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Li S, Zhang L, Zhang W, Chen H, Hong M, Xia J, Zhang W, Luan X, Zheng G, Lu D. Identifying traditional Chinese medicine combinations for breast cancer treatment based on transcriptional regulation and chemical structure. Chin Med 2025; 20:23. [PMID: 39953557 PMCID: PMC11829537 DOI: 10.1186/s13020-025-01074-5] [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: 11/14/2024] [Accepted: 01/24/2025] [Indexed: 02/17/2025] Open
Abstract
Breast cancer (BC) is a prevalent form of cancer among women. Despite the emergence of numerous therapies over the past few decades, few have achieved the ideal therapeutic effect due to the heterogeneity of BC. Drug combination therapy is seen as a promising approach to cancer treatment. Traditional Chinese medicine (TCM), known for its multicomponent nature, has been validated for its anticancer properties, likely due to the synergy effect of the key components. However, identifying effective component combinations from TCM is challenging due to the vast combination possibilities and limited prior knowledge. This study aims to present a strategy for discovering synergistic compounds based on transcriptional regulation and chemical structure. First, BC-related gene sets were used to screen TCM-derived compound combinations guided by synergistic regulation. Then, machine learning models incorporating chemical structural features were established to identify potential compound combinations. Subsequently, the pair of honokiol and neochlorogenic acid was selected by integrating the results of compound combination screening. Finally, cell experiments were conducted to confirm the synergistic effect of the pair against BC. Overall, this study offers an integrated screening strategy to discover compound combinations of TCM against BC. The tumor cell suppression effect of the honokiol and neochlorogenic acid pair validated the effectiveness of the proposed strategy.
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Affiliation(s)
- Shensuo Li
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
- West China School of Public Health and West China Fourth Hospital, and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, 610041, China
| | - Lijun Zhang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Wen Zhang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Hongyu Chen
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Mei Hong
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Jianhua Xia
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
- School of Pharmacy, Second Military Medical University, Shanghai, 200433, China.
| | - Xin Luan
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Guangyong Zheng
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
| | - Dong Lu
- Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China.
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Dong X, Liu H, Tong T, Wu L, Wang J, You T, Wei Y, Yi X, Yang H, Hu J, Wang H, Wang X, Li MJ. Personalized prediction of anticancer potential of non-oncology drugs through learning from genome derived molecular pathways. NPJ Precis Oncol 2025; 9:36. [PMID: 39905223 PMCID: PMC11794852 DOI: 10.1038/s41698-025-00813-z] [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: 11/08/2023] [Accepted: 01/19/2025] [Indexed: 02/06/2025] Open
Abstract
Advances in cancer genomics have significantly expanded our understanding of cancer biology. However, the high cost of drug development limits our ability to translate this knowledge into precise treatments. Approved non-oncology drugs, comprising a large repository of chemical entities, offer a promising avenue for repurposing in cancer therapy. Herein we present CHANCE, a supervised machine learning model designed to predict the anticancer activities of non-oncology drugs for specific patients by simultaneously considering personalized coding and non-coding mutations. Utilizing protein-protein interaction networks, CHANCE harmonizes multilevel mutation annotations and integrates pharmacological information across different drugs into a single model. We systematically benchmarked the performance of CHANCE and show its predictions are better than previous model and highly interpretable. Applying CHANCE to approximately 5000 cancer samples indicated that >30% might respond to at least one non-oncology drug, with 11% non-oncology drugs predicted to have anticancer activities. Moreover, CHANCE predictions suggested an association between SMAD7 mutations and aspirin treatment response. Experimental validation using tumor cells derived from seven patients with pancreatic or esophageal cancer confirmed the potential anticancer activity of at least one non-oncology drug for five of these patients. To summarize, CHANCE offers a personalized and interpretable approach, serving as a valuable tool for mining non-oncology drugs in the precision oncology era.
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Affiliation(s)
- Xiaobao Dong
- Department of Genetics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Precision Medicine Research Center, The Second Hospital of Tianjin Medical University; Tianjin Medical University, Tianjin, China
| | - Huanhuan Liu
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Ting Tong
- Department of Gastroenterology, The Third Xiangya Hospital, Hunan Key Laboratory of Non-resolving Inflammation and Cancer, Central South University, Changsha, China
- Endoscopic Center, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China
| | - Liuxing Wu
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jianhua Wang
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Tianyi You
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yongjian Wei
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xianfu Yi
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hongxi Yang
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jie Hu
- Biobank of Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, China
| | - Haitao Wang
- Department of Oncology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.
| | - Xiaoyan Wang
- Department of Gastroenterology, The Third Xiangya Hospital, Hunan Key Laboratory of Non-resolving Inflammation and Cancer, Central South University, Changsha, China.
| | - Mulin Jun Li
- Department of Genetics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Precision Medicine Research Center, The Second Hospital of Tianjin Medical University; Tianjin Medical University, Tianjin, China.
