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Angelucci F, Ai AR, Piendel L, Cerman J, Hort J. Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials. Curr Opin Struct Biol 2024; 87:102857. [PMID: 38838385 DOI: 10.1016/j.sbi.2024.102857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/15/2024] [Accepted: 05/12/2024] [Indexed: 06/07/2024]
Abstract
The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.
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Affiliation(s)
- Francesco Angelucci
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic.
| | - Alice Ruixue Ai
- Department of Clinical Molecular Biology, University of Oslo and Akershus University Hospital, 1478 Lørenskog, Norway
| | - Lydia Piendel
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; Augusta University/University of Georgia Medical Partnership, Medical College of Georgia, Athens, GA, USA
| | - Jiri Cerman
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic
| | - Jakub Hort
- Memory Clinic, Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic; INDRC, International Neurodegenerative Disorders Research Center, Prague, Czech Republic
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2
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Xie W, Yu J, Huang L, For LS, Zheng Z, Chen X, Wang Y, Liu Z, Peng C, Wong KC. DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning. Comput Biol Med 2024; 175:108487. [PMID: 38653064 DOI: 10.1016/j.compbiomed.2024.108487] [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: 01/09/2024] [Revised: 03/26/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.
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Affiliation(s)
- Weidun Xie
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Jixiang Yu
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lei Huang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Lek Shyuen For
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Xingjian Chen
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuchen Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China
| | - Zhichao Liu
- Sir William Dunn School of Pathology, University of Oxford, UK
| | - Chengbin Peng
- College of Information Science and Engineering, Ningbo University, Ningbo, China
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China; Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China.
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3
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Bi XA, Wang Y, Luo S, Chen K, Xing Z, Xu L. Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7420-7434. [PMID: 36264725 DOI: 10.1109/tnnls.2022.3212700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.
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Xu M, Li W, He J, Wang Y, Lv J, He W, Chen L, Zhi H. DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm. Int J Mol Sci 2024; 25:5267. [PMID: 38791306 PMCID: PMC11121335 DOI: 10.3390/ijms25105267] [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: 02/29/2024] [Revised: 04/25/2024] [Accepted: 05/06/2024] [Indexed: 05/26/2024] Open
Abstract
Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.
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Affiliation(s)
- Manyi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Jiaheng He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Yahui Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin 150000, China;
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
| | - Hui Zhi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150000, China; (M.X.); (W.L.); (J.H.); (Y.W.); (J.L.)
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Gouveia Roque C, Phatnani H, Hengst U. The broken Alzheimer's disease genome. CELL GENOMICS 2024; 4:100555. [PMID: 38697121 PMCID: PMC11099344 DOI: 10.1016/j.xgen.2024.100555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/25/2024] [Accepted: 04/07/2024] [Indexed: 05/04/2024]
Abstract
The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.
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Affiliation(s)
- Cláudio Gouveia Roque
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY 10032, USA
| | - Ulrich Hengst
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Pathology & Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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6
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Young AP, Denovan-Wright EM. JAK1/2 Regulates Synergy Between Interferon Gamma and Lipopolysaccharides in Microglia. J Neuroimmune Pharmacol 2024; 19:14. [PMID: 38642237 DOI: 10.1007/s11481-024-10115-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 04/01/2024] [Indexed: 04/22/2024]
Abstract
Microglia, the resident immune cells of the brain, regulate neuroinflammation which can lead to secondary neuronal damage and cognitive impairment under pathological conditions. Two of the many molecules that can elicit an inflammatory response from microglia are lipopolysaccharide (LPS), a component of gram-negative bacteria, and interferon gamma (IFNγ), an endogenous pro-inflammatory cytokine. We thoroughly examined the concentration-dependent relationship between LPS from multiple bacterial species and IFNγ in cultured microglia and macrophages. We measured the effects that these immunostimulatory molecules have on pro-inflammatory activity of microglia and used a battery of signaling inhibitors to identify the pathways that contribute to the microglial response. We found that LPS and IFNγ interacted synergistically to induce a pro-inflammatory phenotype in microglia, and that inhibition of JAK1/2 completely blunted the response. We determined that this synergistic action of LPS and IFNγ was likely dependent on JNK and Akt signaling rather than typical pro-inflammatory mediators such as NF-κB. Finally, we demonstrated that LPS derived from Escherichia coli, Klebsiella pneumoniae, and Akkermansia muciniphila can elicit different inflammatory responses from microglia and macrophages, but these responses could be consistently prevented using ruxolitinib, a JAK1/2 inhibitor. Collectively, this work reveals a mechanism by which microglia may become hyperactivated in response to the combination of LPS and IFNγ. Given that elevations in circulating LPS and IFNγ occur in a wide variety of pathological conditions, it is critical to understand the pharmacological interactions between these molecules to develop safe and effective treatments to suppress this process.
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Affiliation(s)
- Alexander P Young
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada.
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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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Rani N, Kaushik A, Kardam S, Kag S, Raj VS, Ambasta RK, Kumar P. Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:23-70. [PMID: 38789181 DOI: 10.1016/bs.pmbts.2024.03.029] [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
Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.
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Affiliation(s)
- Neetu Rani
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Aastha Kaushik
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Shefali Kardam
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - Sonika Kag
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University, Delhi, India
| | - V Samuel Raj
- Department of Biotechnology and Microbiology, SRM University, Sonepat, Haryana, 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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Qiu Y, Cheng F. Artificial intelligence for drug discovery and development in Alzheimer's disease. Curr Opin Struct Biol 2024; 85:102776. [PMID: 38335558 DOI: 10.1016/j.sbi.2024.102776] [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: 10/25/2023] [Revised: 12/29/2023] [Accepted: 01/15/2024] [Indexed: 02/12/2024]
Abstract
The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.
