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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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2
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Weng W, Fu J, Cheng F, Wang Y, Zhang J. Integrated Bulk and Single-Cell RNA-Sequencing Reveals the Effects of Circadian Rhythm Disruption on the Metabolic Reprogramming of CD4+ T Cells in Alzheimer's Disease. Mol Neurobiol 2024; 61:6013-6030. [PMID: 38265551 DOI: 10.1007/s12035-023-03907-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 12/22/2023] [Indexed: 01/25/2024]
Abstract
Although growing evidence suggests close correlations between Alzheimer's disease (AD) and circadian rhythm disruption (CRD), few studies have focused on the influence of circadian rhythm on levels of immune cells in AD. We aimed to delineate the mechanism underlying the effects of circadian related genes on T cell immune function in AD. A total of 112 brain samples were used to construct the CRD-related model by performing weighted gene co-expression network analysis and machine learning algorithms (LASSO, SVM-RFE, and RF). The ssGSEA method was used to calculate the CRDscore in order to quantify CRD status. Using single-cell transcriptome data of CSF cells, we investigated the CD4+ T cell metabolism and cell-cell communication in high- and low-risk CRD groups. Connectivity map (CMap) was applied to explore small molecule drugs targeting CRD, and the expression of the signature gene GPR4 was further validated in AD. The CRDscore algorithm, which is based on 23 circadian-related genes, can effectively classify the CRD status in AD datasets. The single-cell analysis revealed that the CD4+ T cells with high CRDscore were characterized by hypometabolism. Cell communication analysis revealed that CD4+ T cells might be involved in promoting CD8+ T cell adhesion under CRD, which may facilitate T cell infiltration into the brain parenchyma. Overall, this study indicates the potential connotation of circadian rhythm in AD, providing insights into understanding T cell metabolic reprogramming under CRD.
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Affiliation(s)
- Weipin Weng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Neurology, Center for Cognitive Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jianhan Fu
- Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fan Cheng
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Yixuan Wang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China
| | - Jie Zhang
- Department of Neurology, The Second Xiangya Hospital of Central South University, Changsha, China.
- Clinical Medical Research Center for Stroke Prevention and Treatment of Hunan Province, Department of Neurology, Second Xiangya Hospital, Central South University, Changsha, China.
- Department of Neurology, Turpan City People's Hospital, Tulufan, China.
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3
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Wang Z, Chen DN, Huang XY, Zhu JM, Lin F, You Q, Lin YZ, Cai H, Wei Y, Xue XY, Zheng QS, Xu N. Machine learning-based autophagy-related prognostic signature for personalized risk stratification and therapeutic approaches in bladder cancer. Int Immunopharmacol 2024; 138:112623. [PMID: 38991630 DOI: 10.1016/j.intimp.2024.112623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 06/21/2024] [Accepted: 07/01/2024] [Indexed: 07/13/2024]
Abstract
OBJECTIVE Bladder cancer (BCa) is a highly lethal urological malignancy characterized by its notable histological heterogeneity. Autophagy has swiftly emerged as a diagnostic and prognostic biomarker in diverse cancer types. Nonetheless, the currently accessible autophagy-related signature specific to BCa remains limited. METHODS A refined autophagy-related signature was developed through a 10-fold cross-validation framework, incorporating 101 combinations of machine learning algorithms. The performance of this signature in predicting prognosis and response to immunotherapy was thoroughly evaluated, along with an exploration of potential drug targets and compounds. In vitro and in vivo experiments were conducted to verify the regulatory mechanism of hub gene. RESULTS The autophagy-related prognostic signature (ARPS) has exhibited superior performance in predicting the prognosis of BCa compared to the majority of clinical features and other developed markers. Higher ARPS is associated with poorer prognosis and reduced sensitivity to immunotherapy. Four potential targets and five therapeutic agents were screened for patients in the high-ARPS group. In vitro and vivo experiments have confirmed that FKBP9 promotes the proliferation, invasion, and metastasis of BCa. CONCLUSIONS Overall, our study developed a valuable tool to optimize risk stratification and decision-making for BCa patients.
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Affiliation(s)
- Zhen Wang
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Dong-Ning Chen
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Xu-Yun Huang
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Jun-Ming Zhu
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Fei Lin
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Qi You
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Yun-Zhi Lin
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Hai Cai
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Yong Wei
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Xue-Yi Xue
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
| | - Qing-Shui Zheng
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China.
| | - Ning Xu
- Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China; Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China.
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4
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Nene L, Flepisi BT, Brand SJ, Basson C, Balmith M. Evolution of Drug Development and Regulatory Affairs: The Demonstrated Power of Artificial Intelligence. Clin Ther 2024:S0149-2918(24)00138-3. [PMID: 38981791 DOI: 10.1016/j.clinthera.2024.05.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 05/27/2024] [Accepted: 05/29/2024] [Indexed: 07/11/2024]
Abstract
PURPOSE Artificial intelligence (AI) refers to technology capable of mimicking human cognitive functions and has important applications across all sectors and industries, including drug development. This has considerable implications for the regulation of drug development processes, as it is expected to transform both the way drugs are brought to market and the systems through which this process is controlled. There is currently insufficient evidence in published literature of the real-world applications of AI. Therefore, this narrative review investigated, collated, and elucidated the applications of AI in drug development and its regulatory processes. METHODS A narrative review was conducted to ascertain the role of AI in streamlining drug development and regulatory processes. FINDINGS The findings of this review revealed that machine learning or deep learning, natural language processing, and robotic process automation were favored applications of AI. Each of them had considerable implications on the operations they were intended to support. Overall, the AI tools facilitated access and provided manageability of information for decision-making across the drug development lifecycle. However, the findings also indicate that additional work is required by regulatory authorities to set out appropriate guidance on applications of the technology, which has critical implications for safety, regulatory process workflow and product development costs. IMPLICATIONS AI has adequately proven its utility in drug development, prompting further investigations into the translational value of its utility based on cost and time saved for the delivery of essential drugs.
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Affiliation(s)
- Linda Nene
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Brian Thabile Flepisi
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Sarel Jacobus Brand
- Center of Excellence for Pharmaceutical Sciences, Department of Pharmacology, North-West University, Potchefstroom, South Africa
| | - Charlise Basson
- Department of Physiology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
| | - Marissa Balmith
- Department of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.
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5
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He H, Xie J, Huang D, Zhang M, Zhao X, Ying Y, Wang J. DRTerHGAT: A drug repurposing method based on the ternary heterogeneous graph attention network. J Mol Graph Model 2024; 130:108783. [PMID: 38677034 DOI: 10.1016/j.jmgm.2024.108783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/21/2024] [Accepted: 04/23/2024] [Indexed: 04/29/2024]
Abstract
Drug repurposing is an effective method to reduce the time and cost of drug development. Computational drug repurposing can quickly screen out the most likely associations from large biological databases to achieve effective drug repurposing. However, building a comprehensive model that integrates drugs, proteins, and diseases for drug repurposing remains challenging. This study proposes a drug repurposing method based on the ternary heterogeneous graph attention network (DRTerHGAT). DRTerHGAT designs a novel protein feature extraction process consisting of a large-scale protein language model and a multi-task autoencoder, so that protein features can be extracted accurately and efficiently from amino acid sequences. The ternary heterogeneous graph of drug-protein-disease comprehensively considering the relationships among the three types of nodes, including three homogeneous and three heterogeneous relationships. Based on the graph and the extracted protein features, the deep features of the drugs and the diseases are extracted by graph convolutional networks (GCN) and heterogeneous graph node attention networks (HGNA). In the experiments, DRTerHGAT is proven superior to existing advanced methods and DRTerHGAT variants. DRTerHGAT's powerful ability for drug repurposing is also demonstrated in Alzheimer's disease.
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Affiliation(s)
- Hongjian He
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Jiang Xie
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Dingkai Huang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Mengfei Zhang
- The School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Xuyu Zhao
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Yiwei Ying
- School of Life Sciences,Shanghai University, Shanghai, China
| | - Jiao Wang
- School of Life Sciences,Shanghai University, Shanghai, China.
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6
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Ganapathiraju MK, Triptish B, Deshpande S, Wesesky M, Wood J, Nimgaonkar VL. Schizophrenia interactome derived repurposable drugs and randomized control trials of two candidates. Biol Psychiatry 2024:S0006-3223(24)01426-4. [PMID: 38950808 DOI: 10.1016/j.biopsych.2024.06.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 07/03/2024]
Abstract
There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses like schizophrenia. Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past two decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are FDA-approved or reached late stages in clinical trials. We developed a rational approach to achieve this computationally for schizophrenia by studying drugs that target the proteins in its protein interaction network ('interactome'). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence: their target networks were enriched for pathways relevant to schizophrenia etiology or for genes that had an association with diseases pathogenically similar to schizophrenia. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials (RCT), where their prior safety approvals obviate the need for time-consuming phase I and II studies. We selected two among the shortlisted candidates based on likely adherence and side effect profiles. We are testing them through adjunctive RCTs for patients with schizophrenia or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in future and should also be exploited for other psychiatric disorders.