- Department of Bioinformatics, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
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Wang J, Zeng Z, Li Z, Liu G, Zhang S, Luo C, Hu S, Wan S, Zhao L. The clinical application of artificial intelligence in cancer precision treatment. J Transl Med 2025; 23:120. [PMID: 39871340 PMCID: PMC11773911 DOI: 10.1186/s12967-025-06139-5] [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: 11/08/2024] [Accepted: 01/14/2025] [Indexed: 01/29/2025] Open
Abstract
BACKGROUND Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking. OBSERVATIONS By collecting and integrating large volumes of data and applying it to clinical tasks across various algorithms and models, artificial intelligence plays a significant role in cancer precision medicine. Here, we describe the general principles of artificial intelligence, including machine learning and deep learning. We further summarize the latest developments in artificial intelligence applications in cancer precision medicine. In tumor precision treatment, artificial intelligence plays a crucial role in individualizing both conventional and emerging therapies. In specific fields, including target prediction, targeted drug generation, immunotherapy response prediction, neoantigen prediction, and identification of long non-coding RNA, artificial intelligence offers promising perspectives. Finally, we outline the current challenges and ethical issues in the field. CONCLUSIONS Recent clinical studies demonstrate that artificial intelligence is involved in cancer precision medicine and has the potential to benefit cancer healthcare, particularly by optimizing conventional therapies, emerging targeted therapies, and individual immunotherapies. This review aims to provide valuable resources to clinicians and researchers and encourage further investigation in this field.
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Affiliation(s)
- Jinyu Wang
- Department of Medical Genetics, West China Second University Hospital, Sichuan University, Chengdu, China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
| | - Ziyi Zeng
- Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China
- Department of Neonatology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Zehua Li
- Department of Plastic and Burn Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Guangyue Liu
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Shunhong Zhang
- Department of Cardiology, Panzhihua Iron and Steel Group General Hospital, Panzhihua, China
| | - Chenchen Luo
- Department of Outpatient Chengbei, the Affiliated Stomatological Hospital, Southwest Medical University, Luzhou, China
| | - Saidi Hu
- Department of Stomatology, Yaan people's Hospital, Yaan, China
| | - Siran Wan
- Department of Gynaecology and Obstetrics, Yaan people's Hospital, Yaan, China
| | - Linyong Zhao
- Department of General Surgery & Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy / Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
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Fan S, Yang K, Lu K, Dong X, Li X, Zhu Q, Li S, Zeng J, Zhou X. DrugRepPT: a deep pretraining and fine-tuning framework for drug repositioning based on drug's expression perturbation and treatment effectiveness. Bioinformatics 2024; 40:btae692. [PMID: 39563444 PMCID: PMC11630837 DOI: 10.1093/bioinformatics/btae692] [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: 06/14/2024] [Revised: 10/16/2024] [Accepted: 11/15/2024] [Indexed: 11/21/2024] Open
Abstract
MOTIVATION Drug repositioning (DR), identifying novel indications for approved drugs, is a cost-effective strategy in drug discovery. Despite numerous proposed DR models, integrating network-based features, differential gene expression, and chemical structures for high-performance DR remains challenging. RESULTS We propose a comprehensive deep pretraining and fine-tuning framework for DR, termed DrugRepPT. Initially, we design a graph pretraining module employing model-augmented contrastive learning on a vast drug-disease heterogeneous graph to capture nuanced interactions and expression perturbations after intervention. Subsequently, we introduce a fine-tuning module leveraging a graph residual-like convolution network to elucidate intricate interactions between diseases and drugs. Moreover, a Bayesian multiloss approach is introduced to balance the existence and effectiveness of drug treatment effectively. Extensive experiments showcase the efficacy of our framework, with DrugRepPT exhibiting remarkable performance improvements compared to SOTA (state of the arts) baseline methods (improvement 106.13% on Hit@1 and 54.45% on mean reciprocal rank). The reliability of predicted results is further validated through two case studies, i.e. gastritis and fatty liver, via literature validation, network medicine analysis, and docking screening. AVAILABILITY AND IMPLEMENTATION The code and results are available at https://github.com/2020MEAI/DrugRepPT.
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Affiliation(s)
- Shuyue Fan
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Kuo Yang
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Kezhi Lu
- Faculty of Engineering and IT, Australian AI Institute, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Xin Dong
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Xianan Li
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Qiang Zhu
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Shao Li
- Department of Automation, MOE Key Laboratory of Bioinformatics/Bioinformatics Division, BNRIST, Institute for TCM-X, Tsinghua University, Beijing 100084, China
| | - Jianyang Zeng
- School of Engineering, Westlake University, Hangzhou 310030, China
| | - Xuezhong Zhou
- Department of Artificial Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer Science & Technology, Beijing Jiaotong University, Beijing 100044, China
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Karampuri A, Jakkula BK, Perugu S. ResisenseNet hybrid neural network model for predicting drug sensitivity and repurposing in breast Cancer. Sci Rep 2024; 14:23949. [PMID: 39397003 PMCID: PMC11471817 DOI: 10.1038/s41598-024-71076-0] [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: 06/04/2024] [Accepted: 08/23/2024] [Indexed: 10/15/2024] Open
Abstract
Breast cancer remains a leading cause of mortality among women worldwide, with drug resistance driven by transcription factors and mutations posing significant challenges. To address this, we present ResisenseNet, a predictive model for drug sensitivity and resistance. ResisenseNet integrates transcription factor expression, genomic markers, drugs, and molecular descriptors, employing a hybrid architecture of 1D-CNN + LSTM and DNN to effectively learn long-range and temporal patterns from amino acid sequences and transcription factor data. The model demonstrated exceptional predictive accuracy, achieving a validation accuracy of 0.9794 and a loss value of 0.042. Comprehensive validation included comparisons with state-of-the-art models and ablation studies, confirming the robustness of the developed architecture. ResisenseNet has been applied to repurpose existing anticancer drugs across 14 different cancers, with a focus on breast cancer. Among the malignancies studied, drugs targeting Low-grade Glioma (LGG) and Lung Adenocarcinoma (LUAD) showed increased sensitivity to breast cancer as per ResisenseNet's assessment. Further evaluation of the predicted sensitive drugs revealed that 14 had no prior history of anticancer activity against breast cancer. These drugs target key signaling pathways involved in breast cancer, presenting novel therapeutic opportunities. ResisenseNet addresses drug resistance by filtering ineffective compounds and enhancing chemotherapy for breast cancer. In vitro studies on sensitive drugs provide valuable insights into breast cancer prognosis, contributing to improved treatment strategies.
