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Picard M, Scott-Boyer MP, Bodein A, Leclercq M, Prunier J, Périn O, Droit A. Target repositioning using multi-layer networks and machine learning: The case of prostate cancer. Comput Struct Biotechnol J 2024; 24:464-475. [PMID: 38983753 PMCID: PMC11231507 DOI: 10.1016/j.csbj.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024] Open
Abstract
The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.
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Affiliation(s)
- Milan Picard
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Julien Prunier
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Transformation and Innovation Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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2
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Bakulin A, Teyssier NB, Kampmann M, Khoroshkin M, Goodarzi H. pyPAGE: A framework for Addressing biases in gene-set enrichment analysis-A case study on Alzheimer's disease. PLoS Comput Biol 2024; 20:e1012346. [PMID: 39236079 PMCID: PMC11421795 DOI: 10.1371/journal.pcbi.1012346] [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: 08/31/2023] [Revised: 09/24/2024] [Accepted: 07/22/2024] [Indexed: 09/07/2024] Open
Abstract
Inferring the driving regulatory programs from comparative analysis of gene expression data is a cornerstone of systems biology. Many computational frameworks were developed to address this problem, including our iPAGE (information-theoretic Pathway Analysis of Gene Expression) toolset that uses information theory to detect non-random patterns of expression associated with given pathways or regulons. Our recent observations, however, indicate that existing approaches are susceptible to the technical biases that are inherent to most real world annotations. To address this, we have extended our information-theoretic framework to account for specific biases and artifacts in biological networks using the concept of conditional information. To showcase pyPAGE, we performed a comprehensive analysis of regulatory perturbations that underlie the molecular etiology of Alzheimer's disease (AD). pyPAGE successfully recapitulated several known AD-associated gene expression programs. We also discovered several additional regulons whose differential activity is significantly associated with AD. We further explored how these regulators relate to pathological processes in AD through cell-type specific analysis of single cell and spatial gene expression datasets. Our findings showcase the utility of pyPAGE as a precise and reliable biomarker discovery in complex diseases such as Alzheimer's disease.
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Affiliation(s)
- Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, Russia
| | - Noam B. Teyssier
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
| | - Martin Kampmann
- Institute for Neurodegenerative Diseases, University of California San Francisco, California, United States of America
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
| | - Matvei Khoroshkin
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, California, United States of America
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, United States of America
- Arc Institute, Palo Alto, California, United States of America
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3
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Zhang J, Zhang Y, Wang J, Xia Y, Zhang J, Chen L. Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies. Signal Transduct Target Ther 2024; 9:211. [PMID: 39174535 PMCID: PMC11344989 DOI: 10.1038/s41392-024-01911-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/18/2024] [Accepted: 07/02/2024] [Indexed: 08/24/2024] Open
Abstract
Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.
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Affiliation(s)
- Jifa Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yinglu Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaxing Wang
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, 38163, TN, USA
| | - Yilin Xia
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Jiaxian Zhang
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Lei Chen
- Department of Neurology, Laboratory of Neuro-system and Multimorbidity and State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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4
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Liu X, Abad L, Chatterjee L, Cristea IM, Varjosalo M. Mapping protein-protein interactions by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024. [PMID: 38742660 DOI: 10.1002/mas.21887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.
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Affiliation(s)
- Xiaonan Liu
- Department of Physiology, Faculty of Medical Sciences in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
| | - Lawrence Abad
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Lopamudra Chatterjee
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, USA
| | - Markku Varjosalo
- Institute of Biotechnology, HiLIFE Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- iCAN Digital Precision Cancer Medicine Flagship, University of Helsinki, Helsinki, Finland
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Romano JD, Truong V, Kumar R, Venkatesan M, Graham BE, Hao Y, Matsumoto N, Li X, Wang Z, Ritchie MD, Shen L, Moore JH. The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research. J Med Internet Res 2024; 26:e46777. [PMID: 38635981 PMCID: PMC11066745 DOI: 10.2196/46777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 06/23/2023] [Accepted: 11/07/2023] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs. OBJECTIVE We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics. METHODS We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base. RESULTS AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones. CONCLUSIONS AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.
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Affiliation(s)
- Joseph D Romano
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Center of Excellence in Environmental Toxicology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Van Truong
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Rachit Kumar
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mythreye Venkatesan
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Britney E Graham
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Yun Hao
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nick Matsumoto
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Xi Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Zhiping Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Li Shen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
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Agu PC, Obulose CN. Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications. Drug Dev Res 2024; 85:e22159. [PMID: 38375772 DOI: 10.1002/ddr.22159] [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: 11/08/2023] [Revised: 01/12/2024] [Accepted: 01/29/2024] [Indexed: 02/21/2024]
Abstract
The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.
