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Xu T, Gao W, Zhu L, Chen W, Niu C, Yin W, Ma L, Zhu X, Ling Y, Gao S, Liu L, Jiao N, Chen W, Zhang G, Zhu R, Wu D. NAFLDkb: A Knowledge Base and Platform for Drug Development against Nonalcoholic Fatty Liver Disease. J Chem Inf Model 2024; 64:2817-2828. [PMID: 37167092 DOI: 10.1021/acs.jcim.3c00395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease with a broad spectrum of histologic manifestations. The rapidly growing prevalence and the complex pathologic mechanisms of NAFLD pose great challenges for treatment development. Despite tremendous efforts devoted to drug development, there are no FDA-approved medicines yet. Here, we present NAFLDkb, a specialized knowledge base and platform for computer-aided drug design against NAFLD. With multiperspective information curated from diverse source materials and public databases, NAFLDkb presents the associations of drug-related entities as individual knowledge graphs. Practical drug discovery tools that facilitate the utilization and expansion of NAFLDkb have also been implemented in the web interface, including chemical structure search, drug-likeness screening, knowledge-based repositioning, and research article annotation. Moreover, case studies of a knowledge graph repositioning model and a generative neural network model are presented herein, where three repositioning drug candidates and 137 novel lead-like compounds were newly established as NAFLD pharmacotherapy options reusing data records and machine learning tools in NAFLDkb, suggesting its clinical reliability and great potential in identifying novel drug-disease associations of NAFLD and generating new insights to accelerate NAFLD drug development. NAFLDkb is freely accessible at https://www.biosino.org/nafldkb and will be updated periodically with the latest findings.
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Affiliation(s)
- Tingjun Xu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Wenxing Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lixin Zhu
- Guangdong Institute of Gastroenterology; Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases; Biomedical Innovation Center, Sun Yat-sen University, Guangzhou 510655, P. R. China
- Department of General Surgery, The Sixth Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, P. R. China
| | - Wanning Chen
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Chaoqun Niu
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Wenjing Yin
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Liangxiao Ma
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Xinyue Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Yunchao Ling
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Sheng Gao
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Lei Liu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Na Jiao
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
| | - Weiming Chen
- Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 LingLing Road, Shanghai 200032, P. R. China
| | - Guoqing Zhang
- Chinese Academy of Sciences Key Laboratory of Computational Biology, Bio-Med Big Data Center, Shanghai Institute of Nutrition and Health, University of the Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. China
| | - Ruixin Zhu
- Putuo People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai 200060, P. R. China
| | - Dingfeng Wu
- National Clinical Research Center for Child Health, the Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, P. R. China
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Jeong E, Hong H, Lee YA, Kim KS. Potential Rheumatoid Arthritis-Associated Interstitial Lung Disease Treatment and Computational Approach for Future Drug Development. Int J Mol Sci 2024; 25:2682. [PMID: 38473928 PMCID: PMC11154459 DOI: 10.3390/ijms25052682] [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: 01/22/2024] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by swelling in at least one joint. Owing to an overactive immune response, extra-articular manifestations are observed in certain cases, with interstitial lung disease (ILD) being the most common. Rheumatoid arthritis-associated interstitial lung disease (RA-ILD) is characterized by chronic inflammation of the interstitial space, which causes fibrosis and the scarring of lung tissue. Controlling inflammation and pulmonary fibrosis in RA-ILD is important because they are associated with high morbidity and mortality. Pirfenidone and nintedanib are specific drugs against idiopathic pulmonary fibrosis and showed efficacy against RA-ILD in several clinical trials. Immunosuppressants and disease-modifying antirheumatic drugs (DMARDs) with anti-fibrotic effects have also been used to treat RA-ILD. Immunosuppressants moderate the overexpression of cytokines and immune cells to reduce pulmonary damage and slow the progression of fibrosis. DMARDs with mild anti-fibrotic effects target specific fibrotic pathways to regulate fibrogenic cellular activity, extracellular matrix homeostasis, and oxidative stress levels. Therefore, specific medications are required to effectively treat RA-ILD. In this review, the commonly used RA-ILD treatments are discussed based on their molecular mechanisms and clinical trial results. In addition, a computational approach is proposed to develop specific drugs for RA-ILD.
