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Jing Y, Luo L, Zeng Z, Zhao X, Huang R, Song C, Chen G, Wei S, Yang H, Tang Y, Jin S. Targeted Screening of Curcumin Derivatives as Pancreatic Lipase Inhibitors Using Computer-Aided Drug Design. ACS OMEGA 2024; 9:27669-27679. [PMID: 38947805 PMCID: PMC11209693 DOI: 10.1021/acsomega.4c03596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 05/30/2024] [Accepted: 06/03/2024] [Indexed: 07/02/2024]
Abstract
Curcumin has demonstrated promising preclinical antiobesity effects, but its low bioavailability makes it difficult to exert its full effect at a suitable dose. The objective of this study was to screen curcumin derivatives with enhanced bioavailability and lipid-lowering activity under the guidance of computer-aided drug design (CADD). CAAD was used to perform virtual assays on curcumin derivatives to assess their pharmacokinetic properties and effects on pancreatic lipase activity. Subsequently, 19 curcumin derivatives containing 5 skeletons were synthesized to confirm the above virtual assay. The in vitro pancreatic lipase inhibition assay was employed to determine the half-maximal inhibitory concentration (IC50) of these 19 curcumin derivatives. Based on CADD analysis and in vitro pancreatic lipase inhibition, 2 curcumin derivatives outperformed curcumin in both aspects. Microscale thermophoresis (MST) experiments were employed to assess the binding equilibrium constants (K d) of the aforementioned 2 curcumin derivatives, curcumin, and the positive control drug with pancreatic lipase. Through virtual screening utilizing a chemoinformatics database and molecular docking, 6 derivatives of curcumin demonstrated superior solubility, absorption, and pancreatic lipase inhibitory activity compared to curcumin. The IC50 value for 1,7-bis(4-hydroxyphenyl)heptane-3,5-dione (C4), which displayed the most effective inhibitory effect, was 42.83 μM, while the IC50 value for 1,7-bis(4-hydroxy-3-methoxyphenyl)heptane-3,5-dione (C6) was 98.62 μM. On the other hand, the IC50 value for curcumin was 142.24 μM. The MST experiment results indicated that the K d values of C4, C6, and curcumin were 2.91, 18.20, and 23.53 μM, respectively. The results of the activity assays exhibited a relatively high degree of concordance with the outcomes yielded by CADD screening. Under the guidance of CADD, the targeted screening of curcumin derivatives with excellent properties in this study exhibited high-efficiency and low-cost benefits.
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Affiliation(s)
- Yuxuan Jing
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Laichun Luo
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Zhaoxiang Zeng
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Xueyan Zhao
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Rongzeng Huang
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Chengwu Song
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
- Center
of Traditional Chinese Medicine Modernization for Liver Diseases, 430065 Wuhan, Hubei, China
- Hubei
Shizhen Laboratory, 430065 Wuhan, Hubei, China
| | - Guiying Chen
- Wuhan
Hongren Biopharmaceutical Inc, 430065 Wuhan, Hubei, China
| | - Sha Wei
- School
of Basic Medical Sciences, Hubei University
of Chinese Medicine, 430065 Wuhan, Hubei, China
| | - Haijun Yang
- School
of Basic Medical Sciences, Hubei University
of Chinese Medicine, 430065 Wuhan, Hubei, China
| | - Yinping Tang
- School
of Pharmacy, Hubei University of Chinese
Medicine, 430065 Wuhan, Hubei, China
| | - Shuna Jin
- Hubei
Shizhen Laboratory, 430065 Wuhan, Hubei, China
- School
of Basic Medical Sciences, Hubei University
of Chinese Medicine, 430065 Wuhan, Hubei, China
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Cavalcante BRR, Freitas RD, Siquara da Rocha LO, Santos RSB, Souza BSDF, Ramos PIP, Rocha GV, Gurgel Rocha CA. In silico approaches for drug repurposing in oncology: a scoping review. Front Pharmacol 2024; 15:1400029. [PMID: 38919258 PMCID: PMC11196849 DOI: 10.3389/fphar.2024.1400029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 05/14/2024] [Indexed: 06/27/2024] Open
Abstract