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Affiliation(s)
- Yunguang Qiu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. https://twitter.com/YunguangQiu
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA.
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Ghandikota SK, Jegga AG. Application of artificial intelligence and machine learning in drug repurposing. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2024; 205:171-211. [PMID: 38789178 DOI: 10.1016/bs.pmbts.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.
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Affiliation(s)
- Sudhir K Ghandikota
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Anil G Jegga
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Das V, Miller JH, Alladi CG, Annadurai N, De Sanctis JB, Hrubá L, Hajdúch M. Antineoplastics for treating Alzheimer's disease and dementia: Evidence from preclinical and observational studies. Med Res Rev 2024. [PMID: 38530106 DOI: 10.1002/med.22033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 02/15/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
As the world population ages, there will be an increasing need for effective therapies for aging-associated neurodegenerative disorders, which remain untreatable. Dementia due to Alzheimer's disease (AD) is one of the leading neurological diseases in the aging population. Current therapeutic approaches to treat this disorder are solely symptomatic, making the need for new molecular entities acting on the causes of the disease extremely urgent. One of the potential solutions is to use compounds that are already in the market. The structures have known pharmacokinetics, pharmacodynamics, toxicity profiles, and patient data available in several countries. Several drugs have been used successfully to treat diseases different from their original purposes, such as autoimmunity and peripheral inflammation. Herein, we divulge the repurposing of drugs in the area of neurodegenerative diseases, focusing on the therapeutic potential of antineoplastics to treat dementia due to AD and dementia. We briefly touch upon the shared pathological mechanism between AD and cancer and drug repurposing strategies, with a focus on artificial intelligence. Next, we bring out the current status of research on the development of drugs, provide supporting evidence from retrospective, clinical, and preclinical studies on antineoplastic use, and bring in new areas, such as repurposing drugs for the prion-like spreading of pathologies in treating AD.
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Affiliation(s)
- Viswanath Das
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
- Czech Advanced Technologies and Research Institute (CATRIN), Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc, Czech Republic
| | - John H Miller
- School of Biological Sciences and Centre for Biodiscovery, Victoria University of Wellington, Wellington, New Zealand
| | - Charanraj Goud Alladi
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Narendran Annadurai
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Juan Bautista De Sanctis
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
- Czech Advanced Technologies and Research Institute (CATRIN), Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc, Czech Republic
| | - Lenka Hrubá
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
- Czech Advanced Technologies and Research Institute (CATRIN), Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc, Czech Republic
| | - Marián Hajdúch
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
- Czech Advanced Technologies and Research Institute (CATRIN), Institute of Molecular and Translational Medicine, Palacký University Olomouc, Olomouc, Czech Republic
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12
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Ahmadi M, Javaheri D, Khajavi M, Danesh K, Hur J. A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction. PLoS One 2024; 19:e0297996. [PMID: 38530836 DOI: 10.1371/journal.pone.0297996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 01/16/2024] [Indexed: 03/28/2024] Open
Abstract
Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.
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Affiliation(s)
- Mohsen Ahmadi
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America
| | - Danial Javaheri
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Matin Khajavi
- Foster School of Businesses, University of Washington, Seattle, Washington, United States of America
| | - Kasra Danesh
- Department of Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, United States of America
| | - Junbeom Hur
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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13
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Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
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14
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Zhang H, Huang J, Xie J, Huang W, Yang Y, Xu M, Lei J, Chen H. GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning. J Chem Inf Model 2024; 64:666-676. [PMID: 38241022 DOI: 10.1021/acs.jcim.3c01700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2024]
Abstract
Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.
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Affiliation(s)
- Hao Zhang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Jinchao Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Junjie Xie
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
| | - Mingyuan Xu
- Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 Xin Dao Huan Bei Road, Guangzhou 510005, China
| | - Jinping Lei
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou 510006, China
| | - Hongming Chen
- Guangzhou National Laboratory, Guangzhou International Bio Island, No. 9 Xin Dao Huan Bei Road, Guangzhou 510005, China
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15
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Azevedo PHRDA, Peçanha BRDB, Flores-Junior LAP, Alves TF, Dias LRS, Muri EMF, Lima CHDS. In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor. J Biomol Struct Dyn 2024; 42:1417-1428. [PMID: 37054524 DOI: 10.1080/07391102.2023.2199868] [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: 10/20/2022] [Accepted: 04/01/2023] [Indexed: 04/15/2023]
Abstract
O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.
