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Khurana P, Varshney R, Gupta A. A Network-Biology led Computational Drug repurposing Strategy to prioritize therapeutic options for COVID-19. Heliyon 2022; 8:e09387. [PMID: 35578630 PMCID: PMC9093055 DOI: 10.1016/j.heliyon.2022.e09387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2021] [Revised: 11/17/2021] [Accepted: 05/03/2022] [Indexed: 12/15/2022] Open
Abstract
The alarming pandemic situation of novel Severe Acute Respiratory Syndrome Coronavirus 2 (nSARS-CoV-2) infection, high drug development cost and slow process of drug discovery have made repositioning of existing drugs for therapeutics a popular alternative. It involves the repurposing of existing safe compounds which results in low overall development costs and shorter development timeline. In the present study, a computational network-biology approach has been used for comparing three candidate drugs i.e. quercetin, N-acetyl cysteine (NAC), and 2-deoxy-glucose (2-DG) to be effectively repurposed against COVID-19. For this, the associations between these drugs and genes of Severe Acute Respiratory Syndrome (SARS) and the Middle East Respiratory Syndrome (MERS) diseases were retrieved and a directed drug-gene-gene-disease interaction network was constructed. Further, to quantify the associations between a target gene and a disease gene, the shortest paths from the target gene to the disease genes were identified. A vector DV was calculated to represent the extent to which a disease gene was influenced by these drugs. Quercetin was quantified as the best among the three drugs, suited for repurposing with DV of -70.19, followed by NAC with DV of -39.99 and 2-DG with DV of -13.71. The drugs were also assessed for their safety and efficacy balance (in terms of therapeutic index) using network properties. It was found that quercetin was a forerunner than other two drugs.
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Yin J, Li X, Li F, Lu Y, Zeng S, Zhu F. Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease. Comput Struct Biotechnol J 2021; 19:2318-2328. [PMID: 33995923 PMCID: PMC8105181 DOI: 10.1016/j.csbj.2021.04.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 04/09/2021] [Accepted: 04/15/2021] [Indexed: 12/14/2022] Open
Abstract
An appropriate therapeutic index is crucial for drug discovery and development since narrow therapeutic index (NTI) drugs with slight dosage variation may induce severe adverse drug reactions or potential treatment failure. To date, the shared characteristics underlying the targets of NTI drugs have been explored by several studies, which have been applied to identify potential drug targets. However, the association between the drug therapeutic index and the related disease has not been dissected, which is important for revealing the NTI drug mechanism and optimizing drug design. Therefore, in this study, two classes of disease (cancers and cardiovascular disorders) with the largest number of NTI drugs were selected, and the target property of the corresponding NTI drugs was analyzed. By calculating the biological system profiles and human protein–protein interaction (PPI) network properties of drug targets and adopting an AI-based algorithm, differentiated features between two diseases were discovered to reveal the distinct underlying mechanisms of NTI drugs in different diseases. Consequently, ten shared features and four unique features were identified for both diseases to distinguish NTI from NNTI drug targets. These computational discoveries, as well as the newly found features, suggest that in the clinical study of avoiding narrow therapeutic index in those diseases, the ability of target to be a hub and the efficiency of target signaling in the human PPI network should be considered, and it could thus provide novel guidance in the drug discovery and clinical research process and help to estimate the drug safety of cancer and cardiovascular disease.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiaoxu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yinjing Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China.,Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Li XX, Yin J, Tang J, Li Y, Yang Q, Xiao Z, Zhang R, Wang Y, Hong J, Tao L, Xue W, Zhu F. Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target. Front Pharmacol 2018; 9:1245. [PMID: 30429792 PMCID: PMC6220079 DOI: 10.3389/fphar.2018.01245] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 10/12/2018] [Indexed: 12/14/2022] Open
Abstract
One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.
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Affiliation(s)
- Xiao Xu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiayi Yin
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yinghong Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Runyuan Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-cancer Chinese Medicine of Zhejiang Province, School of Medicine, Hangzhou Normal University, Hangzhou, China
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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Huang CH, Chen TH, Ng KL. Graph theory and stability analysis of protein complex interaction networks. IET Syst Biol 2016; 10:64-75. [PMID: 26997661 DOI: 10.1049/iet-syb.2015.0007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov-Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological-based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree-based, betweenness-based and brokering-coefficient-based perturbations have the largest effect on network stability.
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Affiliation(s)
- Chien-Hung Huang
- Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin 63205, Taiwan
| | - Teng-Hung Chen
- Department of Computer Science and Information Engineering, National Formosa University, Yun-Lin 63205, Taiwan
| | - Ka-Lok Ng
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 40402, Taiwan.