Introduction: Cancer refers to a group of diseases characterized by the uncontrolled growth and spread of abnormal cells in the body. Due to its complexity, it has been hard to find an ideal medicine to treat all cancer types, although there is an urgent need for it. However, the cost of developing a new drug is high and time-consuming. In this sense, drug repurposing (DR) can hasten drug discovery by giving existing drugs new disease indications. Many computational methods have been applied to achieve DR, but just a few have succeeded. Therefore, this review aims to show in silico DR approaches and the gap between these strategies and their ultimate application in oncology. Methods: The scoping review was conducted according to the Arksey and O'Malley framework and the Joanna Briggs Institute recommendations. Relevant studies were identified through electronic searching of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases, as well as the grey literature. We included peer-reviewed research articles involving in silico strategies applied to drug repurposing in oncology, published between 1 January 2003, and 31 December 2021. Results: We identified 238 studies for inclusion in the review. Most studies revealed that the United States, India, China, South Korea, and Italy are top publishers. Regarding cancer types, breast cancer, lymphomas and leukemias, lung, colorectal, and prostate cancer are the top investigated. Additionally, most studies solely used computational methods, and just a few assessed more complex scientific models. Lastly, molecular modeling, which includes molecular docking and molecular dynamics simulations, was the most frequently used method, followed by signature-, Machine Learning-, and network-based strategies. Discussion: DR is a trending opportunity but still demands extensive testing to ensure its safety and efficacy for the new indications. Finally, implementing DR can be challenging due to various factors, including lack of quality data, patient populations, cost, intellectual property issues, market considerations, and regulatory requirements. Despite all the hurdles, DR remains an exciting strategy for identifying new treatments for numerous diseases, including cancer types, and giving patients faster access to new medications.
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Affiliation(s)
- Bruno Raphael Ribeiro Cavalcante
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | - Raíza Dias Freitas
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Social and Pediatric Dentistry of the School of Dentistry, Federal University of Bahia, Salvador, Brazil
| | - Leonardo de Oliveira Siquara da Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
| | | | - Bruno Solano de Freitas Souza
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Pablo Ivan Pereira Ramos
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Center of Data and Knowledge Integration for Health (CIDACS), Salvador, Brazil
| | - Gisele Vieira Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
| | - Clarissa Araújo Gurgel Rocha
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Brazil
- Department of Pathology and Forensic Medicine of the School of Medicine, Federal University of Bahia, Salvador, Brazil
- D’Or Institute for Research and Education (IDOR), Salvador, Brazil
- Department of Propaedeutics, School of Dentistry of the Federal University of Bahia, Salvador, Brazil
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Valančienė J, Melaika K, Šliachtenko A, Šiaurytė-Jurgelėnė K, Ekkert A, Jatužis D. Stroke genetics and how it Informs novel drug discovery. Expert Opin Drug Discov 2024; 19:553-564. [PMID: 38494780 DOI: 10.1080/17460441.2024.2324916] [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: 12/05/2023] [Accepted: 02/26/2024] [Indexed: 03/19/2024]
Abstract
INTRODUCTION Stroke is one of the main causes of death and disability worldwide. Nevertheless, despite the global burden of this disease, our understanding is limited and there is still a lack of highly efficient etiopathology-based treatment. It is partly due to the complexity and heterogenicity of the disease. It is estimated that around one-third of ischemic stroke is heritable, emphasizing the importance of genetic factors identification and targeting for therapeutic purposes. AREAS COVERED In this review, the authors provide an overview of the current knowledge of stroke genetics and its value in diagnostics, personalized treatment, and prognostication. EXPERT OPINION As the scale of genetic testing increases and the cost decreases, integration of genetic data into clinical practice is inevitable, enabling assessing individual risk, providing personalized prognostic models and identifying new therapeutic targets and biomarkers. Although expanding stroke genetics data provides different diagnostics and treatment perspectives, there are some limitations and challenges to face. One of them is the threat of health disparities as non-European populations are underrepresented in genetic datasets. Finally, a deeper understanding of underlying mechanisms of potential targets is still lacking, delaying the application of novel therapies into routine clinical practice.