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Affiliation(s)
| | | | | | - Tatiana Fialho Alves
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Luiza Rosaria Sousa Dias
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
| | - Estela Maris Freitas Muri
- Laboratório de Química Medicinal, Faculdade de Farmácia, Universidade Federal Fluminense, Niterói, RJ, Brazil
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16
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Späth J, Wang R, Humphrey M, Baumbach J, Loscalzo J. Machine learning-based integration of network features and chemical structure of compounds for SARS-CoV-2 drug effect analysis. CPT Pharmacometrics Syst Pharmacol 2024; 13:257-269. [PMID: 37950385 PMCID: PMC10864927 DOI: 10.1002/psp4.13076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/12/2023] [Accepted: 10/24/2023] [Indexed: 11/12/2023] Open
Abstract
High drug development costs and the limited number of new annual drug approvals increase the need for innovative approaches for drug effect prediction. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), led to a global pandemic with high morbidity and mortality. Although effective preventive measures exist, there are few effective treatments for hospitalized patients with SARS-CoV-2 infection. Drug repurposing and drug effect prediction are promising strategies that could shorten development time and reduce costs compared with de novo drug discovery. In this work, we present a machine learning framework to integrate a variety of target network features and physicochemical properties of compounds, and analyze their influence on the therapeutic effects for SARS-CoV-2 infection and on host cell cytotoxic effects. Random forest models trained on compounds with known experimental effects on SARS-CoV-2 infection and subsequent feature importance analysis based on Shapley values provided insights into the determinants of drug efficacy and cytotoxicity, which can be incorporated into novel drug discovery approaches. Given the complexity of molecular mechanisms of drug action and limited sample sizes, our models achieve a reasonable mean area under the receiver operating characteristic curve (ROC-AUC) of 0.73 on an unseen validation set. To our knowledge, this is the first work to incorporate a combination of network and physicochemical features of compounds into a machine learning model to predict drug effects on SARS-CoV-2 infection. Our systems pharmacology-based machine learning framework can be used to classify other existing drugs for SARS-CoV-2 infection and can easily be adapted to drug effect prediction for future viral outbreaks.
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Affiliation(s)
- Julian Späth
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Rui‐Sheng Wang
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Maeve Humphrey
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jan Baumbach
- Institute of Computational Systems BiologyUniversity of HamburgHamburgGermany
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Corasaniti MT, Bagetta G, Nicotera P, Maione S, Tonin P, Guida F, Scuteri D. Exploitation of Autophagy Inducers in the Management of Dementia: A Systematic Review. Int J Mol Sci 2024; 25:1264. [PMID: 38279266 PMCID: PMC10816917 DOI: 10.3390/ijms25021264] [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: 12/17/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
The social burden of dementia is remarkable since it affects some 57.4 million people all over the world. Impairment of autophagy in age-related diseases, such as dementia, deserves deep investigation for the detection of novel disease-modifying approaches. Several drugs belonging to different classes were suggested to be effective in managing Alzheimer's disease (AD) by means of autophagy induction. Useful autophagy inducers in AD should be endowed with a direct, measurable effect on autophagy, have a safe tolerability profile, and have the capability to cross the blood-brain barrier, at least with poor penetration. According to the PRISMA 2020 recommendations, we propose here a systematic review to appraise the measurable effectiveness of autophagy inducers in the improvement of cognitive decline and neuropsychiatric symptoms in clinical trials and retrospective studies. The systematic search retrieved 3067 records, 10 of which met the eligibility criteria. The outcomes most influenced by the treatment were cognition and executive functioning, pointing at a role for metformin, resveratrol, masitinib and TPI-287, with an overall tolerable safety profile. Differences in sample power, intervention, patients enrolled, assessment, and measure of outcomes prevents generalization of results. Moreover, the domain of behavioral symptoms was found to be less investigated, thus prompting new prospective studies with homogeneous design. PROSPERO registration: CRD42023393456.
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Affiliation(s)
| | - Giacinto Bagetta
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy;
| | - Pierluigi Nicotera
- German Center for Neurodegenerative Diseases (DZNE), 53127 Bonn, Germany;
| | - Sabatino Maione
- Division of Pharmacology, Department of Experimental Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (S.M.); (F.G.)
- Laboratory of Biomolecules, Venoms and Theranostic Application, Institute Pasteur de Tunis, Université Tunis El Manar, Tunis 1002, Tunisia
| | - Paolo Tonin
- Regional Center for Serious Brain Injuries, S. Anna Institute, 88900 Crotone, Italy;
| | - Francesca Guida
- Division of Pharmacology, Department of Experimental Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (S.M.); (F.G.)
| | - Damiana Scuteri
- Department of Health Sciences, University “Magna Graecia” of Catanzaro, 88100 Catanzaro, Italy;
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18
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Faquetti ML, Slappendel L, Bigonne H, Grisoni F, Schneider P, Aichinger G, Schneider G, Sturla SJ, Burden AM. Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2024; 10:e12445. [PMID: 38528988 PMCID: PMC10962475 DOI: 10.1002/trc2.12445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/10/2023] [Accepted: 12/27/2023] [Indexed: 03/27/2024]
Abstract
INTRODUCTION Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off-targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors' off-target interactions in the context of AD remains unclear. METHODS Putative off-targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off-targets were filtered based on their relevance to AD. Targets that had not been previously identified as off-targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell-based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling. RESULTS With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2-α2) and dual leucine zipper kinase (MAP3K12), both with binding constant (K d) values of 5.8 μM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant. CONCLUSION In this study, we extended the list of baricitinib off-targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off-target interaction on AD, the combined approach of ML-based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD. Highlights This study explored JAK inhibitors' off-targets in AD using a multidisciplinary approach.We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off-target interactions of tofacitinib and baricitinib in AD.Previously unknown inhibition of two enzymes (CK2-a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments.Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing.The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.
