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Mohamed EAR, Abdel-Rahman IM, Zaki MEA, Al-Khdhairawi A, Abdelhamid MM, Alqaisi AM, Rahim LBA, Abu-Hussein B, El-Sheikh AAK, Abdelwahab SF, Hassan HA. In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing. J Mol Model 2023; 29:70. [PMID: 36808314 PMCID: PMC9939377 DOI: 10.1007/s00894-023-05457-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/16/2023] [Indexed: 02/23/2023]
Abstract
BACKGROUND In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aimed to discover potent drugs against Omicron, which were previously repurposed for coronavirus disease 2019 (COVID-19). All repurposed anti-COVID-19 drugs were compiled from previous studies and tested against the RBD of SARS-CoV-2 Omicron. METHODS As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site. RESULTS The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔGbinding of - 75.7304 ± 0.98324 and - 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study.
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Affiliation(s)
- Eslam A. R. Mohamed
- Department of Chemistry, Faculty of Science, Minia University, Minia, 61511 Egypt
| | - Islam M. Abdel-Rahman
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Deraya University, New-Minia, 61519 Minia Egypt
| | - Magdi E. A. Zaki
- Department of Chemistry, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia
| | - Ahmad Al-Khdhairawi
- Department of Biological Science and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Malaysia
| | - Mahmoud M. Abdelhamid
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Al-Azhar University, Asyut, 71524 Egypt
| | - Ahmad M. Alqaisi
- Chemistry Department, University of Jordan, Amman, 11942 Jordan
- Present Address: School for Engineering of Matter, Transport and Energy, Arizona State University, Tempe, AZ 85287 USA
| | - Lyana binti Abd Rahim
- Department of Medicine, Hospital Tuanku Ampuan Najihah, Kuala Pilah, Negeri Sembilan Malaysia
| | - Bilal Abu-Hussein
- Albayader Specialty Hospital, Amman, Jordan
- Present Address: Department of General Surgery, Cumberland Infirmary Hospital, Carlisle, England
| | - Azza A. K. El-Sheikh
- Basic Health Sciences Department, College of Medicine, Princess Nourah bint Abdulrahman University, P.O. 13 Box 84428, Riyadh, 11671 Saudi Arabia
| | - Sayed F. Abdelwahab
- Department of Pharmaceutics and Industrial Pharmacy, College of Pharmacy, Taif University, PO Box 11099, Taif, 21944 Saudi Arabia
| | - Heba Ali Hassan
- Department of Pharmacognosy, Faculty of Pharmacy, Sohag University, Sohag, 82524 Egypt
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Cui T, Wang Z, Gu H, Qin P, Wang J. Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection. Front Genet 2023; 14:1095976. [PMID: 36816042 PMCID: PMC9932661 DOI: 10.3389/fgene.2023.1095976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 01/23/2023] [Indexed: 02/05/2023] Open
Abstract
In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R 2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.
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Affiliation(s)
- Tongtong Cui
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China
| | - Zeyuan Wang
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China
| | - Hong Gu
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China
| | - Pan Qin
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China,*Correspondence: Jia Wang, ; Pan Qin,
| | - Jia Wang
- Department of Breast Surgery, Second Hospital of Dalian Medical University, Dalian, Liaoning, China,*Correspondence: Jia Wang, ; Pan Qin,
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Han T, Luo Z, Ji L, Wu P, Li G, Liu X, Lai Y. Identification of natural compounds as SARS-CoV-2 inhibitors via molecular docking and molecular dynamic simulation. Front Microbiol 2023; 13:1095068. [PMID: 36817101 PMCID: PMC9930647 DOI: 10.3389/fmicb.2022.1095068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 12/29/2022] [Indexed: 02/05/2023] Open
Abstract
Background Base mutations increase the contagiousness and transmissibility of the Delta and Lambda strains and lead to the severity of the COVID-19 pandemic. Molecular docking and molecular dynamics (MD) simulations are frequently used for drug discovery and relocation. Small molecular compounds from Chinese herbs have an inhibitory effect on the virus. Therefore, this study used computational simulations to investigate the effects of small molecular compounds on the spike (S) protein and the binding between them and angiotensin-converting enzyme 2 (ACE2) receptors. Methods In this study, molecular docking, MD simulation, and protein-protein analysis were used to explore the medicinal target inhibition of Chinese herbal medicinal plant chemicals on SARS-CoV-2. 12,978 phytochemicals were screened against S proteins of SARS-CoV-2 Lambda and Delta mutants. Results Molecular docking showed that 65.61% and 65.28% of the compounds had the relatively stable binding ability to the S protein of Lambda and Delta mutants (docking score ≤ -6). The top five compounds with binding energy with Lambda and Delta mutants were clematichinenoside AR2 (-9.7), atratoglaucoside,b (-9.5), physalin b (-9.5), atratoglaucoside, a (-9.4), Ochnaflavone (-9.3) and neo-przewaquinone a (-10), Wikstrosin (-9.7), xilingsaponin A (-9.6), ardisianoside G (-9.6), and 23-epi-26-deoxyactein (-9.6), respectively. Four compounds (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) could interact with S protein mutation sites of Lambda and Delta mutants, respectively, and MD simulation results showed that four plant chemicals and spike protein have good energy stable complex formation ability. In addition, protein-protein docking was carried out to evaluate the changes in ACE2 binding ability caused by the formation of four plant chemicals and S protein complexes. The analysis showed that the binding of four plant chemicals to the S protein could reduce the stability of the binding to ACE2, thereby reducing the replication ability of the virus. Conclusion To sum up, the study concluded that four phytochemicals (Casuarictin, Heterophylliin D, Protohypericin, and Glansrin B) had significant effects on the binding sites of the SARS-CoV-2 S protein. This study needs further in vitro and in vivo experimental validation of these major phytochemicals to assess their potential anti-SARS-CoV-2. Graphical abstract.
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Affiliation(s)
- Tiantian Han
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Ziqing Luo
- Laboratory Animal Center, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lichun Ji
- The Third Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Peng Wu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Geng Li
- Laboratory Animal Center, Guangzhou University of Chinese Medicine, Guangzhou, China,*Correspondence: Geng Li, ✉
| | - Xiaohong Liu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China,Xiaohong Liu, ✉
| | - Yanni Lai
- School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China,Yanni Lai, ✉
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4
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Lang X, Liu J, Zhang G, Feng X, Dan W. Knowledge Mapping of Drug Repositioning's Theme and Development. Drug Des Devel Ther 2023; 17:1157-1174. [PMID: 37096060 PMCID: PMC10122475 DOI: 10.2147/dddt.s405906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 04/11/2023] [Indexed: 04/26/2023] Open
Abstract
Background In recent years, the emergence of new diseases and resistance to known diseases have led to increasing demand for new drugs. By means of bibliometric analysis, this paper studied the relevant articles on drug repositioning in recent years and analyzed the current research foci and trends. Methodology The Web of Science database was searched to collect all relevant literature on drug repositioning from 2001 to 2022. These data were imported into CiteSpace and bibliometric online analysis platforms for bibliometric analysis. The processed data and visualized images predict the development trends in the research field. Results The quality and quantity of articles published after 2011 have improved significantly, with 45 of them cited more than 100 times. Articles posted by journals from different countries have high citation values. Authors from other institutions have also collaborated to analyze drug rediscovery. Keywords found in the literature include molecular docking (N=223), virtual screening (N=170), drug discovery (N=126), machine learning (N=125), and drug-target interaction (N=68); these words represent the core content of drug repositioning. Conclusion The key focus of drug research and development is related to the discovery of new indications for drugs. Researchers are starting to retarget drugs after analyzing online databases and clinical trials. More and more drugs are being targeted at other diseases to treat more patients, based on saving money and time. It is worth noting that researchers need more financial and technical support to complete drug development.