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Affiliation(s)
- Anush Karampuri
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Bharath Kumar Jakkula
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India.
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Duo L, Liu Y, Ren J, Tang B, Hirst JD. Artificial intelligence for small molecule anticancer drug discovery. Expert Opin Drug Discov 2024; 19:933-948. [PMID: 39074493 DOI: 10.1080/17460441.2024.2367014] [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: 04/22/2024] [Accepted: 06/07/2024] [Indexed: 07/31/2024]
Abstract
INTRODUCTION The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships. AREA COVERED In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research. EXPERT OPINION The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.
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Affiliation(s)
- Lihui Duo
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Yu Liu
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jianfeng Ren
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Bencan Tang
- Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo, China
| | - Jonathan D Hirst
- School of Chemistry, University of Nottingham University Park, Nottingham, UK
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Sganzerla Martinez G, Garduno A, Toloue Ostadgavahi A, Hewins B, Dutt M, Kumar A, Martin-Loeches I, Kelvin DJ. Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning. Int J Mol Sci 2024; 25:5920. [PMID: 38892107 PMCID: PMC11172967 DOI: 10.3390/ijms25115920] [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: 03/15/2024] [Revised: 05/24/2024] [Accepted: 05/25/2024] [Indexed: 06/21/2024] Open
Abstract
A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.
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Affiliation(s)
- Gustavo Sganzerla Martinez
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
| | - Alexis Garduno
- Department of Clinical Medicine, Trinity College Dublin, D08 NHY1 Dublin, Ireland; (A.G.); (I.M.-L.)
- Department of Intensive Care Medicine, St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - Ali Toloue Ostadgavahi
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
| | - Benjamin Hewins
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
| | - Mansi Dutt
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
| | - Anuj Kumar
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
| | - Ignacio Martin-Loeches
- Department of Clinical Medicine, Trinity College Dublin, D08 NHY1 Dublin, Ireland; (A.G.); (I.M.-L.)
- Department of Intensive Care Medicine, St. James’s Hospital, D08 NHY1 Dublin, Ireland
- Multidisciplinary Intensive Care Research Organization (MICRO), St. James’s Hospital, D08 NHY1 Dublin, Ireland
| | - David J. Kelvin
- Microbiology and Immunology, Dalhousie University, Halifax, NS B3H 4H7, Canada; (G.S.M.); (A.T.O.); (B.H.); (M.D.); (A.K.)
- Department of Pediatrics, Izaak Walton Killam (IWK) Health Center, Canadian Center for Vaccinology, Halifax, NS B3H 4H7, Canada
- Department of Immunology, Shantou University Medical College, Shantou 512025, China
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Satish KS, Saraswathy GR, Ritesh G, Saravanan KS, Krishnan A, Bhargava J, Ushnaa K, Dsouza PL. Exploring cutting-edge strategies for drug repurposing in female cancers - An insight into the tools of the trade. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 207:355-415. [PMID: 38942544 DOI: 10.1016/bs.pmbts.2024.05.002] [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: 06/30/2024]
Abstract
Female cancers, which include breast and gynaecological cancers, represent a significant global health burden for women. Despite advancements in research pertinent to unearthing crucial pathological characteristics of these cancers, challenges persist in discovering potential therapeutic strategies. This is further exacerbated by economic burdens associated with de novo drug discovery and clinical intricacies such as development of drug resistance and metastasis. Drug repurposing, an innovative approach leveraging existing FDA-approved drugs for new indications, presents a promising avenue to expedite therapeutic development. Computational techniques, including virtual screening and analysis of drug-target-disease relationships, enable the identification of potential candidate drugs. Integration of diverse data types, such as omics and clinical information, enhances the precision and efficacy of drug repurposing strategies. Experimental approaches, including high-throughput screening assays, in vitro, and in vivo models, complement computational methods, facilitating the validation of repurposed drugs. This review highlights various target mining strategies based on analysis of differential gene expression, weighted gene co-expression, protein-protein interaction network, and host-pathogen interaction, among others. To unearth drug candidates, the technicalities of leveraging information from databases such as DrugBank, STITCH, LINCS, and ChEMBL, among others are discussed. Further in silico validation techniques encompassing molecular docking, pharmacophore modelling, molecular dynamic simulations, and ADMET analysis are elaborated. Overall, this review delves into the exploration of individual case studies to offer a wide perspective of the ever-evolving field of drug repurposing, emphasizing the multifaceted approaches and methodologies employed for the same to confront female cancers.