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Affiliation(s)
- Maria L. Faquetti
- Department of Chemistry and Applied BiosciencesInstitute of Pharmaceutical SciencesETH ZurichZurichSwitzerland
| | - Laura Slappendel
- Department of Health Sciences and TechnologyInstitute of Food, Nutrition and Health, ETH ZurichZurichSwitzerland
| | - Hélène Bigonne
- Department of Health Sciences and TechnologyInstitute of Food, Nutrition and Health, ETH ZurichZurichSwitzerland
| | - Francesca Grisoni
- Department of Biomedical EngineeringInstitute for Complex Molecular SystemsEindhoven University of TechnologyEindhoventhe Netherlands
- Centre for Living TechnologiesAlliance TU/e, WUR, UU, UMC UtrechtUtrechtthe Netherlands
| | - Petra Schneider
- Department of Chemistry and Applied BiosciencesInstitute of Pharmaceutical SciencesETH ZurichZurichSwitzerland
- inSili.com LLCZurichSwitzerland
| | - Georg Aichinger
- Department of Health Sciences and TechnologyInstitute of Food, Nutrition and Health, ETH ZurichZurichSwitzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied BiosciencesInstitute of Pharmaceutical SciencesETH ZurichZurichSwitzerland
- ETH Singapore SEC LtdSingaporeSingapore
| | - Shana J. Sturla
- Department of Health Sciences and TechnologyInstitute of Food, Nutrition and Health, ETH ZurichZurichSwitzerland
| | - Andrea M. Burden
- Department of Chemistry and Applied BiosciencesInstitute of Pharmaceutical SciencesETH ZurichZurichSwitzerland
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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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20
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Yan R, Wang W, Yang W, Huang M, Xu W. Mitochondria-Related Candidate Genes and Diagnostic Model to Predict Late-Onset Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2024; 99:S299-S315. [PMID: 37334608 PMCID: PMC11091583 DOI: 10.3233/jad-230314] [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] [Accepted: 05/15/2023] [Indexed: 06/20/2023]
Abstract
Background Late-onset Alzheimer's disease (LOAD) is the most common type of dementia, but its pathogenesis remains unclear, and there is a lack of simple and convenient early diagnostic markers to predict the occurrence. Objective Our study aimed to identify diagnostic candidate genes to predict LOAD by machine learning methods. Methods Three publicly available datasets from the Gene Expression Omnibus (GEO) database containing peripheral blood gene expression data for LOAD, mild cognitive impairment (MCI), and controls (CN) were downloaded. Differential expression analysis, the least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature elimination (SVM-RFE) were used to identify LOAD diagnostic candidate genes. These candidate genes were then validated in the validation group and clinical samples, and a LOAD prediction model was established. Results LASSO and SVM-RFE analyses identified 3 mitochondria-related genes (MRGs) as candidate genes, including NDUFA1, NDUFS5, and NDUFB3. In the verification of 3 MRGs, the AUC values showed that NDUFA1, NDUFS5 had better predictability. We also verified the candidate MRGs in MCI groups, the AUC values showed good performance. We then used NDUFA1, NDUFS5 and age to build a LOAD diagnostic model and AUC was 0.723. Results of qRT-PCR experiments with clinical blood samples showed that the three candidate genes were expressed significantly lower in the LOAD and MCI groups when compared to CN. Conclusion Two mitochondrial-related candidate genes, NDUFA1 and NDUFS5, were identified as diagnostic markers for LOAD and MCI. Combining these two candidate genes with age, a LOAD diagnostic prediction model was successfully constructed.
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Affiliation(s)
- Ran Yan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Wang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wen Yang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Masha Huang
- Department of Biochemistry and Molecular Cell Biology, Shanghai Key Laboratory for Tumor Microenvironment and Inflammation, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wei Xu
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Neurology, Ruijin Hospital, Zhoushan Branch, Shanghai Jiaotong University School of Medicine, Shanghai, China
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Ndukwe K, Serrano PA, Rockwell P, Xie L, Figueiredo-Pereira M. Histone deacetylase inhibitor RG2833 has therapeutic potential for Alzheimer's disease in females. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.573348. [PMID: 38234827 PMCID: PMC10793399 DOI: 10.1101/2023.12.26.573348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Nearly two-thirds of patients with Alzheimer's are women. Identifying therapeutics specific for women is critical to lowering their elevated risk for developing this major cause of adult dementia. Moreover, targeting epigenetic processes that regulate multiple cellular pathways is advantageous given Alzheimer's multifactorial nature. Histone acetylation is an epigenetic process heavily involved in memory consolidation. Its disruption is linked to Alzheimer's. Through our computational studies, we predicted that the investigational drug RG2833 (N-[6-(2-aminoanilino)-6-oxohexyl]-4-methylbenzamide) has repurposing potential for Alzheimer's. RG2833 is a histone deacetylase HDAC1/3 inhibitor that is orally bioavailable and permeates the blood-brain-barrier. We investigated the RG2833 therapeutic potential in TgF344-AD rats, which are a model of Alzheimer's that exhibits age-dependent progression, thus mimicking this aspect of Alzheimer's patients that is difficult to establish in animal models. We investigated the RG2833 effects on cognitive performance, gene expression, and AD-like pathology in 11-month TgF344-AD female and male rats. A total of 89 rats were used: wild type n = 45 (17 females, 28 males), and TgF344-AD n = 44 (24 females, 20 males)] across multiple cohorts. No obvious toxicity was detected in the TgF344-AD rats up to 6 months of RG2833-treatment starting at 5 months of age administering the drug in rodent chow at ∼30mg/kg of body weight. We started treatment early in the course of pathology when therapeutic intervention is predicted to be more effective than in later stages of the disease. The drug-treatment significantly mitigated hippocampal-dependent spatial memory deficits in 11-month TgF344-AD females but not in males, compared to wild type littermates. This female sex-specific drug effect has not been previously reported. RG2833-treatment failed to ameliorate amyloid beta accumulation and microgliosis in female and male TgF344-AD rats. However, RNAseq analysis of hippocampal tissue from TgF344-AD rats showed that drug-treatment in females upregulated the expression of immediate early genes, such as Arc, Egr1 and c-Fos, and other genes involved in synaptic plasticity and memory consolidation. Remarkably, out of 17,168 genes analyzed for each sex, no significant changes in gene expression were detected in males at P < 0.05, false discovery rate < 0.05, and fold-change ≥ 1.5. Our data suggest that histone modifying therapeutics such as RG2833 improve cognitive behavior by modulating the expression of immediate early, neuroprotective and synaptic plasticity genes. Our preclinical study supports that RG2833 has therapeutic potential specifically for female Alzheimer's patients. RG2833 evaluations using other AD-related models is necessary to confirm our findings.