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Zhang J, Wang Y, Shang D, Yu F, Liu W, Zhang Y, Feng C, Wang Q, Xu Y, Liu Y, Bai X, Li X, Li C. Characterizing and optimizing human anticancer drug targets based on topological properties in the context of biological pathways. J Biomed Inform 2015; 54:132-40. [PMID: 25724580 DOI: 10.1016/j.jbi.2015.02.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Revised: 02/16/2015] [Accepted: 02/17/2015] [Indexed: 01/14/2023]
Abstract
One of the challenging problems in drug discovery is to identify the novel targets for drugs. Most of the traditional methods for drug targets optimization focused on identifying the particular families of "druggable targets", but ignored their topological properties based on the biological pathways. In this study, we characterized the topological properties of human anticancer drug targets (ADTs) in the context of biological pathways. We found that the ADTs tended to present the following seven topological properties: influence the number of the pathways related to cancer, be localized at the start or end of the pathways, interact with cancer related genes, exhibit higher connectivity, vulnerability, betweenness, and closeness than other genes. We first ranked ADTs based on their topological property values respectively, then fused them into one global-rank using the joint cumulative distribution of an N-dimensional order statistic to optimize human ADTs. We applied the optimization method to 13 anticancer drugs, respectively. Results demonstrated that over 70% of known ADTs were ranked in the top 20%. Furthermore, the performance for mercaptopurine was significant: 6 known targets (ADSL, GMPR2, GMPR, HPRT1, AMPD3, AMPD2) were ranked in the top 15 and other four out of the top 15 (MAT2A, CDKN1A, AREG, JUN) have the potentialities to become new targets for cancer therapy.
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Affiliation(s)
- Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Yan Wang
- Majorbio Bio-Pharm Technology Co., Ltd., Shanghai 201203, PR China
| | - Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Fulong Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Wei Liu
- Department of Mathematics, Heilongjiang Institute of Technology, Harbin 150050, PR China
| | - Yan Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Qiuyu Wang
- School of Nursing, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China
| | - Yuejuan Liu
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Xuefeng Bai
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Xuecang Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, PR China.
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Mora A, Donaldson IM. Effects of protein interaction data integration, representation and reliability on the use of network properties for drug target prediction. BMC Bioinformatics 2012; 13:294. [PMID: 23146171 PMCID: PMC3534413 DOI: 10.1186/1471-2105-13-294] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2012] [Accepted: 11/02/2012] [Indexed: 11/21/2022] Open
Abstract
Background Previous studies have noted that drug targets appear to be associated with higher-degree or higher-centrality proteins in interaction networks. These studies explicitly or tacitly make choices of different source databases, data integration strategies, representation of proteins and complexes, and data reliability assumptions. Here we examined how the use of different data integration and representation techniques, or different notions of reliability, may affect the efficacy of degree and centrality as features in drug target prediction. Results Fifty percent of drug targets have a degree of less than nine, and ninety-five percent have a degree of less than ninety. We found that drug targets are over-represented in higher degree bins – this relationship is only seen for the consolidated interactome and it is not dependent on n-ary interaction data or its representation. Degree acts as a weak predictive feature for drug-target status and using more reliable subsets of the data does not increase this performance. However, performance does increase if only cancer-related drug targets are considered. We also note that a protein’s membership in pathway records can act as a predictive feature that is better than degree and that high-centrality may be an indicator of a drug that is more likely to be withdrawn. Conclusions These results show that protein interaction data integration and cleaning is an important consideration when incorporating network properties as predictive features for drug-target status. The provided scripts and data sets offer a starting point for further studies and cross-comparison of methods.
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Affiliation(s)
- Antonio Mora
- Department for Molecular Biosciences, University of Oslo, Norway
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Arnedos M, André F, Farace F, Lacroix L, Besse B, Robert C, Soria JC, Eggermont AMM. The challenge to bring personalized cancer medicine from clinical trials into routine clinical practice: the case of the Institut Gustave Roussy. Mol Oncol 2012; 6:204-10. [PMID: 22483534 DOI: 10.1016/j.molonc.2012.02.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2011] [Revised: 02/27/2012] [Accepted: 02/27/2012] [Indexed: 11/18/2022] Open
Abstract
Research with high throughput technologies has propitiated the segmentation of different types of tumors into very small subgroups characterized by the presence of very rare molecular alterations. The identification of these subgroups and the apparition of new agents targeting these infrequent alterations are already affecting the way in which clinical trials are being conducted with an increased need to identify those patients harboring specific molecular alterations. In this review we describe some of the currently ongoing and future studies at the Institut Gustave Roussy that aim for the identification of potential therapeutic targets for cancer patients with the incorporation of high throughput technologies into daily practice including aCGH, next generation sequencing and the creation of a software that allows for target identification specific for each tumor. The initial intention is to enrich clinical trials with cancer patients carrying certain molecular alterations in order to increase the possibility of demonstrating benefit from a targeted agent. Mid and long term aims are to facilitate and speed up the process of drug development as well as to implement the concept of personalized medicine.
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Affiliation(s)
- Monica Arnedos
- Department of Medical Oncology, Institut de Cancerologie Gustave Roussy, 94805 Villejuif, France
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