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Affiliation(s)
- Kshreeraja S Satish
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Ganesan Rajalekshmi Saraswathy
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India.
| | - Giri Ritesh
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kamatchi Sundara Saravanan
- Department of Pharmacognosy, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Aarti Krishnan
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Janhavi Bhargava
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Kuri Ushnaa
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
| | - Prizvan Lawrence Dsouza
- Department of Pharmacy Practice, Faculty of Pharmacy, M.S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India
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Karampuri A, Kundur S, Perugu S. Exploratory drug discovery in breast cancer patients: A multimodal deep learning approach to identify novel drug candidates targeting RTK signaling. Comput Biol Med 2024; 174:108433. [PMID: 38642491 DOI: 10.1016/j.compbiomed.2024.108433] [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: 02/01/2024] [Revised: 04/04/2024] [Accepted: 04/07/2024] [Indexed: 04/22/2024]
Abstract
Breast cancer, a highly formidable and diverse malignancy predominantly affecting women globally, poses a significant threat due to its intricate genetic variability, rendering it challenging to diagnose accurately. Various therapies such as immunotherapy, radiotherapy, and diverse chemotherapy approaches like drug repurposing and combination therapy are widely used depending on cancer subtype and metastasis severity. Our study revolves around an innovative drug discovery strategy targeting potential drug candidates specific to RTK signalling, a prominently targeted receptor class in cancer. To accomplish this, we have developed a multimodal deep neural network (MM-DNN) based QSAR model integrating omics datasets to elucidate genomic, proteomic expression data, and drug responses, validated rigorously. The results showcase an R2 value of 0.917 and an RMSE value of 0.312, affirming the model's commendable predictive capabilities. Structural analogs of drug molecules specific to RTK signalling were sourced from the PubChem database, followed by meticulous screening to eliminate dissimilar compounds. Leveraging the MM-DNN-based QSAR model, we predicted the biological activity of these molecules, subsequently clustering them into three distinct groups. Feature importance analysis was performed. Consequently, we successfully identified prime drug candidates tailored for each potential downstream regulatory protein within the RTK signalling pathway. This method makes the early stages of drug development faster by removing inactive compounds, providing a hopeful path in combating breast cancer.
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Affiliation(s)
- Anush Karampuri
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Sunitha Kundur
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India
| | - Shyam Perugu
- Department of Biotechnology, National Institute of Technology, Warangal, 500604, India.
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Mehrotra S, Sharma S, Pandey RK. A journey from omics to clinicomics in solid cancers: Success stories and challenges. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2024; 139:89-139. [PMID: 38448145 DOI: 10.1016/bs.apcsb.2023.11.008] [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: 03/08/2024]
Abstract
The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.
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11
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Gogoshin G, Rodin AS. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends. Cancers (Basel) 2023; 15:5858. [PMID: 38136405 PMCID: PMC10742144 DOI: 10.3390/cancers15245858] [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: 10/23/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 12/24/2023] Open
Abstract
Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.
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Affiliation(s)
- Grigoriy Gogoshin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
| | - Andrei S. Rodin
- Department of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA
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12
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B S N, P K KN, Akey KS, Sankaran S, Raman RK, Natarajan J, Selvaraj J. Vitamin D analog calcitriol for breast cancer therapy; an integrated drug discovery approach. J Biomol Struct Dyn 2023; 41:11017-11043. [PMID: 37054526 DOI: 10.1080/07391102.2023.2199866] [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: 07/25/2022] [Accepted: 12/11/2022] [Indexed: 04/15/2023]
Abstract
As breast cancer remains leading cause of cancer death globally, it is essential to develop an affordable breast cancer therapy in underdeveloped countries. Drug repurposing offers potential to address gaps in breast cancer treatment. Molecular networking studies were performed for drug repurposing approach by using heterogeneous data. The PPI networks were built to select the target genes from the EGFR overexpression signaling pathway and its associated family members. The selected genes EGFR, ErbB2, ErbB4 and ErbB3 were allowed to interact with 2637 drugs, leads to PDI network construction of 78, 61, 15 and 19 drugs, respectively. As drugs approved for treating non cancer-related diseases or disorders are clinically safe, effective, and affordable, these drugs were given considerable attention. Calcitriol had shown significant binding affinities with all four receptors than standard neratinib. The RMSD, RMSF, and H-bond analysis of protein-ligand complexes from molecular dynamics simulation (100 ns), confirmed the stable binding of calcitriol with ErbB2 and EGFR receptors. In addition, MMGBSA and MMP BSA also affirmed the docking results. These in-silico results were validated with in-vitro cytotoxicity studies in SK-BR-3 and Vero cells. The IC50 value of calcitriol (43.07 mg/ml) was found to be lower than neratinib (61.50 mg/ml) in SK-BR-3 cells. In Vero cells the IC50 value of calcitriol (431.05 mg/ml) was higher than neratinib (404.95 mg/ml). It demonstrates that calcitriol suggestively downregulated the SK-BR-3 cell viability in a dose-dependent manner. These implications revealed calcitriol has shown better cytotoxicity and decreased the proliferation rate of breast cancer cells than neratinib.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Nagaraj B S
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Krishnan Namboori P K
- Amrita Molecular Modeling and Synthesis (AMMAS) Research lab, Amrita Vishwavidyapeetham, Coimbatore, Tamilnadu, India
| | - Krishna Swaroop Akey
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Sathianarayanan Sankaran
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Karpagam Academy of Higher Education, Coimbatore, Tamilnadu, India
| | - Rajesh Kumar Raman
- Department of Pharmaceutical Biotechnology, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jawahar Natarajan
- Department of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
| | - Jubie Selvaraj
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Ooty, Tamilnadu, India
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Behera RN, Bisht VS, Giri K, Ambatipudi K. Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment. Proteomics Clin Appl 2023; 17:e2300016. [PMID: 37259687 DOI: 10.1002/prca.202300016] [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: 02/16/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023]
Abstract
Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.