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Affiliation(s)
- Madhavi K Ganapathiraju
- Department of Biomedical Informatics and Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Carnegie Mellon University in Qatar, Doha, Qatar.
| | - Bhatia Triptish
- Dept. of Psychiatry, Centre of Excellence in Mental Health, ABVIMS- Dr Ram Manohar Lohia Hospital, New Delhi 110001, India
| | - Smita Deshpande
- Department of Psychiatry, St John's Medical College Hospital, Koramangala, Bengaluru, Karnataka 560034, India
| | - Maribeth Wesesky
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joel Wood
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vishwajit L Nimgaonkar
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Veterans Administration Pittsburgh Healthcare System, Pittsburgh, PA, USA.
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7
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Paudel KR, Singh M, De Rubis G, Kumbhar P, Mehndiratta S, Kokkinis S, El-Sherkawi T, Gupta G, Singh SK, Malik MZ, Mohammed Y, Oliver BG, Disouza J, Patravale V, Hansbro PM, Dua K. Computational and biological approaches in repurposing ribavirin for lung cancer treatment: Unveiling antitumorigenic strategies. Life Sci 2024; 352:122859. [PMID: 38925223 DOI: 10.1016/j.lfs.2024.122859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Revised: 03/11/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024]
Abstract
Lung cancer is among leading causes of death worldwide. The five-year survival rate of this disease is extremely low (17.8 %), mainly due to difficult early diagnosis and to the limited efficacy of currently available chemotherapeutics. This underlines the necessity to develop innovative therapies for lung cancer. In this context, drug repurposing represents a viable approach, as it reduces the turnaround time of drug development removing costs associated to safety testing of new molecular entities. Ribavirin, an antiviral molecule used to treat hepatitis C virus infections, is particularly promising as repurposed drug for cancer treatment, having shown therapeutic activity against glioblastoma, acute myeloid leukemia, and nasopharyngeal carcinoma. In the present study, we thoroughly investigated the in vitro anticancer activity of ribavirin against A549 human lung adenocarcinoma cells. From a functional standpoint, ribavirin significantly inhibits cancer hallmarks such as cell proliferation, migration, and colony formation. Mechanistically, ribavirin downregulates the expression of numerous proteins and genes regulating cell migration, proliferation, apoptosis, and cancer angiogenesis. The anticancer potential of ribavirin was further investigated in silico through gene ontology pathway enrichment and protein-protein interaction networks, identifying five putative molecular interactors of ribavirin (Erb-B2 Receptor Tyrosine Kinase 4 (Erb-B4); KRAS; Intercellular Adhesion Molecule 1 (ICAM-1); amphiregulin (AREG); and neuregulin-1 (NRG1)). These interactions were characterized via molecular docking and molecular dynamic simulations. The results of this study highlight the potential of ribavirin as a repurposed chemotherapy against lung cancer, warranting further studies to ascertain the in vivo anticancer activity of this molecule.
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Affiliation(s)
- Keshav Raj Paudel
- Centre of Inflammation, Centenary Institute and University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW 2007, Australia
| | - Manisha Singh
- Department of Biotechnology, Jaypee Institute of Information Technology (JIIT), Noida, Uttar Pradesh, India; Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Gabriele De Rubis
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Popat Kumbhar
- Department of Pharmaceutics, Tatyasaheb Kore College of Pharmacy, Warananagar, Tal: Panhala, Dist: Kolhapur, Maharashtra 416113, India
| | - Samir Mehndiratta
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Sofia Kokkinis
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Tammam El-Sherkawi
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Gaurav Gupta
- Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, 140401, Punjab, India; Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | - Sachin Kumar Singh
- Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia; School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar-Delhi GT Road, Phagwara 144411, Punjab, India
| | - Md Zubbair Malik
- Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Dasman, Kuwait city 15462, Kuwait
| | - Yousuf Mohammed
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Brian G Oliver
- Woolcock Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia; School of Life Sciences, University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - John Disouza
- Department of Pharmaceutics, Tatyasaheb Kore College of Pharmacy, Warananagar, Tal: Panhala, Dist: Kolhapur, Maharashtra 416113, India
| | - Vandana Patravale
- Department of Pharmaceutical Sciences and Technology, Institute of Chemical Technology, Nathalal Parekh Marg, Matunga, Mumbai 400019, Maharashtra, India
| | - Philip Michael Hansbro
- Centre of Inflammation, Centenary Institute and University of Technology Sydney, Faculty of Science, School of Life Sciences, Sydney, NSW 2007, Australia.
| | - Kamal Dua
- Discipline of Pharmacy, Graduate School of Health, University of Technology Sydney, Sydney, NSW 2007, Australia; Faculty of Health, Australian Research Centre in Complementary and Integrative Medicine, University of Technology Sydney, Ultimo, NSW 2007, Australia.
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Vijayakumar S, DiGuiseppi JA, Dabestani PJ, Ryan WG, Quevedo RV, Li Y, Diers J, Tu S, Fleegel J, Nguyen C, Rhoda LM, Imami AS, Hamoud ARA, Lovas S, McCullumsmith RE, Zallocchi M, Zuo J. In silico transcriptome screens identify epidermal growth factor receptor inhibitors as therapeutics for noise-induced hearing loss. SCIENCE ADVANCES 2024; 10:eadk2299. [PMID: 38896614 PMCID: PMC11186505 DOI: 10.1126/sciadv.adk2299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 05/14/2024] [Indexed: 06/21/2024]
Abstract
Noise-induced hearing loss (NIHL) is a common sensorineural hearing impairment that lacks U.S. Food and Drug Administration-approved drugs. To fill the gap in effective screening models, we used an in silico transcriptome-based drug screening approach, identifying 22 biological pathways and 64 potential small molecule treatments for NIHL. Two of these, afatinib and zorifertinib [epidermal growth factor receptor (EGFR) inhibitors], showed efficacy in zebrafish and mouse models. Further tests with EGFR knockout mice and EGF-morpholino zebrafish confirmed their protective role against NIHL. Molecular studies in mice highlighted EGFR's crucial involvement in NIHL and the protective effect of zorifertinib. When given orally, zorifertinib was found in the perilymph with favorable pharmacokinetics. In addition, zorifertinib combined with AZD5438 (a cyclin-dependent kinase 2 inhibitor) synergistically prevented NIHL in zebrafish. Our results underscore the potential for in silico transcriptome-based drug screening in diseases lacking efficient models and suggest EGFR inhibitors as potential treatments for NIHL, meriting clinical trials.
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Affiliation(s)
- Sarath Vijayakumar
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Joseph A. DiGuiseppi
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Parinaz Jila Dabestani
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - William G. Ryan
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA.
| | - Rene Vielman Quevedo
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Yuju Li
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Jack Diers
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Shu Tu
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Jonathan Fleegel
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Cassidy Nguyen
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Lauren M. Rhoda
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Ali Sajid Imami
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA.
| | | | - Sándor Lovas
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Robert E. McCullumsmith
- Department of Neurosciences, University of Toledo, Toledo, OH 43614, USA.
- Neurosciences Institute, ProMedica, Toledo, OH 43606, USA
| | - Marisa Zallocchi
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
| | - Jian Zuo
- Department of Biomedical Sciences, School of Medicine, Creighton University, Omaha, NE 68178, USA
- Ting Therapeutics, University of California San Diego, 9310 Athena Circle, San Diego, CA 92037, USA
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9
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Bai H, Lu S, Zhang T, Cui H, Nakaguchi T, Xuan P. Graph reasoning method enhanced by relational transformers and knowledge distillation for drug-related side effect prediction. iScience 2024; 27:109571. [PMID: 38799562 PMCID: PMC11126883 DOI: 10.1016/j.isci.2024.109571] [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: 08/06/2023] [Revised: 09/29/2023] [Accepted: 03/22/2024] [Indexed: 05/29/2024] Open
Abstract
Identifying the side effects related to drugs is beneficial for reducing the risk of drug development failure and saving the drug development cost. We proposed a graph reasoning method, RKDSP, to fuse the semantics of multiple connection relationships, the local knowledge within each meta-path, the global knowledge among multiple meta-paths, and the attributes of the drug and side effect node pairs. We constructed drug-side effect heterogeneous graphs consisting of the drugs, side effects, and their similarity and association connections. Multiple relational transformers were established to learn node features from diverse meta-path semantic perspectives. A knowledge distillation module was constructed to learn local and global knowledge of multiple meta-paths. Finally, an adaptive convolutional neural network-based strategy was presented to adaptively encode the attributes of each drug-side effect node pair. The experimental results demonstrated that RKDSP outperforms the compared state-of-the-art prediction approaches.