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Affiliation(s)
- Xiaona Lang
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Jinlei Liu
- Cardiology Department, Guang ‘anmen Hospital, Chinese Academy of Traditional Chinese Medicine, Beijing, People’s Republic of China
| | - Guangzhong Zhang
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
| | - Xin Feng
- Pharmacy Department, Tianjin Hospital, Tianjin, People’s Republic of China
| | - Wenchao Dan
- Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China
- Correspondence: Wenchao Dan, Dermatological Department, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, People’s Republic of China, Tel +86 13652001152, Email
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5
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Guo W, Liu J, Dong F, Chen R, Das J, Ge W, Xu X, Hong H. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3376. [PMID: 36234502 PMCID: PMC9565823 DOI: 10.3390/nano12193376] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/24/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.
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Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ru Chen
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Jayanti Das
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Weigong Ge
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Xiaoming Xu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
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6
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Ahmed F, Lee JW, Samantasinghar A, Kim YS, Kim KH, Kang IS, Memon FH, Lim JH, Choi KH. SperoPredictor: An Integrated Machine Learning and Molecular Docking-Based Drug Repurposing Framework With Use Case of COVID-19. Front Public Health 2022; 10:902123. [PMID: 35784208 PMCID: PMC9244710 DOI: 10.3389/fpubh.2022.902123] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022] Open
Abstract
The global spread of the SARS coronavirus 2 (SARS-CoV-2), its manifestation in human hosts as a contagious disease, and its variants have induced a pandemic resulting in the deaths of over 6,000,000 people. Extensive efforts have been devoted to drug research to cure and refrain the spread of COVID-19, but only one drug has received FDA approval yet. Traditional drug discovery is inefficient, costly, and unable to react to pandemic threats. Drug repurposing represents an effective strategy for drug discovery and reduces the time and cost compared to de novo drug discovery. In this study, a generic drug repurposing framework (SperoPredictor) has been developed which systematically integrates the various types of drugs and disease data and takes the advantage of machine learning (Random Forest, Tree Ensemble, and Gradient Boosted Trees) to repurpose potential drug candidates against any disease of interest. Drug and disease data for FDA-approved drugs (n = 2,865), containing four drug features and three disease features, were collected from chemical and biological databases and integrated with the form of drug-disease association tables. The resulting dataset was split into 70% for training, 15% for testing, and the remaining 15% for validation. The testing and validation accuracies of the models were 99.3% for Random Forest and 99.03% for Tree Ensemble. In practice, SperoPredictor identified 25 potential drug candidates against 6 human host-target proteomes identified from a systematic review of journals. Literature-based validation indicated 12 of 25 predicted drugs (48%) have been already used for COVID-19 followed by molecular docking and re-docking which indicated 4 of 13 drugs (30%) as potential candidates against COVID-19 to be pre-clinically and clinically validated. Finally, SperoPredictor results illustrated the ability of the platform to be rapidly deployed to repurpose the drugs as a rapid response to emergent situations (like COVID-19 and other pandemics).
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jae Wook Lee
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
| | | | | | - Kyung Hwan Kim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - In Suk Kang
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Fida Hussain Memon
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Jong Hwan Lim
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Jeju, South Korea
- BioSpero, Inc., Jeju, South Korea
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Dhanalakshmi M, Das K, Pandya M, Shah S, Gadnayak A, Dave S, Das J. Artificial Neural Network-Based Study Predicts GS-441524 as a Potential Inhibitor of SARS-CoV-2 Activator Protein Furin: a Polypharmacology Approach. Appl Biochem Biotechnol 2022; 194:4511-4529. [PMID: 35507249 PMCID: PMC9066385 DOI: 10.1007/s12010-022-03928-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Furin, a pro-protein convertase, plays a significant role as a biological scissor in bacterial, viral, and even mammalian substrates which in turn decides the fate of many viral and bacterial infections along with the numerous ailments caused by cancer, diabetes, inflammations, and neurological disorders. In the wake of the current pandemic caused by the virus SARS-CoV-2, furin has become the center of attraction for researchers as the spike protein contains a polybasic furin cleavage site. In the present work, we have searched for novel inhibitors against this interesting human target from FDA-approved antiviral. To enhance the selection of new inhibitors, we employed Kohonen's artificial neural network-based self-organizing maps for ligand-based virtual screening. Promising results were obtained which can help in drug repurposing and network pharmacology studies can address the errors generated due to promiscuity/polypharmacology. We found 15 existing FDA antiviral drugs having the potential to inhibit furin. Among these, six compounds have targets on important human proteins (LDLR, FCGR1A, PCK1, TLR7, DNA, and PNP). The role of these 15 drugs inhibiting furin can be established by studying further on patients infected with number of viruses including SARS-CoV-2. Here we propose two promising candidate FDA drugs GS-441524 and Grazoprevir (MK-5172) for repurposing as inhibitors of furin. The best results were observed with GS-441524.
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Affiliation(s)
- M Dhanalakshmi
- Research and Development Centre, Bharathiar University, Marudhamalai Rd, Coimbatore, Tamil Nadu, India
| | - Kajari Das
- Department of Biotechnology, College of Basic Science and Humanities, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India
| | - Medha Pandya
- The KPES Science College, Maharaja krishnakumarsinhji Bhavnagar University, Bhavnagar, Gujarat, India
| | - Sejal Shah
- Department of Microbiology, Faculty of Science, Marwadi University, Rajkot, Gujarat, India
| | - Ayushman Gadnayak
- Centre for Genomics & Biomedical Informatics, IMS and SUM Hospital, Siksha "O" Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India
| | - Sushma Dave
- Department of Applied Sciences, JIET, Jodhpur, Rajasthan, India.
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Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
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Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
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9
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Wei P, Long D, Tan Y, Xing W, Li X, Yang K, Liu H. Integrated Pharmacogenetics Analysis of the Three Fangjis Decoctions for Treating Arrhythmias Based on Molecular Network Patterns. Front Cardiovasc Med 2022; 8:726694. [PMID: 35004871 PMCID: PMC8739471 DOI: 10.3389/fcvm.2021.726694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 11/23/2021] [Indexed: 11/30/2022] Open
Abstract
Aim: To explore the diverse target distribution and variable mechanisms of different fangjis prescriptions when treating arrhythmias based on the systems pharmacology. Methods: The active ingredients and their corresponding targets were acquired from the three fangjis [Zhigancao Tang (ZT), Guizhigancao Longgumuli Tang (GLT), and Huanglian E'jiao Tang (HET)] and the arrhythmia-related genes were identified based on comprehensive database screening. Networks were constructed between the fangjis and arrhythmia and used to define arrhythmia modules. Common and differential gene targets were identified within the arrhythmia network modules and the cover rate (CR) matrix was applied to compare the contributions of the fangjis to the network and modules. Comparative pharmacogenetics analyses were then conducted to define the arrhythmia-related signaling pathways regulated by the fangjis prescriptions. Finally, the divergence and convergence points of the arrhythmia pathways were deciphered based on databases and the published literature. Results: A total of 187, 105, and 68 active ingredients and 1,139, 1,195, and 811 corresponding gene targets of the three fangjis were obtained and 102 arrhythmia-related genes were acquired. An arrhythmia network was constructed and subdivided into 4 modules. For the target distribution analysis, 65.4% of genes were regulated by the three fangjis within the arrhythmia network. ZT and GLT were more similar to each other, mainly regulated by module two, whereas HET was divided among all the modules. From the perspective of signal transduction, calcium-related pathways [calcium, cyclic guanosine 3′,5′-monophosphate (cGMP)-PKG, and cyclic adenosine 3′,5′-monophosphate (cAMP)] and endocrine system-related pathways (oxytocin signaling pathway and renin secretion pathways) were associated with all the three fangjis prescriptions. Nevertheless, heterogeneity existed between the biological processes and pathway distribution among the three prescriptions. GLT and HET were particularly inclined toward the conditions involving abnormal hormone secretion, whereas ZT tended toward renin-angiotensin-aldosterone system (RAAS) disorders. However, calcium signaling-related pathways prominently feature in the pharmacological activities of the decoctions. Experimental validation indicated that ZT, GLT, and HET significantly shortened the duration of ventricular arrhythmia (VA) and downregulated the expression of CALM2 and interleukin-6 (IL-6) messenger RNAs (mRNAs); GLT and HET downregulated the expression of CALM1 and NOS3 mRNAs; HET downregulated the expression of CRP mRNA. Conclusion: Comparing the various distributions of the three fangjis, pathways provide evidence with respect to precise applications toward individualized arrhythmia treatments.
