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Li X, Zan X, Liu T, Dong X, Zhang H, Li Q, Bao Z, Lin J. Integrated edge information and pathway topology for drug-disease associations. iScience 2024; 27:110025. [PMID: 38974972 PMCID: PMC11226970 DOI: 10.1016/j.isci.2024.110025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/06/2024] [Accepted: 05/15/2024] [Indexed: 07/09/2024] Open
Abstract
Drug repurposing is a promising approach to find new therapeutic indications for approved drugs. Many computational approaches have been proposed to prioritize candidate anticancer drugs by gene or pathway level. However, these methods neglect the changes in gene interactions at the edge level. To address the limitation, we develop a computational drug repurposing method (iEdgePathDDA) based on edge information and pathway topology. First, we identify drug-induced and disease-related edges (the changes in gene interactions) within pathways by using the Pearson correlation coefficient. Next, we calculate the inhibition score between drug-induced edges and disease-related edges. Finally, we prioritize drug candidates according to the inhibition score on all disease-related edges. Case studies show that our approach successfully identifies new drug-disease pairs based on CTD database. Compared to the state-of-the-art approaches, the results demonstrate our method has the superior performance in terms of five metrics across colorectal, breast, and lung cancer datasets.
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Affiliation(s)
- Xianbin Li
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Xiangzhen Zan
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, Guangdong 520000, China
| | - Tao Liu
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Xiwei Dong
- School of Computer and Big Data Science, Jiujiang University, Jiujiang, Jiangxi 332000, China
| | - Haqi Zhang
- Department of Digital Media Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China
| | - Qizhang Li
- Innovative Drug R&D Center, School of Life Sciences, Huaibei Normal University, Huaibei, Anhui 235000, China
| | - Zhenshen Bao
- College of Information Engineering, Taizhou University, Taizhou 225300, Jiangsu, China
| | - Jie Lin
- Department of Pharmacy, the Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325200, Zhejiang Province, China
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2
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Słyk Ż, Stachowiak N, Małecki M. Recombinant Adeno-Associated Virus Vectors for Gene Therapy of the Central Nervous System: Delivery Routes and Clinical Aspects. Biomedicines 2024; 12:1523. [PMID: 39062095 PMCID: PMC11274884 DOI: 10.3390/biomedicines12071523] [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: 04/19/2024] [Revised: 06/23/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
The Central Nervous System (CNS) is vulnerable to a range of diseases, including neurodegenerative and oncological conditions, which present significant treatment challenges. The blood-brain barrier (BBB) restricts molecule penetration, complicating the achievement of therapeutic concentrations in the CNS following systemic administration. Gene therapy using recombinant adeno-associated virus (rAAV) vectors emerges as a promising strategy for treating CNS diseases, demonstrated by the registration of six gene therapy products in the past six years and 87 ongoing clinical trials. This review explores the implementation of rAAV vectors in CNS disease treatment, emphasizing AAV biology and vector engineering. Various administration methods-such as intravenous, intrathecal, and intraparenchymal routes-and experimental approaches like intranasal and intramuscular administration are evaluated, discussing their advantages and limitations in different CNS contexts. Additionally, the review underscores the importance of optimizing therapeutic efficacy through the pharmacokinetics (PK) and pharmacodynamics (PD) of rAAV vectors. A comprehensive analysis of clinical trials reveals successes and challenges, including barriers to commercialization. This review provides insights into therapeutic strategies using rAAV vectors in neurological diseases and identifies areas requiring further research, particularly in optimizing rAAV PK/PD.
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Affiliation(s)
- Żaneta Słyk
- Department of Applied Pharmacy, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland
- Laboratory of Gene Therapy, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Natalia Stachowiak
- Department of Applied Pharmacy, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland
| | - Maciej Małecki
- Department of Applied Pharmacy, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland
- Laboratory of Gene Therapy, Faculty of Pharmacy, Medical University of Warsaw, 02-091 Warsaw, Poland
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3
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Kori M, Kasavi C, Arga KY. Exploring COVID-19 Pandemic Disparities with Transcriptomic Meta-analysis from the Perspective of Personalized Medicine. J Microbiol 2024:10.1007/s12275-024-00154-9. [PMID: 38980578 DOI: 10.1007/s12275-024-00154-9] [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: 03/12/2024] [Revised: 06/06/2024] [Accepted: 06/17/2024] [Indexed: 07/10/2024]
Abstract
Infection with SARS-CoV2, which is responsible for COVID-19, can lead to differences in disease development, severity and mortality rates depending on gender, age or the presence of certain diseases. Considering that existing studies ignore these differences, this study aims to uncover potential differences attributable to gender, age and source of sampling as well as viral load using bioinformatics and multi-omics approaches. Differential gene expression analyses were used to analyse the phenotypic differences between SARS-CoV-2 patients and controls at the mRNA level. Pathway enrichment analyses were performed at the gene set level to identify the activated pathways corresponding to the differences in the samples. Drug repurposing analysis was performed at the protein level, focusing on host-mediated drug candidates to uncover potential therapeutic differences. Significant differences (i.e. the number of differentially expressed genes and their characteristics) were observed for COVID-19 at the mRNA level depending on the sample source, gender and age of the samples. The results of the pathway enrichment show that SARS-CoV-2 can be combated more effectively in the respiratory tract than in the blood samples. Taking into account the different sample sources and their characteristics, different drug candidates were identified. Evaluating disease prediction, prevention and/or treatment strategies from a personalised perspective is crucial. In this study, we not only evaluated the differences in COVID-19 from a personalised perspective, but also provided valuable data for further experimental and clinical efforts. Our findings could shed light on potential pandemics.