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Affiliation(s)
- Honglei Bai
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Siyuan Lu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- School of Mathematical Science, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Ping Xuan
- Department of Computer Science and Technology, Shantou University, Shantou, China
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10
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Santos AS, Costa VAF, Freitas VAQ, Dos Anjos LRB, de Almeida Santos ES, Arantes TD, Costa CR, de Sene Amâncio Zara AL, do Rosário Rodrigues Silva M, Neves BJ. Drug to genome to drug: a computational large-scale chemogenomics screening for novel drug candidates against sporotrichosis. Braz J Microbiol 2024:10.1007/s42770-024-01406-x. [PMID: 38888692 DOI: 10.1007/s42770-024-01406-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 05/28/2024] [Indexed: 06/20/2024] Open
Abstract
Sporotrichosis is recognized as the predominant subcutaneous mycosis in South America, attributed to pathogenic species within the Sporothrix genus. Notably, in Brazil, Sporothrix brasiliensis emerges as the principal species, exhibiting significant sapronotic, zoonotic and enzootic epidemic potential. Consequently, the discovery of novel therapeutic agents for the treatment of sporotrichosis is imperative. The present study is dedicated to the repositioning of pharmaceuticals for sporotrichosis therapy. To achieve this goal, we designed a pipeline with the following steps: (a) compilation and preparation of Sporothrix genome data; (b) identification of orthologous proteins among the species; (c) identification of homologous proteins in publicly available drug-target databases; (d) selection of Sporothrix essential targets using validated genes from Saccharomyces cerevisiae; (e) molecular modeling studies; and (f) experimental validation of selected candidates. Based on this approach, we were able to prioritize eight drugs for in vitro experimental validation. Among the evaluated compounds, everolimus and bifonazole demonstrated minimum inhibitory concentration (MIC) values of 0.5 µg/mL and 4.0 µg/mL, respectively. Subsequently, molecular docking studies suggest that bifonazole and everolimus may target specific proteins within S. brasiliensis- namely, sterol 14-α-demethylase and serine/threonine-protein kinase TOR, respectively. These findings shed light on the potential binding affinities and binding modes of bifonazole and everolimus with their probable targets, providing a preliminary understanding of the antifungal mechanism of action of these compounds. In conclusion, our research advances the understanding of the therapeutic potential of bifonazole and everolimus, supporting their further investigation as antifungal agents for sporotrichosis in prospective hit-to-lead and preclinical investigations.
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Affiliation(s)
- Andressa Santana Santos
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | | | - Laura Raniere Borges Dos Anjos
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - Thales Domingos Arantes
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Carolina Rodrigues Costa
- Institute of Tropical Pathology and Public Health, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Ana Laura de Sene Amâncio Zara
- Postgraduate Program in Health Technology Assistance and Assessment (PPG-AAS), Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - Bruno Junior Neves
- Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil.
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11
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Park JH, Cho YR. Computational drug repositioning with attention walking. Sci Rep 2024; 14:10072. [PMID: 38698208 PMCID: PMC11066070 DOI: 10.1038/s41598-024-60756-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 04/26/2024] [Indexed: 05/05/2024] Open
Abstract
Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.
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Affiliation(s)
- Jong-Hoon Park
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea
| | - Young-Rae Cho
- Division of Software, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
- Division of Digital Healthcare, Yonsei University Mirae Campus, Wonju-si, 26493, Gangwon-do, Korea.
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12
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Mishra A, Vasanthan M, Malliappan SP. Drug Repurposing: A Leading Strategy for New Threats and Targets. ACS Pharmacol Transl Sci 2024; 7:915-932. [PMID: 38633585 PMCID: PMC11019736 DOI: 10.1021/acsptsci.3c00361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/01/2024] [Accepted: 03/06/2024] [Indexed: 04/19/2024]
Abstract
Less than 6% of rare illnesses have an appropriate treatment option. Repurposed medications for new indications are a cost-effective and time-saving strategy that results in excellent success rates, which may significantly lower the risk associated with therapeutic development for rare illnesses. It is becoming a realistic alternative to repurposing "conventional" medications to treat joint and rare diseases considering the significant failure rates, high expenses, and sluggish stride of innovative medication advancement. This is due to delisted compounds, cheaper research fees, and faster development time frames. Repurposed drug competitors have been developed using strategic decisions based on data analysis, interpretation, and investigational approaches, but technical and regulatory restrictions must also be considered. Combining experimental and computational methodologies generates innovative new medicinal applications. It is a one-of-a-kind strategy for repurposing human-safe pharmaceuticals to treat uncommon and difficult-to-treat ailments. It is a very effective method for discovering and creating novel medications. Several pharmaceutical firms have developed novel therapies by repositioning old medications. Repurposing drugs is practical, cost-effective, and speedy and generally involves lower risks when compared to developing a new drug from the beginning.
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Affiliation(s)
- Ashish
Sriram Mishra
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Manimaran Vasanthan
- Department
of Pharmaceutics, SRM College of Pharmacy, SRM Institute of Science and Technology, Kattankulathur, 603202, Tamil Nadu, India
| | - Sivakumar Ponnurengam Malliappan
- School
of Medicine and Pharmacy, Duy Tan University, Da Nang Vietnam, Institute
of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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13
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Israr J, Alam S, Kumar A. System biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:221-245. [PMID: 38789180 DOI: 10.1016/bs.pmbts.2024.03.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.
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Affiliation(s)
- Juveriya Israr
- Institute of Biosciences and Technology, Shri Ramswaroop Memorial University, Lucknow-Deva Road, Barabanki, Uttar Pradesh, India; Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Shabroz Alam
- Department of Biotechnology Era University, Lucknow, Uttar Pradesh, India
| | - Ajay Kumar
- Department of Biotechnology, Faculty of Engineering and Technology, Rama University, Mandhana, Kanpur, Uttar Pradesh, India.
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14
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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15
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Luo H, Yin W, Wang J, Zhang G, Liang W, Luo J, Yan C. Drug-drug interactions prediction based on deep learning and knowledge graph: A review. iScience 2024; 27:109148. [PMID: 38405609 PMCID: PMC10884936 DOI: 10.1016/j.isci.2024.109148] [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: 02/27/2024] Open
Abstract
Drug-drug interactions (DDIs) can produce unpredictable pharmacological effects and lead to adverse events that have the potential to cause irreversible damage to the organism. Traditional methods to detect DDIs through biological or pharmacological analysis are time-consuming and expensive, therefore, there is an urgent need to develop computational methods to effectively predict drug-drug interactions. Currently, deep learning and knowledge graph techniques which can effectively extract features of entities have been widely utilized to develop DDI prediction methods. In this research, we aim to systematically review DDI prediction researches applying deep learning and graph knowledge. The available biomedical data and public databases related to drugs are firstly summarized in this review. Then, we discuss the existing drug-drug interactions prediction methods which have utilized deep learning and knowledge graph techniques and group them into three main classes: deep learning-based methods, knowledge graph-based methods, and methods that combine deep learning with knowledge graph. We comprehensively analyze the commonly used drug related data and various DDI prediction methods, and compare these prediction methods on benchmark datasets. Finally, we briefly discuss the challenges related to drug-drug interactions prediction, including asymmetric DDIs prediction and high-order DDI prediction.
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Affiliation(s)
- Huimin Luo
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Weijie Yin
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Jianlin Wang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
| | - Ge Zhang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China
| | - Wenjuan Liang
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Junwei Luo
- College of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China
| | - Chaokun Yan
- School of Computer and Information Engineering, Henan University, Kaifeng, China
- Academy for Advanced Interdisciplinary Studies, Zhengzhou, China
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16
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Ma J, Zhao Z, Li T, Liu Y, Ma J, Zhang R. GraphsformerCPI: Graph Transformer for Compound-Protein Interaction Prediction. Interdiscip Sci 2024:10.1007/s12539-024-00609-y. [PMID: 38457109 DOI: 10.1007/s12539-024-00609-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 01/01/2024] [Accepted: 01/08/2024] [Indexed: 03/09/2024]
Abstract
Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.
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Affiliation(s)
- Jun Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
- School of Information Engineering, Lanzhou University of Finance and Economics, Lanzhou, 730020, China.
| | - Zhili Zhao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Tongfeng Li
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
- Computer College, Qinghai Normal University, Xi'ning, 810016, China
| | - Yunwu Liu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Jun Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China
| | - Ruisheng Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China.
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17
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Jin S, Zhang Y, Yu H, Lu M. SADR: Self-Supervised Graph Learning With Adaptive Denoising for Drug Repositioning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:265-277. [PMID: 38190661 DOI: 10.1109/tcbb.2024.3351079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications.
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18
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Jin Q, Xie J, Huang D, Zhao C, He H. MSFF-MA-DDI: Multi-Source Feature Fusion with Multiple Attention blocks for predicting Drug-Drug Interaction events. Comput Biol Chem 2024; 108:108001. [PMID: 38154317 DOI: 10.1016/j.compbiolchem.2023.108001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 11/30/2023] [Accepted: 12/03/2023] [Indexed: 12/30/2023]
Abstract
The interaction of multiple drugs could lead to severe events, which cause medical injuries and expenses. Accurate prediction of drug-drug interaction (DDI) events can help clinicians make effective decisions and establish appropriate therapy programs. However, there exist two issues worthy of further consideration. (i) The global features of drug molecules should be paid attention to, rather than just their local characteristics. (ii) The fusion of multi-source features should also be studied to capture the comprehensive features of the drug. This study designs a Multi-Source Feature Fusion framework with Multiple Attention blocks named MSFF-MA-DDI that utilizes multimodal data for DDI event prediction. MSFF-MA-DDI can (i) encode global correlations between long-distance atoms in drug molecular sequences by a self-attention layer based on a position embedding block and (ii) fuse drug sequence features and heterogeneous features (chemical substructure, target, and enzyme) through a multi-head attention block to better represent the features of drugs. Experiments on real-world datasets show that MSFF-MA-DDI can achieve performance that is close to or even better than state-of-the-art models. Especially in cold start scenarios, the model can achieve the best performance. The effectiveness of the model is also supported by the case study on nervous system drugs. The source codes and data are available at https://github.com/BioCenter-SHU/MSFF-MA-DDI.