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Affiliation(s)
- Rama N Behera
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Vinod S Bisht
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kuldeep Giri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kiran Ambatipudi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
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14
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Ahmed F, Yang YJ, Samantasinghar A, Kim YW, Ko JB, Choi KH. Network-based drug repurposing for HPV-associated cervical cancer. Comput Struct Biotechnol J 2023; 21:5186-5200. [PMID: 37920815 PMCID: PMC10618120 DOI: 10.1016/j.csbj.2023.10.038] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 10/17/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023] Open
Abstract
In women, cervical cancer (CC) is the fourth most common cancer around the world with average cases of 604,000 and 342,000 deaths per year. Approximately 50% of high-grade CC are attributed to human papillomavirus (HPV) types 16 and 18. Chances of CC in HPV-positive patients are 6 times more than HPV-negative patients which demands timely and effective treatment. Repurposing of drugs is considered a viable approach to drug discovery which makes use of existing drugs, thus potentially reducing the time and costs associated with de-novo drug discovery. In this study, we present an integrative drug repurposing framework based on a systems biology-enabled network medicine platform. First, we built an HPV-induced CC protein interaction network named HPV2C following the CC signatures defined by the omics dataset, obtained from GEO database. Second, the drug target interaction (DTI) data obtained from DrugBank, and related databases was used to model the DTI network followed by drug target network proximity analysis of HPV-host associated key targets and DTIs in the human protein interactome. This analysis identified 142 potential anti-HPV repurposable drugs to target HPV induced CC pathways. Third, as per the literature survey 51 of the predicted drugs are already used for CC and 33 of the remaining drugs have anti-viral activity. Gene set enrichment analysis of potential drugs in drug-gene signatures and in HPV-induced CC-specific transcriptomic data in human cell lines additionally validated the predictions. Finally, 13 drug combinations were found using a network based on overlapping exposure. To summarize, the study provides effective network-based technique to quickly identify suitable repurposable drugs and drug combinations that target HPV-associated CC.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, South Korea
| | - Young Jin Yang
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | | | - Young Woo Kim
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | - Jeong Beom Ko
- Korea Institute of Industrial Technology, 102 Jejudaehak-ro, Jeju-si 63243, South Korea
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, South Korea
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15
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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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16
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Li X, Liao M, Wang B, Zan X, Huo Y, Liu Y, Bao Z, Xu P, Liu W. A drug repurposing method based on inhibition effect on gene regulatory network. Comput Struct Biotechnol J 2023; 21:4446-4455. [PMID: 37731599 PMCID: PMC10507583 DOI: 10.1016/j.csbj.2023.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/05/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023] Open
Abstract
Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.
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Affiliation(s)
- Xianbin Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Minzhen Liao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Bing Wang
- School of Medicine, Southeast University, Nanjing, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yanhao Huo
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Yue Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
| | - Zhenshen Bao
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Peng Xu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
- School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Wenbin Liu
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China
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17
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Shoaib TH, Ibraheem W, Abdelrahman M, Osman W, Sherif AE, Ashour A, Ibrahim SRM, Ghazawi KF, Miski SF, Almadani SA, ALsiyud DF, Mohamed GA, Alzain AA. Exploring the potential of approved drugs for triple-negative breast cancer treatment by targeting casein kinase 2: Insights from computational studies. PLoS One 2023; 18:e0289887. [PMID: 37578958 PMCID: PMC10424868 DOI: 10.1371/journal.pone.0289887] [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: 05/04/2023] [Accepted: 07/27/2023] [Indexed: 08/16/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive malignancy that requires effective targeted drug therapy. In this study, we employed in silico methods to evaluate the efficacy of seven approved drugs against human ck2 alpha kinase, a significant modulator of TNBC metastasis and invasiveness. Molecular docking revealed that the co-crystallized reference inhibitor 108600 achieved a docking score of (-7.390 kcal/mol). Notably, among the seven approved drugs tested, sunitinib, bazedoxifene, and etravirine exhibited superior docking scores compared to the reference inhibitor. Specifically, their respective docking scores were -10.401, -7.937, and -7.743 kcal/mol. Further analysis using MM/GBSA demonstrated that these three top-ranked drugs possessed better binding energies than the reference ligand. Subsequent molecular dynamics simulations identified etravirine, an FDA-approved antiviral drug, as the only repurposed drug that demonstrated a stable and reliable binding mode with the human ck2 alpha protein, based on various analysis measures including RMSD, RMSF, and radius of gyration. Principal component analysis indicated that etravirine exhibited comparable stability of motion as a complex with human ck2 alpha protein, similar to the co-crystallized inhibitor. Additionally, Density functional theory (DFT) calculations were performed on a complex of etravirine and a representative gold atom positioned at different sites relative to the heteroatoms of etravirine. The results of the DFT calculations revealed low-energy complexes that could potentially serve as guides for experimental trials involving gold nanocarriers of etravirine, enhancing its delivery to malignant cells and introducing a new drug delivery route. Based on the results obtained in this research study, etravirine shows promise as a potential antitumor agent targeting TNBC, warranting further investigation through experimental and clinical assessments.