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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23
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Li Z, Yin B, Zhang S, Lan Z, Zhang L. Targeting protein kinases for the treatment of Alzheimer's disease: Recent progress and future perspectives. Eur J Med Chem 2023; 261:115817. [PMID: 37722288 DOI: 10.1016/j.ejmech.2023.115817] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 09/02/2023] [Accepted: 09/11/2023] [Indexed: 09/20/2023]
Abstract
Alzheimer's disease (AD) is a serious neurodegenerative disease characterized by memory impairment, mental retardation, impaired motor balance, loss of self-care and even death. Among the complex and diverse pathological changes in AD, protein kinases are deeply involved in abnormal phosphorylation of Tau proteins to form intracellular neuronal fiber tangles, neuronal loss, extracellular β-amyloid (Aβ) deposits to form amyloid plaques, and synaptic disturbances. As a disease of the elderly, the growing geriatric population is directly driving the market demand for AD therapeutics, and protein kinases are potential targets for the future fight against AD. This perspective provides an in-depth look at the role of the major protein kinases (GSK-3β, CDK5, p38 MAPK, ERK1/2, and JNK3) in the pathogenesis of AD. At the same time, the development of different protein kinase inhibitors and the current state of clinical advancement are also outlined.
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Affiliation(s)
- Zhijia Li
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Bo Yin
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Shuangqian Zhang
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China
| | - Zhigang Lan
- Department of Neurosurgery, West China Hospital of Sichuan University, Chengdu, 610041, China.
| | - Lan Zhang
- Sichuan Engineering Research Center for Biomimetic Synthesis of Natural Drugs, School of Life Science and Engineering, Southwest Jiaotong University, Chengdu, 610031, China.
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24
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Majeed J, Sabbagh MN, Kang MH, Lawrence JJ, Pruitt K, Bacus S, Reyna E, Brown M, Decourt B. Cancer drugs with high repositioning potential for Alzheimer's disease. Expert Opin Emerg Drugs 2023; 28:311-332. [PMID: 38100555 PMCID: PMC10877737 DOI: 10.1080/14728214.2023.2296079] [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: 08/25/2023] [Accepted: 12/13/2023] [Indexed: 12/17/2023]
Abstract
INTRODUCTION Despite the recent full FDA approval of lecanemab, there is currently no disease modifying therapy (DMT) that can efficiently slow down the progression of Alzheimer's disease (AD) in the general population. This statement emphasizes the need to identify novel DMTs in the shortest time possible to prevent a global epidemic of AD cases as the world population experiences an increase in lifespan. AREAS COVERED Here, we review several classes of anti-cancer drugs that have been or are being investigated in Phase II/III clinical trials for AD, including immunomodulatory drugs, RXR agonists, sex hormone therapies, tyrosine kinase inhibitors, and monoclonal antibodies. EXPERT OPINION Given the overall course of brain pathologies during the progression of AD, we express a great enthusiasm for the repositioning of anti-cancer drugs as possible AD DMTs. We anticipate an increasing number of combinatorial therapy strategies to tackle AD symptoms and their underlying pathologies. However, we strongly encourage improvements in clinical trial study designs to better assess target engagement and possible efficacy over sufficient periods of drug exposure.
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Affiliation(s)
- Jad Majeed
- University of Arizona Honors College, Tucson, Arizona, USA
| | - Marwan N. Sabbagh
- Alzheimer’s and Memory Disorders Division, Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Min H. Kang
- Department of Pediatrics, Cancer Center, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
| | - J. Josh Lawrence
- Department of Pharmacology and Neuroscience, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
| | - Kevin Pruitt
- Department of Pharmacology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Ellie Reyna
- Department of Pharmacology and Neuroscience, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
| | - Maddy Brown
- Department of Pharmacology and Neuroscience, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
| | - Boris Decourt
- Department of Pharmacology and Neuroscience, School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA
- Roseman University of Health Sciences, Las Vegas, Nevada, USA
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25
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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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26
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Samantasinghar A, Ahmed F, Rahim CSA, Kim KH, Kim S, Choi KH. Artificial intelligence-assisted repurposing of lubiprostone alleviates tubulointerstitial fibrosis. Transl Res 2023; 262:75-88. [PMID: 37541485 DOI: 10.1016/j.trsl.2023.07.010] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/19/2023] [Accepted: 07/28/2023] [Indexed: 08/06/2023]
Abstract
Tubulointerstitial fibrosis (TIF) is the most prominent cause which leads to chronic kidney disease (CKD) and end-stage renal failure. Despite extensive research, there have been many clinical trial failures, and there is currently no effective treatment to cure renal fibrosis. This demonstrates the necessity of more effective therapies and better preclinical models to screen potential drugs for TIF. In this study, we investigated the antifibrotic effect of the machine learning-based repurposed drug, lubiprostone, validated through an advanced proximal tubule on a chip system and in vivo UUO mice model. Lubiprostone significantly downregulated TIF biomarkers including connective tissue growth factor (CTGF), extracellular matrix deposition (Fibronectin and collagen), transforming growth factor (TGF-β) downstream signaling markers especially, Smad-2/3, matrix metalloproteinase (MMP2/9), plasminogen activator inhibitor-1 (PAI-1), EMT and JAK/STAT-3 pathway expression in the proximal tubule on a chip model and UUO model compared to the conventional 2D culture. These findings suggest that the proximal tubule on a chip model is a more physiologically relevant model for studying and identifying potential biomarkers for fibrosis compared to conventional in vitro 2D culture and alternative of an animal model. In conclusion, the high throughput Proximal tubule-on-chip system shows improved in vivo-like function and indicates the potential utility for renal fibrosis drug screening. Additionally, repurposed Lubiprostone shows an effective potency to treat TIF via inhibiting 3 major profibrotic signaling pathways such as TGFβ/Smad, JAK/STAT, and epithelial-mesenchymal transition (EMT), and restores kidney function.