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Affiliation(s)
- Qi Jin
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China.
| | - Dingkai Huang
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Chang Zhao
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Hongjian He
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
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19
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Huang Y, Wang S, Zhang X, Yang C, Wang S, Cheng H, Ke A, Gao C, Guo K. Identification of Fasudil as a collaborator to promote the anti-tumor effect of lenvatinib in hepatocellular carcinoma by inhibiting GLI2-mediated hedgehog signaling pathway. Pharmacol Res 2024; 200:107082. [PMID: 38280440 DOI: 10.1016/j.phrs.2024.107082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 01/29/2024]
Abstract
Lenvatinib is a frontline tyrosine kinase inhibitor for patients with advanced hepatocellular carcinoma (HCC). However, just 25% of patients benefit from the treatment, and acquired resistance always develops. To date, there are neither effective medications to combat lenvatinib resistance nor accurate markers that might predict how well a patient would respond to the lenvatinib treatment. Thus, novel strategies to recognize and deal with lenvatinib resistance are desperately needed. In the current study, a robust Lenvatinib Resistance index (LRi) model to predict lenvatinib response status in HCC was first established. Subsequently, five candidate drugs (Mercaptopurine, AACOCF3, NU1025, Fasudil, and Exisulind) that were capable of reversing lenvatinib resistance signature were initially selected by performing the connectivity map (CMap) analysis, and fasudil finally stood out by conducting a series of cellular functional assays in vitro and xenograft mouse model. Transcriptomics revealed that the co-administration of lenvatinib and fasudil overcame lenvatinib resistance by remodeling the hedgehog signaling pathway. Mechanistically, the feedback activation of EGFR by lenvatinib led to the activation of the GLI2-ABCC1 pathway, which supported the HCC cell's survival and proliferation. Notably, co-administration of lenvatinib and fasudil significantly inhibited IHH, the upstream switch of the hedgehog pathway, to counteract GLI2 activation and finally enhance the effectiveness of lenvatinib. These findings elucidated a novel EGFR-mediated mechanism of lenvatinib resistance and provided a practical approach to overcoming drug resistance in HCC through meaningful drug repurposing strategies.
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Affiliation(s)
- Yilan Huang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China
| | - Siwei Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China; Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaojun Zhang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Yang
- State Key Laboratory of Systems Medicine for Cancer, Shanghai Cancer Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sikai Wang
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China
| | - Hongxia Cheng
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China
| | - Aiwu Ke
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China.
| | - Chao Gao
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China.
| | - Kun Guo
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Key Laboratory of Carcinogenesis and Cancer Invasion Ministry of Education, Shanghai, China.
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20
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Otero-Carrasco B, Ugarte Carro E, Prieto-Santamaría L, Diaz Uzquiano M, Caraça-Valente Hernández JP, Rodríguez-González A. Identifying patterns to uncover the importance of biological pathways on known drug repurposing scenarios. BMC Genomics 2024; 25:43. [PMID: 38191292 PMCID: PMC10775474 DOI: 10.1186/s12864-023-09913-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
BACKGROUND Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing. METHODS Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test. RESULTS A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05). CONCLUSIONS Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.
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Affiliation(s)
- Belén Otero-Carrasco
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Esther Ugarte Carro
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | - Lucía Prieto-Santamaría
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain
| | - Marina Diaz Uzquiano
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain
| | | | - Alejandro Rodríguez-González
- Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Pozuelo de Alarcón, 28223, Spain.
- ETS Ingenieros Informáticos, Universidad Politécnica de Madrid, Boadilla del Monte, 28660, Spain.
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21
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He S, Lv X, He X, Guo J, Pan R, Jin Y, Tian Z, Pan L, Zhang S. Drug Repositioning for Amyloid Transthyretin Amyloidosis by Interactome Network Corrected by Graph Neural Networks and Transcriptome Analysis. Hum Gene Ther 2024; 35:70-79. [PMID: 37756369 DOI: 10.1089/hum.2021.222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023] Open
Abstract
Amyloid transthyretin (ATTR) amyloidosis caused by transthyretin misfolded into amyloid deposits in nerve and heart is a progressive rare disease. The unknown pathogenesis and the lack of therapy make the 5-year survival prognosis extremely poor. Currently available ATTR drugs can only relieve symptoms and slow down progression, but no drug has demonstrated curable effect for this disease. The growing volume of pharmacological data and large-scale genome and transcriptome data bring new opportunities to find potential new ATTR drugs through computational drug repositioning. We collected the ATTR-related in the disease pathogenesis and differentially expressed (DE) genes from five public databases and Gene Expression Omnibus expression profiles, respectively, then screened drug candidates by a corrected protein-protein network analysis of the ATTR-related genes as well as the drug targets from DrugBank database, and then filtered the drug candidates on the basis of gene expression data perturbed by compounds. We collected 139 and 56 ATTR-related genes from five public databases and transcriptome data, respectively, and performed functional enrichment analysis. We screened out 355 drug candidates based on the proximity to ATTR-related genes in the corrected interactome network, refined by graph neural networks. An Inverted Gene Set Enrichment analysis was further applied to estimate the effect of perturbations on ATTR-related and DE genes. High probability drug candidates were discussed. Drug repositioning using systematic computational processes on an interactome network with transcriptome data were performed to screen out several potential new drug candidates for ATTR.
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Affiliation(s)
- Shan He
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - XiaoYing Lv
- Global Health Drug Discovery Institute, Beijing, China
| | - XinYue He
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - JinJiang Guo
- Global Health Drug Discovery Institute, Beijing, China
| | - RuoKai Pan
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - YuTong Jin
- Global Health Drug Discovery Institute, Beijing, China
| | - Zhuang Tian
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - LuRong Pan
- Global Health Drug Discovery Institute, Beijing, China
| | - ShuYang Zhang
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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22
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Djeddi WE, Hermi K, Ben Yahia S, Diallo G. Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining. BMC Bioinformatics 2023; 24:488. [PMID: 38114937 PMCID: PMC10731821 DOI: 10.1186/s12859-023-05593-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/30/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND The pharmaceutical field faces a significant challenge in validating drug target interactions (DTIs) due to the time and cost involved, leading to only a fraction being experimentally verified. To expedite drug discovery, accurate computational methods are essential for predicting potential interactions. Recently, machine learning techniques, particularly graph-based methods, have gained prominence. These methods utilize networks of drugs and targets, employing knowledge graph embedding (KGE) to represent structured information from knowledge graphs in a continuous vector space. This phenomenon highlights the growing inclination to utilize graph topologies as a means to improve the precision of predicting DTIs, hence addressing the pressing requirement for effective computational methodologies in the field of drug discovery. RESULTS The present study presents a novel approach called DTIOG for the prediction of DTIs. The methodology employed in this study involves the utilization of a KGE strategy, together with the incorporation of contextual information obtained from protein sequences. More specifically, the study makes use of Protein Bidirectional Encoder Representations from Transformers (ProtBERT) for this purpose. DTIOG utilizes a two-step process to compute embedding vectors using KGE techniques. Additionally, it employs ProtBERT to determine target-target similarity. Different similarity measures, such as Cosine similarity or Euclidean distance, are utilized in the prediction procedure. In addition to the contextual embedding, the proposed unique approach incorporates local representations obtained from the Simplified Molecular Input Line Entry Specification (SMILES) of drugs and the amino acid sequences of protein targets. CONCLUSIONS The effectiveness of the proposed approach was assessed through extensive experimentation on datasets pertaining to Enzymes, Ion Channels, and G-protein-coupled Receptors. The remarkable efficacy of DTIOG was showcased through the utilization of diverse similarity measures in order to calculate the similarities between drugs and targets. The combination of these factors, along with the incorporation of various classifiers, enabled the model to outperform existing algorithms in its ability to predict DTIs. The consistent observation of this advantage across all datasets underlines the robustness and accuracy of DTIOG in the domain of DTIs. Additionally, our case study suggests that the DTIOG can serve as a valuable tool for discovering new DTIs.
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Affiliation(s)
- Warith Eddine Djeddi
- LR11ES14, Faculty of Sciences of Tunis, University of Tunis El Manar, Campus Universitaire, 2092, Tunis, Tunisia.