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Affiliation(s)
- Peter Chinedu Agu
- Department of Biochemistry, College of Science, Evangel University, Akaeze, Ebonyi State, Nigeria
| | - Chidiebere Nwiboko Obulose
- Department of Computer Sciences, Our Savior Institute of Science, Agriculture, and Technology (OSISATECH Polytechnic), Enugu, Nigeria
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7
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Mansour HM. The interference between SARS-COV-2 and Alzheimer's disease: Potential immunological and neurobiological crosstalk from a kinase perspective reveals a delayed pandemic. Ageing Res Rev 2024; 94:102195. [PMID: 38244862 DOI: 10.1016/j.arr.2024.102195] [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: 11/14/2022] [Revised: 01/06/2024] [Accepted: 01/08/2024] [Indexed: 01/22/2024]
Abstract
Coronavirus disease 2019 (COVID-19) has infected over 700 million people, with up to 30% developing neurological manifestations, including dementias. However, there is a lack of understanding of common molecular brain markers causing Alzheimer's disease (AD). COVID-19 has etiological cofactors with AD, making patients with AD a vulnerable population at high risk of experiencing more severe symptoms and worse consequences. Both AD and COVID-19 have upregulated several shared kinases, leading to the repositioning of kinase inhibitors (KIs) for the treatment of both diseases. This review provides an overview of the interactions between the immune system and the nervous system in relation to receptor tyrosine kinases, including epidermal growth factor receptors, vascular growth factor receptors, and non-receptor tyrosine kinases such as Bruton tyrosine kinase, spleen tyrosine kinase, c-ABL, and JAK/STAT. We will discuss the promising results of kinase inhibitors in pre-clinical and clinical studies for both COVID-19 and Alzheimer's disease (AD), as well as the challenges in repositioning KIs for these diseases. Understanding the shared kinases between AD and COVID-19 could help in developing therapeutic approaches for both.
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Affiliation(s)
- Heba M Mansour
- General Administration of Innovative Products, Central Administration of Biological, Innovative Products, and Clinical Studies (Bio-INN), Egyptian Drug Authority (EDA), Giza, Egypt.
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8
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Torabi M, Yasami-Khiabani S, Sardari S, Golkar M, Pérez-Sánchez H, Ghasemi F. Identification of new potential candidates to inhibit EGF via machine learning algorithm. Eur J Pharmacol 2024; 963:176176. [PMID: 38000720 DOI: 10.1016/j.ejphar.2023.176176] [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: 06/19/2023] [Revised: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023]
Abstract
One of the cost-effective alternative methods to find new inhibitors has been the repositioning approach of existing drugs. The advantage of computational drug repositioning method is saving time and cost to remove the pre-clinical step and accelerate the drug discovery process. Hence, an ensemble computational-experimental approach, consisting of three steps, a machine learning model, simulation of drug-target interaction and experimental characterization, was developed. The machine learning type used here was a different tree classification method, which is one of the best randomize machine learning model to identify potential inhibitors from weak inhibitors. This model was trained more than one-hundred times, and forty top trained models were extracted for the drug repositioning step. The machine learning step aimed to discover the approved drugs with the highest possible success rate in the experimental step. Therefore, among all the identified molecules with more than 0.9 probability in more than 70% of the models, nine compounds, were selected. Besides, out of the nine chosen drugs, seven compounds have been confirmed to inhibit EGF in the published articles since 2019. Hence, two identified compounds, in addition to gefitinib, as a positive control, five weak-inhibitors and one neutral, were considered via molecular docking study. Finally, the eight proposed drugs, including gefitinib, were investigated using MTT assay and In-Cell ELISA to characterize the drugs' effect on A431 cell growth and EGF-signaling. From our experiments, we could conclude that salicylic acid and piperazine could play an EGF-inhibitor role like gefitinib.
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Affiliation(s)
- Mohammadreza Torabi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran
| | | | - Soroush Sardari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Majid Golkar
- Department of Parasitology, Pasteur Institute of Iran, Tehran, Iran
| | - Horacio Pérez-Sánchez
- Structural Bioinformatics and High Performance Computing Reseach Group (BIO-HPC), Computer Engineering Department, UCAM Universidad Católica de Murcia, Murcia, E30107, Spain
| | - Fahimeh Ghasemi
- Department of Bioinformatics and Systems Biology, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Iran; Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran.