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Affiliation(s)
- Medi Kori
- Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, 34752, Istanbul, Turkey.
- Faculty of Health Sciences, Acibadem Mehmet Ali Aydinlar University, 34752, Istanbul, Turkey.
| | - Ceyda Kasavi
- Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, 34722, Istanbul, Turkey
- Genetic and Metabolic Diseases Research and Investigation Center, Marmara University, 34722, Istanbul, Turkey
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4
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Alidoost M, Wilson JL. Preclinical side effect prediction through pathway engineering of protein interaction network models. CPT Pharmacometrics Syst Pharmacol 2024; 13:1180-1200. [PMID: 38736280 PMCID: PMC11247120 DOI: 10.1002/psp4.13150] [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: 11/14/2023] [Revised: 03/01/2024] [Accepted: 04/08/2024] [Indexed: 05/14/2024] Open
Abstract
Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.
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Affiliation(s)
- Mohammadali Alidoost
- Department of Bioengineering, University of California, Los Angeles, California, USA
| | - Jennifer L Wilson
- Department of Bioengineering, University of California, Los Angeles, California, USA
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Liao Y, Yu H, Zhang Y, Lu Z, Sun Y, Guo L, Guo J, Kang Z, Feng X, Sun Y, Wang G, Su Z, Lu T, Yang Y, Li W, Lv L, Yan H, Zhang D, Yue W. Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation. Mol Psychiatry 2024; 29:1857-1868. [PMID: 38336841 DOI: 10.1038/s41380-024-02447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024]
Abstract
Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.
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Affiliation(s)
- Yundan Liao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong, 272067, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
| | - Zhe Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yaoyao Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Liangkun Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Jing Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Zhewei Kang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Xiaoyang Feng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yutao Sun
- No.5 Hospital, Tangshan, Hebei, 063000, China
| | - Guishan Wang
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Zhonghua Su
- The Second Affiliated Hospital of Jining Medical College, Jining, 272051, China
| | - Tianlan Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Yongfeng Yang
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Wenqiang Li
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Luxian Lv
- The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, 453002, Henan, China
| | - Hao Yan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
| | - Dai Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China
- Chinese Institute for Brain Research, Beijing, 102206, China
- Institute for Brain Research and Rehabilitation (IBRR), Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Weihua Yue
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing, 100191, China.
- National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), Beijing, 100191, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871, China.
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Waseem T, Rajput TA, Mushtaq MS, Babar MM, Rajadas J. Computational biology approaches for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:91-109. [PMID: 38789189 DOI: 10.1016/bs.pmbts.2024.03.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.
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Affiliation(s)
- Tanya Waseem
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | - Tausif Ahmed Rajput
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan
| | | | - Mustafeez Mujtaba Babar
- Shifa College of Pharmaceutical Sciences, Shifa Tameer-e-Millat University, Islamabad, Pakistan; Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States.