- High Institute of Informatics in Kef, University of Jendouba, Saleh Ayech, 8189, Jendouba, Tunisia.
| | - Khalil Hermi
- High Institute of Informatics in Kef, University of Jendouba, Saleh Ayech, 8189, Jendouba, Tunisia
| | - Sadok Ben Yahia
- Department of Software Science, Tallinn University of Technology, Ehitajate tee-5, 12618, Tallinn, Estonia
- The Maersk Mc-Kinney Moller Institute, Southern Syddansk Universitet, Alsion 2, 6400, Sønderborg, Denmark
| | - Gayo Diallo
- Bordeaux Population Health Inserm 1219, University of Bordeaux, rue Léo Saignat, 33000, Bordeaux, France
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23
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Amiri R, Razmara J, Parvizpour S, Izadkhah H. A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks. BMC Bioinformatics 2023; 24:442. [PMID: 37993777 PMCID: PMC10664633 DOI: 10.1186/s12859-023-05572-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/17/2023] [Indexed: 11/24/2023] Open
Abstract
Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.
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Affiliation(s)
- Ramin Amiri
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
| | - Jafar Razmara
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran.
| | - Sepideh Parvizpour
- Research Center for Pharmaceutical Nanotechnology, Biomedicine Institute, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biotechnology, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Habib Izadkhah
- Department of Computer Science, Faculty of Mathematics, Statistics and Computer Science, University of Tabriz, Tabriz, Iran
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24
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Li V, Lee Y, Lee C, Kim H. Repurposing existing drugs for monkeypox: applications of virtual screening methods. Genes Genomics 2023; 45:1347-1355. [PMID: 37713070 PMCID: PMC10587275 DOI: 10.1007/s13258-023-01449-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/28/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Monkeypox is endemic to African region and has become of Global concern recently due to its outbreaks in non-endemic countries. Although, the disease was first recorded in 1970, no monkeypox specific drug or vaccine exists as of now. METHODS We applied drug repositioning method, testing effectiveness of currently approved drugs against emerging disease, as one of the most affordable approaches for discovering novel treatment measures. Techniques such as virtual ligand-based and structure-based screening were applied to identify potential drug candidates against monkeypox. RESULTS We narrowed down our results to 6 antiviral and 20 anti-tumor drugs that exhibit theoretically higher potency than tecovirimat, the currently approved drug for monkeypox disease. CONCLUSIONS Our results indicated that selected drug compounds displayed strong binding affinity for p37 receptor of monkeypox virus and therefore can potentially be used in future studies to confirm their effectiveness against the disease.
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Affiliation(s)
- Vladimir Li
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Youngho Lee
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea
| | - Chul Lee
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY, USA
| | - Heebal Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
- Department of Agricultural Biotechnology, Research Institute for Agriculture and Life Sciences, Seoul National University, Gwanak-gu 1, Gwanak-ro, Seoul, 08826, Republic of Korea.
- eGnome, Seoul, Republic of Korea.
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25
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Ji X, Williams KP, Zheng W. Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer. Cancer Inform 2023; 22:11769351231202588. [PMID: 37846218 PMCID: PMC10576937 DOI: 10.1177/11769351231202588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 09/01/2023] [Indexed: 10/18/2023] Open
Abstract
The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.
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Affiliation(s)
- Xiaojia Ji
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Kevin P Williams
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
| | - Weifan Zheng
- BRITE Institute and Department of Pharmaceutical Sciences, College of Health and Sciences, North Carolina Central University, Durham, NC, USA
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26
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To KKW, Chow JCH, Cheung KM, Cho WCS. Circumvention of Gefitinib Resistance by Repurposing Flunarizine via Histone Deacetylase Inhibition. ACS Pharmacol Transl Sci 2023; 6:1531-1543. [PMID: 37854628 PMCID: PMC10580381 DOI: 10.1021/acsptsci.3c00202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 10/20/2023]
Abstract
Gefitinib is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR TKI) for treating advanced non-small cell lung cancer (NSCLC). However, drug resistance seriously impedes the clinical efficacy of gefitinib. This study investigated the repositioning of the non-oncology drug capable of inhibiting histone deacetylases (HDACs) to overcome gefitinib resistance. A few drug candidates were identified using the in silico repurposing tool "DRUGSURV" and tested for HDAC inhibition. Flunarizine, originally indicated for migraine prophylaxis and vertigo treatment, was selected for detailed investigation in NSCLC cell lines harboring a range of different gefitinib resistance mechanisms (EGFR T790M, KRAS G12S, MET amplification, or PTEN loss). The circumvention of gefitinib resistance by flunarizine was further demonstrated in an EGFR TKI (erlotinib)-refractory patient-derived tumor xenograft (PDX) model in vivo. The acetylation level of cellular histone protein was increased by flunarizine in a concentration- and time-dependent manner. Among the NSCLC cell lines evaluated, the extent of gefitinib resistance circumvention by flunarizine was found to be the most pronounced in EGFR T790M-bearing H1975 cells. The gefitinib-flunarizine combination was shown to induce the apoptotic protein Bim but reduce the antiapoptotic protein Bcl-2, which apparently circumvented gefitinib resistance. The induction of Bim by flunarizine was accompanied by an increase in the histone acetylation and E2F1 interaction with the BIM gene promoter. Flunarizine was also found to upregulate E-cadherin but downregulate the vimentin expression, which subsequently inhibited cancer cell migration and invasion. Importantly, flunarizine was also shown to significantly potentiate the tumor growth suppressive effect of gefitinib in EGFR TKI-refractory PDX in vivo. The findings advocate for the translational application of flunarizine to circumvent gefitinib resistance in the clinic.
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Affiliation(s)
- Kenneth K. W. To
- School
of Pharmacy, Faculty of Medicine, The Chinese
University of Hong Kong, Hong Kong, SAR, China
| | - James C. H. Chow
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - Ka-Man Cheung
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
| | - William C. S. Cho
- Department
of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, SAR, China
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27
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Gonzalez-Cavazos AC, Tanska A, Mayers M, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. Sci Data 2023; 10:632. [PMID: 37717042 PMCID: PMC10505144 DOI: 10.1038/s41597-023-02534-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
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Affiliation(s)
- Adriana Carolina Gonzalez-Cavazos
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Michael Mayers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Patrick A Rewers
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA
| | - Andrew I Su
- The Scripps Research Institute, Department of Integrative Structural and Computational Biology, 10550 N Torrey Pines Rd, La Jolla, CA, 92037, USA.
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28
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Xuan P, Li P, Cui H, Wang M, Nakaguchi T, Zhang T. Learning Multi-Types of Neighbor Node Attributes and Semantics by Heterogeneous Graph Transformer and Multi-View Attention for Drug-Related Side-Effect Prediction. Molecules 2023; 28:6544. [PMID: 37764319 PMCID: PMC10537290 DOI: 10.3390/molecules28186544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
Since side-effects of drugs are one of the primary reasons for their failure in clinical trials, predicting their side-effects can help reduce drug development costs. We proposed a method based on heterogeneous graph transformer and capsule networks for side-effect-drug-association prediction (TCSD). The method encodes and integrates attributes from multiple types of neighbor nodes, connection semantics, and multi-view pairwise information. In each drug-side-effect heterogeneous graph, a target node has two types of neighbor nodes, the drug nodes and the side-effect ones. We proposed a new heterogeneous graph transformer-based context representation learning module. The module is able to encode specific topology and the contextual relations among multiple kinds of nodes. There are similarity and association connections between the target node and its various types of neighbor nodes, and these connections imply semantic diversity. Therefore, we designed a new strategy to measure the importance of a neighboring node to the target node and incorporate different semantics of the connections between the target node and its multi-type neighbors. Furthermore, we designed attentions at the neighbor node type level and at the graph level, respectively, to obtain enhanced informative neighbor node features and multi-graph features. Finally, a pairwise multi-view feature learning module based on capsule networks was built to learn the pairwise attributes from the heterogeneous graphs. Our prediction model was evaluated using a public dataset, and the cross-validation results showed it achieved superior performance to several state-of-the-art methods. Ablation experiments undertaken demonstrated the effectiveness of heterogeneous graph transformer-based context encoding, the position enhanced pairwise attribute learning, and the neighborhood node category-level attention. Case studies on five drugs further showed TCSD's ability in retrieving potential drug-related side-effect candidates, and TCSD inferred the candidate side-effects for 708 drugs.
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Affiliation(s)
- Ping Xuan
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
- Department of Computer Science, School of Engineering, Shantou University, Shantou 515000, China
| | - Peiru Li
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne 3086, Australia
| | - Meng Wang
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin 130407, China
- School of Mathematical Science, Heilongjiang University, Harbin 130407, China
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29
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Xuan P, Xu K, Cui H, Nakaguchi T, Zhang T. Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects. Front Pharmacol 2023; 14:1257842. [PMID: 37731739 PMCID: PMC10507253 DOI: 10.3389/fphar.2023.1257842] [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: 07/13/2023] [Accepted: 08/17/2023] [Indexed: 09/22/2023] Open
Abstract
Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug-side effect heterogeneous graphs have not been completely exploited. Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space. Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC's ability in discovering the potential drug-related side effect candidates. Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations.
