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Abbas A, Ye F. Computational methods and key considerations for in silico design of proteolysis targeting chimera (PROTACs). Int J Biol Macromol 2024; 277:134293. [PMID: 39084437 DOI: 10.1016/j.ijbiomac.2024.134293] [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: 05/29/2024] [Revised: 07/19/2024] [Accepted: 07/28/2024] [Indexed: 08/02/2024]
Abstract
Proteolysis-targeting chimeras (PROTACs), as heterobifunctional molecules, have garnered significant attention for their ability to target previously undruggable proteins. Due to the challenges in obtaining crystal structures of PROTAC molecules in the ternary complex, a plethora of computational tools have been developed to aid in PROTAC design. These computational tools can be broadly classified into artificial intelligence (AI)-based or non-AI-based methods. This review aims to provide a comprehensive overview of the latest computational methods for the PROTAC design process, covering both AI and non-AI approaches, from protein selection to ternary complex modeling and prediction. Key considerations for in silico PROTAC design are discussed, along with additional considerations for deploying AI-based models. These considerations are intended to guide subsequent model development in the PROTAC design process. Finally, future directions and recommendations are provided.
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Affiliation(s)
- Amr Abbas
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China; Pharmaceutical Chemistry Department, Faculty of Pharmacy, Cairo University, Cairo 11562, Egypt
| | - Fei Ye
- College of Life Sciences and Medicine, Zhejiang Sci-Tech University, Hangzhou 310018, China.
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2
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Manen-Freixa L, Antolin AA. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery. Expert Opin Drug Discov 2024; 19:1043-1069. [PMID: 39004919 DOI: 10.1080/17460441.2024.2376643] [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: 03/15/2024] [Accepted: 07/02/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology. AREAS COVERED This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples. EXPERT OPINION Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.
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Affiliation(s)
- Leticia Manen-Freixa
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
| | - Albert A Antolin
- Oncobell Division, Bellvitge Biomedical Research Institute (IDIBELL) and ProCURE Department, Catalan Institute of Oncology (ICO), Barcelona, Spain
- Center for Cancer Drug Discovery, The Division of Cancer Therapeutics, The Institute of Cancer Research, London, UK
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Zheng Y, Ma Y, Xiong Q, Zhu K, Weng N, Zhu Q. The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects. Pharmacol Res 2024; 208:107381. [PMID: 39218422 DOI: 10.1016/j.phrs.2024.107381] [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: 06/11/2024] [Revised: 08/06/2024] [Accepted: 08/26/2024] [Indexed: 09/04/2024]
Abstract
Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.
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Affiliation(s)
- Ying Zheng
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Yifei Ma
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Qunli Xiong
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu, Sichuan 610041, China
| | - Kai Zhu
- Department of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350011, PR China
| | - Ningna Weng
- Department of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian 350011, PR China
| | - Qing Zhu
- Division of Abdominal Tumor Multimodality Treatment, Cancer Center, West China Hospital, Sichuan University, No.37 Guoxue Alley, Chengdu, Sichuan 610041, China.
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Zeng X, Zhong KY, Meng PY, Li SJ, Lv SQ, Wen ML, Li Y. MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs. BMC Biol 2024; 22:182. [PMID: 39183297 PMCID: PMC11346193 DOI: 10.1186/s12915-024-01981-3] [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: 05/23/2024] [Accepted: 08/13/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. RESULTS We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. CONCLUSIONS During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Kai-Yang Zhong
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Pei-Yan Meng
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control & Prevention, Dali, 671000, China
| | - Shuang-Qing Lv
- Institute of Surveying and Information Engineering, West Yunnan University of Applied Science, Dali, 671000, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, Kunming, 650000, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, Dali, 671003, China.
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5
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Li X, Sun Y, Zhou Z, Li J, Liu S, Chen L, Shi Y, Wang M, Zhu Z, Wang G, Lu Q. Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308970. [PMID: 38454653 PMCID: PMC11095145 DOI: 10.1002/advs.202308970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/15/2024] [Indexed: 03/09/2024]
Abstract
Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.
