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Kowshik AV, Manoj M, Sowmyanarayan S, Chatterjee J. Drug repurposing: databases and pipelines. CNS Spectr 2024; 29:6-9. [PMID: 37489503 DOI: 10.1017/s1092852923002365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/26/2023]
Abstract
The concept of drug repurposing is focused on the repositioning of drug molecules that have already undergone safety trials. There are different strategies for drug repurposing. Network-based strategy focuses on the evaluation of drug combinations in a molecular environment with multi-target hits and analysis of drug interactions. Implementation of any in silico strategy requires several databases and pipelines for executing the process of shortlisting appropriate drugs.
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Affiliation(s)
- Arjun V Kowshik
- Department of Biotechnology, PES University, Bengaluru, India
| | - Megha Manoj
- Department of Biotechnology, PES University, Bengaluru, India
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Zhao C, Tan T, Zhang E, Wang T, Gong H, Jia Q, Liu T, Yang X, Zhao J, Wu Z, Wei H, Xiao J, Yang C. A chronicle review of new techniques that facilitate the understanding and development of optimal individualized therapeutic strategies for chordoma. Front Oncol 2022; 12:1029670. [PMID: 36465398 PMCID: PMC9708744 DOI: 10.3389/fonc.2022.1029670] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 10/19/2022] [Indexed: 09/01/2023] Open
Abstract
Chordoma is a rare malignant bone tumor that mainly occurs in the sacrum and the clivus/skull base. Surgical resection is the treatment of choice for chordoma, but the local recurrence rate is high with unsatisfactory prognosis. Compared with other common tumors, there is not much research and individualized treatment for chordoma, partly due to the rarity of the disease and the lack of appropriate disease models, which delay the discovery of therapeutic strategies. Recent advances in modern techniques have enabled gaining a better understanding of a number of rare diseases, including chordoma. Since the beginning of the 21st century, various chordoma cell lines and animal models have been reported, which have partially revealed the intrinsic mechanisms of tumor initiation and progression with the use of next-generation sequencing (NGS) techniques. In this study, we performed a systematic overview of the chordoma models and related sequencing studies in a chronological manner, from the first patient-derived chordoma cell line (U-CH1) to diverse preclinical models such as the patient-derived organoid-based xenograft (PDX) and patient-derived organoid (PDO) models. The use of modern sequencing techniques has discovered mutations and expression signatures that are considered potential treatment targets, such as the expression of Brachyury and overactivated receptor tyrosine kinases (RTKs). Moreover, computational and bioinformatics techniques have made drug repositioning/repurposing and individualized high-throughput drug screening available. These advantages facilitate the research and development of comprehensive and personalized treatment strategies for indicated patients and will dramatically improve their prognoses in the near feature.
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Affiliation(s)
- Chenglong Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tao Tan
- Department of Orthopedics, 905 Hospital of People’s Liberation Army Navy, Shanghai, China
| | - E. Zhang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Ting Wang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haiyi Gong
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Qi Jia
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Tielong Liu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Xinghai Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jian Zhao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Zhipeng Wu
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Haifeng Wei
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Jianru Xiao
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
| | - Cheng Yang
- Spinal Tumor Center, Department of Orthopedic Oncology, Changzheng Hospital, Shanghai, China
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Gu Y, Zheng S, Yin Q, Jiang R, Li J. REDDA: Integrating multiple biological relations to heterogeneous graph neural network for drug-disease association prediction. Comput Biol Med 2022; 150:106127. [PMID: 36182762 DOI: 10.1016/j.compbiomed.2022.106127] [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: 06/18/2022] [Revised: 07/27/2022] [Accepted: 09/18/2022] [Indexed: 11/03/2022]
Abstract
Computational drug repositioning is an effective way to find new indications for existing drugs, thus can accelerate drug development and reduce experimental costs. Recently, various deep learning-based repurposing methods have been established to identify the potential drug-disease associations (DDA). However, effective utilization of the relations of biological entities to capture the biological interactions to enhance the drug-disease association prediction is still challenging. To resolve the above problem, we proposed a heterogeneous graph neural network called REDDA (Relations-Enhanced Drug-Disease Association prediction). Assembled with three attention mechanisms, REDDA can sequentially learn drug/disease representations by a general heterogeneous graph convolutional network-based node embedding block, a topological subnet embedding block, a graph attention block, and a layer attention block. Performance comparisons on our proposed benchmark dataset show that REDDA outperforms 8 advanced drug-disease association prediction methods, achieving relative improvements of 0.76% on the area under the receiver operating characteristic curve (AUC) score and 13.92% on the precision-recall curve (AUPR) score compared to the suboptimal method. On the other benchmark dataset, REDDA also obtains relative improvements of 2.48% on the AUC score and 4.93% on the AUPR score. Specifically, case studies also indicate that REDDA can give valid predictions for the discovery of -new indications for drugs and new therapies for diseases. The overall results provide an inspiring potential for REDDA in the in silico drug development. The proposed benchmark dataset and source code are available in https://github.com/gu-yaowen/REDDA.
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Affiliation(s)
- Yaowen Gu
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China
| | - Si Zheng
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China; Institute for Artificial Intelligence, Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China
| | - Qijin Yin
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Jiao Li
- Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing, 100020, China.
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Eichberg DG, Komotar RJ, Ivan ME. Commentary: Computational Drug Repositioning Identifies Potentially Active Therapies for Chordoma. Neurosurgery 2021; 88:E203-E204. [PMID: 33009573 DOI: 10.1093/neuros/nyaa403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 07/12/2020] [Indexed: 11/14/2022] Open
Affiliation(s)
- Daniel G Eichberg
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida
| | - Ricardo J Komotar
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida.,Sylvester Comprehensive Cancer Center, Miami, Florida
| | - Michael E Ivan
- Department of Neurological Surgery, University of Miami Miller School of Medicine, Miami, Florida.,Sylvester Comprehensive Cancer Center, Miami, Florida
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