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Affiliation(s)
- Ping Xuan
- Department of Computer Science, School of Engineering, Shantou University, Shantou, China
| | - Kai Xu
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
| | - Hui Cui
- Department of Computer Science and Information Technology, La Trobe University, Melbourne, VI, Australia
| | - Toshiya Nakaguchi
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| | - Tiangang Zhang
- School of Computer Science and Technology, Heilongjiang University, Harbin, China
- School of Mathematical Science, Heilongjiang University, Harbin, China
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30
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Zhong J, Cui P, Zhu Y, Xiao Q, Qu Z. DAHNGC: A Graph Convolution Model for Drug-Disease Association Prediction by Using Heterogeneous Network. J Comput Biol 2023; 30:1019-1033. [PMID: 37702623 DOI: 10.1089/cmb.2023.0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
In the field of drug development and repositioning, the prediction of drug-disease associations is a critical task. A recently proposed method for predicting drug-disease associations based on graph convolution relies heavily on the features of adjacent nodes within the homogeneous network for characterizing information. However, this method lacks node attribute information from heterogeneous networks, which could hardly provide valuable insights for predicting drug-disease associations. In this study, a novel drug-disease association prediction model called DAHNGC is proposed, which is based on a graph convolutional neural network. This model includes two feature extraction methods that are specifically designed to extract the attribute characteristics of drugs and diseases from both homogeneous and heterogeneous networks. First, the DropEdge technique is added to the graph convolutional neural network to alleviate the oversmoothing problem and obtain the characteristics of the same nodes of drugs or diseases in the homogeneous network. Then, an automatic feature extraction method in the heterogeneous network is designed to obtain the features of drugs or diseases at different nodes. Finally, the obtained features are put into the fully connected network for nonlinear transformation, and the potential drug-disease pairs are obtained by bilinear decoding. Experimental results demonstrate that the DAHNGC model exhibits good predictive performance for drug-disease associations.
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Affiliation(s)
- Jiancheng Zhong
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Pan Cui
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Yihong Zhu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Qiu Xiao
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
| | - Zuohang Qu
- School of Information Science and Engineering, Hunan Normal University, Changsha, China
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31
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Ai C, Yang H, Ding Y, Tang J, Guo F. Low Rank Matrix Factorization Algorithm Based on Multi-Graph Regularization for Detecting Drug-Disease Association. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3033-3043. [PMID: 37159322 DOI: 10.1109/tcbb.2023.3274587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot in recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed and low cost, which greatly accelerate the progress of predicting the drug-disease association. In this study, we propose a novel similarity-based method of low-rank matrix decomposition based on multi-graph regularization. On the basis of low-rank matrix factorization with L2 regularization, the multi-graph regularization constraint is constructed by combining a variety of similarity matrices from drugs and diseases respectively. In the experiments, we analyze the difference in the combination of different similarities, resulting that combining all the similarity information on drug space is unnecessary, and only a part of the similarity information can achieve the desired performance. Then our method is compared with other existing models on three data sets (Fdataset, Cdataset and LRSSLdataset) and have a good advantage in the evaluation measurement of AUPR. Besides, a case study experiment is conducted and showing that the superior ability for predicting the potential disease-related drugs of our model. Finally, we compare our model with some methods on six real world datasets, and our model has a good performance in detecting real world data.
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32
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Huang Z, Chen S, Yu L. Predicting new drug indications based on double variational autoencoders. Comput Biol Med 2023; 164:107261. [PMID: 37487382 DOI: 10.1016/j.compbiomed.2023.107261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/29/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023]
Abstract
Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.
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Affiliation(s)
- Zhaoyang Huang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Shengjian Chen
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, Shaanxi, China.
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Anjos LRBD, Costa VAF, Neves BJ, Junqueira-Kipnis AP, Kipnis A. Repurposing miconazole and tamoxifen for the treatment of Mycobacterium abscessus complex infections through in silico chemogenomics approach. World J Microbiol Biotechnol 2023; 39:273. [PMID: 37553519 DOI: 10.1007/s11274-023-03718-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 07/28/2023] [Indexed: 08/10/2023]
Abstract
Drug repositioning is an alternative to overcome the complexity of the drug discovery and approval procedures for the treatment of Mycobacterium abscessus Complex (MABSC) infections that are increasing globally due to the emergency of antimicrobial resistance mechanisms. Here, an in silico chemogenomics approach was performed to compare the sequences from 4942 M. abscessus subsp. abscessus (M. abscessus) proteins with 5258 or 3473 therapeutic targets registered in the DrugBank or Therapeutic Target Database, respectively. This comparison identified 446 drugs or drug candidates whose targets were homologous to M. abscessus proteins. These identified drugs were considered potential inhibitors of MABSC (anti-MABSC activity). Further screening and inspection resulted in the selection of ezetimibe, furosemide, itraconazole, miconazole (MCZ), tamoxifen (TAM), and thiabendazole (THI) for experimental validation. Among them, MCZ and TAM showed minimum inhibitory concentrations (MIC) of 32 and 24 µg mL-1 against M. abscessus, respectively. For M. bolletii and M. massiliense strains, MCZ and TAM showed MICs of 16 and 24 µg mL-1, in this order. Subsequently, the antibacterial activity of MCZ was confirmed in vivo, indicating its potential to reduce the bacterial load in the lungs of infected mice. These results show that MCZ and TAM can serve as molecular scaffolds for the prospective hit-2-lead optimization of new analogs with greater potency, selectivity, and permeability.
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Affiliation(s)
| | | | - Bruno Junior Neves
- Faculty of Pharmacy, Laboratory of Cheminformatics (LabChem), Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - André Kipnis
- Department of Biosciences and Technology, Federal University of Goiás, Goiânia, Goiás, Brazil.
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To KKW, Cheung KM, Cho WCS. Repurposing of triamterene as a histone deacetylase inhibitor to overcome cisplatin resistance in lung cancer treatment. J Cancer Res Clin Oncol 2023; 149:7217-7234. [PMID: 36905422 DOI: 10.1007/s00432-023-04641-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 02/07/2023] [Indexed: 03/12/2023]
Abstract
PURPOSE Cisplatin is the core chemotherapeutic drug used for first-line treatment of advanced non-small cell lung cancer (NSCLC). However, drug resistance is severely hindering its clinical efficacy. This study investigated the circumvention of cisplatin resistance by repurposing non-oncology drugs with putative histone deacetylase (HDAC) inhibitory effect. METHODS A few clinically approved drugs were identified by a computational drug repurposing tool called "DRUGSURV" and evaluated for HDAC inhibition. Triamterene, originally indicated as a diuretic, was chosen for further investigation in pairs of parental and cisplatin-resistant NSCLC cell lines. Sulforhodamine B assay was used to evaluate cell proliferation. Western blot analysis was performed to examine histone acetylation. Flow cytometry was used to examine apoptosis and cell cycle effects. Chromatin immunoprecipitation was conducted to investigate the interaction of transcription factors to the promoter of genes regulating cisplatin uptake and cell cycle progression. The circumvention of cisplatin resistance by triamterene was further verified in a patient-derived tumor xenograft (PDX) from a cisplatin-refractory NSCLC patient. RESULTS Triamterene was found to inhibit HDACs. It was shown to enhance cellular cisplatin accumulation and potentiate cisplatin-induced cell cycle arrest, DNA damage, and apoptosis. Mechanistically, triamterene was found to induce histone acetylation in chromatin, thereby reducing the association of HDAC1 but promoting the interaction of Sp1 with the gene promoter of hCTR1 and p21. Triamterene was further shown to potentiate the anti-cancer effect of cisplatin in cisplatin-resistant PDX in vivo. CONCLUSION The findings advocate further clinical evaluation of the repurposing use of triamterene to overcome cisplatin resistance.
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Affiliation(s)
- Kenneth K W To
- School of Pharmacy, Faculty of Medicine, The Chinese University of Hong Kong, Room 801N, Lo Kwee-Seong Integrated Biomedical Sciences Building, Area 39, Shatin, New Territories, Hong Kong SAR, China.
| | - Ka M Cheung
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - William C S Cho
- Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong SAR, China
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Chen L, Chen K, Zhou B. Inferring drug-disease associations by a deep analysis on drug and disease networks. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:14136-14157. [PMID: 37679129 DOI: 10.3934/mbe.2023632] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Drugs, which treat various diseases, are essential for human health. However, developing new drugs is quite laborious, time-consuming, and expensive. Although investments into drug development have greatly increased over the years, the number of drug approvals each year remain quite low. Drug repositioning is deemed an effective means to accelerate the procedures of drug development because it can discover novel effects of existing drugs. Numerous computational methods have been proposed in drug repositioning, some of which were designed as binary classifiers that can predict drug-disease associations (DDAs). The negative sample selection was a common defect of this method. In this study, a novel reliable negative sample selection scheme, named RNSS, is presented, which can screen out reliable pairs of drugs and diseases with low probabilities of being actual DDAs. This scheme considered information from k-neighbors of one drug in a drug network, including their associations to diseases and the drug. Then, a scoring system was set up to evaluate pairs of drugs and diseases. To test the utility of the RNSS, three classic classification algorithms (random forest, bayes network and nearest neighbor algorithm) were employed to build classifiers using negative samples selected by the RNSS. The cross-validation results suggested that such classifiers provided a nearly perfect performance and were significantly superior to those using some traditional and previous negative sample selection schemes.