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9
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Zhang YH, Zhao P, Gao HL, Zhong ML, Li JY. Screening Targets and Therapeutic Drugs for Alzheimer's Disease Based on Deep Learning Model and Molecular Docking. J Alzheimers Dis 2024; 100:863-878. [PMID: 38995776 DOI: 10.3233/jad-231389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
Background Alzheimer's disease (AD) is a neurodegenerative disorder caused by a complex interplay of various factors. However, a satisfactory cure for AD remains elusive. Pharmacological interventions based on drug targets are considered the most cost-effective therapeutic strategy. Therefore, it is paramount to search potential drug targets and drugs for AD. Objective We aimed to provide novel targets and drugs for the treatment of AD employing transcriptomic data of AD and normal control brain tissues from a new perspective. Methods Our study combined the use of a multi-layer perceptron (MLP) with differential expression analysis, variance assessment and molecular docking to screen targets and drugs for AD. Results We identified the seven differentially expressed genes (DEGs) with the most significant variation (ANKRD39, CPLX1, FABP3, GABBR2, GNG3, PPM1E, and WDR49) in transcriptomic data from AD brain. A newly built MLP was used to confirm the association between the seven DEGs and AD, establishing these DEGs as potential drug targets. Drug databases and molecular docking results indicated that arbaclofen, baclofen, clozapine, arbaclofen placarbil, BML-259, BRD-K72883421, and YC-1 had high affinity for GABBR2, and FABP3 bound with oleic, palmitic, and stearic acids. Arbaclofen and YC-1 activated GABAB receptor through PI3K/AKT and PKA/CREB pathways, respectively, thereby promoting neuronal anti-apoptotic effect and inhibiting p-tau and Aβ formation. Conclusions This study provided a new strategy for the identification of targets and drugs for the treatment of AD using deep learning. Seven therapeutic targets and ten drugs were selected by using this method, providing new insight for AD treatment.
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Affiliation(s)
- Ya-Hong Zhang
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - Pu Zhao
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - Hui-Ling Gao
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - Man-Li Zhong
- College of Life and Health Sciences, Northeastern University, Shenyang, China
| | - Jia-Yi Li
- Health Sciences Institute, China Medical University, Shenyang, China
- Department of Experimental Medical Science, Neuronal Plasticity and Repair Unit, Wallenberg Neuroscience Center, Lund University, Lund, Sweden
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10
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Geng C, Wang Z, Tang Y. Machine learning in Alzheimer's disease drug discovery and target identification. Ageing Res Rev 2024; 93:102172. [PMID: 38104638 DOI: 10.1016/j.arr.2023.102172] [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: 10/13/2023] [Revised: 11/28/2023] [Accepted: 12/13/2023] [Indexed: 12/19/2023]
Abstract
Alzheimer's disease (AD) stands as a formidable neurodegenerative ailment that poses a substantial threat to the elderly population, with no known curative or disease-slowing drugs in existence. Among the vital and time-consuming stages in the drug discovery process, disease modeling and target identification hold particular significance. Disease modeling allows for a deeper comprehension of disease progression mechanisms and potential therapeutic avenues. On the other hand, target identification serves as the foundational step in drug development, exerting a profound influence on all subsequent phases and ultimately determining the success rate of drug development endeavors. Machine learning (ML) techniques have ushered in transformative breakthroughs in the realm of target discovery. Leveraging the strengths of large dataset analysis, multifaceted data processing, and the exploration of intricate biological mechanisms, ML has become instrumental in the quest for effective AD treatments. In this comprehensive review, we offer an account of how ML methodologies are being deployed in the pursuit of drug discovery for AD. Furthermore, we provide an overview of the utilization of ML in uncovering potential intervention strategies and prospective therapeutic targets for AD. Finally, we discuss the principal challenges and limitations currently faced by these approaches. We also explore the avenues for future research that hold promise in addressing these challenges.
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Affiliation(s)
- Chaofan Geng
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - ZhiBin Wang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Tang
- Department of Neurology & Innovation Center for Neurological Disorders, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China; Neurodegenerative Laboratory of Ministry of Education of the People's Republic of China, Beijing, China.
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11
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Doherty T, Yao Z, Khleifat AAL, Tantiangco H, Tamburin S, Albertyn C, Thakur L, Llewellyn DJ, Oxtoby NP, Lourida I, Ranson JM, Duce JA. Artificial intelligence for dementia drug discovery and trials optimization. Alzheimers Dement 2023; 19:5922-5933. [PMID: 37587767 DOI: 10.1002/alz.13428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/26/2023] [Accepted: 07/05/2023] [Indexed: 08/18/2023]
Abstract
Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation.