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Affiliation(s)
| | | | | | - Kamilė Šiaurytė-Jurgelėnė
- Department of Human and Medical Genetics, Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | | | - Dalius Jatužis
- Center of Neurology, Vilnius University, Vilnius, Lithuania
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Sahu M, Vashishth S, Kukreti N, Gulia A, Russell A, Ambasta RK, Kumar P. Synergizing drug repurposing and target identification for neurodegenerative diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:111-169. [PMID: 38789177 DOI: 10.1016/bs.pmbts.2024.03.023] [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
Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shrutikirti Vashishth
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Neha Kukreti
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashima Gulia
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Ashish Russell
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Rashmi K Ambasta
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India.
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5
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Takundwa MM, Thimiri Govinda Raj DB. Novel strategies for drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:9-21. [PMID: 38789188 DOI: 10.1016/bs.pmbts.2024.03.021] [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
Synthetic biology, precision medicine, and nanobiotechnology are the three main emerging areas that drive translational innovation toward commercialization. There are several strategies used in precision medicine and drug repurposing is one of the key approaches as it addresses the challenges in drug discovery (high cost and time). Here, we provide a perspective on various new approaches to drug repurposing for cancer precision medicine. We report here our optimized wound healing methodology that can be used to validate drug sensitivity and drug repurposing. Using HeLa as our benchmark, we demonstrated that the assay can be applied to identify drugs that limit cell proliferation. From a future perspective, this assay can be expanded to ex vivo culturing of solid tumors in 2D culture and leukemia in 3D culture.
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Affiliation(s)
- Mutsa Monica Takundwa
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - Deepak B Thimiri Govinda Raj
- Synthetic Nanobiotechnology and Biomachines, Synthetic Biology and Precision Medicine Centre, Future Production Chemicals Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa.
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Habib M, Lalagkas PN, Melamed RD. Mapping drug biology to disease genetics to discover drug impacts on the human phenome. BIOINFORMATICS ADVANCES 2024; 4:vbae038. [PMID: 38736684 PMCID: PMC11087821 DOI: 10.1093/bioadv/vbae038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/18/2024] [Accepted: 03/07/2024] [Indexed: 05/14/2024]
Abstract
Motivation Medications can have unexpected effects on disease, including not only harmful drug side effects, but also beneficial drug repurposing. These effects on disease may result from hidden influences of drugs on disease gene networks. Then, discovering how biological effects of drugs relate to disease biology can both provide insight into the mechanism of latent drug effects, and can help predict new effects. Results Here, we develop Draphnet, a model that integrates molecular data on 429 drugs and gene associations of nearly 200 common phenotypes to learn a network that explains drug effects on disease in terms of these molecular signals. We present evidence that our method can both predict drug effects, and can provide insight into the biology of unexpected drug effects on disease. Using Draphnet to map a drug's known molecular effects to downstream effects on the disease genome, we put forward disease genes impacted by drugs, and we suggest a new grouping of drugs based on shared effects on the disease genome. Our approach has multiple applications, including predicting drug uses and learning drug biology, with implications for personalized medicine. Availability and implementation Code to reproduce the analysis is available at https://github.com/RDMelamed/drug-phenome.