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Affiliation(s)
- Penglu Wei
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Dehuai Long
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Yupei Tan
- Beijing University of Chinese Medicine, Beijing, China
| | - Wenlong Xing
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Xiang Li
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Kuo Yang
- School of Computer and Information Technology, Institute of Medical Intelligence, Beijing Jiaotong University, Beijing, China
| | - Hongxu Liu
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
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10
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Masoudi-Sobhanzadeh Y, Salemi A, Pourseif MM, Jafari B, Omidi Y, Masoudi-Nejad A. Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries. Brief Bioinform 2021; 22:bbab113. [PMID: 33993214 PMCID: PMC8194848 DOI: 10.1093/bib/bbab113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 02/15/2021] [Accepted: 03/13/2021] [Indexed: 01/09/2023] Open
Abstract
To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.
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Affiliation(s)
- Yosef Masoudi-Sobhanzadeh
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Aysan Salemi
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Mohammad M Pourseif
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Jafari
- Department of Medicinal Chemistry, Faculty of Pharmacy, Urmia University of Medical Sciences, Urmia, Iran
| | - Yadollah Omidi
- Nova Southeastern University College of Pharmacy, Florida, USA
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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11
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Habibi M, Taheri G. Topological network based drug repurposing for coronavirus 2019. PLoS One 2021; 16:e0255270. [PMID: 34324563 PMCID: PMC8320924 DOI: 10.1371/journal.pone.0255270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022] Open
Abstract
The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus's performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.
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Affiliation(s)
- Mahnaz Habibi
- Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
- * E-mail:
| | - Golnaz Taheri
- Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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12
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Coates JTT, Pirovano G, El Naqa I. Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges. J Med Imaging (Bellingham) 2021; 8:031902. [PMID: 33768134 PMCID: PMC7985651 DOI: 10.1117/1.jmi.8.3.031902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 01/12/2021] [Indexed: 12/14/2022] Open
Abstract
The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, next-generation platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.
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Affiliation(s)
- James T. T. Coates
- Massachusetts General Hospital & Harvard Medical School, Center for Cancer Research, Boston, Massachusetts, United States
| | - Giacomo Pirovano
- Memorial Sloan Kettering Cancer Center, Department of Radiology, New York, New York, United States
| | - Issam El Naqa
- Moffitt Cancer Center and Research Institute, Department of Machine Learning, Tampa, Florida, United States
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13
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Drug repurposing using transcriptome sequencing and virtual drug screening in a patient with glioblastoma. Invest New Drugs 2020; 39:670-685. [PMID: 33313992 PMCID: PMC8068653 DOI: 10.1007/s10637-020-01037-7] [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: 09/24/2020] [Accepted: 11/19/2020] [Indexed: 12/02/2022]
Abstract
Background Precision medicine and drug repurposing are attractive strategies, especially for tumors with worse prognosis. Glioblastoma is a highly malignant brain tumor with limited treatment options and short survival times. We identified novel BRAF (47-438del) and PIK3R1 (G376R) mutations in a glioblastoma patient by RNA-sequencing. Methods The protein expression of BRAF and PIK3R1 as well as the lack of EGFR expression as analyzed by immunohistochemistry corroborated RNA-sequencing data. The expression of additional markers (AKT, SRC, mTOR, NF-κB, Ki-67) emphasized the aggressiveness of the tumor. Then, we screened a chemical library of > 1500 FDA-approved drugs and > 25,000 novel compounds in the ZINC database to find established drugs targeting BRAF47-438del and PIK3R1-G376R mutated proteins. Results Several compounds (including anthracyclines) bound with higher affinities than the control drugs (sorafenib and vemurafenib for BRAF and PI-103 and LY-294,002 for PIK3R1). Subsequent cytotoxicity analyses showed that anthracyclines might be suitable drug candidates. Aclarubicin revealed higher cytotoxicity than both sorafenib and vemurafenib, whereas idarubicin and daunorubicin revealed higher cytotoxicity than LY-294,002. Liposomal formulations of anthracyclines may be suitable to cross the blood brain barrier. Conclusions In conclusion, we identified novel small molecules via a drug repurposing approach that could be effectively used for personalized glioblastoma therapy especially for patients carrying BRAF47-438del and PIK3R1-G376R mutations.
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14
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Chiu YC, Chen HIH, Gorthi A, Mostavi M, Zheng S, Huang Y, Chen Y. Deep learning of pharmacogenomics resources: moving towards precision oncology. Brief Bioinform 2020; 21:2066-2083. [PMID: 31813953 PMCID: PMC7711267 DOI: 10.1093/bib/bbz144] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 08/22/2019] [Accepted: 10/18/2019] [Indexed: 12/13/2022] Open
Abstract
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology. Deep learning (DL) methodology has emerged as a powerful technique to characterize and learn from rapidly accumulating pharmacogenomics data. We introduce the fundamentals and typical model architectures of DL. We review the use of DL in classification of cancers and cancer subtypes (diagnosis and treatment stratification of patients), prediction of drug response and drug synergy for individual tumors (treatment prioritization for a patient), drug repositioning and discovery and the study of mechanism/mode of action of treatments. For each topic, we summarize current genomics and pharmacogenomics data resources such as pan-cancer genomics data for cancer cell lines (CCLs) and tumors, and systematic pharmacologic screens of CCLs. By revisiting the published literature, including our in-house analyses, we demonstrate the unprecedented capability of DL enabled by rapid accumulation of data resources to decipher complex drug response patterns, thus potentially improving cancer medicine. Overall, this review provides an in-depth summary of state-of-the-art DL methods and up-to-date pharmacogenomics resources and future opportunities and challenges to realize the goal of precision oncology.