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Affiliation(s)
- Thomas Doherty
- Eisai Europe Ltd, Hatfield, UK
- University of Westminster, London, UK
| | | | - Ahmad A L Khleifat
- Institute of Psychiatry, Psychology & Neuroscience, Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Stefano Tamburin
- University of Verona, Department of Neurosciences, Biomedicine & Movement Sciences, Verona, Italy
| | - Chris Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lokendra Thakur
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- Alan Turing Institute, London, UK
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | | | | | - James A Duce
- The ALBORADA Drug Discovery Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
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Dobbs Spendlove M, M. Gibson T, McCain S, Stone BC, Gill T, Pickett BE. Pathway2Targets: an open-source pathway-based approach to repurpose therapeutic drugs and prioritize human targets. PeerJ 2023; 11:e16088. [PMID: 37790614 PMCID: PMC10544355 DOI: 10.7717/peerj.16088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 08/22/2023] [Indexed: 10/05/2023] Open
Abstract
Background Recent efforts to repurpose existing drugs to different indications have been accompanied by a number of computational methods, which incorporate protein-protein interaction networks and signaling pathways, to aid with prioritizing existing targets and/or drugs. However, many of these existing methods are focused on integrating additional data that are only available for a small subset of diseases or conditions. Methods We have designed and implemented a new R-based open-source target prioritization and repurposing method that integrates both canonical intracellular signaling information from five public pathway databases and target information from public sources including OpenTargets.org. The Pathway2Targets algorithm takes a list of significant pathways as input, then retrieves and integrates public data for all targets within those pathways for a given condition. It also incorporates a weighting scheme that is customizable by the user to support a variety of use cases including target prioritization, drug repurposing, and identifying novel targets that are biologically relevant for a different indication. Results As a proof of concept, we applied this algorithm to a public colorectal cancer RNA-sequencing dataset with 144 case and control samples. Our analysis identified 430 targets and ~700 unique drugs based on differential gene expression and signaling pathway enrichment. We found that our highest-ranked predicted targets were significantly enriched in targets with FDA-approved therapeutics for colorectal cancer (p-value < 0.025) that included EGFR, VEGFA, and PTGS2. Interestingly, there was no statistically significant enrichment of targets for other cancers in this same list suggesting high specificity of the results. We also adjusted the weighting scheme to prioritize more novel targets for CRC. This second analysis revealed epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase (PI3K), and two mitogen-activated protein kinases (MAPK14 and MAPK3). These observations suggest that our open-source method with a customizable weighting scheme can accurately prioritize targets that are specific and relevant to the disease or condition of interest, as well as targets that are at earlier stages of development. We anticipate that this method will complement other approaches to repurpose drugs for a variety of indications, which can contribute to the improvement of the quality of life and overall health of such patients.
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Affiliation(s)
- Mauri Dobbs Spendlove
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Trenton M. Gibson
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Shaney McCain
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | - Benjamin C. Stone
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
| | | | - Brett E. Pickett
- Microbiology and Molecular Biology, Brigham Young University, Provo, UT, United States of America
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13
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Grabowska ME, Huang A, Wen Z, Li B, Wei WQ. Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review. Front Pharmacol 2023; 14:1257700. [PMID: 37745051 PMCID: PMC10512468 DOI: 10.3389/fphar.2023.1257700] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.
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Affiliation(s)
- Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annabelle Huang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, United States
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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14
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Wu X, Li Z, Chen G, Yin Y, Chen CYC. Hybrid neural network approaches to predict drug-target binding affinity for drug repurposing: screening for potential leads for Alzheimer's disease. Front Mol Biosci 2023; 10:1227371. [PMID: 37441162 PMCID: PMC10334190 DOI: 10.3389/fmolb.2023.1227371] [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: 05/29/2023] [Accepted: 06/13/2023] [Indexed: 07/15/2023] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease that primarily affects elderly individuals. Recent studies have found that sigma-1 receptor (S1R) agonists can maintain endoplasmic reticulum stress homeostasis, reduce neuronal apoptosis, and enhance mitochondrial function and autophagy, making S1R a target for AD therapy. Traditional experimental methods are costly and inefficient, and rapid and accurate prediction methods need to be developed, while drug repurposing provides new ways and options for AD treatment. In this paper, we propose HNNDTA, a hybrid neural network for drug-target affinity (DTA) prediction, to facilitate drug repurposing for AD treatment. The study combines protein-protein interaction (PPI) network analysis, the HNNDTA model, and molecular docking to identify potential leads for AD. The HNNDTA model was constructed using 13 drug encoding networks and 9 target encoding networks with 2506 FDA-approved drugs as the candidate drug library for S1R and related proteins. Seven potential drugs were identified using network pharmacology and DTA prediction results of the HNNDTA model. Molecular docking simulations were further performed using the AutoDock Vina tool to screen haloperidol and bromperidol as lead compounds for AD treatment. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation results indicated that both compounds had good pharmacokinetic properties and were virtually non-toxic. The study proposes a new approach to computer-aided drug design that is faster and more economical, and can improve hit rates for new drug compounds. The results of this study provide new lead compounds for AD treatment, which may be effective due to their multi-target action. HNNDTA is freely available at https://github.com/lizhj39/HNNDTA.
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Affiliation(s)
- Xialin Wu
- School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
- Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Zhuojian Li
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Guanxing Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Yiyang Yin
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-Sen University, Shenzhen, China
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan
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15
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Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, Garcia-Fandino R. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel) 2023; 16:891. [PMID: 37375838 DOI: 10.3390/ph16060891] [Citation(s) in RCA: 55] [Impact Index Per Article: 55.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) has the potential to revolutionize the drug discovery process, offering improved efficiency, accuracy, and speed. However, the successful application of AI is dependent on the availability of high-quality data, the addressing of ethical concerns, and the recognition of the limitations of AI-based approaches. In this article, the benefits, challenges, and drawbacks of AI in this field are reviewed, and possible strategies and approaches for overcoming the present obstacles are proposed. The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods, as well as the potential advantages of AI in pharmaceutical research, are also discussed. Overall, this review highlights the potential of AI in drug discovery and provides insights into the challenges and opportunities for realizing its potential in this field. Note from the human authors: This article was created to test the ability of ChatGPT, a chatbot based on the GPT-3.5 language model, in terms of assisting human authors in writing review articles. The text generated by the AI following our instructions (see Supporting Information) was used as a starting point, and its ability to automatically generate content was evaluated. After conducting a thorough review, the human authors practically rewrote the manuscript, striving to maintain a balance between the original proposal and the scientific criteria. The advantages and limitations of using AI for this purpose are discussed in the last section.