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Affiliation(s)
- Tagyedeen H. Shoaib
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, University of Gezira, Gezira, Sudan
| | - Walaa Ibraheem
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, University of Gezira, Gezira, Sudan
| | - Mohammed Abdelrahman
- Faculty of Pharmacy, Department of Pharmaceutics, University of Gezira, Gezira, Sudan
| | - Wadah Osman
- Faculty of Pharmacy, Department of Pharmacognosy, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
- Faculty of Pharmacy, Department of Pharmacognosy, University of Khartoum, Khartoum, Sudan
| | - Asmaa E. Sherif
- Faculty of Pharmacy, Department of Pharmacognosy, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
- Faculty of Pharmacy, Department of Pharmacognosy, Mansoura University, Mansoura, Egypt
| | - Ahmed Ashour
- Faculty of Pharmacy, Department of Pharmacognosy, Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia
- Faculty of Pharmacy, Department of Pharmacognosy, Mansoura University, Mansoura, Egypt
| | - Sabrin R. M. Ibrahim
- Department of Chemistry, Preparatory Year Program, Batterjee Medical College, Jeddah, Saudi Arabia
- Faculty of Pharmacy, Department of Pharmacognosy, Assiut University, Assiut, Egypt
| | - Kholoud F. Ghazawi
- Clinical Pharmacy Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Samar F. Miski
- Department of Pharmacology and Toxicology, College of Pharmacy, Taibah University, Al-Madinah Al-Munawwarah, Saudi Arabia
| | - Sara A. Almadani
- Department of Pharmacology and Toxicology, College of Pharmacy, Taibah University, Al-Madinah Al-Munawwarah, Saudi Arabia
| | - Duaa Fahad ALsiyud
- Department of Medical Laboratories—Hematology, King Fahd Armed Forces Hospital, Corniche Road, Andalus, Jeddah, Saudi Arabia
| | - Gamal A. Mohamed
- Department of Natural Products and Alternative Medicine, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulrahim A. Alzain
- Faculty of Pharmacy, Department of Pharmaceutical Chemistry, University of Gezira, Gezira, Sudan
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18
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Zhou H, Liu H, Yu Y, Yuan X, Xiao L. Informatics on Drug Repurposing for Breast Cancer. Drug Des Devel Ther 2023; 17:1933-1943. [PMID: 37405253 PMCID: PMC10315146 DOI: 10.2147/dddt.s417563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/17/2023] [Indexed: 07/06/2023] Open
Abstract
Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.
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Affiliation(s)
- Hui Zhou
- Department of Lymphoma and Hematology, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
- Department of Lymphoma and Hematology, Hunan Cancer Hospital, Changsha, Hunan, People’s Republic of China
| | - Hongdou Liu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Yan Yu
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
| | - Xiao Yuan
- Department of Laboratory Diagnosis, Changsha Kingmed Center for Clinical Laboratory, Changsha, Hunan, People’s Republic of China
- Department of Laboratory Diagnosis, Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, People’s Republic of China
| | - Ling Xiao
- Department of Histology and Embryology of Xiangya School of Medicine, Central South University, Changsha, Hunan, People’s Republic of China
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Ahmed F, Samantasinghar A, Manzoor Soomro A, Kim S, Hyun Choi K. A systematic review of computational approaches to understand cancer biology for informed drug repurposing. J Biomed Inform 2023; 142:104373. [PMID: 37120047 DOI: 10.1016/j.jbi.2023.104373] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/25/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023]
Abstract
Cancer is the second leading cause of death globally, trailing only heart disease. In the United States alone, 1.9 million new cancer cases and 609,360 deaths were recorded for 2022. Unfortunately, the success rate for new cancer drug development remains less than 10%, making the disease particularly challenging. This low success rate is largely attributed to the complex and poorly understood nature of cancer etiology. Therefore, it is critical to find alternative approaches to understanding cancer biology and developing effective treatments. One such approach is drug repurposing, which offers a shorter drug development timeline and lower costs while increasing the likelihood of success. In this review, we provide a comprehensive analysis of computational approaches for understanding cancer biology, including systems biology, multi-omics, and pathway analysis. Additionally, we examine the use of these methods for drug repurposing in cancer, including the databases and tools that are used for cancer research. Finally, we present case studies of drug repurposing, discussing their limitations and offering recommendations for future research in this area.
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Affiliation(s)
- Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea
| | | | | | - Sejong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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20
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Flori L, Brogi S, Sirous H, Calderone V. Disruption of Irisin Dimerization by FDA-Approved Drugs: A Computational Repurposing Approach for the Potential Treatment of Lipodystrophy Syndromes. Int J Mol Sci 2023; 24:ijms24087578. [PMID: 37108741 PMCID: PMC10145865 DOI: 10.3390/ijms24087578] [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: 03/22/2023] [Revised: 04/12/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023] Open
Abstract
In this paper, we present the development of a computer-based repurposing approach to identify FDA-approved drugs that are potentially able to interfere with irisin dimerization. It has been established that altered levels of irisin dimers are a pure hallmark of lipodystrophy (LD) syndromes. Accordingly, the identification of compounds capable of slowing down or precluding the irisin dimers' formation could represent a valuable therapeutic strategy in LD. Combining several computational techniques, we identified five FDA-approved drugs with satisfactory computational scores (iohexol, XP score = -7.70 kcal/mol, SP score = -5.5 kcal/mol, ΔGbind = -61.47 kcal/mol, ΔGbind (average) = -60.71 kcal/mol; paromomycin, XP score = -7.23 kcal/mol, SP score = -6.18 kcal/mol, ΔGbind = -50.14 kcal/mol, ΔGbind (average) = -49.13 kcal/mol; zoledronate, XP score = -6.33 kcal/mol, SP score = -5.53 kcal/mol, ΔGbind = -32.38 kcal/mol, ΔGbind (average) = -29.42 kcal/mol; setmelanotide, XP score = -6.10 kcal/mol, SP score = -7.24 kcal/mol, ΔGbind = -56.87 kcal/mol, ΔGbind (average) = -62.41 kcal/mol; and theophylline, XP score = -5.17 kcal/mol, SP score = -5.55 kcal/mol, ΔGbind = -33.25 kcal/mol, ΔGbind (average) = -35.29 kcal/mol) that are potentially able to disrupt the dimerization of irisin. For this reason, they deserve further investigation to characterize them as irisin disruptors. Remarkably, the identification of drugs targeting this process can offer novel therapeutic opportunities for the treatment of LD. Furthermore, the identified drugs could provide a starting point for a repositioning approach, synthesizing novel analogs with improved efficacy and selectivity against the irisin dimerization process.