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Affiliation(s)
- Mamoon Habib
- Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
| | | | - Rachel D Melamed
- Department of Biological Science, University of Massachusetts Lowell, Lowell, MA 01854, United States
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7
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Hukerikar N, Hingorani AD, Asselbergs FW, Finan C, Schmidt AF. Prioritising genetic findings for drug target identification and validation. Atherosclerosis 2024; 390:117462. [PMID: 38325120 DOI: 10.1016/j.atherosclerosis.2024.117462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 01/25/2024] [Indexed: 02/09/2024]
Abstract
The decreasing costs of high-throughput genetic sequencing and increasing abundance of sequenced genome data have paved the way for the use of genetic data in identifying and validating potential drug targets. However, the number of identified potential drug targets is often prohibitively large to experimentally evaluate in wet lab experiments, highlighting the need for systematic approaches for target prioritisation. In this review, we discuss principles of genetically guided drug development, specifically addressing loss-of-function analysis, colocalization and Mendelian randomisation (MR), and the contexts in which each may be most suitable. We subsequently present a range of biomedical resources which can be used to annotate and prioritise disease-associated proteins identified by these studies including 1) ontologies to map genes, proteins, and disease, 2) resources for determining the druggability of a potential target, 3) tissue and cell expression of the gene encoding the potential target, and 4) key biological pathways involving the potential target. We illustrate these concepts through a worked example, identifying a prioritised set of plasma proteins associated with non-alcoholic fatty liver disease (NAFLD). We identified five proteins with strong genetic support for involvement with NAFLD: CYB5A, NT5C, NCAN, TGFBI and DAPK2. All of the identified proteins were expressed in both liver and adipose tissues, with TGFBI and DAPK2 being potentially druggable. In conclusion, the current review provides an overview of genetic evidence for drug target identification, and how biomedical databases can be used to provide actionable prioritisation, fully informing downstream experimental validation.
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Affiliation(s)
- Nikita Hukerikar
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK.
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Folkert W Asselbergs
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK; Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Amand F Schmidt
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK; The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK; Department of Cardiology, Division Heart and Lungs, University Medical Centre Utrecht, Utrecht University, Utrecht, the Netherlands; Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical, Centre, University of Amsterdam, Amsterdam, the Netherlands
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8
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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 2024:10.1038/s41380-024-02447-2. [PMID: 38336841 DOI: 10.1038/s41380-024-02447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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Pinakhina D, Loboda A, Sergushichev A, Artomov M. Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease. HGG ADVANCES 2023; 4:100203. [PMID: 37250495 PMCID: PMC10209737 DOI: 10.1016/j.xhgg.2023.100203] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/25/2023] [Indexed: 05/31/2023] Open
Abstract
We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.
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Affiliation(s)
- Daria Pinakhina
- ITMO University, 197101 Saint Petersburg, Russia
- Bekhterev National Medical Research Center, 192019 Saint Petersburg, Russia
| | - Alexander Loboda
- ITMO University, 197101 Saint Petersburg, Russia
- Almazov National Medical Research Center, 191014 Saint Petersburg, Russia
| | | | - Mykyta Artomov
- ITMO University, 197101 Saint Petersburg, Russia
- Broad Institute, Cambridge, MA 02142, USA
- Massachusetts General Hospital, Boston, MA 02114, USA
- The Steve and Cindy Rasmussen Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH 43205, USA
- Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH 43210, USA
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Lee C, Lin J, Prokop A, Gopalakrishnan V, Hanna RN, Papa E, Freeman A, Patel S, Yu W, Huhn M, Sheikh AS, Tan K, Sellman BR, Cohen T, Mangion J, Khan FM, Gusev Y, Shameer K. StarGazer: A Hybrid Intelligence Platform for Drug Target Prioritization and Digital Drug Repositioning Using Streamlit. Front Genet 2022; 13:868015. [PMID: 35711912 PMCID: PMC9197487 DOI: 10.3389/fgene.2022.868015] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 04/29/2022] [Indexed: 01/26/2023] Open
Abstract
Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.