| | - Jayakumar Rajadas
- Advanced Drug Delivery and Regenerative Biomaterials Laboratory, Cardiovascular Institute and Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford University, Palo Alto, CA, United States
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Shi Y, Dong H, Sun S, Wu X, Fang J, Zhao J, Han J, Li Z, Wu H, Liu L, Wu W, Tian Y, Yuan G, Fan X, Xu C. Protein-centric omics analysis reveals circulating complements linked to non-viral liver diseases as potential therapeutic targets. Clin Mol Hepatol 2024; 30:80-97. [PMID: 38061333 PMCID: PMC10776287 DOI: 10.3350/cmh.2023.0343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/04/2023] [Accepted: 12/07/2023] [Indexed: 01/11/2024] Open
Abstract
BACKGROUND/AIMS To evaluate the causal correlation between complement components and non-viral liver diseases and their potential use as druggable targets. METHODS We conducted Mendelian randomization (MR) to assess the causal role of circulating complements in the risk of non-viral liver diseases. A complement-centric protein interaction network was constructed to explore biological functions and identify potential therapeutic options. RESULTS In the MR analysis, genetically predicted levels of complement C1q C chain (C1QC) were positively associated with the risk of autoimmune hepatitis (odds ratio 1.125, 95% confidence interval 1.018-1.244), while complement factor H-related protein 5 (CFHR5) was positively associated with the risk of primary sclerosing cholangitis (PSC;1.193, 1.048- 1.357). On the other hand, CFHR1 (0.621, 0.497-0.776) and CFHR2 (0.824, 0.703-0.965) were inversely associated with the risk of alcohol-related cirrhosis. There were also significant inverse associations between C8 gamma chain (C8G) and PSC (0.832, 0.707-0.979), as well as the risk of metabolic dysfunction-associated steatotic liver disease (1.167, 1.036-1.314). Additionally, C1S (0.111, 0.018-0.672), C7 (1.631, 1.190-2.236), and CFHR2 (1.279, 1.059-1.546) were significantly associated with the risk of hepatocellular carcinoma. Proteins from the complement regulatory networks and various liver diseaserelated proteins share common biological processes. Furthermore, potential therapeutic drugs for various liver diseases were identified through drug repurposing based on the complement regulatory network. CONCLUSION Our study suggests that certain complement components, including C1S, C1QC, CFHR1, CFHR2, CFHR5, C7, and C8G, might play a role in non-viral liver diseases and could be potential targets for drug development.
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Affiliation(s)
- Yingzhou Shi
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Hang Dong
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Shiwei Sun
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Xiaoqin Wu
- Northern Ohio Alcohol Center, Department of Inflammation and Immunity, Cleveland Clinic, Cleveland, OH, USA
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Jianbo Zhao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Clinical Medical College, Ningxia Medical University, Yinchuan, Ningxia, China
| | - Junming Han
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Zongyue Li
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Huixiao Wu
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Luna Liu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Wanhong Wu
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Yang Tian
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Guandou Yuan
- Division of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Xiude Fan
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
| | - Chao Xu
- Department of Endocrinology, Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education; Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China
- Shandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, China
- Shandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, China
- “Chuangxin China” Innovation Base of Stem Cell and Gene Therapy for Endocrine Metabolic diseases, Jinan, Shandong, China
- Shandong Engineering Laboratory of Prevention and Control for Endocrine and Metabolic Diseases, Jinan, Shandong, China
- Shandong Engineering Research Center of Stem Cell and Gene Therapy for Endocrine and Metabolic Diseases, Jinan, Shandong, China
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8
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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [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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9
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Bolz SN, Schroeder M. Promiscuity in drug discovery on the verge of the structural revolution: recent advances and future chances. Expert Opin Drug Discov 2023; 18:973-985. [PMID: 37489516 DOI: 10.1080/17460441.2023.2239700] [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: 06/09/2023] [Accepted: 07/19/2023] [Indexed: 07/26/2023]
Abstract
INTRODUCTION Promiscuity denotes the ability of ligands and targets to specifically interact with multiple binding partners. Despite negative aspects like side effects, promiscuity is receiving increasing attention in drug discovery as it can enhance drug efficacy and provides a molecular basis for drug repositioning. The three-dimensional structure of ligand-target complexes delivers exclusive insights into the molecular mechanisms of promiscuity and structure-based methods enable the identification of promiscuous interactions. With the recent breakthrough in protein structure prediction, novel possibilities open up to reveal unknown connections in ligand-target interaction networks. AREAS COVERED This review highlights the significance of structure in the identification and characterization of promiscuity and evaluates the potential of protein structure prediction to advance our knowledge of drug-target interaction networks. It discusses the definition and relevance of promiscuity in drug discovery and explores different approaches to detecting promiscuous ligands and targets. EXPERT OPINION Examination of structural data is essential for understanding and quantifying promiscuity. The recent advancements in structure prediction have resulted in an abundance of targets that are well-suited for structure-based methods like docking. In silico approaches may eventually completely transform our understanding of drug-target networks by complementing the millions of predicted protein structures with billions of predicted drug-target interactions.