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Affiliation(s)
- Xinuo Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yuan Sun
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Zheng Zhou
- Department of Computer ScienceRWTH Aachen University52074AachenGermany
| | - Jinran Li
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Sai Liu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Long Chen
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Yiting Shi
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Min Wang
- Affiliated Brain Hospital of Nanjing Medical UniversityNanjing210029China
| | - Zheying Zhu
- School of PharmacyThe University of NottinghamNottinghamNG7 2RDUK
| | - Guangji Wang
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
| | - Qiulun Lu
- Jiangsu Provincial Key Laboratory of Drug Metabolism and PharmacokineticsState Key Laboratory of Natural MedicinesChina Pharmaceutical UniversityNanjing211166China
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Nandi S, Bhaduri S, Das D, Ghosh P, Mandal M, Mitra P. Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence. Mol Pharm 2024; 21:1563-1590. [PMID: 38466810 DOI: 10.1021/acs.molpharmaceut.3c01161] [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: 03/13/2024]
Abstract
Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.
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Affiliation(s)
- Suvendu Nandi
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Soumyadeep Bhaduri
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Debraj Das
- Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Priya Ghosh
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Mahitosh Mandal
- School of Medical Science and Technology, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
| | - Pralay Mitra
- Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India
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Siddique F, Anwaar A, Bashir M, Nadeem S, Rawat R, Eyupoglu V, Afzal S, Bibi M, Bin Jardan YA, Bourhia M. Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach. Front Chem 2024; 12:1380266. [PMID: 38576849 PMCID: PMC10991842 DOI: 10.3389/fchem.2024.1380266] [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: 02/01/2024] [Accepted: 03/05/2024] [Indexed: 04/06/2024] Open
Abstract
Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.
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Affiliation(s)
- Farhan Siddique
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Ahmar Anwaar
- Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Maryam Bashir
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
- Southern Punjab Institute of Health Sciences, Multan, Pakistan
| | - Sumaira Nadeem
- Department of Pharmacy, The Women University, Multan, Pakistan
| | - Ravi Rawat
- School of Health Sciences & Technology, UPES University, Dehradun, India
| | - Volkan Eyupoglu
- Department of Chemistry, Cankırı Karatekin University, Cankırı, Türkiye
| | - Samina Afzal
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Mehvish Bibi
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
| | - Yousef A. Bin Jardan
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Mohammed Bourhia
- Laboratory of Biotechnology and Natural Resources Valorization, Faculty of Sciences, Ibn Zohr University, Agadir, Morocco
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Wang Y, Xu H, Huang X, Zhang Y, Lu Y, Cheng J, Xu X, Li J, Yao H, Chen X. Orchestrating Precision within the Tumor Microenvironment by Biomimetic Nanoprodrugs for Effective Tumor Therapy. ACS APPLIED MATERIALS & INTERFACES 2024; 16:8484-8498. [PMID: 38334265 DOI: 10.1021/acsami.3c18239] [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: 02/10/2024]
Abstract
Malignant tumors are still one of the most deadly diseases that threaten human life and health. However, developing new drugs is challenging due to lengthy trials, funding constraints, and regulatory approval procedures. Consequently, researchers have devoted themselves to transforming some clinically approved old drugs into antitumor drugs with certain active ingredients, which have become an attractive alternative. Disulfiram (DSF), an antialcohol medication, can rapidly metabolize in the physiological environment into diethyldithiocarbamate (DTC) which can readily react with Cu2+ ions in situ to form the highly toxic bis(N,N-diethyldithiocarbamate)-copper(II) (CuET) complex. In this study, DSF is loaded into mesoporous dopamine nanocarriers and surface-chelated with tannin and Cu2+ to construct M-MDTC nanoprodrugs under the camouflage of K7 tumor cell membranes. After intravenous injection, M-MDTC nanoprodrugs successfully reach the tumor sites with the help of mediated cell membranes. Under slightly acidic pH and photothermal stimulation conditions, DSF and Cu2+ are simultaneously released, forming a highly toxic CuET to kill tumor cells in situ. The generated CuET can also induce immunogenic cell death of tumor cells, increase the proportion of CD86+ CD80+ cells, and promote dendritic cell maturation. In vitro and in vivo studies of M-MDTC nanoprodrugs have shown excellent tumor-cell-killing ability and solid tumor suppression. This approach enables in situ amplification of chemotherapy in the tumor microenvironment, achieving an effective antitumor treatment.