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Affiliation(s)
- Yu-Chiao Chiu
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Hung-I Harry Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Aparna Gorthi
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Milad Mostavi
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Siyuan Zheng
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yufei Huang
- Department of Electrical and Computer Engineering, the University of Texas at San Antonio, San Antonio, TX 78249, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
| | - Yidong Chen
- Greehey Children’s Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA
- Department of Population Health Sciences, University of Texas Health San Antonio, San Antonio, TX 78229, USA
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15
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Yadav M, Dhagat S, Eswari JS. Emerging strategies on in silico drug development against COVID-19: challenges and opportunities. Eur J Pharm Sci 2020; 155:105522. [PMID: 32827661 PMCID: PMC7438372 DOI: 10.1016/j.ejps.2020.105522] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 08/14/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022]
Abstract
The importance of coronaviruses as human pathogen has been highlighted by the recent outbreak of SARS-CoV-2 leading to the search of suitable drugs to overcome respiratory infections caused by the virus. Due to the lack of specific drugs against coronavirus, the existing antiviral and antimalarial drugs are currently being administered to the patients infected with SARS-CoV-2. The scientists are also considering repurposing of some of the existing drugs as a suitable option in search of effective drugs against coronavirus till the establishment of a potent drug and/or vaccine. Computer-aided drug discovery provides a promising attempt to enable scientists to develop new and target specific drugs to combat any disease. The discovery of novel targets for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins present in the virus. Targeting viral proteins will make the drug specific against the virus, thereby, increasing the chances of viral mortality. Hence, this review provides the structure of SARS-CoV-2 virus along with the important viral components involved in causing infection. It also focuses on the role of various target proteins in disease, the mechanism by which currently administered drugs act against the virus and the repurposing of few drugs. The gap arising from the absence of specific drugs is addressed by proposing potential antiviral drug targets which might provide insights into structure-based drug development against SARS-CoV-2.
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Affiliation(s)
- Manisha Yadav
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India
| | - Swasti Dhagat
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India
| | - J Satya Eswari
- Department of Biotechnology, National Institute of Technology Raipur, C.G., 492010, India.
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16
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Deshpande RR, Tiwari AP, Nyayanit N, Modak M. In silico molecular docking analysis for repurposing therapeutics against multiple proteins from SARS-CoV-2. Eur J Pharmacol 2020; 886:173430. [PMID: 32758569 PMCID: PMC7398085 DOI: 10.1016/j.ejphar.2020.173430] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2020] [Revised: 07/25/2020] [Accepted: 07/26/2020] [Indexed: 12/16/2022]
Abstract
SARS-CoV-2 has devastated the world with its rapid spread and fatality. The researchers across the globe are struggling hard to search a drug to treat this infection. Understanding the time constraint, the best approach is to study clinically approved drugs for control of this deadly pandemic of COVID 19. The repurposing of such drugs can be supported with the study of molecular interactions to enhance the possibility of application. The present work is a molecular docking study of proteins responsible for viral propagation namely 3Clpro, Nsp10/16, Spike protein, SARS protein receptor binding domain, Nsp 9 viral single strand binding protein and viral helicase. The protein through virus enters the host cell-human angiotensin-converting enzyme 2 (ACE2) receptor, is also used as a target for molecular docking. The docking was done with most discussed drugs for SARS-CoV-2 like Ritonavir, Lopinavir, Remdesivir, Chloroquine, Hydroxychloroquine (HCQ), routine antiviral drugs like Oseltamivir and Ribavirin. In addition, small molecules with anti-inflammatory actions like Mycophenolic acid (MPA), Pemirolast, Isoniazid and Eriodictyol were also tested. The generated data confirms the potential of Ritonavir, Lopinavir and Remdesivir as a therapeutic candidate against SARS-CoV-2. It is observed that Eriodictyol binds to almost all selected target proteins with good binding energy, suggesting its importance in treatment of COVID 19. Molecular interactions of Ritonavir, Lopinavir and Remdesivir against SARS-CoV-2 proteins enhanced their potential as a candidate drug for treatment of COVID-19. Eriodictyol had emerged as a new repurposing drug that can be used in COVID-19.
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Affiliation(s)
| | - Arpita Pandey Tiwari
- Department of Stem Cell and Regenerative Medicine, Center for Interdisciplinary Research, D.Y. Patil Education Society (Institution Deemed to be University), Kolhapur, India,Corresponding author
| | | | - Manisha Modak
- Department of Zoology, Sir Parashurambhau College, Pune, India,Corresponding author
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17
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Fang J, Pieper AA, Nussinov R, Lee G, Bekris L, Leverenz JB, Cummings J, Cheng F. Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing. Med Res Rev 2020; 40:2386-2426. [PMID: 32656864 PMCID: PMC7561446 DOI: 10.1002/med.21709] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 06/23/2020] [Accepted: 06/27/2020] [Indexed: 12/16/2022]
Abstract
Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.
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Affiliation(s)
- Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong 510006, China
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Andrew A Pieper
- Harrington Discovery Institute, University Hospital Case Medical Center; Department of Psychiatry, Case Western Reserve University, Geriatric Research Education and Clinical Centers, Louis Stokes Cleveland VAMC, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Garam Lee
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Lynn Bekris
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - James B. Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jeffrey Cummings
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
- Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio 44106, USA
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18
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Castrillon JA, Eng C, Cheng F. Pharmacogenomics for immunotherapy and immune-related cardiotoxicity. Hum Mol Genet 2020; 29:R186-R196. [PMID: 32620943 PMCID: PMC7574958 DOI: 10.1093/hmg/ddaa137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 06/25/2020] [Accepted: 07/01/2020] [Indexed: 12/20/2022] Open
Abstract
Immune checkpoint blockade (ICB) has become a standard of care in a subset of solid tumors. Although cancer survivorship has extended, rates of durable response of ICB remain poor; furthermore, cardiac adverse effects are emerging, which impact several mechanical aspects of the heart. Cardio-oncology programs implement a clinical assessment to curtail cardiovascular disease progression but are limited to the current clinical parameters used in cardiology. Pharmacogenomics provides the potential to unveil heritable and somatic genetic variations for guiding precision immunotherapy treatment to reduce the risk of immune-related cardiotoxicity. A better understanding of pharmacogenomics will optimize the current treatment selection and dosing of immunotherapy. Here, we summarize the recent pharmacogenomics studies in immunotherapy responsiveness and its related cardiotoxicity and highlight how patient genetics and epigenetics can facilitate researchers and clinicians in designing new approaches for precision immunotherapy. We highlight and discuss how single-cell technologies, human-induced pluripotent stem cells and systems pharmacogenomics accelerate future studies of precision cardio-oncology.
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Affiliation(s)
- Jessica A Castrillon
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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19
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Hockings JK, Castrillon JA, Cheng F. Pharmacogenomics meets precision cardio-oncology: is there synergistic potential? Hum Mol Genet 2020; 29:R177-R185. [PMID: 32601683 PMCID: PMC7574955 DOI: 10.1093/hmg/ddaa134] [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: 05/18/2020] [Revised: 06/23/2020] [Accepted: 06/24/2020] [Indexed: 11/12/2022] Open
Abstract
An individual's inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.
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Affiliation(s)
- Jennifer K Hockings
- Department of Pharmacy, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jessica A Castrillon
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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20
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Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepDR: a network-based deep learning approach to in silico drug repositioning. Bioinformatics 2020; 35:5191-5198. [PMID: 31116390 DOI: 10.1093/bioinformatics/btz418] [Citation(s) in RCA: 249] [Impact Index Per Article: 62.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/22/2019] [Accepted: 05/14/2019] [Indexed: 12/23/2022] Open
Abstract
MOTIVATION Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. RESULTS In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). AVAILABILITY AND IMPLEMENTATION Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. SUPPLEMENTARY INFORMATION Supplementary data are available online at Bioinformatics.