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Affiliation(s)
- Alexandre Blanco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
- MD.USE Innovations S.L., Edificio Emprendia, 15782 Santiago de Compostela, Spain
| | - Alfonso Cabezón
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Alejandro Seco-González
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Daniel Conde-Torres
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Paula Antelo-Riveiro
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Ángel Piñeiro
- Soft Matter & Molecular Biophysics Group, Department of Applied Physics, Faculty of Physics, University of Santiago de Compostela, 15705 Santiago de Compostela, Spain
| | - Rebeca Garcia-Fandino
- Department of Organic Chemistry, Center for Research in Biological Chemistry and Molecular Materials, University of Santiago de Compostela, CIQUS, 15705 Santiago de Compostela, Spain
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16
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Wei W, Yao JX, Zhang TT, Wen JY, Zhang Z, Luo YM, Cao Y, Li H. Network pharmacology reveals that Berberine may function against Alzheimer's disease via the AKT signaling pathway. Front Neurosci 2023; 17:1059496. [PMID: 37214397 PMCID: PMC10192713 DOI: 10.3389/fnins.2023.1059496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Accepted: 04/12/2023] [Indexed: 05/24/2023] Open
Abstract
Objective To investigate the mechanism underlying the effects of berberine (BBR) in the treatment of Alzheimer's disease (AD). Methods 3 × Tg AD mice were treated with BBR for 3 months, then the open field test (OFT), the novel object recognition test (NOR) and the Morris water maze (MWM) test were performed to assess behavioral performance. Hematoxylin-eosin (HE) staining, Nissl staining were used to examine histopathological changes. The pharmacological and molecular properties of BBR were obtained from the TCMSP database. BBR-associated AD targets were identified using the PharmMapper (PM), the comparative toxicogenomics database (CTD), DisGeNet and the human gene database (GeneCards). Core networks and BBR targets for the treatment of AD were identified using PPI network and functional enrichment analyses. AutoDock software was used to model the interaction between BBR and potential targets. Finally, RT-qPCR, western blotting were used to validate the expression of core targets. Results Behavioral experiments, HE staining and Nissl staining have shown that BBR can improve memory task performance and neuronal damage in the hippocampus of AD mice. 117 BBR-associated targets for the treatment of AD were identified, and 43 genes were used for downstream functional enrichment analysis in combination with the results of protein-protein interaction (PPI) network analysis. 2,230 biological processes (BP) terms, 67 cell components (CC) terms, 243 molecular function (MF) terms and 118 KEGG terms were identified. ALB, EGFR, CASP3 and five targets in the PI3K-AKT signaling pathway including AKT1, HSP90AA1, SRC, HRAS, IGF1 were selected by PPI network analysis, validated by molecular docking analysis and RT-q PCR as core targets for further analysis. Akt1 mRNA expression levels were significantly decreased in AD mice and significantly increased after BBR treatment (p < 0.05). Besides, AKT and ERK phosphorylation decreased in the model group, and BBR significantly increased their phosphorylation levels. Conclusion AKT1, HSP90AA1, SRC, HRAS, IGF1 and ALB, EGFR, CASP3 were core targets of BBR in the treatment of AD. BBR may exert a neuroprotective effect by modulating the ERK and AKT signaling pathways.
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Affiliation(s)
- Wei Wei
- Wangjing Hospital, China Academy of Chinese Medical Science, Beijing, China
- Institute of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Jiu-xiu Yao
- College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Ting-ting Zhang
- Wangjing Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Jia-yu Wen
- Institute of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Zhen Zhang
- Institute of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Yi-miao Luo
- Institute of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Yu Cao
- Institute of Geriatrics, Xiyuan Hospital, China Academy of Chinese Medical Science, Beijing, China
| | - Hao Li
- Wangjing Hospital, China Academy of Chinese Medical Science, Beijing, China
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17
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Ranson JM, Bucholc M, Lyall D, Newby D, Winchester L, Oxtoby NP, Veldsman M, Rittman T, Marzi S, Skene N, Al Khleifat A, Foote IF, Orgeta V, Kormilitzin A, Lourida I, Llewellyn DJ. Harnessing the potential of machine learning and artificial intelligence for dementia research. Brain Inform 2023; 10:6. [PMID: 36829050 PMCID: PMC9958222 DOI: 10.1186/s40708-022-00183-3] [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: 05/31/2022] [Accepted: 12/26/2022] [Indexed: 02/26/2023] Open
Abstract
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.