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Affiliation(s)
- Yuemin Wang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
| | - Hong Xu
- Orthopedic Research Institution, Department of Orthopedics, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Xiaoming Huang
- Sichuan Eye Hospital, AIER Eye Hospital Group, No. 153, Tianfu Fourth Street, High-Tech Zone, Chengdu 610047, China
| | - Yuyue Zhang
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
| | - Yongping Lu
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
| | - Jing Cheng
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
| | - Xinyuan Xu
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
| | - Jianshu Li
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
- State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Med-X Center for Materials, Sichuan University, Chengdu 610041, China
| | - Haochen Yao
- Hepatobiliary and Pancreatic Surgery Department, General Surgery Center, First Hospital of Jilin University, No. 1 Xinmin Street, Changchun, Jilin 130021, China
| | - Xingyu Chen
- College of Polymer Science and Engineering, State Key Laboratory of Polymer Materials Engineering, Sichuan University, Chengdu 610065, China
- College of Medicine, Southwest Jiaotong University, Chengdu 610003, China
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Liu F, Patt A, Chen C, Huang R, Xu Y, Mathé EA, Zhu Q. Exploring NCATS in-house biomedical data for evidence-based drug repurposing. PLoS One 2024; 19:e0289518. [PMID: 38271343 PMCID: PMC10810548 DOI: 10.1371/journal.pone.0289518] [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: 07/19/2023] [Accepted: 11/08/2023] [Indexed: 01/27/2024] Open
Abstract
Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.
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Affiliation(s)
- Fang Liu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Andrew Patt
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland, United States of America
| | - Chloe Chen
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Ruili Huang
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland, United States of America
| | - Yanji Xu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Ewy A. Mathé
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland, United States of America
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland, United States of America
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10
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Zhu Y, Ning Z, Li X, Lin Z. Machine Learning Algorithms Identify Target Genes and the Molecular Mechanism of Matrine against Diffuse Large B-cell Lymphoma. Curr Comput Aided Drug Des 2024; 20:847-859. [PMID: 37605410 DOI: 10.2174/1573409920666230821102806] [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: 03/30/2023] [Revised: 06/22/2023] [Accepted: 07/13/2023] [Indexed: 08/23/2023]
Abstract
BACKGROUND Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease. OBJECTIVE The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL. METHODS Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms. RESULTS A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway. CONCLUSION Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.
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Affiliation(s)
- Yidong Zhu
- Department of Traditional Chinese Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China
| | - Zhongping Ning
- Department of Cardiology, Shanghai Pudong New District Zhoupu Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Ximing Li
- Department of Cardiology, Shanghai Pudong New District Zhoupu Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Zhikang Lin
- Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 200032, China
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11
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Brilhante RSN, Costa ADC, de Mesquita JRL, dos Santos Araújo G, Freire RS, Nunes JVS, Nobre AFD, Fernandes MR, Rocha MFG, Pereira Neto WDA, Crouzier T, Schimpf U, Viera RS. Antifungal Activity of Chitosan against Histoplasma capsulatum in Planktonic and Biofilm Forms: A Therapeutic Strategy in the Future? J Fungi (Basel) 2023; 9:1201. [PMID: 38132801 PMCID: PMC10744476 DOI: 10.3390/jof9121201] [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: 10/30/2023] [Revised: 12/09/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023] Open
Abstract
Histoplasmosis is a respiratory disease caused by Histoplasma capsulatum, a dimorphic fungus, with high mortality and morbidity rates, especially in immunocompromised patients. Considering the small existing therapeutic arsenal, new treatment approaches are still required. Chitosan, a linear polysaccharide obtained from partial chitin deacetylation, has anti-inflammatory, antimicrobial, biocompatibility, biodegradability, and non-toxicity properties. Chitosan with different deacetylation degrees and molecular weights has been explored as a potential agent against fungal pathogens. In this study, the chitosan antifungal activity against H. capsulatum was evaluated using the broth microdilution assay, obtaining minimum inhibitory concentrations (MIC) ranging from 32 to 128 µg/mL in the filamentous phase and 8 to 64 µg/mL in the yeast phase. Chitosan combined with classical antifungal drugs showed a synergic effect, reducing chitosan's MICs by 32 times, demonstrating that there were no antagonistic interactions relating to any of the strains tested. A synergism between chitosan and amphotericin B or itraconazole was detected in the yeast-like form for all strains tested. For H. capsulatum biofilms, chitosan reduced biomass and metabolic activity by about 40% at 512 µg/mL. In conclusion, studying chitosan as a therapeutic strategy against Histoplasma capsulatum is promising, mainly considering its numerous possible applications, including its combination with other compounds.