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Affiliation(s)
- Chiyun Lee
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Junxia Lin
- Georgetown University, Washington, DC, United States
| | | | | | - Richard N. Hanna
- Early Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Eliseo Papa
- Research Data and Analytics, R&D IT, AstraZeneca, Cambridge, United Kingdom
| | - Adrian Freeman
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Saleha Patel
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Wen Yu
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Monika Huhn
- Biometrics and Information Sciences, BioPharmaceuticals R&D, AstraZeneca, Mölndal, Sweden
| | - Abdul-Saboor Sheikh
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Keith Tan
- Neuroscience, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Bret R. Sellman
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Taylor Cohen
- Discovery Microbiome, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Jonathan Mangion
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Faisal M. Khan
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States
| | - Yuriy Gusev
- Georgetown University, Washington, DC, United States
| | - Khader Shameer
- Data Science and Artificial Intelligence, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, United States,*Correspondence: Khader Shameer,
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Abstract
Drug repurposing is the use of a given therapeutic agent for indications other than that for which it was originally designed or intended. The concept is appealing because of potentially lower development costs and shorter timelines than are needed to produce a new drug. To date, drug repurposing for cardiovascular indications has been opportunistic and driven by knowledge of disease mechanisms or serendipitous observation rather than by systematic endeavours to match an existing drug to a new indication. Innovations in two areas of personalized medicine — computational approaches to associate drug effects with disease signatures and predictive model systems to screen drugs for disease-modifying activities — support efforts that together create an efficient pipeline to systematically repurpose drugs to treat cardiovascular disease. Furthermore, new experimental strategies that guide the medicinal chemistry re-engineering of drugs could improve repurposing efforts by tailoring a medicine to its new indication. In this Review, we summarize the historical approach to repurposing and discuss the technological advances that have created a new landscape of opportunities. Drugs can be repurposed for new therapeutic indications. In this Review, Mercola and colleagues summarize the latest techniques for systematic drug repurposing and re-engineering, which could increase the pace, efficiency and cost-effectiveness of drug discovery for the treatment of cardiovascular disease. Contemporary technologies are expected to make drug repurposing large-scale, systematic and deliberate rather than opportunistic. New experimental and computational tools harness patient genomics for drug repurposing. Discovery of repurposed drugs on the basis of patient genomics has implications for precision prescribing of medicines to treat individual patients. The treatment of rare, monogenic diseases, which often provide too little return on investment to incentivize conventional drug discovery, might benefit because the molecular aetiologies of these diseases are well suited to the discovery of drug repurposing candidates.
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Evaluating the detection ability of a range of epistasis detection methods on simulated data for pure and impure epistatic models. PLoS One 2022; 17:e0263390. [PMID: 35180244 PMCID: PMC8856572 DOI: 10.1371/journal.pone.0263390] [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: 08/24/2021] [Accepted: 01/18/2022] [Indexed: 11/19/2022] Open
Abstract
Background Numerous approaches have been proposed for the detection of epistatic interactions within GWAS datasets in order to better understand the drivers of disease and genetics. Methods A selection of state-of-the-art approaches were assessed. These included the statistical tests, fast-epistasis, BOOST, logistic regression and wtest; swarm intelligence methods, namely AntEpiSeeker, epiACO and CINOEDV; and data mining approaches, including MDR, GSS, SNPRuler and MPI3SNP. Data were simulated to provide randomly generated models with no individual main effects at different heritabilities (pure epistasis) as well as models based on penetrance tables with some main effects (impure epistasis). Detection of both two and three locus interactions were assessed across a total of 1,560 simulated datasets. The different methods were also applied to a section of the UK biobank cohort for Atrial Fibrillation. Results For pure, two locus interactions, PLINK’s implementation of BOOST recovered the highest number of correct interactions, with 53.9% and significantly better performing than the other methods (p = 4.52e − 36). For impure two locus interactions, MDR exhibited the best performance, recovering 62.2% of the most significant impure epistatic interactions (p = 6.31e − 90 for all but one test). The assessment of three locus interaction prediction revealed that wtest recovered the highest number (17.2%) of pure epistatic interactions(p = 8.49e − 14). wtest also recovered the highest number of three locus impure epistatic interactions (p = 6.76e − 48) while AntEpiSeeker ranked as the most significant the highest number of such interactions (40.5%). Finally, when applied to a real dataset for Atrial Fibrillation, most notably finding an interaction between SYNE2 and DTNB.