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Affiliation(s)
- Lorenzo Flori
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
| | - Simone Brogi
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
- Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Hajar Sirous
- Bioinformatics Research Center, School of Pharmacy and Pharmaceutical Sciences, Isfahan University of Medical Sciences, Isfahan 81746-73461, Iran
| | - Vincenzo Calderone
- Department of Pharmacy, University of Pisa, Via Bonanno 6, 56126 Pisa, Italy
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21
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Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, Liu X. A compact review of progress and prospects of deep learning in drug discovery. J Mol Model 2023; 29:117. [PMID: 36976427 DOI: 10.1007/s00894-023-05492-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.
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Affiliation(s)
- Huijun Li
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Lin Zou
- College of Medicine, Guangxi University, Nanning, 530004, China
| | | | - Dongqiong He
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Zifan Liu
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xuejie Ding
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Hao Wen
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Lisheng Wang
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Mingqing Yuan
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xu Liu
- College of Medicine, Guangxi University, Nanning, 530004, China.
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22
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Ünsal Ü, Cüvitoğlu A, Turhan K, Işık Z. NMSDR: Drug repurposing approach based on transcriptome data and network module similarity. Mol Inform 2023; 42:e2200077. [PMID: 36411244 DOI: 10.1002/minf.202200077] [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: 04/05/2022] [Revised: 09/19/2022] [Accepted: 11/21/2022] [Indexed: 11/23/2022]
Abstract
Computational drug repurposing aims to discover new treatment regimens by analyzing approved drugs on the market. This study proposes previously approved compounds that can change the expression profile of disease-causing proteins by developing a network theory-based drug repurposing approach. The novelty of the proposed approach is an exploration of module similarity between a disease-causing network and a compound-specific interaction network; thus, such an association leads to more realistic modeling of molecular cell responses at a system biology level. The overlap of the disease network and each compound-specific network is calculated based on a shortest-path similarity of networks by accounting for all protein pairs between networks. A higher similarity score indicates a significant potential of a compound. The approach was validated for breast and lung cancers. When all compounds are sorted by their normalized-similarity scores, 36 and 16 drugs are proposed as new candidates for breast and lung cancer treatment, respectively. A literature survey on candidate compounds revealed that some of our predictions have been clinically investigated in phase II/III trials for the treatment of two cancer types. As a summary, the proposed approach has provided promising initial results by modeling biochemical cell responses in a network-level data representation.
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Affiliation(s)
- Ülkü Ünsal
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye.,Department of Health Management, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Ali Cüvitoğlu
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
| | - Kemal Turhan
- Department of Biostatistics and Medical Informatics, Karadeniz Technical University, 61080, Trabzon, Türkiye
| | - Zerrin Işık
- Department of Computer Engineering, Dokuz Eylul University, 35390, İzmir, Türkiye
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23
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Gao Y, Chen S, Tong J, Fu X. Topology-enhanced molecular graph representation for anti-breast cancer drug selection. BMC Bioinformatics 2022; 23:382. [PMID: 36123643 PMCID: PMC9484163 DOI: 10.1186/s12859-022-04913-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/24/2022] [Indexed: 12/24/2022] Open
Abstract
Background Breast cancer is currently one of the cancers with a higher mortality rate in the world. The biological research on anti-breast cancer drugs focuses on the activity of estrogen receptors alpha (ER\documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α), the pharmacokinetic properties and the safety of the compounds, which, however, is an expensive and time-consuming process. Developments of deep learning bring potential to efficiently facilitate the candidate drug selection against breast cancer. Methods In this paper, we propose an Anti-Breast Cancer Drug selection method utilizing Gated Graph Neural Networks (ABCD-GGNN) to topologically enhance the molecular representation of candidate drugs. By constructing atom-level graphs through atomic descriptors for each distinct compound, ABCD-GGNN can topologically learn both the implicit structure and substructure characteristics of a candidate drug and then integrate the representation with explicit discrete molecular descriptors to generate a molecule-level representation. As a result, the representation of ABCD-GGNN can inductively predict the ER\documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α, the pharmacokinetic properties and the safety of each candidate drug. Finally, we design a ranking operator whose inputs are the predicted properties so as to statistically select the appropriate drugs against breast cancer. Results Extensive experiments conducted on our collected anti-breast cancer candidate drug dataset demonstrate that our proposed method outperform all the other representative methods in the tasks of predicting ER\documentclass[12pt]{minimal}
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\begin{document}$$\alpha$$\end{document}α, and the pharmacokinetic properties and safety of the compounds. Extended result analysis demonstrates the efficiency and biological rationality of the operator we design to calculate the candidate drug ranking from the predicted properties. Conclusion In this paper, we propose the ABCD-GGNN representation method to efficiently integrate the topological structure and substructure features of the molecules with the discrete molecular descriptors. With a ranking operator applied, the predicted properties efficiently facilitate the candidate drug selection against breast cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04913-6.
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Affiliation(s)
- Yue Gao
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.,Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China
| | - Songling Chen
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China.,Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China
| | - Junyi Tong
- School of Science, Beijing University of Posts and Telecommunications, Beijing, China
| | - Xiangling Fu
- School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, China. .,Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education, Beijing, China.