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Affiliation(s)
- Raimunda Sâmia Nogueira Brilhante
- Department of Pathology and Legal Medicine, School of Medicine, Specialized Medical Mycology Center, Postgraduate Program in Medical Sciences, Federal University of Ceará, Rua Barão de Canindé, 210, Montese, Fortaleza 60425-540, CE, Brazil; (A.d.C.C.); (A.F.D.N.); (M.R.F.); (W.d.A.P.N.)
| | - Anderson da Cunha Costa
- Department of Pathology and Legal Medicine, School of Medicine, Specialized Medical Mycology Center, Postgraduate Program in Medical Sciences, Federal University of Ceará, Rua Barão de Canindé, 210, Montese, Fortaleza 60425-540, CE, Brazil; (A.d.C.C.); (A.F.D.N.); (M.R.F.); (W.d.A.P.N.)
| | | | - Gessica dos Santos Araújo
- Postgraduate in Veterinary Sciences, Faculty of Veterinary, State University of Ceará, Dr. Silas Munguba Avenue, 1700, Itaperi Campus, Fortaleza 60714-903, CE, Brazil; (G.d.S.A.); (M.F.G.R.)
| | - Rosemeyre Souza Freire
- Analytical Center, Department of Physics, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil; (R.S.F.); (J.V.S.N.)
| | - João Victor Serra Nunes
- Analytical Center, Department of Physics, Federal University of Ceará, Fortaleza 60020-181, CE, Brazil; (R.S.F.); (J.V.S.N.)
| | - Augusto Feynman Dias Nobre
- Department of Pathology and Legal Medicine, School of Medicine, Specialized Medical Mycology Center, Postgraduate Program in Medical Sciences, Federal University of Ceará, Rua Barão de Canindé, 210, Montese, Fortaleza 60425-540, CE, Brazil; (A.d.C.C.); (A.F.D.N.); (M.R.F.); (W.d.A.P.N.)
| | - Mirele Rodrigues Fernandes
- Department of Pathology and Legal Medicine, School of Medicine, Specialized Medical Mycology Center, Postgraduate Program in Medical Sciences, Federal University of Ceará, Rua Barão de Canindé, 210, Montese, Fortaleza 60425-540, CE, Brazil; (A.d.C.C.); (A.F.D.N.); (M.R.F.); (W.d.A.P.N.)
| | - Marcos Fábio Gadelha Rocha
- Postgraduate in Veterinary Sciences, Faculty of Veterinary, State University of Ceará, Dr. Silas Munguba Avenue, 1700, Itaperi Campus, Fortaleza 60714-903, CE, Brazil; (G.d.S.A.); (M.F.G.R.)
| | - Waldemiro de Aquino Pereira Neto
- Department of Pathology and Legal Medicine, School of Medicine, Specialized Medical Mycology Center, Postgraduate Program in Medical Sciences, Federal University of Ceará, Rua Barão de Canindé, 210, Montese, Fortaleza 60425-540, CE, Brazil; (A.d.C.C.); (A.F.D.N.); (M.R.F.); (W.d.A.P.N.)
| | - Thomas Crouzier
- KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Chemistry, Division of Glycoscience, AlbaNova University Center, 106 91 Stockholm, Sweden; (T.C.); (U.S.)