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Lin WZ, Liu YC, Lee MC, Tang CT, Wu GJ, Chang YT, Chu CM, Shiau CY. From GWAS to drug screening: repurposing antipsychotics for glioblastoma. J Transl Med 2022; 20:70. [PMID: 35120529 PMCID: PMC8815269 DOI: 10.1186/s12967-021-03209-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/19/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Glioblastoma is currently an incurable cancer. Genome-wide association studies have demonstrated that 41 genetic variants are associated with glioblastoma and may provide an option for drug development. METHODS We investigated FDA-approved antipsychotics for their potential treatment of glioblastoma based on genome-wide association studies data using a 'pathway/gene-set analysis' approach. RESULTS The in-silico screening led to the discovery of 12 candidate drugs. DepMap portal revealed that 42 glioma cell lines show higher sensitivities to 12 candidate drugs than to Temozolomide, the current standard treatment for glioblastoma. CONCLUSION In particular, cell lines showed significantly higher sensitivities to Norcyclobenzaprine and Protriptyline which were predicted to bind targets to disrupt a certain molecular function such as DNA repair, response to hormones, or DNA-templated transcription, and may lead to an effect on survival-related pathways including cell cycle arrest, response to ER stress, glucose transport, and regulation of autophagy. However, it is recommended that their mechanism of action and efficacy are further determined.
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Affiliation(s)
- Wei-Zhi Lin
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Yen-Chun Liu
- School of Medicine, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Meng-Chang Lee
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chi-Tun Tang
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Neurological Surgery, Tri-Service General Hospital, No. 325, Sec. 2, Chenggong Rd., Neihu District, Taipei, 11490 Taiwan
| | - Gwo-Jang Wu
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei, Taiwan
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, No. 325, Sec. 2, Chenggong Rd., Neihu District, Taipei, 11490 Taiwan
| | - Yu-Tien Chang
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chi-Ming Chu
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
- School of Public Health, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
| | - Chia-Yang Shiau
- Graduate Institute of Life Sciences, National Defense Medical Center, No.161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
- Fidelity Regulation Therapeutics Inc., 161, Sec. 6, Minquan E. Rd., Neihu Dist., Taipei City, 11490 Taiwan
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14
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Adikusuma W, Chou WH, Lin MR, Ting J, Irham LM, Perwitasari DA, Chang WP, Chang WC. Identification of Druggable Genes for Asthma by Integrated Genomic Network Analysis. Biomedicines 2022; 10:biomedicines10010113. [PMID: 35052792 PMCID: PMC8773254 DOI: 10.3390/biomedicines10010113] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/22/2021] [Accepted: 12/24/2021] [Indexed: 02/01/2023] Open
Abstract
Asthma is a common and heterogeneous disease characterized by chronic airway inflammation. Currently, the two main types of asthma medicines are inhaled corticosteroids and long-acting β2-adrenoceptor agonists (LABAs). In addition, biological drugs provide another therapeutic option, especially for patients with severe asthma. However, these drugs were less effective in preventing severe asthma exacerbation, and other drug options are still limited. Herein, we extracted asthma-associated single nucleotide polymorphisms (SNPs) from the genome-wide association studies (GWAS) and phenome-wide association studies (PheWAS) catalog and prioritized candidate genes through five functional annotations. Genes enriched in more than two categories were defined as “biological asthma risk genes.” Then, DrugBank was used to match target genes with FDA-approved medications and identify candidate drugs for asthma. We discovered 139 biological asthma risk genes and identified 64 drugs targeting 22 of these genes. Seven of them were approved for asthma, including reslizumab, mepolizumab, theophylline, dyphylline, aminophylline, oxtriphylline, and enprofylline. We also found 17 drugs with clinical or preclinical evidence in treating asthma. In addition, eleven of the 40 candidate drugs were further identified as promising asthma therapy. Noteworthy, IL6R is considered a target for asthma drug repurposing based on its high target scores. Through in silico drug repurposing approach, we identified sarilumab and satralizumab as the most promising drug for asthma treatment.
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Affiliation(s)
- Wirawan Adikusuma
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; (W.A.); (W.-H.C.); (M.-R.L.); (J.T.)