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Affiliation(s)
- Xiangxiang Zeng
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Siyi Zhu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Xiangrong Liu
- Department of Computer Science, Xiamen University, Xiamen 361005, China
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.,Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.,Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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21
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BR B, Damle H, Ganju S, Damle L. In silico screening of known small molecules to bind ACE2 specific RBD on Spike glycoprotein of SARS-CoV-2 for repurposing against COVID-19. F1000Res 2020; 9:663. [PMID: 32765844 PMCID: PMC7385708 DOI: 10.12688/f1000research.24143.1] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/17/2020] [Indexed: 12/19/2022] Open
Abstract
Background: Human coronavirus (SARS-CoV-2) is causing a pandemic with significant morbidity and mortality. As no effective novel drugs are available currently, drug repurposing is an alternative intervention strategy. Here we present an in silico drug repurposing study that implements successful concepts of computer-aided drug design (CADD) technology for repurposing known drugs to interfere with viral cellular entry via the spike glycoprotein (SARS-CoV-2-S), which mediates host cell entry via the hACE2 receptor. Methods: A total of 4015 known and approved small molecules were screened for interaction with SARS-CoV-2-S through docking studies and 15 lead molecules were shortlisted. Additionally, streptomycin, ciprofloxacin, and glycyrrhizic acid (GA) were selected based on their reported anti-viral activity, safety, availability and affordability. The 18 molecules were subjected to molecular dynamics (MD) simulation. Results: The MD simulation results indicate that GA of plant origin may be repurposed for SARS-CoV-2 intervention, pending further studies. Conclusions: Repurposing is a beneficial strategy for treating COVID-19 with existing drugs. It is aimed at using docking studies to screen molecules for clinical application and investigating their efficacy in inhibiting SARS-CoV-2-S. SARS-CoV-2-S is a key pathogenic protein that mediates pathogen-host interaction. Hence, the molecules screened for inhibitory properties against SARS-CoV-2-S can be clinically used to treat COVID-19 since the safety profile is already known.
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Affiliation(s)
- Bharath BR
- Computational Biology, Atrimed Biotech LLP, Banglore, 560100, India
| | | | - Shiban Ganju
- Atrimed Pharmaceuticals Pvt. Ltd, Banglore, 560001, India
| | - Latha Damle
- Computational Biology, Atrimed Biotech LLP, Banglore, 560100, India
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22
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Investigation of Some Antiviral N-Heterocycles as COVID 19 Drug: Molecular Docking and DFT Calculations. Int J Mol Sci 2020; 21:ijms21113922. [PMID: 32486229 PMCID: PMC7312990 DOI: 10.3390/ijms21113922] [Citation(s) in RCA: 112] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 04/29/2020] [Accepted: 04/30/2020] [Indexed: 02/07/2023] Open
Abstract
The novel coronavirus, COVID-19, caused by SARS-CoV-2, is a global health pandemic that started in December 2019. The effective drug target among coronaviruses is the main protease Mpro, because of its essential role in processing the polyproteins that are translated from the viral RNA. In this study, the bioactivity of some selected heterocyclic drugs named Favipiravir (1), Amodiaquine (2), 2'-Fluoro-2'-deoxycytidine (3), and Ribavirin (4) was evaluated as inhibitors and nucleotide analogues for COVID-19 using computational modeling strategies. The density functional theory (DFT) calculations were performed to estimate the thermal parameters, dipole moment, polarizability, and molecular electrostatic potential of the present drugs; additionally, Mulliken atomic charges of the drugs as well as the chemical reactivity descriptors were investigated. The nominated drugs were docked on SARS-CoV-2 main protease (PDB: 6LU7) to evaluate the binding affinity of these drugs. Besides, the computations data of DFT the docking simulation studies was predicted that the Amodiaquine (2) has the least binding energy (-7.77 Kcal/mol) and might serve as a good inhibitor to SARS-CoV-2 comparable with the approved medicines, hydroxychloroquine, and remdesivir which have binding affinity -6.06 and -4.96 Kcal/mol, respectively. The high binding affinity of 2 was attributed to the presence of three hydrogen bonds along with different hydrophobic interactions between the drug and the critical amino acids residues of the receptor. Finally, the estimated molecular electrostatic potential results by DFT were used to illustrate the molecular docking findings. The DFT calculations showed that drug 2 has the highest of lying HOMO, electrophilicity index, basicity, and dipole moment. All these parameters could share with different extent to significantly affect the binding affinity of these drugs with the active protein sites.
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Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F. Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov 2020; 6:14. [PMID: 32194980 PMCID: PMC7073332 DOI: 10.1038/s41421-020-0153-3] [Citation(s) in RCA: 990] [Impact Index Per Article: 247.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/02/2020] [Indexed: 02/07/2023] Open
Abstract
Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Yin Huang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - William Martin
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195 USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195 USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106 USA
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24
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Deng Y, Luo S, Deng C, Luo T, Yin W, Zhang H, Zhang Y, Zhang X, Lan Y, Ping Y, Xiao Y, Li X. Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability. Brief Bioinform 2019; 20:254-266. [PMID: 28968730 DOI: 10.1093/bib/bbx109] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Indexed: 02/06/2023] Open
Abstract
Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.
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Affiliation(s)
- Yulan Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Shangyi Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Wenkang Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Hongyi Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yujia Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yanyan Ping
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
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25
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Zhang Y, Liao G, Bai J, Zhang X, Xu L, Deng C, Yan M, Xie A, Luo T, Long Z, Xiao Y, Li X. Identifying Cancer Driver lncRNAs Bridged by Functional Effectors through Integrating Multi-omics Data in Human Cancers. MOLECULAR THERAPY. NUCLEIC ACIDS 2019; 17:362-373. [PMID: 31302496 PMCID: PMC6626872 DOI: 10.1016/j.omtn.2019.05.030] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 04/23/2019] [Accepted: 05/15/2019] [Indexed: 01/18/2023]
Abstract
The accumulation of somatic driver mutations in the human genome enables cells to gradually acquire a growth advantage and contributes to tumor development. Great efforts on protein-coding cancer drivers have yielded fruitful discoveries and clinical applications. However, investigations on cancer drivers in non-coding regions, especially long non-coding RNAs (lncRNAs), are extremely scarce due to the limitation of functional understanding. Thus, to identify driver lncRNAs integrating multi-omics data in human cancers, we proposed a computational framework, DriverLncNet, which dissected the functional impact of somatic copy number alteration (CNA) of lncRNAs on regulatory networks and captured key functional effectors in dys-regulatory networks. Applying it to 5 cancer types from The Cancer Genome Atlas (TCGA), we portrayed the landscape of 117 driver lncRNAs and revealed their associated cancer hallmarks through their functional effectors. Moreover, lncRNA RP11-571M6.8 was detected to be highly associated with immunotherapeutic targets (PD-1, PD-L1, and CTLA-4) and regulatory T cell infiltration level and their markers (IL2RA and FCGR2B) in glioblastoma multiforme, highlighting its immunosuppressive function. Meanwhile, a high expression of RP11-1020A11.1 in bladder carcinoma was predictive of poor survival independent of clinical characteristics, and CTD-2256P15.2 in lung adenocarcinoma responded to the sensitivity of methyl ethyl ketone (MEK) inhibitors. In summary, this study provided a framework to decipher the mechanisms of tumorigenesis from driver lncRNA level, established a new landscape of driver lncRNAs in human cancers, and offered potential clinical implications for precision oncology.