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Affiliation(s)
- Janice M Ranson
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK.
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Donald Lyall
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Danielle Newby
- Department of Psychiatry, University of Oxford, Oxford, UK
| | | | - Neil P Oxtoby
- Department of Computer Science, UCL Centre for Medical Image Computing, University College London, London, UK
| | | | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Sarah Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Nathan Skene
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, King's College London, London, UK
| | | | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, UK
| | | | - Ilianna Lourida
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
| | - David J Llewellyn
- University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK
- The Alan Turing Institute, London, UK
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18
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Astillero-Lopez V, Gonzalez-Rodriguez M, Villar-Conde S, Flores-Cuadrado A, Martinez-Marcos A, Ubeda-Banon I, Saiz-Sanchez D. Neurodegeneration and astrogliosis in the entorhinal cortex in Alzheimer's disease: Stereological layer-specific assessment and proteomic analysis. Alzheimers Dement 2022; 18:2468-2480. [PMID: 35142030 DOI: 10.1002/alz.12580] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/30/2021] [Accepted: 12/12/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The entorhinal cortex is among the earliest areas involved in Alzheimer's disease. Volume reduction and neural loss in this area have been widely reported. Human entorhinal cortex atrophy is, in part, due to neural loss, but microglial and/or astroglial involvement in the different layers remains unclear. Additionally, -omic approaches in the human entorhinal cortex are scarce. METHODS Herein, stereological layer-specific and proteomic analyses were carried out in the human brain. RESULTS Neurodegeneration, microglial reduction, and astrogliosis have been demonstrated, and proteomic data have revealed relationships with up- (S100A6, PPP1R1B, BAG3, and PRDX6) and downregulated (GSK3B, SYN1, DLG4, and RAB3A) proteins. Namely, clusters of these proteins were related to synaptic, neuroinflammatory, and oxidative stress processes. DISCUSSION Differential layer involvement among neural and glial populations determined by proteinopathies and identified proteins related to neurodegeneration and astrogliosis could explain how the cortical circuitry facilitates pathological spreading within the medial temporal lobe.
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Affiliation(s)
- Veronica Astillero-Lopez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Melania Gonzalez-Rodriguez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Sandra Villar-Conde
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Alicia Flores-Cuadrado
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Alino Martinez-Marcos
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Isabel Ubeda-Banon
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
| | - Daniel Saiz-Sanchez
- Neuroplasticity and Neurodegeneration Laboratory, CRIB, Ciudad Real Medical School, University of Castilla-La Mancha, Ciudad Real, Spain
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Wang X, Ezeana CF, Wang L, Puppala M, Huang Y, He Y, Yu X, Yin Z, Zhao H, Lai EC, Wong STC. Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2022; 8:e12351. [PMID: 36204350 PMCID: PMC9520763 DOI: 10.1002/trc2.12351] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 11/26/2022]
Abstract
Introduction Geriatric patients with dementia incur higher healthcare costs and longer hospital stays than other geriatric patients. We aimed to identify risk factors for hospitalization outcomes that could be mitigated early to improve outcomes and impact overall quality of life. Methods We identified risk factors, that is, demographics, hospital complications, pre-admission, and post-admission risk factors including medical history and comorbidities, affecting hospitalization outcomes determined by hospital stays and discharge dispositions. Over 150 clinical and demographic factors of 15,678 encounters (8407 patients) were retrieved from our institution's data warehouse. We further narrowed them down to twenty factors through feature selection engineering by using analysis of variance (ANOVA) and Glmnet. We developed an explainable machine-learning model to predict hospitalization outcomes among geriatric patients with dementia. Results Our model is based on stacking ensemble learning and achieved accuracy of 95.6% and area under the curve (AUC) of 0.757. It outperformed prevalent methods of risk assessment for encounters of patients with Alzheimer's disease dementia (ADD) (4993), vascular dementia (VD) (4173), Parkinson's disease with dementia (PDD) (3735), and other unspecified dementias (OUD) (2777). Top identified hospitalization outcome risk factors, mostly from medical history, include encephalopathy, number of medical problems at admission, pressure ulcers, urinary tract infections, falls, admission source, age, race, anemia, etc., with several overlaps in multi-dementia groups. Discussion Our model identified several predictive factors that can be modified or intervened so that efforts can be made to prevent recurrence or mitigate their adverse effects. Knowledge of the modifiable risk factors would help guide early interventions for patients at high risk for poor hospitalization outcome as defined by hospital stays longer than seven days, undesirable discharge disposition, or both. The interventions include starting specific protocols on modifiable risk factors like encephalopathy, falls, and infections, where non-existent or not routine, to improve hospitalization outcomes of geriatric patients with dementia. Highlights A total 15,678 encounters of Geriatrics with dementia with a final 20 risk factors.Developed a predictive model for hospitalization outcomes for multi-dementia types.Risk factors for each type were identified including those amenable to interventions.Top factors are encephalopathy, pressure ulcers, urinary tract infection (UTI), falls, and admission source.With accuracy of 95.6%, our ensemble predictive model outperforms other models.