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Affiliation(s)
- Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Kaiyu Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
| | - Bo Zhou
- Shanghai University of Medicine & Health Sciences, Shanghai 201318, China
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Vijayakumar S, DiGuiseppi JA, Dabestani J, Ryan WG, Vielman Quevedo R, Li Y, Diers J, Tu S, Fleegel J, Nguyen C, Rhoda LM, Imami AS, Hamoud AAR, Lovas S, McCullumsmith R, Zallocchi M, Zuo J. In Silico Transcriptome-based Screens Identify Epidermal Growth Factor Receptor Inhibitors as Therapeutics for Noise-induced Hearing Loss. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.544128. [PMID: 37333346 PMCID: PMC10274759 DOI: 10.1101/2023.06.07.544128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Noise-Induced Hearing Loss (NIHL) represents a widespread disease for which no therapeutics have been approved by the Food and Drug Administration (FDA). Addressing the conspicuous void of efficacious in vitro or animal models for high throughput pharmacological screening, we utilized an in silico transcriptome-oriented drug screening strategy, unveiling 22 biological pathways and 64 promising small molecule candidates for NIHL protection. Afatinib and zorifertinib, both inhibitors of the Epidermal Growth Factor Receptor (EGFR), were validated for their protective efficacy against NIHL in experimental zebrafish and murine models. This protective effect was further confirmed with EGFR conditional knockout mice and EGF knockdown zebrafish, both demonstrating protection against NIHL. Molecular analysis using Western blot and kinome signaling arrays on adult mouse cochlear lysates unveiled the intricate involvement of several signaling pathways, with particular emphasis on EGFR and its downstream pathways being modulated by noise exposure and Zorifertinib treatment. Administered orally, Zorifertinib was successfully detected in the perilymph fluid of the inner ear in mice with favorable pharmacokinetic attributes. Zorifertinib, in conjunction with AZD5438 - a potent inhibitor of cyclin dependent kinase 2 - produced synergistic protection against NIHL in the zebrafish model. Collectively, our findings underscore the potential application of in silico transcriptome-based drug screening for diseases bereft of efficient screening models and posit EGFR inhibitors as promising therapeutic agents warranting clinical exploration for combatting NIHL. Highlights In silico transcriptome-based drug screens identify pathways and drugs against NIHL.EGFR signaling is activated by noise but reduced by zorifertinib in mouse cochleae.Afatinib, zorifertinib and EGFR knockout protect against NIHL in mice and zebrafish.Orally delivered zorifertinib has inner ear PK and synergizes with a CDK2 inhibitor.
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Zabihian A, Sayyad FZ, Hashemi SM, Shami Tanha R, Hooshmand M, Gharaghani S. DEDTI versus IEDTI: efficient and predictive models of drug-target interactions. Sci Rep 2023; 13:9238. [PMID: 37286613 DOI: 10.1038/s41598-023-36438-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 06/03/2023] [Indexed: 06/09/2023] Open
Abstract
Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.
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Affiliation(s)
- Arash Zabihian
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Department of Bioinformatics, Kish International Campus, University of Tehran, Kish, Iran
| | - Faeze Zakaryapour Sayyad
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Seyyed Morteza Hashemi
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Reza Shami Tanha
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran
| | - Mohsen Hooshmand
- Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran.
| | - Sajjad Gharaghani
- Laboratory of Bioinformatics and Drug Design (LBD), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Amiri Souri E, Chenoweth A, Karagiannis SN, Tsoka S. Drug repurposing and prediction of multiple interaction types via graph embedding. BMC Bioinformatics 2023; 24:202. [PMID: 37193964 DOI: 10.1186/s12859-023-05317-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 04/30/2023] [Indexed: 05/18/2023] Open
Abstract
BACKGROUND Finding drugs that can interact with a specific target to induce a desired therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore, both identifying new drug-target links, as well as delineating the type of drug interaction, are important in drug repurposing studies. RESULTS A computational drug repurposing approach was proposed to predict novel drug-target interactions (DTIs), as well as to predict the type of interaction induced. The methodology is based on mining a heterogeneous graph that integrates drug-drug and protein-protein similarity networks, together with verified drug-disease and protein-disease associations. In order to extract appropriate features, the three-layer heterogeneous graph was mapped to low dimensional vectors using node embedding principles. The DTI prediction problem was formulated as a multi-label, multi-class classification task, aiming to determine drug modes of action. DTIs were defined by concatenating pairs of drug and target vectors extracted from graph embedding, which were used as input to classification via gradient boosted trees, where a model is trained to predict the type of interaction. After validating the prediction ability of DT2Vec+, a comprehensive analysis of all unknown DTIs was conducted to predict the degree and type of interaction. Finally, the model was applied to propose potential approved drugs to target cancer-specific biomarkers. CONCLUSION DT2Vec+ showed promising results in predicting type of DTI, which was achieved via integrating and mapping triplet drug-target-disease association graphs into low-dimensional dense vectors. To our knowledge, this is the first approach that addresses prediction between drugs and targets across six interaction types.
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Affiliation(s)
- E Amiri Souri
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK
| | - A Chenoweth
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, Guy's Hospital, King's College London, London, SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy's Cancer Centre, King's College London, London, SE1 9RT, UK
| | - S N Karagiannis
- St. John's Institute of Dermatology, School of Basic and Medical Biosciences, Guy's Hospital, King's College London, London, SE1 9RT, UK
- Breast Cancer Now Research Unit, School of Cancer and Pharmaceutical Sciences, Guy's Cancer Centre, King's College London, London, SE1 9RT, UK
| | - S Tsoka
- Department of Informatics, Faculty of Natural, Mathematical and Engineering Sciences, King's College London, Bush House, London, WC2B 4BG, UK.
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Gonzalez-Cavazos AC, Tanska A, Mayers MD, Carvalho-Silva D, Sridharan B, Rewers PA, Sankarlal U, Jagannathan L, Su AI. DrugMechDB: A Curated Database of Drug Mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.01.538993. [PMID: 37205439 PMCID: PMC10187194 DOI: 10.1101/2023.05.01.538993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to disease predictions. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repurposing models or as a valuable resource for training such models.
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Affiliation(s)
| | - Anna Tanska
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Michael D. Mayers
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Denise Carvalho-Silva
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Brindha Sridharan
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Patrik A. Rewers
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Umasri Sankarlal
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Lakshmanan Jagannathan
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
| | - Andrew I. Su
- The Scripps Research Institute, Department of Integrative and Structural Biology, 10550 N Torrey Pines Rd. La Jolla, CA, 92037, USA
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Wang Z, Mehmood A, Yao J, Zhang H, Wang L, Al-Shehri M, Kaushik AC, Wei DQ. Combination of furosemide, gold, and dopamine as a potential therapy for breast cancer. Funct Integr Genomics 2023; 23:94. [PMID: 36943579 DOI: 10.1007/s10142-023-01007-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/23/2023]
Abstract
Breast cancer is one of the leading causes of death in women worldwide. Initially, it develops in the epithelium of the ducts or lobules of the breast glandular tissues with limited growth and the potential to metastasize. It is a highly heterogeneous malignancy; however, the common molecular mechanisms could help identify new targeted drugs for treating its subtypes. This study uses computational drug repositioning approaches to explore fresh drug candidates for breast cancer treatment. We also implemented reversal gene expression and gene expression-based signatures to explore novel drug candidates computationally. The drug activity profiles and related gene expression changes were acquired from the DrugBank, PubChem, and LINCS databases, and then in silico drug screening, molecular dynamics (MD) simulation, replica exchange MD simulations, and simulated annealing molecular dynamics (SAMD) simulations were conducted to discover and verify the valid drug candidates. We have found that compounds like furosemide, gold, and dopamine showed significant outcomes. Furthermore, the expression of genes related to breast cancer was observed to be reversed by these shortlisted drugs. Therefore, we postulate that combining furosemide, gold, and dopamine would be a potential combination therapy measurement for breast cancer patients.
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Affiliation(s)
- Zhen Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Aamir Mehmood
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China
| | - Jia Yao
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Hui Zhang
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Li Wang
- Wuxi School of Medicine, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Mohammed Al-Shehri
- Department of Biology, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | | | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai, 200240, China.
- Zhongjing Research and Industrialization Institute of Chinese Medicine, Zhongguancun Scientific Park, Nanyang, Henan, China.
- Peng Cheng Laboratory, Nanshan District, Shenzhen, Guangdong, China.
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Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinformatics 2023; 24:38. [PMID: 36737694 PMCID: PMC9896467 DOI: 10.1186/s12859-023-05153-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.