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Affiliation(s)
- Yong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Jing Bai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Xinxin Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Chunyu Deng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Aimin Xie
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Tao Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Zhilin Long
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150086, China; Key Laboratory of Cardiovascular Medicine Research, Harbin Medical University, Ministry of Education, Harbin, Heilongjiang 150086, China.
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26
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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27
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Dai E, Wang J, Yang F, Zhou X, Song Q, Wang S, Yu X, Liu D, Yang Q, Dai H, Jiang W, Ling H. Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities. Int J Cancer 2018; 142:1427-1439. [PMID: 29143332 DOI: 10.1002/ijc.31158] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Accepted: 11/07/2017] [Indexed: 01/28/2023]
Abstract
Selecting the available treatment for each cancer patient from genomic context is a core goal of precision medicine, but innovative approaches with mechanism interpretation and improved performance are still highly needed. Through utilizing in vitro chemotherapy response data coupled with gene and miRNA expression profiles, we applied a network-based approach that identified markers not as individual molecules but as functional groups extracted from the integrated transcription factor and miRNA regulatory network. Based on the identified chemoresponse communities, the predictors of drug resistance achieved high accuracy in cross-validation and were more robust and reproducible than conventional single-molecule markers. Meanwhile, as candidate communities not only enriched abundant cellular process but also covered a variety of drug enzymes, transporters, and targets, these resulting chemoresponse communities furnished novel models to interpret multiple kinds of potential regulatory mechanism, such as dysregulation of cancer cell apoptosis or disturbance of drug metabolism. Moreover, compounds were linked based on the enrichment of their common chemoresponse communities to uncover undetected response patterns and possible multidrug resistance phenotype. Finally, an omnibus repository named ChemoCommunity (http://www.jianglab.cn/ChemoCommunity/) was constructed, which furnished a user-friendly interface for a convenient retrieval of the detailed information on chemoresponse communities. Taken together, our method, and the accompanying database, improved the performance of classifiers for drug resistance and provided novel model to uncover the possible regulatory mechanism of individual response to drug.
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Affiliation(s)
- Enyu Dai
- Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Parasitology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Heilongjiang Provincial Key Laboratory of Infection and Immunity, Harbin, 150081, People's Republic of China.,Key Laboratory of Pathogen Biology, Harbin, 150081, People's Republic of China.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Jing Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Feng Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Xu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Qian Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Shuyuan Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Xuexin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Dianming Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Qian Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Hong Dai
- The 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, People's Republic of China
| | - Wei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People's Republic of China
| | - Hong Ling
- Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081, People's Republic of China.,Department of Parasitology, Harbin Medical University, Harbin, 150081, People's Republic of China.,Heilongjiang Provincial Key Laboratory of Infection and Immunity, Harbin, 150081, People's Republic of China.,Key Laboratory of Pathogen Biology, Harbin, 150081, People's Republic of China
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28
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Abbruzzese C, Matteoni S, Signore M, Cardone L, Nath K, Glickson JD, Paggi MG. Drug repurposing for the treatment of glioblastoma multiforme. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2017; 36:169. [PMID: 29179732 PMCID: PMC5704391 DOI: 10.1186/s13046-017-0642-x] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 11/17/2017] [Indexed: 01/07/2023]
Abstract
Background Glioblastoma Multiforme is the deadliest type of brain tumor and is characterized by very poor prognosis with a limited overall survival. Current optimal therapeutic approach has essentially remained unchanged for more than a decade, consisting in maximal surgical resection followed by radiotherapy plus temozolomide. Main body Such a dismal patient outcome represents a compelling need for innovative and effective therapeutic approaches. Given the development of new drugs is a process presently characterized by an immense increase in costs and development time, drug repositioning, finding new uses for existing approved drugs or drug repurposing, re-use of old drugs when novel molecular findings make them attractive again, are gaining significance in clinical pharmacology, since it allows faster and less expensive delivery of potentially useful drugs from the bench to the bedside. This is quite evident in glioblastoma, where a number of old drugs is now considered for clinical use, often in association with the first-line therapeutic intervention. Interestingly, most of these medications are, or have been, widely employed for decades in non-neoplastic pathologies without relevant side effects. Now, the refinement of their molecular mechanism(s) of action through up-to-date technologies is paving the way for their use in the therapeutic approach of glioblastoma as well as other cancer types. Short conclusion The spiraling costs of new antineoplastic drugs and the long time required for them to reach the market demands a profoundly different approach to keep lifesaving therapies affordable for cancer patients. In this context, repurposing can represent a relatively inexpensive, safe and fast approach to glioblastoma treatment. To this end, pros and cons must be accurately considered.
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Affiliation(s)
- Claudia Abbruzzese
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy
| | - Silvia Matteoni
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy
| | - Michele Signore
- RPPA Unit, Proteomics Area, Core Facilities, Istituto Superiore di Sanità, Rome, Italy
| | - Luca Cardone
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Regina Elena National Cancer Institute, IRCCS, Rome, Italy
| | - Kavindra Nath
- Laboratory of Molecular Imaging, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jerry D Glickson
- Laboratory of Molecular Imaging, Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marco G Paggi
- Department of Research, Advanced Diagnostics and Technological Innovation, Unit of Cellular Networks and Therapeutic Targets, Proteomics Area, Regina Elena National Cancer Institute, IRCCS, Via Elio Chianesi, 53, Rome, Italy.
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29
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Fang J, Wu Z, Cai C, Wang Q, Tang Y, Cheng F. Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy. J Chem Inf Model 2017; 57:2657-2671. [PMID: 28956927 DOI: 10.1021/acs.jcim.7b00216] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
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Affiliation(s)
- Jiansong Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237, China
| | - Chuipu Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Qi Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine , 12 Jichang Road, Guangzhou 510405, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , 130 Meilong Road, Shanghai 200237, China
| | - Feixiong Cheng
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts 02215, United States.,Center for Complex Networks Research (CCNR), Northeastern University , Boston, Massachusetts 02115, United States
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30
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Precision medicine for hepatocelluar carcinoma using molecular pattern diagnostics: results from a preclinical pilot study. Cell Death Dis 2017; 8:e2867. [PMID: 28594404 PMCID: PMC5520889 DOI: 10.1038/cddis.2017.229] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 04/07/2017] [Accepted: 04/11/2017] [Indexed: 02/07/2023]
Abstract
The aim of this study was to design a road map for personalizing cancer therapy in hepatocellular carcinoma (HCC) by using molecular pattern diagnostics. As an exploratory study, we investigated molecular patterns of tissues of two tumors from individual HCC patients, which in previous experiments had shown contrasting reactions to the phase 2 transforming growth factor beta receptor 1 inhibitor galunisertib. Cancer-driving molecular patterns encompass – inter alias – altered transcription profiles and somatic mutations in coding regions differentiating tumors from their respective peritumoral tissues and from each other. Massive analysis of cDNA ends and all-exome sequencing demonstrate a highly divergent transcriptional and mutational landscape, respectively, for the two tumors, that offers potential explanations for the tumors contrasting responses to galunisertib. Molecular pattern diagnostics (MPDs) suggest alternative, individual-tumor-specific therapies, which in both cases deviate from the standard sorafenib treatment and from each other. Suggested personalized therapies use kinase inhibitors and immune-focused drugs as well as low-toxicity natural compounds identified using an advanced bioinformatics routine included in the MPD protocol. The MPD pipeline we describe here for the prediction of suitable drugs for treatment of two contrasting HCCs may serve as a blueprint for the design of therapies for various types of cancer.