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Affiliation(s)
- Eunji Jeong
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
| | - Hyunseok Hong
- Yale College, Yale University, New Haven, CT 06520, USA;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Yeon-Ah Lee
- Division of Rheumatology, Department of Internal Medicine, Kyung Hee University Hospital, Seoul 02447, Republic of Korea;
| | - Kyoung-Soo Kim
- Department of Medicine, College of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea;
- Department of Clinical Pharmacology and Therapeutics, School of Medicine, Kyung Hee University, Seoul 02447, Republic of Korea
- East-West Bone & Joint Disease Research Institute, Kyung Hee University Hospital at Gangdong, Seoul 05278, Republic of Korea
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Wang EY, Zhao Y, Okhovatian S, Smith JB, Radisic M. Intersection of stem cell biology and engineering towards next generation in vitro models of human fibrosis. Front Bioeng Biotechnol 2022; 10:1005051. [PMID: 36338120 PMCID: PMC9630603 DOI: 10.3389/fbioe.2022.1005051] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/26/2022] [Indexed: 08/31/2023] Open
Abstract
Human fibrotic diseases constitute a major health problem worldwide. Fibrosis involves significant etiological heterogeneity and encompasses a wide spectrum of diseases affecting various organs. To date, many fibrosis targeted therapeutic agents failed due to inadequate efficacy and poor prognosis. In order to dissect disease mechanisms and develop therapeutic solutions for fibrosis patients, in vitro disease models have gone a long way in terms of platform development. The introduction of engineered organ-on-a-chip platforms has brought a revolutionary dimension to the current fibrosis studies and discovery of anti-fibrotic therapeutics. Advances in human induced pluripotent stem cells and tissue engineering technologies are enabling significant progress in this field. Some of the most recent breakthroughs and emerging challenges are discussed, with an emphasis on engineering strategies for platform design, development, and application of machine learning on these models for anti-fibrotic drug discovery. In this review, we discuss engineered designs to model fibrosis and how biosensor and machine learning technologies combine to facilitate mechanistic studies of fibrosis and pre-clinical drug testing.
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Affiliation(s)
- Erika Yan Wang
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Yimu Zhao
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Sargol Okhovatian
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
| | - Jacob B. Smith
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
| | - Milica Radisic
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada
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Li A, Chen JY, Hsu CL, Oyang YJ, Huang HC, Juan HF. A Single-Cell Network-Based Drug Repositioning Strategy for Post-COVID-19 Pulmonary Fibrosis. Pharmaceutics 2022; 14:pharmaceutics14050971. [PMID: 35631558 PMCID: PMC9147547 DOI: 10.3390/pharmaceutics14050971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/22/2022] [Accepted: 04/29/2022] [Indexed: 12/04/2022] Open
Abstract
Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.
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Affiliation(s)
- Albert Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Jhih-Yu Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Chia-Lang Hsu
- Department of Medical Research, National Taiwan University Hospital, Taipei 106, Taiwan;
- Graduate Institute of Medical Genomics and Proteomics, National Taiwan University, Taipei 106, Taiwan
| | - Yen-Jen Oyang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
| | - Hsuan-Cheng Huang
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
| | - Hsueh-Fen Juan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 106, Taiwan; (A.L.); (J.-Y.C.); (Y.-J.O.)
- Department of Life Science, National Taiwan University, Taipei 106, Taiwan
- Center for Computational and Systems Biology, National Taiwan University, Taipei 106, Taiwan
- Correspondence: (H.-C.H.); (H.-F.J.)
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 286] [Impact Index Per Article: 95.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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