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Affiliation(s)
| | - Faheem Ahmed
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
| | | | | | - Sejoong Kim
- Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea; Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hyun Choi
- Department of Mechatronics Engineering, Jeju National University, Republic of Korea.
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27
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Shiwani T, Relton S, Evans R, Kale A, Heaven A, Clegg A, Todd O. New Horizons in artificial intelligence in the healthcare of older people. Age Ageing 2023; 52:afad219. [PMID: 38124256 PMCID: PMC10733173 DOI: 10.1093/ageing/afad219] [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: 05/12/2023] [Indexed: 12/23/2023] Open
Abstract
Artificial intelligence (AI) in healthcare describes algorithm-based computational techniques which manage and analyse large datasets to make inferences and predictions. There are many potential applications of AI in the care of older people, from clinical decision support systems that can support identification of delirium from clinical records to wearable devices that can predict the risk of a fall. We held four meetings of older people, clinicians and AI researchers. Three priority areas were identified for AI application in the care of older people. These included: monitoring and early diagnosis of disease, stratified care and care coordination between healthcare providers. However, the meetings also highlighted concerns that AI may exacerbate health inequity for older people through bias within AI models, lack of external validation amongst older people, infringements on privacy and autonomy, insufficient transparency of AI models and lack of safeguarding for errors. Creating effective interventions for older people requires a person-centred approach to account for the needs of older people, as well as sufficient clinical and technological governance to meet standards of generalisability, transparency and effectiveness. Education of clinicians and patients is also needed to ensure appropriate use of AI technologies, with investment in technological infrastructure required to ensure equity of access.
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Affiliation(s)
- Taha Shiwani
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Samuel Relton
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Ruth Evans
- Leeds Institute of Health Sciences, University of Leeds, Leeds, UK
| | - Aditya Kale
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Anne Heaven
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Andrew Clegg
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
| | - Oliver Todd
- Academic Unit for Ageing & Stroke Research, Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Duckworth Lane, Bradford, West Yorkshire BD9 6RJ, UK
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28
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Bucholc M, James C, Khleifat AA, Badhwar A, Clarke N, Dehsarvi A, Madan CR, Marzi SJ, Shand C, Schilder BM, Tamburin S, Tantiangco HM, Lourida I, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia research methods optimization. Alzheimers Dement 2023; 19:5934-5951. [PMID: 37639369 DOI: 10.1002/alz.13441] [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: 04/03/2023] [Revised: 07/19/2023] [Accepted: 07/23/2023] [Indexed: 08/31/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
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Affiliation(s)
- Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK
| | - Charlotte James
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Ahmad Al Khleifat
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
- Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada
- Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada
| | - Natasha Clarke
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Amir Dehsarvi
- Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK
| | | | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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29
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Ali M, Park IH, Kim J, Kim G, Oh J, You JS, Kim J, Shin JS, Yoon SS. How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors. Biomedicines 2023; 11:3134. [PMID: 38137356 PMCID: PMC10740425 DOI: 10.3390/biomedicines11123134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/15/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
The integration of artificial intelligence (AI) into drug discovery has markedly advanced the search for effective therapeutics. In our study, we employed a comprehensive computational-experimental approach to identify potential anti-SARS-CoV-2 compounds. We developed a predictive model to assess the activities of compounds based on their structural features. This model screened a library of approximately 700,000 compounds, culminating in the selection of the top 100 candidates for experimental validation. In vitro assays on human intestinal epithelial cells (Caco-2) revealed that 19 of these compounds exhibited inhibitory activity. Notably, eight compounds demonstrated dose-dependent activity in Vero cell lines, with half-maximal effective concentration (EC50) values ranging from 1 μM to 7 μM. Furthermore, we utilized a clustering approach to pinpoint potential nucleoside analog inhibitors, leading to the discovery of two promising candidates: azathioprine and its metabolite, thioinosinic acid. Both compounds showed in vitro activity against SARS-CoV-2, with thioinosinic acid also significantly reducing viral loads in mouse lungs. These findings underscore the utility of AI in accelerating drug discovery processes.
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Affiliation(s)
- Mohammed Ali
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - In Ho Park
- Department of Biomedical Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea;
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Junebeom Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Gwanghee Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jooyeon Oh
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jin Sun You
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jieun Kim
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Jeon-Soo Shin
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Sang Sun Yoon
- Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea; (M.A.); (J.K.); (G.K.); (J.O.); (J.S.Y.); (J.K.)