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31
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Fang J, Cai C, Wang Q, Lin P, Zhao Z, Cheng F. Systems Pharmacology-Based Discovery of Natural Products for Precision Oncology Through Targeting Cancer Mutated Genes. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:177-187. [PMID: 28294568 PMCID: PMC5356618 DOI: 10.1002/psp4.12172] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2016] [Revised: 01/09/2017] [Accepted: 01/10/2017] [Indexed: 02/05/2023]
Abstract
Massive cancer genomics data have facilitated the rapid revolution of a novel oncology drug discovery paradigm through targeting clinically relevant driver genes or mutations for the development of precision oncology. Natural products with polypharmacological profiles have been demonstrated as promising agents for the development of novel cancer therapies. In this study, we developed an integrated systems pharmacology framework that facilitated identifying potential natural products that target mutated genes across 15 cancer types or subtypes in the realm of precision medicine. High performance was achieved for our systems pharmacology framework. In case studies, we computationally identified novel anticancer indications for several US Food and Drug Administration-approved or clinically investigational natural products (e.g., resveratrol, quercetin, genistein, and fisetin) through targeting significantly mutated genes in multiple cancer types. In summary, this study provides a powerful tool for the development of molecularly targeted cancer therapies through targeting the clinically actionable alterations by exploiting the systems pharmacology of natural products.
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Affiliation(s)
- J Fang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - C Cai
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - Q Wang
- Institute of Clinical Pharmacology, Guangzhou University of Chinese Medicine, Guangzhou, P.R. China
| | - P Lin
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China
| | - Z Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, USA.,Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - F Cheng
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, P.R. China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
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32
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Mounika Inavolu S, Renbarger J, Radovich M, Vasudevaraja V, Kinnebrew GH, Zhang S, Cheng L. IODNE: An integrated optimization method for identifying the deregulated subnetwork for precision medicine in cancer. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:168-176. [PMID: 28266149 PMCID: PMC5351413 DOI: 10.1002/psp4.12167] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Revised: 01/05/2017] [Accepted: 01/06/2017] [Indexed: 12/18/2022]
Abstract
Subnetwork analysis can explore complex patterns of entire molecular pathways for the purpose of drug target identification. In this article, the gene expression profiles of a cohort of patients with breast cancer are integrated with protein‐protein interaction (PPI) networks using, simultaneously, both edge scoring and node scoring. A novel optimization algorithm, integrated optimization method to identify deregulated subnetwork (IODNE), is developed to search for the optimal dysregulated subnetwork of the merged gene and protein network. IODNE is applied to select subnetworks for Luminal‐A breast cancer from The Cancer Genome Atlas (TCGA) data. A large fraction of cancer‐related genes and the well‐known clinical targets, ER1/PR and HER2, are found by IODNE. This validates the utility of IODNE. When applying IODNE to the triple‐negative breast cancer (TNBC) subtype data, we identified subnetworks that contain genes such as ERBB2, HRAS, PGR, CAD, POLE, and SLC2A1.
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Affiliation(s)
- S Mounika Inavolu
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - J Renbarger
- Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - M Radovich
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - V Vasudevaraja
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - G H Kinnebrew
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - S Zhang
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - L Cheng
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Medical and Molecular Genetics, School of Medicine, Indiana University, Indianapolis, Indiana, USA.,Department of Pediatrics, Hematology/Oncology, School of Medicine, Indiana University, Indianapolis, Indiana, USA
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Lu W, Yao X, Ouyang P, Dong N, Wu D, Jiang X, Wu Z, Zhang C, Xu Z, Tang Y, Zou S, Liu M, Li J, Zeng M, Lin P, Cheng F, Huang J. Drug Repurposing of Histone Deacetylase Inhibitors That Alleviate Neutrophilic Inflammation in Acute Lung Injury and Idiopathic Pulmonary Fibrosis via Inhibiting Leukotriene A4 Hydrolase and Blocking LTB4 Biosynthesis. J Med Chem 2017; 60:1817-1828. [PMID: 28218840 DOI: 10.1021/acs.jmedchem.6b01507] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Acute lung injury (ALI) and idiopathic pulmonary fibrosis (IPF) are both serious public health problems with high incidence and mortality rate in adults, and with few drugs available for the efficient treatment in clinic. In this study, we identified that two known histone deacetylase (HDAC) inhibitors, suberanilohydroxamic acid (SAHA, 1) and its analogue 4-(dimethylamino)-N-[7-(hydroxyamino)-7-oxoheptyl]benzamide (2), are effective inhibitors of Leukotriene A4 hydrolase (LTA4H), a key enzyme in the biosynthesis of leukotriene B4 (LTB4), across a panel of 18 HDAC inhibitors, using enzymatic assay, thermofluor assay, and X-ray crystallographic investigation. Importantly, both 1 and 2 markedly diminish early neutrophilic inflammation in mouse models of ALI and IPF under a clinical safety dose. Detailed mechanisms of down-regulation of proinflammatory cytokines by 1 or 2 were determined in vivo. Collectively, 1 and 2 would provide promising agents with well-known clinical safety for potential treatment in patients with ALI and IPF via pharmacologically inhibiting LAT4H and blocking LTB4 biosynthesis.
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Affiliation(s)
- Weiqiang Lu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China.,Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Xue Yao
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Ping Ouyang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Ningning Dong
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Xingwu Jiang
- Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Chen Zhang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Zhongyu Xu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Shien Zou
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University , Shanghai 200011, China
| | - Mingyao Liu
- Shanghai Key Laboratory of Regulatory Biology, The Institute of Biomedical Sciences and School of Life Sciences, East China Normal University , Shanghai 200241, China
| | - Jian Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
| | - Minghua Zeng
- Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources (Ministry of Education), School of Chemistry & Chemical Engineering, Guangxi Normal University , Guilin 541004, China
| | - Ping Lin
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu 610041, Sichuan, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy , Chengdu 610041, Sichuan, China.,Center for Complex Networks Research, Northeastern University , Boston, Massachusetts 02115, United States.,Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School , Boston, Massachusetts 02215, United States
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology , Shanghai 200237, China
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Wu Z, Lu W, Wu D, Luo A, Bian H, Li J, Li W, Liu G, Huang J, Cheng F, Tang Y. In silico prediction of chemical mechanism of action via an improved network-based inference method. Br J Pharmacol 2016; 173:3372-3385. [PMID: 27646592 DOI: 10.1111/bph.13629] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 08/26/2016] [Accepted: 09/10/2016] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND AND PURPOSE Deciphering chemical mechanism of action (MoA) enables the development of novel therapeutics (e.g. drug repositioning) and evaluation of drug side effects. Development of novel computational methods for chemical MoA assessment under a systems pharmacology framework would accelerate drug discovery and development with greater efficiency and low cost. EXPERIMENTAL APPROACH In this study, we proposed an improved network-based inference method, balanced substructure-drug-target network-based inference (bSDTNBI), to predict MoA for old drugs, clinically failed drugs and new chemical entities. Specifically, three parameters were introduced into network-based resource diffusion processes to adjust the initial resource allocation of different node types, the weighted values of different edge types and the influence of hub nodes. The performance of the method was systematically validated by benchmark datasets and bioassays. KEY RESULTS High performance was yielded for bSDTNBI in both 10-fold and leave-one-out cross validations. A global drug-target network was built to explore MoA of anticancer drugs and repurpose old drugs for 15 cancer types/subtypes. In a case study, 27 predicted candidates among 56 commercially available compounds were experimentally validated to have binding affinities on oestrogen receptor α with IC50 or EC50 values ≤10 μM. Furthermore, two dual ligands with both agonistic and antagonistic activities ≤1 μM would provide potential lead compounds for the development of novel targeted therapy in breast cancer or osteoporosis. CONCLUSION AND IMPLICATIONS In summary, bSDTNBI would provide a powerful tool for the MoA assessment on both old drugs and novel compounds in drug discovery and development.