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Affiliation(s)
- Xin Wang
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Chika F. Ezeana
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Lin Wang
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Mamta Puppala
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | | | - Yunjie He
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Xiaohui Yu
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Zheng Yin
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Hong Zhao
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Eugene C. Lai
- Neurological InstituteHouston Methodist HospitalHoustonTexasUSA
| | - Stephen T. C. Wong
- T.T. & W.F. Chao Center for BRAINHouston Methodist Academic InstituteHouston Methodist HospitalHoustonTexasUSA
- Brain and Mind Research InstituteWeill Cornell Medical CollegeNew YorkUSA
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20
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Arrué L, Cigna-Méndez A, Barbosa T, Borrego-Muñoz P, Struve-Villalobos S, Oviedo V, Martínez-García C, Sepúlveda-Lara A, Millán N, Márquez Montesinos JCE, Muñoz J, Santana PA, Peña-Varas C, Barreto GE, González J, Ramírez D. New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools. Pharmaceutics 2022; 14:1914. [PMID: 36145662 PMCID: PMC9503559 DOI: 10.3390/pharmaceutics14091914] [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: 08/05/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 11/30/2022] Open
Abstract
Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer's disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.
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Affiliation(s)
- Lily Arrué
- Centro de Investigación de Estudios Avanzados del Maule (CIEAM), Vicerrectoría de Investigación y Postgrado, Universidad Católica del Maule, Talca 3480094, Chile
| | - Alexandra Cigna-Méndez
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Tábata Barbosa
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Paola Borrego-Muñoz
- Escuela de Medicina, Fundación Universitaria Juan N. Corpas, Bogotá 110311, Colombia
| | - Silvia Struve-Villalobos
- Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Temuco 4780000, Chile
| | - Victoria Oviedo
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Claudia Martínez-García
- Departamento de Farmacia, Facultad de Ciencias, Universidad Nacional de Colombia, Bogotá 111321, Colombia
| | - Alexis Sepúlveda-Lara
- Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Temuco 4780000, Chile
| | - Natalia Millán
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | | | - Juana Muñoz
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - Paula A. Santana
- Facultad de Ingeniería, Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, Santiago 8910060, Chile
| | - Carlos Peña-Varas
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
| | - George E. Barreto
- Department of Biological Sciences, University of Limerick, V94 T9PX Limerick, Ireland
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
| | - David Ramírez
- Departamento de Farmacología, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4030000, Chile
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21
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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22
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Padhi D, Govindaraju T. Mechanistic Insights for Drug Repurposing and the Design of Hybrid Drugs for Alzheimer's Disease. J Med Chem 2022; 65:7088-7105. [PMID: 35559617 DOI: 10.1021/acs.jmedchem.2c00335] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The heterogeneity and complex nature of Alzheimer's disease (AD) is attributed to several genetic risk factors and molecular culprits. The slow pace and increasing failure rate of conventional drug discovery has led to the exploration of complementary strategies based on repurposing approved drugs to treat AD. Drug repurposing (DR) is a cost-effective, low-risk, and efficient approach for identifying novel therapeutic candidates for AD treatment. Similarly, hybrid drug design through the integration of distinct pharmacophores from known or failed drugs and natural products is an interesting strategy to target the multifactorial nature of AD. In this Perspective, we discuss the potential of DR and highlight promising drug candidates that can be advanced for clinical trials, backed by a detailed discussion on their plausible mechanisms of action. Our article fosters research on the hidden potential of DR and hybrid drug design with the goal of unravelling new drugs and targets to tackle AD.
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Affiliation(s)
- Dikshaa Padhi
- Bioorganic Chemistry Laboratory, New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur P.O., Bengaluru, Karnataka 560064, India
| | - Thimmaiah Govindaraju
- Bioorganic Chemistry Laboratory, New Chemistry Unit, Jawaharlal Nehru Centre for Advanced Scientific Research (JNCASR), Jakkur P.O., Bengaluru, Karnataka 560064, India
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23
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Tucker Edmister S, Del Rosario Hernández T, Ibrahim R, Brown CA, Gore SV, Kakodkar R, Kreiling JA, Creton R. Novel use of FDA-approved drugs identified by cluster analysis of behavioral profiles. Sci Rep 2022; 12:6120. [PMID: 35449173 PMCID: PMC9023506 DOI: 10.1038/s41598-022-10133-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/01/2022] [Indexed: 12/20/2022] Open
Abstract
Repurposing FDA-approved drugs is an efficient and cost-effective approach in the development of therapeutics for a broad range of diseases. However, prediction of function can be challenging, especially in the brain. We screened a small-molecule library with FDA-approved drugs for effects on behavior. The studies were carried out using zebrafish larvae, imaged in a 384-well format. We found that various drugs affect activity, habituation, startle responses, excitability, and optomotor responses. The changes in behavior were organized in behavioral profiles, which were examined by hierarchical cluster analysis. One of the identified clusters includes the calcineurin inhibitors cyclosporine (CsA) and tacrolimus (FK506), which are immunosuppressants and potential therapeutics in the prevention of Alzheimer's disease. The calcineurin inhibitors form a functional cluster with seemingly unrelated drugs, including bromocriptine, tetrabenazine, rosiglitazone, nebivolol, sorafenib, cabozantinib, tamoxifen, meclizine, and salmeterol. We propose that drugs with 'CsA-type' behavioral profiles are promising candidates for the prevention and treatment of Alzheimer's disease.