- Brain Korea 21 Project for Medical Sciences, Department of Microbiology and Immunology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- Institute of Immunology and Immunological Diseases, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
- BioMe Inc., Seoul 02455, Republic of Korea
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30
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Jensen LT, Attfield KE, Feldmann M, Fugger L. Allosteric TYK2 inhibition: redefining autoimmune disease therapy beyond JAK1-3 inhibitors. EBioMedicine 2023; 97:104840. [PMID: 37863021 PMCID: PMC10589750 DOI: 10.1016/j.ebiom.2023.104840] [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: 08/14/2023] [Revised: 09/28/2023] [Accepted: 10/05/2023] [Indexed: 10/22/2023] Open
Abstract
JAK inhibitors impact multiple cytokine pathways simultaneously, enabling high efficacy in treating complex diseases such as cancers and immune-mediated disorders. However, their broad reach also poses safety concerns, which have fuelled a demand for increasingly selective JAK inhibitors. Deucravacitinib, a first-in-class allosteric TYK2 inhibitor, represents a remarkable advancement in the field. Rather than competing at kinase domain catalytic sites as classical JAK1-3 inhibitors, deucravacitinib targets the regulatory pseudokinase domain of TYK2. It strikingly mirrors the functional effect of an evolutionary conserved naturally occurring TYK2 variant, P1104A, known to protect against multiple autoimmune diseases yet provide sufficient TYK2-mediated cytokine signalling required to prevent immune deficiency. The unprecedentedly high functional selectivity and efficacy-safety profile of deucravacitinib, initially demonstrated in psoriasis, combined with genetic support, and promising outcomes in early SLE clinical trials make this inhibitor ripe for exploration in other autoimmune diseases for which better, safe, and efficacious treatments are urgently needed.
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Affiliation(s)
- Lise Torp Jensen
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus 8200, Denmark
| | - Kathrine E Attfield
- Nuffield Department of Clinical Neurosciences, Oxford Centre for Neuroinflammation, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Marc Feldmann
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, The Kennedy Institute for Rheumatology, Botnar Research Institute, University of Oxford, Oxford OX3 7LD, UK
| | - Lars Fugger
- Department of Clinical Medicine, Aarhus University Hospital, Aarhus 8200, Denmark; Nuffield Department of Clinical Neurosciences, Oxford Centre for Neuroinflammation, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; MRC Human Immunology Unit, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DS, UK.
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31
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Akkaya UM, Kalkan H. A New Approach for Multimodal Usage of Gene Expression and Its Image Representation for the Detection of Alzheimer's Disease. Biomolecules 2023; 13:1563. [PMID: 38002245 PMCID: PMC10669658 DOI: 10.3390/biom13111563] [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: 09/09/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 11/26/2023] Open
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder and the multifaceted nature of it requires innovative approaches that integrate various data modalities to enhance its detection. However, due to the cost of collecting multimodal data, multimodal datasets suffer from an insufficient number of samples. To mitigate the impact of a limited sample size on classification, we introduce a novel deep learning method (One2MFusion) which combines gene expression data with their corresponding 2D representation as a new modality. The gene vectors were first mapped to a discriminative 2D image for training a convolutional neural network (CNN). In parallel, the gene sequences were used to train a feed forward neural network (FNN) and the outputs of the FNN and CNN were merged, and a joint deep network was trained for the binary classification of AD, normal control (NC), and mild cognitive impairment (MCI) samples. The fusion of the gene expression data and gene-originated 2D image increased the accuracy (area under the curve) from 0.86 (obtained using a 2D image) to 0.91 for AD vs. NC and from 0.76 (obtained using a 2D image) to 0.88 for MCI vs. NC. The results show that representing gene expression data in another discriminative form increases the classification accuracy when fused with base data.
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Affiliation(s)
| | - Habil Kalkan
- Department of Computer Engineering, Gebze Technical University, 41400 Gebze, Turkey;
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Munteanu C, Iordan DA, Hoteteu M, Popescu C, Postoiu R, Onu I, Onose G. Mechanistic Intimate Insights into the Role of Hydrogen Sulfide in Alzheimer's Disease: A Recent Systematic Review. Int J Mol Sci 2023; 24:15481. [PMID: 37895161 PMCID: PMC10607039 DOI: 10.3390/ijms242015481] [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: 09/22/2023] [Revised: 10/15/2023] [Accepted: 10/21/2023] [Indexed: 10/29/2023] Open
Abstract
In the rapidly evolving field of Alzheimer's Disease (AD) research, the intricate role of Hydrogen Sulfide (H2S) has garnered critical attention for its diverse involvement in both pathological substrates and prospective therapeutic paradigms. While conventional pathophysiological models of AD have primarily emphasized the significance of amyloid-beta (Aβ) deposition and tau protein hyperphosphorylation, this targeted systematic review meticulously aggregates and rigorously appraises seminal contributions from the past year elucidating the complex mechanisms of H2S in AD pathogenesis. Current scholarly literature accentuates H2S's dual role, delineating its regulatory functions in critical cellular processes-such as neurotransmission, inflammation, and oxidative stress homeostasis-while concurrently highlighting its disruptive impact on quintessential AD biomarkers. Moreover, this review illuminates the nuanced mechanistic intimate interactions of H2S in cerebrovascular and cardiovascular pathology associated with AD, thereby exploring avant-garde therapeutic modalities, including sulfurous mineral water inhalations and mud therapy. By emphasizing the potential for therapeutic modulation of H2S via both donors and inhibitors, this review accentuates the imperative for future research endeavors to deepen our understanding, thereby potentially advancing novel diagnostic and therapeutic strategies in AD.