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24
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Liu J, Zhou Z, Kong S, Ma Z. Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs. Front Oncol 2022; 12:956705. [PMID: 35936743 PMCID: PMC9353770 DOI: 10.3389/fonc.2022.956705] [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: 05/30/2022] [Accepted: 06/28/2022] [Indexed: 11/19/2022] Open
Abstract
The optimization of drug properties in the process of cancer drug development is very important to save research and development time and cost. In order to make the anti-breast cancer drug candidates with good biological activity, this paper collected 1974 compounds, firstly, the top 20 molecular descriptors that have the most influence on biological activity were screened by using XGBoost-based data feature selection; secondly, on this basis, take pIC50 values as feature data and use a variety of machine learning algorithms to compare, soas to select a most suitable algorithm to predict the IC50 and pIC50 values. It is preliminarily found that the effects of Random Forest, XGBoost and Gradient-enhanced algorithms are good and have little difference, and the Support vector machine is the worst. Then, using the Semi-automatic parameter adjustment method to adjust the parameters of Random Forest, XGBoost and Gradient-enhanced algorithms to find the optimal parameters. It is found that the Random Forest algorithm has high accuracy and excellent anti over fitting, and the algorithm is stable. Its prediction accuracy is 0.745. Finally, the accuracy of the results is verified by training the model with the preliminarily selected data, which provides an innovative solution for the optimization of the properties of anti- breast cancer drugs, and can provide better support for the early research and development of anti-breast cancer drugs.
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Affiliation(s)
- Jiajia Liu
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
| | - Zhihui Zhou
- College of Science, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, China
| | - Shanshan Kong
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- The Key Laboratory of Engineering Computing in Tangshan City, North China University of Science and Technology, Tangshan, China
- Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan, China
- Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, China
- *Correspondence: Shanshan Kong,
| | - Zezhong Ma
- College of Science, North China University of Science and Technology, Tangshan, China
- Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan, China
- Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan, China
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25
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Zhang Y, Lei X, Pan Y, Wu FX. Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks. Front Pharmacol 2022; 13:872785. [PMID: 35620297 PMCID: PMC9127467 DOI: 10.3389/fphar.2022.872785] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 04/11/2022] [Indexed: 11/29/2022] Open
Abstract
The understanding of therapeutic properties is important in drug repositioning and drug discovery. However, chemical or clinical trials are expensive and inefficient to characterize the therapeutic properties of drugs. Recently, artificial intelligence (AI)-assisted algorithms have received extensive attention for discovering the potential therapeutic properties of drugs and speeding up drug development. In this study, we propose a new method based on GraphSAGE and clustering constraints (DRGCC) to investigate the potential therapeutic properties of drugs for drug repositioning. First, the drug structure features and disease symptom features are extracted. Second, the drug–drug interaction network and disease similarity network are constructed according to the drug–gene and disease–gene relationships. Matrix factorization is adopted to extract the clustering features of networks. Then, all the features are fed to the GraphSAGE to predict new associations between existing drugs and diseases. Benchmark comparisons on two different datasets show that our method has reliable predictive performance and outperforms other six competing. We have also conducted case studies on existing drugs and diseases and aimed to predict drugs that may be effective for the novel coronavirus disease 2019 (COVID-19). Among the predicted anti-COVID-19 drug candidates, some drugs are being clinically studied by pharmacologists, and their binding sites to COVID-19-related protein receptors have been found via the molecular docking technology.
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Affiliation(s)
- Yuchen Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi'an, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
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26
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You Y, Lai X, Pan Y, Zheng H, Vera J, Liu S, Deng S, Zhang L. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022; 7:156. [PMID: 35538061 PMCID: PMC9090746 DOI: 10.1038/s41392-022-00994-0] [Citation(s) in RCA: 142] [Impact Index Per Article: 47.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/14/2022] [Accepted: 04/05/2022] [Indexed: 02/08/2023] Open
Abstract
Artificial intelligence is an advanced method to identify novel anticancer targets and discover novel drugs from biology networks because the networks can effectively preserve and quantify the interaction between components of cell systems underlying human diseases such as cancer. Here, we review and discuss how to employ artificial intelligence approaches to identify novel anticancer targets and discover drugs. First, we describe the scope of artificial intelligence biology analysis for novel anticancer target investigations. Second, we review and discuss the basic principles and theory of commonly used network-based and machine learning-based artificial intelligence algorithms. Finally, we showcase the applications of artificial intelligence approaches in cancer target identification and drug discovery. Taken together, the artificial intelligence models have provided us with a quantitative framework to study the relationship between network characteristics and cancer, thereby leading to the identification of potential anticancer targets and the discovery of novel drug candidates.
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Affiliation(s)
- Yujie You
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Xin Lai
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Room D513, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, China
| | - Huiru Zheng
- School of Computing, Ulster University, Belfast, BT15 1ED, UK
| | - Julio Vera
- Laboratory of Systems Tumor Immunology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Universitätsklinikum Erlangen, Erlangen, 91052, Germany
| | - Suran Liu
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Senyi Deng
- Institute of Thoracic Oncology, Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610065, China.
| | - Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China.
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27
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Xiang H, Li A, Lin X. An Optimization Method for Drug-Target Interaction Prediction Based on RandSAS Strategy. LECTURE NOTES IN COMPUTER SCIENCE 2022:547-555. [DOI: 10.1007/978-3-031-13829-4_47] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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28
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Deep learning in target prediction and drug repositioning: Recent advances and challenges. Drug Discov Today 2021; 27:1796-1814. [PMID: 34718208 DOI: 10.1016/j.drudis.2021.10.010] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/02/2021] [Accepted: 10/21/2021] [Indexed: 12/12/2022]
Abstract
Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.
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