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Affiliation(s)
- Zengrui Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weiqiang Lu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China
| | - Dang Wu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Anqi Luo
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Hanping Bian
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jie Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Jin Huang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Feixiong Cheng
- State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.,Center for Complex Networks Research, Northeastern University, Boston, Massachusetts, USA
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
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Cheng F, Zhao J, Hanker AB, Brewer MR, Arteaga CL, Zhao Z. Transcriptome- and proteome-oriented identification of dysregulated eIF4G, STAT3, and Hippo pathways altered by PIK3CA H1047R in HER2/ER-positive breast cancer. Breast Cancer Res Treat 2016; 160:457-474. [PMID: 27771839 PMCID: PMC10183099 DOI: 10.1007/s10549-016-4011-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 10/05/2016] [Indexed: 01/25/2023]
Abstract
PURPOSE Phosphatidylinositol 3-kinase (PI3K)/AKT pathway aberrations are common in human breast cancer. Furthermore, PIK3CA mutations are commonly associated with resistance to anti-epidermal growth factor receptor 2 (HER2) or anti-estrogen receptor (ER) agents in HER2 or ER positive (HER2+/ER+) breast cancer. Hence, deciphering the underlying mechanisms of PIK3CA mutations in HER2+/ER+ breast cancer would provide novel insights into elucidating resistance to anti-HER2/ER therapies. METHODS In this study, we systematically investigated the biological consequences of PIK3CA H1047R in HER2+/ER+ breast cancer by uniquely incorporating mRNA transcriptomic data from The Cancer Genome Atlas and proteomic data from reverse-phase protein arrays. RESULTS Our integrative bioinformatics analyses revealed that several important pathways such as STAT3 and VEGF/hypoxia were selectively altered by PIK3CA H1047R in HER2+/ER+ breast cancer. Protein differential expression analysis indicated that an elevated eIF4G might promote tumor angiogenesis and growth via regulation of the hypoxia-activated switch in HER2+ PIK3CA H1047R breast cancer. We observed hypo-phosphorylation of EGFR in HER2+ PIK3CA H1047R breast cancer versus HER2+PIK3CAwild-type (PIK3CA WT). In addition, ER and PIK3CA H1047R might cooperate to activate STAT3, MAPK, AKT, and Hippo pathways in ER+ PIK3CA H1047R breast cancer. A higher YAPpS127 level was observed in ER+ PIK3CA H1047R patients than that in an ER+ PIK3CA WT subgroup. By examining breast cancer cell lines having both microarray gene expression and drug treatment data from the Genomics of Drug Sensitivity in Cancer and the Stand Up to Cancer datasets, we found that the elevated YAP1 mRNA expression was associated with the resistance of BCL-2 family inhibitors, but with the sensitivity to MEK/MAPK inhibitors in breast cancer cells. CONCLUSIONS In summary, these findings shed light on the functional consequences of PIK3CA H1047R-driven breast tumorigenesis and resistance to the existing therapeutic agents in HER2+/ER+ breast cancer.
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Affiliation(s)
- Feixiong Cheng
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, 02215, USA.,Center for Complex Networks Research, Northeastern University, Boston, MA, 02115, USA
| | - Junfei Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA.,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Ariella B Hanker
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Monica Red Brewer
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Carlos L Arteaga
- Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. .,Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA. .,Department of Cancer Biology, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA. .,Breast Cancer Research Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
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Systems Biology-Based Investigation of Cellular Antiviral Drug Targets Identified by Gene-Trap Insertional Mutagenesis. PLoS Comput Biol 2016; 12:e1005074. [PMID: 27632082 PMCID: PMC5025164 DOI: 10.1371/journal.pcbi.1005074] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 07/22/2016] [Indexed: 02/05/2023] Open
Abstract
Viruses require host cellular factors for successful replication. A comprehensive systems-level investigation of the virus-host interactome is critical for understanding the roles of host factors with the end goal of discovering new druggable antiviral targets. Gene-trap insertional mutagenesis is a high-throughput forward genetics approach to randomly disrupt (trap) host genes and discover host genes that are essential for viral replication, but not for host cell survival. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses, 1 gram-negative bacterium, and 5 toxins. We herein reported 712 candidate cellular genes, characterizing distinct topological network and evolutionary signatures, and occupying central hubs in the human interactome. Cell cycle phase-specific network analysis showed that host cell cycle programs played critical roles during viral replication (e.g. MYC and TAF4 regulating G0/1 phase). Moreover, the viral perturbation of host cellular networks reflected disease etiology in that host genes (e.g. CTCF, RHOA, and CDKN1B) identified were frequently essential and significantly associated with Mendelian and orphan diseases, or somatic mutations in cancer. Computational drug repositioning framework via incorporating drug-gene signatures from the Connectivity Map into the virus-host interactome identified 110 putative druggable antiviral targets and prioritized several existing drugs (e.g. ajmaline) that may be potential for antiviral indication (e.g. anti-Ebola). In summary, this work provides a powerful methodology with a tight integration of gene-trap insertional mutagenesis testing and systems biology to identify new antiviral targets and drugs for the development of broadly acting and targeted clinical antiviral therapeutics. Infectious diseases result in millions of deaths and cost billions of dollars annually. Hence, there is urgency for developing more innovative and effective antiviral therapeutics. In this study, we used libraries of randomly mutagenized cells to discover cellular genes that are essential for the replication of 10 distinct cytotoxic mammalian viruses. We herein reported over 700 candidate cellular genes, over 20% of which were independently selected by multiple viruses in one or more cell types. Using systems biology-based analysis, we found that host genes associated with viral replication tended to occupy central hubs in the human protein interactome and to be ancient genes with low evolutionary rates, compared to non-virus-associated genes. Cell cycle phase-specific sub-network analysis showed that host cell cycle program played important roles during viral replication by regulating specific cell cycle phases. Moreover, we presented novel evidences to suggest that host genes supporting viral replication were frequently implicated in Mendelian and orphan diseases, or played critical roles in cancer. Importantly, we found approximately 110 new putative druggable antiviral targets by merging genome-wide gene-trap insertional mutagenesis, drug-gene network, and bioinformatics data. Furthermore, we have demonstrated the use of a computable representation of genetic testing to effectively identify new potential antiviral indications for existing drugs. In summary, this study presents new and important methodologies for developing broadly active antiviral therapeutics.
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Cheng F. Gender Dimorphism Creates Divergent Cancer Susceptibilities. Trends Cancer 2016; 2:325-326. [PMID: 28741533 DOI: 10.1016/j.trecan.2016.06.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Accepted: 06/01/2016] [Indexed: 12/16/2022]
Abstract
To date, the cellular and molecular mechanisms underlying sexual dimorphism in the incidence, prognosis, and treatment responses of cancer remain unclear. In a recent article published in Cancer Cell, Yuan et al. applied a pan-cancer analysis to identify sex-biased molecular signatures and revealed two sex-effect groups characterized by distinct incidence and mortality profiles.
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Affiliation(s)
- Feixiong Cheng
- Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA; Center for Complex Networks Research, Northeastern University, Boston, MA 02115, USA.
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