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Affiliation(s)
- Sara Tucker Edmister
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Thaís Del Rosario Hernández
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Rahma Ibrahim
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Cameron A Brown
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Sayali V Gore
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Rohit Kakodkar
- Center for Computation and Visualization, Brown University, Providence, Rhode Island, USA
| | - Jill A Kreiling
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA
| | - Robbert Creton
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA.
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24
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The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence. Biomedicines 2022; 10:biomedicines10020315. [PMID: 35203524 PMCID: PMC8869403 DOI: 10.3390/biomedicines10020315] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 02/05/2023] Open
Abstract
Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.
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25
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Pagan FL, Torres‐Yaghi Y, Hebron ML, Wilmarth B, Turner RS, Matar S, Ferrante D, Ahn J, Moussa C. Safety, target engagement, and biomarker effects of bosutinib in dementia with Lewy bodies. ALZHEIMER'S & DEMENTIA: TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS 2022; 8:e12296. [PMID: 35662832 PMCID: PMC9157583 DOI: 10.1002/trc2.12296] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/16/2022] [Accepted: 03/24/2022] [Indexed: 11/08/2022]
Affiliation(s)
- Fernando L. Pagan
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
- MedStar Georgetown University Hospital Movement Disorders Clinic Department of Neurology Washington DC USA
| | - Yasar Torres‐Yaghi
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
- MedStar Georgetown University Hospital Movement Disorders Clinic Department of Neurology Washington DC USA
| | - Michaeline L. Hebron
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
| | - Barbara Wilmarth
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
- MedStar Georgetown University Hospital Movement Disorders Clinic Department of Neurology Washington DC USA
| | - R. Scott Turner
- Memory Disorders Program Department of Neurology Georgetown University Medical Center Washington DC USA
| | - Sara Matar
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
| | - Dalila Ferrante
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
| | - Jaeil Ahn
- Department of Biostatistics Bioinformatics and Biomathematics Georgetown University Medical Center Washington DC USA
| | - Charbel Moussa
- Translational Neurotherapeutics Program Laboratory for Dementia and Parkinsonism Department of Neurology Lewy Body Dementia Association Research Center of Excellence Georgetown University Medical Center Washington DC USA
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26
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Jian C, Wei L, Mo R, Li R, Liang L, Chen L, Zou C, Meng Y, Liu Y, Zou D. Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication. Front Aging Neurosci 2021; 13:731180. [PMID: 34616287 PMCID: PMC8488208 DOI: 10.3389/fnagi.2021.731180] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Accepted: 09/01/2021] [Indexed: 01/23/2023] Open
Abstract
Alzheimer's disease (AD) is a common neurodegenerative disease. Its onset is insidious and its progression is slow, making diagnosis difficult. In addition, its underlying molecular and cellular mechanisms remain unclear. In this study, clustering analysis was performed on single-cell RNA sequencing (scRNA-seq) data from the prefrontal cortex of 48 AD patients. Each sample module was identified to be a specific AD cell type, eight main brain cell types were identified, and the dysfunctional evolution of each cell type was further explored by pseudo-time analysis. Correlation analysis was then used to explore the relationship between AD cell types and pathological characteristics. In particular, intercellular communication between neurons and glial cells in AD patients was investigated by cell communication analysis. In patients, neuronal cells and glial cells significantly correlated with pathological features, and glial cells appear to play a key role in the development of AD through ligand-receptor axis communication. Marker genes involved in communication between these two cell types were identified using five types of modeling: logistic regression, multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). LASSO modeling identified CXCR4, EGFR, MAP4K4, and IGF1R as key genes in this communication. Our results support the idea that microglia play a role in the occurrence and development of AD through ligand-receptor axis communication. In particular, our analyses identify CXCR4, EGFR, MAP4K4, and IGF1R as potential biomarkers and therapeutic targets in AD.
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Affiliation(s)
- Chongdong Jian
- Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Lei Wei
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ruikang Mo
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rongjie Li
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Lucong Liang
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Liechun Chen
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chun Zou
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Youshi Meng
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ying Liu
- Department of General Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
- Department of Geriatrics, The First People’s Hospital of Nanning, Nanning, China
| | - Donghua Zou
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
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