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Affiliation(s)
- Sarah Naomi Bolz
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
| | - Michael Schroeder
- Biotechnology Center (BIOTEC), CMCB, Technische Universität Dresden, Dresden, Germany
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10
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Bennett AN, Huang RX, He Q, Lee NP, Sung WK, Chan KHK. Drug repositioning for esophageal squamous cell carcinoma. Front Genet 2022; 13:991842. [PMID: 36246638 PMCID: PMC9554346 DOI: 10.3389/fgene.2022.991842] [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: 07/12/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Esophageal cancer (EC) remains a significant challenge globally, having the 8th highest incidence and 6th highest mortality worldwide. Esophageal squamous cell carcinoma (ESCC) is the most common form of EC in Asia. Crucially, more than 90% of EC cases in China are ESCC. The high mortality rate of EC is likely due to the limited number of effective therapeutic options. To increase patient survival, novel therapeutic strategies for EC patients must be devised. Unfortunately, the development of novel drugs also presents its own significant challenges as most novel drugs do not make it to market due to lack of efficacy or safety concerns. A more time and cost-effective strategy is to identify existing drugs, that have already been approved for treatment of other diseases, which can be repurposed to treat EC patients, with drug repositioning. This can be achieved by comparing the gene expression profiles of disease-states with the effect on gene-expression by a given drug. In our analysis, we used previously published microarray data and identified 167 differentially expressed genes (DEGs). Using weighted key driver analysis, 39 key driver genes were then identified. These driver genes were then used in Overlap Analysis and Network Analysis in Pharmomics. By extracting drugs common to both analyses, 24 drugs are predicted to demonstrate therapeutic effect in EC patients. Several of which have already been shown to demonstrate a therapeutic effect in EC, most notably Doxorubicin, which is commonly used to treat EC patients, and Ixazomib, which was recently shown to induce apoptosis and supress growth of EC cell lines. Additionally, our analysis predicts multiple psychiatric drugs, including Venlafaxine, as repositioned drugs. This is in line with recent research which suggests that psychiatric drugs should be investigated for use in gastrointestinal cancers such as EC. Our study shows that a drug repositioning approach is a feasible strategy for identifying novel ESCC therapies and can also improve the understanding of the mechanisms underlying the drug targets.
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Affiliation(s)
- Adam N. Bennett
- Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon, Hong Kong SAR, China
| | - Rui Xuan Huang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Qian He
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Nikki P. Lee
- Department of Surgery, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Wing-Kin Sung
- Department of Computer Sciences, National University of Singapore, Singapore, Singapore
| | - Kei Hang Katie Chan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Epidemiology, Centre for Global Cardiometabolic Health, Brown University, Providence, RI, United States
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11
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Huang RX, Siriwanna D, Cho WC, Wan TK, Du YR, Bennett AN, He QE, Liu JD, Huang XT, Chan KHK. Lung adenocarcinoma-related target gene prediction and drug repositioning. Front Pharmacol 2022; 13:936758. [PMID: 36081949 PMCID: PMC9445420 DOI: 10.3389/fphar.2022.936758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Lung cancer is the leading cause of cancer deaths globally, and lung adenocarcinoma (LUAD) is the most common type of lung cancer. Gene dysregulation plays an essential role in the development of LUAD. Drug repositioning based on associations between drug target genes and LUAD target genes are useful to discover potential new drugs for the treatment of LUAD, while also reducing the monetary and time costs of new drug discovery and development. Here, we developed a pipeline based on machine learning to predict potential LUAD-related target genes through established graph attention networks (GATs). We then predicted potential drugs for the treatment of LUAD through gene coincidence-based and gene network distance-based methods. Using data from 535 LUAD tissue samples and 59 precancerous tissue samples from The Cancer Genome Atlas, 48,597 genes were identified and used for the prediction model building of the GAT. The GAT model achieved good predictive performance, with an area under the receiver operating characteristic curve of 0.90. 1,597 potential LUAD-related genes were identified from the GAT model. These LUAD-related genes were then used for drug repositioning. The gene overlap and network distance with the target genes were calculated for 3,070 drugs and 672 preclinical compounds approved by the US Food and Drug Administration. At which, bromoethylamine was predicted as a novel potential preclinical compound for the treatment of LUAD, and cimetidine and benzbromarone were predicted as potential therapeutic drugs for LUAD. The pipeline established in this study presents new approach for developing targeted therapies for LUAD.
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Affiliation(s)
- Rui Xuan Huang
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Damrongrat Siriwanna
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - William C. Cho
- >Department of Clinical Oncology, Queen Elizabeth Hospital, Hong Kong, China
| | - Tsz Kin Wan
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
| | - Yan Rong Du
- >Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong, China
| | - Adam N. Bennett
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Qian Echo He
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Jun Dong Liu
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
| | - Xiao Tai Huang
- >School of Computer Science and Technology, Xidian University, Xi’an, China
| | - Kei Hang Katie Chan
- >Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
- >Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China
- >Department of Epidemiology and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States
- *Correspondence: Kei Hang Katie Chan,
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Truong TTT, Panizzutti B, Kim JH, Walder K. Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:1464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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Affiliation(s)
- Trang T. T. Truong
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Bruna Panizzutti
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
| | - Jee Hyun Kim
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
- Mental Health Theme, The Florey Institute of Neuroscience and Mental Health, Parkville 3010, Australia
| | - Ken Walder
- IMPACT, The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Deakin University, Geelong 3220, Australia; (T.T.T.T.); (B.P.); (J.H.K.)
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