in this study contains comprehensive drug-target associations with confidence scores. Its application to the SCZ drug repurposing demonstrated that the WinDTome is promising to serve as a useful resource for drug repurposing.
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Target deconvolution of bioactive small molecules: the heart of chemical biology and drug discovery. Arch Pharm Res 2015; 38:1627-41. [DOI: 10.1007/s12272-015-0618-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2015] [Accepted: 05/19/2015] [Indexed: 01/01/2023]
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18
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Zhang X, Fan HR, Li YZ, Xiao XF, Liu R, Qi JW, Wang J, Zhang ZP, Liu CX, Shen XP. Development and Application of Network Toxicology in Safety Research of Chinese Materia Medica. CHINESE HERBAL MEDICINES 2015. [DOI: 10.1016/s1674-6384(15)60016-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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19
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Emmerstorfer A, Wriessnegger T, Hirz M, Pichler H. Overexpression of membrane proteins from higher eukaryotes in yeasts. Appl Microbiol Biotechnol 2014; 98:7671-98. [PMID: 25070595 DOI: 10.1007/s00253-014-5948-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2014] [Revised: 07/08/2014] [Accepted: 07/09/2014] [Indexed: 02/08/2023]
Abstract
Heterologous expression and characterisation of the membrane proteins of higher eukaryotes is of paramount interest in fundamental and applied research. Due to the rather simple and well-established methods for their genetic modification and cultivation, yeast cells are attractive host systems for recombinant protein production. This review provides an overview on the remarkable progress, and discusses pitfalls, in applying various yeast host strains for high-level expression of eukaryotic membrane proteins. In contrast to the cell lines of higher eukaryotes, yeasts permit efficient library screening methods. Modified yeasts are used as high-throughput screening tools for heterologous membrane protein functions or as benchmark for analysing drug-target relationships, e.g., by using yeasts as sensors. Furthermore, yeasts are powerful hosts for revealing interactions stabilising and/or activating membrane proteins. We also discuss the stress responses of yeasts upon heterologous expression of membrane proteins. Through co-expression of chaperones and/or optimising yeast cultivation and expression strategies, yield-optimised hosts have been created for membrane protein crystallography or efficient whole-cell production of fine chemicals.
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Affiliation(s)
- Anita Emmerstorfer
- ACIB-Austrian Centre of Industrial Biotechnology, Petersgasse 14, 8010, Graz, Austria
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20
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Cheng F, Zhao Z. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc 2014; 21:e278-86. [PMID: 24644270 DOI: 10.1136/amiajnl-2013-002512] [Citation(s) in RCA: 179] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
OBJECTIVE Drug-drug interactions (DDIs) are an important consideration in both drug development and clinical application, especially for co-administered medications. While it is necessary to identify all possible DDIs during clinical trials, DDIs are frequently reported after the drugs are approved for clinical use, and they are a common cause of adverse drug reactions (ADR) and increasing healthcare costs. Computational prediction may assist in identifying potential DDIs during clinical trials. METHODS Here we propose a heterogeneous network-assisted inference (HNAI) framework to assist with the prediction of DDIs. First, we constructed a comprehensive DDI network that contained 6946 unique DDI pairs connecting 721 approved drugs based on DrugBank data. Next, we calculated drug-drug pair similarities using four features: phenotypic similarity based on a comprehensive drug-ADR network, therapeutic similarity based on the drug Anatomical Therapeutic Chemical classification system, chemical structural similarity from SMILES data, and genomic similarity based on a large drug-target interaction network built using the DrugBank and Therapeutic Target Database. Finally, we applied five predictive models in the HNAI framework: naive Bayes, decision tree, k-nearest neighbor, logistic regression, and support vector machine, respectively. RESULTS The area under the receiver operating characteristic curve of the HNAI models is 0.67 as evaluated using fivefold cross-validation. Using antipsychotic drugs as an example, several HNAI-predicted DDIs that involve weight gain and cytochrome P450 inhibition were supported by literature resources. CONCLUSIONS Through machine learning-based integration of drug phenotypic, therapeutic, structural, and genomic similarities, we demonstrated that HNAI is promising for uncovering DDIs in drug development and postmarketing surveillance.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, USA Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Network-assisted prediction of potential drugs for addiction. BIOMED RESEARCH INTERNATIONAL 2014; 2014:258784. [PMID: 24689033 PMCID: PMC3932722 DOI: 10.1155/2014/258784] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Accepted: 12/30/2013] [Indexed: 12/19/2022]
Abstract
Drug addiction is a chronic and complex brain disease, adding much burden on the community. Though numerous efforts have been made to identify the effective treatment, it is necessary to find more novel therapeutics for this complex disease. As network pharmacology has become a promising approach for drug repurposing, we proposed to apply the approach to drug addiction, which might provide new clues for the development of effective addiction treatment drugs. We first extracted 44 addictive drugs from the NIDA and their targets from DrugBank. Then, we constructed two networks: an addictive drug-target network and an expanded addictive drug-target network by adding other drugs that have at least one common target with these addictive drugs. By performing network analyses, we found that those addictive drugs with similar actions tended to cluster together. Additionally, we predicted 94 nonaddictive drugs with potential pharmacological functions to the addictive drugs. By examining the PubMed data, 51 drugs significantly cooccurred with addictive keywords than expected. Thus, the network analyses provide a list of candidate drugs for further investigation of their potential in addiction treatment or risk.
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Abstract
Background Drug addiction is a complex and chronic mental disease, which places a large burden on the American healthcare system due to its negative effects on patients and their families. Recently, network pharmacology is emerging as a promising approach to drug discovery by integrating network biology and polypharmacology, allowing for a deeper understanding of molecular mechanisms of drug actions at the systems level. This study seeks to apply this approach for investigation of illicit drugs and their targets in order to elucidate their interaction patterns and potential secondary drugs that can aid future research and clinical care. Results In this study, we extracted 188 illicit substances and their related information from the DrugBank database. The data process revealed 86 illicit drugs targeting a total of 73 unique human genes, which forms an illicit drug-target network. Compared to the full drug-target network from DrugBank, illicit drugs and their target genes tend to cluster together and form four subnetworks, corresponding to four major medication categories: depressants, stimulants, analgesics, and steroids. External analysis of Anatomical Therapeutic Chemical (ATC) second sublevel classifications confirmed that the illicit drugs have neurological functions or act via mechanisms of stimulants, opioids, and steroids. To further explore other drugs potentially having associations with illicit drugs, we constructed an illicit-extended drug-target network by adding the drugs that have the same target(s) as illicit drugs to the illicit drug-target network. After analyzing the degree and betweenness of the network, we identified hubs and bridge nodes, which might play important roles in the development and treatment of drug addiction. Among them, 49 non-illicit drugs might have potential to be used to treat addiction or have addictive effects, including some results that are supported by previous studies. Conclusions This study presents the first systematic review of the network characteristics of illicit drugs, their targets, and other drugs that share the targets of these illicit drugs. The results, though preliminary, provide some novel insights into the molecular mechanisms of drug addiction. The observation of illicit-related drugs, with partial verification from previous studies, demonstrated that the network-assisted approach is promising for the identification of drug repositioning.
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Issa NT, Kruger J, Byers SW, Dakshanamurthy S. Drug repurposing a reality: from computers to the clinic. Expert Rev Clin Pharmacol 2013; 6:95-7. [PMID: 23473587 PMCID: PMC4489563 DOI: 10.1586/ecp.12.79] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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