- Department of Pharmacy, Faculty of Health Science, University of Muhammadiyah Mataram, Mataram 83127, Indonesia
| | - Wan-Hsuan Chou
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; (W.A.); (W.-H.C.); (M.-R.L.); (J.T.)
| | - Min-Rou Lin
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; (W.A.); (W.-H.C.); (M.-R.L.); (J.T.)
| | - Jafit Ting
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; (W.A.); (W.-H.C.); (M.-R.L.); (J.T.)
| | - Lalu Muhammad Irham
- Faculty of Pharmacy, University of Ahmad Dahlan, Yogyakarta 55164, Indonesia; (L.M.I.); (D.A.P.)
| | - Dyah Aryani Perwitasari
- Faculty of Pharmacy, University of Ahmad Dahlan, Yogyakarta 55164, Indonesia; (L.M.I.); (D.A.P.)
| | - Wei-Pin Chang
- School of Health Care Administration, College of Management, Taipei Medical University, Taipei 11031, Taiwan
- Correspondence: (W.-P.C.); (W.-C.C.)
| | - Wei-Chiao Chang
- Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 11031, Taiwan; (W.A.); (W.-H.C.); (M.-R.L.); (J.T.)
- TMU Research Center of Cancer Translational Medicine, Taipei 11031, Taiwan
- Department of Pharmacy, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
- Integrative Research Center for Critical Care, Wan Fang Hospital, Taipei Medical University, Taipei 11696, Taiwan
- Department of Pharmacology, National Defense Medical Center, Taipei 11490, Taiwan
- Correspondence: (W.-P.C.); (W.-C.C.)
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15
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Mirza N, Stevelink R, Taweel B, Koeleman BPC, Marson AG. Using common genetic variants to find drugs for common epilepsies. Brain Commun 2021; 3:fcab287. [PMID: 34988442 PMCID: PMC8710935 DOI: 10.1093/braincomms/fcab287] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 09/17/2021] [Accepted: 10/20/2021] [Indexed: 12/18/2022] Open
Abstract
Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.
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Affiliation(s)
- Nasir Mirza
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK
| | - Remi Stevelink
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CX, the Netherlands; member of the ERN EpiCARE
- Department of Child Neurology, University Medical Center Utrecht Brain Center, Utrecht 3584 CX, the Netherlands
| | - Basel Taweel
- School of Medicine, University of Liverpool, Liverpool L69 3GE, UK
| | - Bobby P C Koeleman
- Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht 3584 CX, the Netherlands; member of the ERN EpiCARE
| | - Anthony G Marson
- Department of Pharmacology & Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 3GE, UK
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16
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Combining Human Genetics of Multiple Sclerosis with Oxidative Stress Phenotype for Drug Repositioning. Pharmaceutics 2021; 13:pharmaceutics13122064. [PMID: 34959343 PMCID: PMC8705550 DOI: 10.3390/pharmaceutics13122064] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 11/26/2021] [Accepted: 11/30/2021] [Indexed: 01/08/2023] Open
Abstract
In multiple sclerosis (MS), oxidative stress (OS) is implicated in the neurodegenerative processes that occur from the beginning of the disease. Unchecked OS initiates a vicious circle caused by its crosstalk with inflammation, leading to demyelination, axonal damage and neuronal loss. The failure of MS antioxidant therapies relying on the use of endogenous and natural compounds drives the application of novel approaches to assess target relevance to the disease prior to preclinical testing of new drug candidates. To identify drugs that can act as regulators of intracellular oxidative homeostasis, we applied an in silico approach that links genome-wide MS associations and molecular quantitative trait loci (QTLs) to proteins of the OS pathway. We found 10 drugs with both central nervous system and oral bioavailability, targeting five out of the 21 top-scoring hits, including arginine methyltransferase (CARM1), which was first linked to MS. In particular, the direction of brain expression QTLs for CARM1 and protein kinase MAPK1 enabled us to select BIIB021 and PEITC drugs with the required target modulation. Our study highlights OS-related molecules regulated by functional MS variants that could be targeted by existing drugs as a supplement to the approved disease-modifying treatments.