| | - Ulrike Schimpf
- KTH Royal Institute of Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health, Department of Chemistry, Division of Glycoscience, AlbaNova University Center, 106 91 Stockholm, Sweden; (T.C.); (U.S.)
| | - Rodrigo Silveira Viera
- Department of Chemical Engineering, Federal University of Ceará, Fortaleza 60440-900, CE, Brazil;
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12
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Boudin M, Diallo G, Drancé M, Mougin F. The OREGANO knowledge graph for computational drug repurposing. Sci Data 2023; 10:871. [PMID: 38057380 PMCID: PMC10700660 DOI: 10.1038/s41597-023-02757-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/16/2023] [Indexed: 12/08/2023] Open
Abstract
Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).
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Affiliation(s)
- Marina Boudin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France.
| | - Gayo Diallo
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Martin Drancé
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
| | - Fleur Mougin
- AHeaD team, Bordeaux Population Health Inserm U1219, Univ. Bordeaux, F-33000, Bordeaux, France
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13
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Wang L, Zhou Y, Chen Q. AMMVF-DTI: A Novel Model Predicting Drug-Target Interactions Based on Attention Mechanism and Multi-View Fusion. Int J Mol Sci 2023; 24:14142. [PMID: 37762445 PMCID: PMC10531525 DOI: 10.3390/ijms241814142] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 09/09/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Accurate identification of potential drug-target interactions (DTIs) is a crucial task in drug development and repositioning. Despite the remarkable progress achieved in recent years, improving the performance of DTI prediction still presents significant challenges. In this study, we propose a novel end-to-end deep learning model called AMMVF-DTI (attention mechanism and multi-view fusion), which leverages a multi-head self-attention mechanism to explore varying degrees of interaction between drugs and target proteins. More importantly, AMMVF-DTI extracts interactive features between drugs and proteins from both node-level and graph-level embeddings, enabling a more effective modeling of DTIs. This advantage is generally lacking in existing DTI prediction models. Consequently, when compared to many of the start-of-the-art methods, AMMVF-DTI demonstrated excellent performance on the human, C. elegans, and DrugBank baseline datasets, which can be attributed to its ability to incorporate interactive information and mine features from both local and global structures. The results from additional ablation experiments also confirmed the importance of each module in our AMMVF-DTI model. Finally, a case study is presented utilizing our model for COVID-19-related DTI prediction. We believe the AMMVF-DTI model can not only achieve reasonable accuracy in DTI prediction, but also provide insights into the understanding of potential interactions between drugs and targets.
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14
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Liu F, Patt A, Chen C, Huang R, Xu Y, Mathé EA, Zhu Q. Exploring NCATS In-House Biomedical Data for Evidence-based Drug Repurposing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.21.550045. [PMID: 37546930 PMCID: PMC10401966 DOI: 10.1101/2023.07.21.550045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Drug repurposing is a strategy for identifying new uses of approved or investigational drugs that are outside the scope of the original medical indication. Even though many repurposed drugs have been found serendipitously in the past, the increasing availability of large volumes of biomedical data has enabled more systemic, data-driven approaches for drug candidate identification. At National Center of Advancing Translational Sciences (NCATS), we invent new methods to generate new data and information publicly available to spur innovation and scientific discovery. In this study, we aimed to explore and demonstrate biomedical data generated and collected via two NCATS research programs, the Toxicology in the 21st Century program (Tox21) and the Biomedical Data Translator (Translator) for the application of drug repurposing. These two programs provide complementary types of biomedical data from uncovering underlying biological mechanisms with bioassay screening data from Tox21 for chemical clustering, to enrich clustered chemicals with scientific evidence mined from the Translator towards drug repurposing. 129 chemical clusters have been generated and three of them have been further investigated for drug repurposing candidate identification, which is detailed as case studies.