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Affiliation(s)
- Lingzhi Hu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Chengzhou Fu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Zhonglu Ren
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Yongming Cai
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Jin Yang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
| | - Siwen Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Wenhua Xu
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China
| | - Deyu Tang
- grid.411847.f0000 0004 1804 4300School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, People’s Republic of China ,grid.79703.3a0000 0004 1764 3838School of Computer Science and Engineering, South China University of Technology, Guangzhou, People’s Republic of China ,Guangdong Province Precise Medicine Big Data of Traditional Chinese Medicine Engineering Technology Research Center, Guangzhou, People’s Republic of China
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Han C, Chen J, Huang J, Zhu R, Zeng J, Yu H, He Z. Single-cell transcriptome analysis reveals the metabolic changes and the prognostic value of malignant hepatocyte subpopulations and predict new therapeutic agents for hepatocellular carcinoma. Front Oncol 2023; 13:1104262. [PMID: 36860314 PMCID: PMC9969971 DOI: 10.3389/fonc.2023.1104262] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 01/02/2023] [Indexed: 02/04/2023] Open
Abstract
Background The development of HCC is often associated with extensive metabolic disturbances. Single cell RNA sequencing (scRNA-seq) provides a better understanding of cellular behavior in the context of complex tumor microenvironments by analyzing individual cell populations. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data was employed to investigate the metabolic pathways in HCC. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analysis were applied to identify six cell subpopulations, namely, T/NK cells, hepatocytes, macrophages, endothelial cells, fibroblasts, and B cells. The gene set enrichment analysis (GSEA) was performed to explore the existence of pathway heterogeneity across different cell subpopulations. Univariate Cox analysis was used to screen genes differentially related to The Overall Survival in TCGA-LIHC patients based on scRNA-seq and bulk RNA-seq datasets, and LASSO analysis was used to select significant predictors for incorporation into multivariate Cox regression. Connectivity Map (CMap) was applied to analysis drug sensitivity of risk models and targeting of potential compounds in high risk groups. Results Analysis of TCGA-LIHC survival data revealed the molecular markers associated with HCC prognosis, including MARCKSL1, SPP1, BSG, CCT3, LAGE3, KPNA2, SF3B4, GTPBP4, PON1, CFHR3, and CYP2C9. The RNA expression of 11 prognosis-related differentially expressed genes (DEGs) in normal human hepatocyte cell line MIHA and HCC cell lines HCC-LM3 and HepG2 were compared by qPCR. Higher KPNA2, LAGE3, SF3B4, CCT3 and GTPBP4 protein expression and lower CYP2C9 and PON1 protein expression in HCC tissues from Gene Expression Profiling Interactive Analysis (GEPIA) and Human Protein Atlas (HPA) databases. The results of target compound screening of risk model showed that mercaptopurine is a potential anti-HCC drug. Conclusion The prognostic genes associated with glucose and lipid metabolic changes in a hepatocyte subpopulation and comparison of liver malignancy cells to normal liver cells may provide insight into the metabolic characteristics of HCC and the potential prognostic biomarkers of tumor-related genes and contribute to developing new treatment strategies for individuals.
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Affiliation(s)
- Cuifang Han
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China,*Correspondence: Cuifang Han, ; Hongbing Yu, ; Zhiwei He,
| | - Jiaru Chen
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China,School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Jing Huang
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China
| | - Riting Zhu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China,School of Pharmacy, Guangdong Medical University, Dongguan, China
| | - Jincheng Zeng
- Dongguan Key Laboratory of Medical Bioactive Molecular Developmental and Translational Research, Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, Guangdong Medical University, Dongguan, China
| | - Hongbing Yu
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China,*Correspondence: Cuifang Han, ; Hongbing Yu, ; Zhiwei He,
| | - Zhiwei He
- Guangdong Provincial Key Laboratory of Medical Molecular Diagnostics, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, China,*Correspondence: Cuifang Han, ; Hongbing Yu, ; Zhiwei He,
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Yang K, Yang Y, Fan S, Xia J, Zheng Q, Dong X, Liu J, Liu Q, Lei L, Zhang Y, Li B, Gao Z, Zhang R, Liu B, Wang Z, Zhou X. DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning. Brief Bioinform 2023; 24:6958501. [PMID: 36562715 DOI: 10.1093/bib/bbac518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 12/24/2022] Open
Abstract
As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.
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Affiliation(s)
- Kuo Yang
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | | | - Shuyue Fan
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jianan Xia
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Qiguang Zheng
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Xin Dong
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Lei Lei
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China
| | - Zhuye Gao
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center for Chinese Medicine Cardiology, China
| | - Runshun Zhang
- Guanganmen Hospital, China Academy of Chinese Medical Sciences, China
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Xuezhong Zhou
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
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Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
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Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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Qureshi R, Zou B, Alam T, Wu J, Lee VHF, Yan H. Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:238-255. [PMID: 35007197 DOI: 10.1109/tcbb.2022.3141697] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.
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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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Wang MN, Xie XJ, You ZH, Ding DW, Wong L. A weighted non-negative matrix factorization approach to predict potential associations between drug and disease. J Transl Med 2022; 20:552. [PMID: 36463215 PMCID: PMC9719187 DOI: 10.1186/s12967-022-03757-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 11/06/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Associations of drugs with diseases provide important information for expediting drug development. Due to the number of known drug-disease associations is still insufficient, and considering that inferring associations between them through traditional in vitro experiments is time-consuming and costly. Therefore, more accurate and reliable computational methods urgent need to be developed to predict potential associations of drugs with diseases. METHODS In this study, we present the model called weighted graph regularized collaborative non-negative matrix factorization for drug-disease association prediction (WNMFDDA). More specifically, we first calculated the drug similarity and disease similarity based on the chemical structures of drugs and medical description information of diseases, respectively. Then, to extend the model to work for new drugs and diseases, weighted [Formula: see text] nearest neighbor was used as a preprocessing step to reconstruct the interaction score profiles of drugs with diseases. Finally, a graph regularized non-negative matrix factorization model was used to identify potential associations between drug and disease. RESULTS During the cross-validation process, WNMFDDA achieved the AUC values of 0.939 and 0.952 on Fdataset and Cdataset under ten-fold cross validation, respectively, which outperforms other competing prediction methods. Moreover, case studies for several drugs and diseases were carried out to further verify the predictive performance of WNMFDDA. As a result, 13(Doxorubicin), 13(Amiodarone), 12(Obesity) and 12(Asthma) of the top 15 corresponding candidate diseases or drugs were confirmed by existing databases. CONCLUSIONS The experimental results adequately demonstrated that WNMFDDA is a very effective method for drug-disease association prediction. We believe that WNMFDDA is helpful for relevant biomedical researchers in follow-up studies.
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Affiliation(s)
- Mei-Neng Wang
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Xue-Jun Xie
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Zhu-Hong You
- grid.440588.50000 0001 0307 1240School of Computer Science, Northwestern Polytechnical University, Xi’an, 710072 China
| | - De-Wu Ding
- grid.449868.f0000 0000 9798 3808School of Mathematics and Computer Science, Yichun University, Yichun, 336000 Jiangxi China
| | - Leon Wong
- grid.9227.e0000000119573309Xinjiang Technical Institutes of Physics and Chemistry, Chinese Academy of Sciences, Urumqi, 830011 China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, 100049 China
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Albuquerque PC, Zicker F, Fonseca BP. Advancing drug repurposing research: Trends, collaborative networks, innovation and knowledge leaders. Drug Discov Today 2022; 27:103396. [DOI: 10.1016/j.drudis.2022.103396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/08/2022] [Accepted: 10/06/2022] [Indexed: 11/03/2022]
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A deep learning method for predicting molecular properties and compound-protein interactions. J Mol Graph Model 2022; 117:108283. [PMID: 35994925 DOI: 10.1016/j.jmgm.2022.108283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/19/2022] [Accepted: 07/26/2022] [Indexed: 01/14/2023]
Abstract
Predicting molecular properties and compound-protein interactions (CPIs) are two important areas of drug design and discovery. They are also an essential way to discover lead compounds in virtual screening. Recently, in silico methods based on deep learning have demonstrated excellent performance in various challenges. It is imperative to develop efficient computational methods to predict accurately both molecular properties and CPIs in drug research using deep learning techniques. In this paper, we propose a deep learning method applicable to both molecular property prediction and CPI prediction based on the idea that both are generally influenced by chemical structure and sequence information of compounds and proteins. Molecular properties are inferred by integrating the molecular structure and sequence information of compounds, and CPIs are predicted by integrating protein sequence and compound structure. The method combines topological structure and sequence fingerprint information of molecules, extracts adequately raw data features, and generates highly representative features for prediction. Molecular property prediction experiments were conducted on BACE, P53 and hERG datasets, and CPI prediction experiments were conducted on Human, C. elegans and KIBA datasets. MG-S achieves outperformance in molecular property prediction on P53, the differences in AUC, Precision and MCC are 0.030, 0.050 and 0.100, respectively, over the suboptimal baseline model, and provides consistently good results on BACE and hERG.The model also achieves impressive performance in CPI prediction, the differences in AUC, Precision and MCC on KIBA are 0.141, 0.138, 0.090 and 0.082, respectively, compared with the state-of-the-art models. The comprehensive results show that the MG-S model has higher performance, better classification ability, and faster convergence. MG-S will serve as a useful method to predict compound properties and CPIs in the early stages of drug design and discovery.Our code and datasets are available at: https://github.com/happay-ending/cpi_cpp.
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Anyama CA, Louis H, Inah BE, Gber TE, Ogar JO, Ayi AA. Hydrothermal Synthesis, crystal structure, DFT studies, and molecular docking of Zn-BTC MOF as potential antiprotozoal agents. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.134825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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