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17
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Malacrida AM, Salci TP, Negri M, Svidzinski TI. Insight into the antifungals used to address human infection due to Trichosporon spp.: a scoping review. Future Microbiol 2021; 16:1277-1288. [PMID: 34689610 PMCID: PMC8544482 DOI: 10.2217/fmb-2021-0048] [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] [Indexed: 11/21/2022] Open
Abstract
Trichosporonosis infections have been increasing worldwide. Providing adequate treatment for these infections remains a challenge. This scoping review contains information about potential antifungals to treat this pathology. Using online databases, we found 76 articles published between 2010 and 2020 related to this topic. Classic antifungals, molecules and biomolecules, repositioned drugs and natural products have been tested against species of Trichosporon. Experimental research has lacked depth or was limited to in vitro and in vivo tests, so there are no promising new candidates for the clinical treatment of patients with trichosporonosis. Furthermore, most studies did not present appropriate scientific criteria for drug tests, compromising their quality.
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Affiliation(s)
- Amanda M Malacrida
- Departament of Clinical Analyses and Biomedicine, Universidade Estadual de Maringá, Maringá, Paraná, CEP, 87020-900, Brazil
| | - Tânia P Salci
- Departament of Pharmacy and Science, Faculdade Integrado de Campo Mourão, Campo Mourão, Paraná, CEP, 87300-970, Brazil
| | - Melyssa Negri
- Departament of Clinical Analyses and Biomedicine, Universidade Estadual de Maringá, Maringá, Paraná, CEP, 87020-900, Brazil
| | - Terezinha Ie Svidzinski
- Departament of Clinical Analyses and Biomedicine, Universidade Estadual de Maringá, Maringá, Paraná, CEP, 87020-900, Brazil
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18
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Fadason T, Farrow S, Gokuladhas S, Golovina E, Nyaga D, O'Sullivan JM, Schierding W. Assigning function to SNPs: Considerations when interpreting genetic variation. Semin Cell Dev Biol 2021; 121:135-142. [PMID: 34446357 DOI: 10.1016/j.semcdb.2021.08.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 08/12/2021] [Indexed: 12/26/2022]
Abstract
Assigning function to single nucleotide polymorphisms (SNPs) to understand the mechanisms that link genetic and phenotypic variation and disease is an area of intensive research that is necessary to contribute to the continuing development of precision medicine. However, despite the apparent simplicity that is captured in the name SNP - 'single nucleotide' changes are not easy to functionally characterize. This complexity arises from multiple features of the genome including the fact that function is development and environment specific. As such, we are often fooled by our terminology and underlying assumptions that there is a single function for a SNP. Here we discuss some of what is known about SNPs, their functions and how we can go about characterizing them.
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Affiliation(s)
- Tayaza Fadason
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | - Sophie Farrow
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
| | | | - Evgeniia Golovina
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Denis Nyaga
- Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand; Garvan Institute of Medical Research, Sydney, New South Wales, Australia; MRC Lifecourse Epidemiology Unit, University of Southampton, United Kingdom.
| | - William Schierding
- Liggins Institute, The University of Auckland, Auckland, New Zealand; The Maurice Wilkins Centre, The University of Auckland, Auckland, New Zealand
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19
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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20
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Lu H, Zhang J, Chen YE, Garcia-Barrio MT. Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases. Cardiovasc Drugs Ther 2021; 35:637-654. [PMID: 33856594 DOI: 10.1007/s10557-021-07175-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/18/2021] [Indexed: 12/11/2022]
Abstract
Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.
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Affiliation(s)
- Haocheng Lu
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA
| | - Jifeng Zhang
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.,Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA
| | - Y Eugene Chen
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA. .,Center for Advanced Models for Translational Sciences and Therapeutics, University of Michigan Medical Center, Ann Arbor, MI, 48109, USA.
| | - Minerva T Garcia-Barrio
- Department of Internal Medicine, University of Michigan Medical Center, 2800 Plymouth Rd, Ann Arbor, MI, 48109-2800, USA.
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