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Affiliation(s)
- Fang Liu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD
| | - Andrew Patt
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
| | - Chloe Chen
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD
| | - Ruili Huang
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
| | - Yanji Xu
- Division of Rare Diseases Research Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD
| | - Ewy A Mathé
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
| | - Qian Zhu
- Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD
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15
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Fairlamb AH, Wyllie S. The critical role of mode of action studies in kinetoplastid drug discovery. FRONTIERS IN DRUG DISCOVERY 2023; 3:fddsv.2023.1185679. [PMID: 37600222 PMCID: PMC7614965 DOI: 10.3389/fddsv.2023.1185679] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Understanding the target and mode of action of compounds identified by phenotypic screening can greatly facilitate the process of drug discovery and development. Here, we outline the tools currently available for target identification against the neglected tropical diseases, human African trypanosomiasis, visceral leishmaniasis and Chagas' disease. We provide examples how these tools can be used to identify and triage undesirable mechanisms, to identify potential toxic liabilities in patients and to manage a balanced portfolio of target-based campaigns. We review the primary targets of drugs that are currently in clinical development that were initially identified via phenotypic screening, and whose modes of action affect protein turnover, RNA trans-splicing or signalling in these protozoan parasites.
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Affiliation(s)
- Alan H. Fairlamb
- Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, United Kingdom
| | - Susan Wyllie
- Wellcome Centre for Anti-Infectives Research, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee, United Kingdom
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16
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Hu S, Chen J, Cao JX, Zhang SS, Gu SX, Chen FE. Quinolines and isoquinolines as HIV-1 inhibitors: Chemical structures, action targets, and biological activities. Bioorg Chem 2023; 136:106549. [PMID: 37119785 DOI: 10.1016/j.bioorg.2023.106549] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/09/2023] [Accepted: 04/13/2023] [Indexed: 05/01/2023]
Abstract
Human immunodeficiency virus type 1 (HIV-1), a lentivirus that causes acquired immunodeficiency syndrome (AIDS), poses a serious threat to global public health. Since the advent of the first drug zidovudine, a number of anti-HIV agents acting on different targets have been approved to combat HIV/AIDS. Among the abundant heterocyclic families, quinoline and isoquinoline moieties are recognized as promising scaffolds for HIV inhibition. This review intends to highlight the advances in diverse chemical structures and abundant biological activity of quinolines and isoquinolines as anti-HIV agents acting on different targets, which aims to provide useful references and inspirations to design and develop novel HIV inhibitors for medicinal chemists.
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Affiliation(s)
- Sha Hu
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China
| | - Jiong Chen
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China
| | - Jin-Xu Cao
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; Pharmaceutical Research Institute, Wuhan Institute of Technology, Wuhan 430205, China; Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, Wuhan Institute of Technology, Wuhan 430205, China
| | - Shuang-Shuang Zhang
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; Pharmaceutical Research Institute, Wuhan Institute of Technology, Wuhan 430205, China; Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, Wuhan Institute of Technology, Wuhan 430205, China
| | - Shuang-Xi Gu
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; Pharmaceutical Research Institute, Wuhan Institute of Technology, Wuhan 430205, China; Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, Wuhan Institute of Technology, Wuhan 430205, China.
| | - Fen-Er Chen
- Key Laboratory for Green Chemical Process of Ministry of Education, School of Chemical Engineering & Pharmacy, Wuhan Institute of Technology, Wuhan 430205, China; Pharmaceutical Research Institute, Wuhan Institute of Technology, Wuhan 430205, China; Hubei Key Laboratory of Novel Reactor and Green Chemical Technology, Wuhan Institute of Technology, Wuhan 430205, China; Department of Chemistry, Fudan University, Shanghai 200433, China.
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17
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Tan S, Lu R, Yao D, Wang J, Gao P, Xie G, Liu H, Yao X. Identification of LRRK2 Inhibitors through Computational Drug Repurposing. ACS Chem Neurosci 2023; 14:481-493. [PMID: 36649061 DOI: 10.1021/acschemneuro.2c00672] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder that affects more than ten million people worldwide. However, the current PD treatments are still limited and alternative treatment strategies are urgently required. Leucine-rich repeat kinase 2 (LRRK2) has been recognized as a promising target for PD treatment. However, there are no approved LRRK2 inhibitors on the market. To rapidly identify potential drug repurposing candidates that inhibit LRRK2 kinase, we report a structure-based drug repurposing workflow that combines molecular docking, recursive partitioning model, molecular dynamics (MD) simulation, and molecular mechanics-generalized Born surface area (MM-GBSA) calculation. Thirteen compounds screened from our drug repurposing workflow were further evaluated through the experiment. The experimental results showed six drugs (Abivertinib, Aumolertinib, Encorafenib, Bosutinib, Rilzabrutinib, and Mobocertinib) with IC50 less than 5 μM that were identified as potential LRRK2 kinase inhibitors. The most potent compound Abivertinib showed potent inhibitions with IC50 toward G2019S mutation and wild-type LRRK2 of 410.3 nM and 177.0 nM, respectively. Our combination screening strategy had a 53% hit rate in this repurposing task. MD simulations and MM-GBSA free energy analysis further revealed the atomic binding mechanism between the identified drugs and G2019S LRRK2. In summary, the results showed that our drug repurposing workflow could be used to identify potent compounds for LRRK2. The potent inhibitors discovered in our work can be a starting point to develop more effective LRRK2 inhibitors.
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Affiliation(s)
- Shuoyan Tan
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China
| | - Ruiqiang Lu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China
| | - Dahong Yao
- School of Pharmaceutical Sciences, Shenzhen Technology University, Shenzhen518060, China
| | - Jun Wang
- Ping An Healthcare Technology, Beijing100000, China
| | - Peng Gao
- Ping An Healthcare Technology, Beijing100000, China
| | - Guotong Xie
- Ping An Healthcare Technology, Beijing100000, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou730000, China.,State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, China
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18
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Sadeghi S, Lu J, Ngom A. An Integrative Heterogeneous Graph Neural Network-Based Method for Multi-Labeled Drug Repurposing. Front Pharmacol 2022; 13:908549. [PMID: 35873597 PMCID: PMC9298882 DOI: 10.3389/fphar.2022.908549] [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: 03/30/2022] [Accepted: 05/09/2022] [Indexed: 12/01/2022] Open
Abstract
Drug repurposing is the process of discovering new indications (i.e., diseases or conditions) for already approved drugs. Many computational methods have been proposed for predicting new associations between drugs and diseases. In this article, we proposed a new method, called DR-HGNN, an integrative heterogeneous graph neural network-based method for multi-labeled drug repurposing, to discover new indications for existing drugs. For this purpose, we first used the DTINet dataset to construct a heterogeneous drug–protein–disease (DPD) network, which is a graph composed of four types of nodes (drugs, proteins, diseases, and drug side effects) and eight types of edges. Second, we labeled each drug–protein edge, dpi,j = (di, pj), of the DPD network with a set of diseases, {δi,j,1, … , δi,j,k} associated with both di and pj and then devised multi-label ranking approaches which incorporate neural network architecture that operates on the heterogeneous graph-structured data and which leverages both the interaction patterns and the features of drug and protein nodes. We used a derivative of the GraphSAGE algorithm, HinSAGE, on the heterogeneous DPD network to learn low-dimensional vector representation of features of drugs and proteins. Finally, we used the drug–protein network to learn the embeddings of the drug–protein edges and then predict the disease labels that act as bridges between drugs and proteins. The proposed method shows better results than existing methods applied to the DTINet dataset, with an AUC of 0.964.
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Affiliation(s)
| | - Jianguo Lu
- School of Computer Science, University of Windsor, Windsor, ON, Canada
| | - Alioune Ngom
- School of Computer Science, University of Windsor, Windsor, ON, Canada
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19
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Zhu S, Bai Q, Li L, Xu T. Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents. Comput Struct Biotechnol J 2022; 20:2839-2847. [PMID: 35765655 PMCID: PMC9189996 DOI: 10.1016/j.csbj.2022.05.057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 12/19/2022] Open
Abstract
Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.
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Affiliation(s)
- Sha Zhu
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
| | - Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu 730000, PR China
- Corresponding author.
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21
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DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network. Curr Issues Mol Biol 2022; 44:2287-2299. [PMID: 35678684 PMCID: PMC9164023 DOI: 10.3390/cimb44050155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 05/08/2022] [Accepted: 05/14/2022] [Indexed: 11/17/2022] Open
Abstract
Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.
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22
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Zouboulis VA, Zouboulis KC, Zouboulis CC. Hidradenitis Suppurativa and Comorbid Disorder Biomarkers, Druggable Genes, New Drugs and Drug Repurposing-A Molecular Meta-Analysis. Pharmaceutics 2021; 14:pharmaceutics14010044. [PMID: 35056940 PMCID: PMC8779519 DOI: 10.3390/pharmaceutics14010044] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/20/2021] [Accepted: 12/23/2021] [Indexed: 12/28/2022] Open
Abstract
Chronic inflammation and dysregulated epithelial differentiation, especially of hair follicle keratinocytes, have been suggested as the major pathogenetic pathways of hidradenitis suppurativa/acne inversa (HS). On the other hand, obesity and metabolic syndrome have additionally been considered as an important risk factor. With adalimumab, a drug has already been approved and numerous other compounds are in advanced-stage clinical studies. A systematic review was conducted to detect and corroborate HS pathogenetic mechanisms at the molecular level and identify HS molecular markers. The obtained data were used to confirm studied and off-label administered drugs and to identify additional compounds for drug repurposing. A robust, strongly associated group of HS biomarkers was detected. The triad of HS pathogenesis, namely upregulated inflammation, altered epithelial differentiation and dysregulated metabolism/hormone signaling was confirmed, the molecular association of HS with certain comorbid disorders, such as inflammatory bowel disease, arthritis, type I diabetes mellitus and lipids/atherosclerosis/adipogenesis was verified and common biomarkers were identified. The molecular suitability of compounds in clinical studies was confirmed and 31 potential HS repurposing drugs, among them 10 drugs already launched for other disorders, were detected. This systematic review provides evidence for the importance of molecular studies to advance the knowledge regarding pathogenesis, future treatment and biomarker-supported clinical course follow-up in HS.
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Affiliation(s)
- Viktor A. Zouboulis
- Faculty of Medicine, Universitaetsklinikum Hamburg-Eppendorf (UKE), 20251 Hamburg, Germany;
| | - Konstantin C. Zouboulis
- Department of Chemistry and Applied Biosciences, Swiss Federal Institute of Technology (ETH) Zurich, 8092 Zurich, Switzerland;
| | - Christos C. Zouboulis
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Brandenburg Medical School Theodor Fontane and Faculty of Health Sciences Brandenburg, 06847 Dessau, Germany
- Correspondence: ; Tel.: +49-340-5014000
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23
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Xiao YC, Yu JL, Dai QQ, Li G, Li GB. Targeting Metalloenzymes by Boron-Containing Metal-Binding Pharmacophores. J Med Chem 2021; 64:17706-17727. [PMID: 34875836 DOI: 10.1021/acs.jmedchem.1c01691] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Metalloenzymes have critical roles in a wide range of biological processes and are directly involved in many human diseases; hence, they are considered as important targets for therapeutic intervention. The specific characteristics of metal ion(s)-containing active sites make exploitation of metal-binding pharmacophores (MBPs) critical to inhibitor development targeting metalloenzymes. This Perspective focuses on boron-containing MBPs, which display unique binding modes with metalloenzyme active sites, particularly via mimicking native substrates or tetrahedral transition states. The design concepts regarding boron-containing MBPs are highlighted through the case analyses on five distinct classes of clinically relevant nucleophilic metalloenzymes from medicinal chemistry perspectives. The challenges (e.g., selectivity) faced by some boron-containing MBPs and possible strategies (e.g., bioisosteres) for metalloenzyme inhibitor transformation are also discussed.
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Affiliation(s)
- You-Cai Xiao
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Jun-Lin Yu
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Qing-Qing Dai
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Gen Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
| | - Guo-Bo Li
- Key Laboratory of Drug-Targeting and Drug Delivery System of the Education Ministry and Sichuan Province, Sichuan Engineering Laboratory for Plant-Sourced Drug and Sichuan Research Center for Drug Precision Industrial Technology, West China School of Pharmacy, Sichuan University, Chengdu 610041, China
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