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Affiliation(s)
- Constantin Munteanu
- Faculty of Medical Bioengineering, University of Medicine and Pharmacy “Grigore T. Popa”, 700454 Iași, Romania;
- Teaching Emergency Hospital “Bagdasar-Arseni” (TEHBA), 041915 Bucharest, Romania; (M.H.); (R.P.); (G.O.)
| | - Daniel Andrei Iordan
- Department of Individual Sports and Kinetotherapy, Faculty of Physical Education and Sport, ‘Dunarea de Jos’ University of Galati, 800008 Galati, Romania;
| | - Mihail Hoteteu
- Teaching Emergency Hospital “Bagdasar-Arseni” (TEHBA), 041915 Bucharest, Romania; (M.H.); (R.P.); (G.O.)
| | - Cristina Popescu
- Teaching Emergency Hospital “Bagdasar-Arseni” (TEHBA), 041915 Bucharest, Romania; (M.H.); (R.P.); (G.O.)
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila” (UMPCD), 020022 Bucharest, Romania
| | - Ruxandra Postoiu
- Teaching Emergency Hospital “Bagdasar-Arseni” (TEHBA), 041915 Bucharest, Romania; (M.H.); (R.P.); (G.O.)
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila” (UMPCD), 020022 Bucharest, Romania
| | - Ilie Onu
- Faculty of Medical Bioengineering, University of Medicine and Pharmacy “Grigore T. Popa”, 700454 Iași, Romania;
| | - Gelu Onose
- Teaching Emergency Hospital “Bagdasar-Arseni” (TEHBA), 041915 Bucharest, Romania; (M.H.); (R.P.); (G.O.)
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila” (UMPCD), 020022 Bucharest, Romania
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Hannawi Y. Cerebral Small Vessel Disease: a Review of the Pathophysiological Mechanisms. Transl Stroke Res 2023:10.1007/s12975-023-01195-9. [PMID: 37864643 DOI: 10.1007/s12975-023-01195-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/02/2023] [Accepted: 09/18/2023] [Indexed: 10/23/2023]
Abstract
Cerebral small vessel disease (cSVD) refers to the age-dependent pathological processes involving the brain small vessels and leading to vascular cognitive impairment, intracerebral hemorrhage, and acute lacunar ischemic stroke. Despite the significant public health burden of cSVD, disease-specific therapeutics remain unavailable due to the incomplete understanding of the underlying pathophysiological mechanisms. Recent advances in neuroimaging acquisition and processing capabilities as well as findings from cSVD animal models have revealed critical roles of several age-dependent processes in cSVD pathogenesis including arterial stiffness, vascular oxidative stress, low-grade systemic inflammation, gut dysbiosis, and increased salt intake. These factors interact to cause a state of endothelial cell dysfunction impairing cerebral blood flow regulation and breaking the blood brain barrier. Neuroinflammation follows resulting in neuronal injury and cSVD clinical manifestations. Impairment of the cerebral waste clearance through the glymphatic system is another potential process that has been recently highlighted contributing to the cognitive decline. This review details these mechanisms and attempts to explain their complex interactions. In addition, the relevant knowledge gaps in cSVD mechanistic understanding are identified and a systematic approach to future translational and early phase clinical research is proposed in order to reveal new cSVD mechanisms and develop disease-specific therapeutics.
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Affiliation(s)
- Yousef Hannawi
- Division of Cerebrovascular Diseases and Neurocritical Care, Department of Neurology, The Ohio State University, 333 West 10th Ave, Graves Hall 3172C, Columbus, OH, 43210, USA.
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Hill J, Shalaby KE, Bihaqi SW, Alansi BH, Barlock B, Parang K, Thompson R, Ouararhni K, Zawia NH. Tolfenamic Acid Derivatives: A New Class of Transcriptional Modulators with Potential Therapeutic Applications for Alzheimer's Disease and Related Disorders. Int J Mol Sci 2023; 24:15216. [PMID: 37894896 PMCID: PMC10607430 DOI: 10.3390/ijms242015216] [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: 09/19/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
The field of Alzheimer's disease (AD) has witnessed recent breakthroughs in the development of disease-modifying biologics and diagnostic markers. While immunotherapeutic interventions have provided much-awaited solutions, nucleic acid-based tools represent other avenues of intervention; however, these approaches are costly and invasive, and they have serious side effects. Previously, we have shown in AD animal models that tolfenamic acid (TA) can lower the expression of AD-related genes and their products and subsequently reduce pathological burden and improve cognition. Using TA as a scaffold and the zinc finger domain of SP1 as a pharmacophore, we developed safer and more potent brain-penetrating analogs that interfere with sequence-specific DNA binding at transcription start sites and predominantly modulate the expression of SP1 target genes. More importantly, the proteome of treated cells displayed ~75% of the downregulated products as SP1 targets. Specific levels of SP1-driven genes and AD biomarkers such as amyloid precursor protein (APP) and Tau proteins were also decreased as part of this targeted systemic response. These small molecules, therefore, offer a viable alternative to achieving desired therapeutic outcomes by interfering with both amyloid and Tau pathways with limited off-target systemic changes.
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Affiliation(s)
- Jaunetta Hill
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA; (J.H.); (S.W.B.); (B.H.A.); (B.B.)
| | - Karim E. Shalaby
- Neurological Disorder Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar; (K.E.S.); (K.O.)
| | - Syed W. Bihaqi
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA; (J.H.); (S.W.B.); (B.H.A.); (B.B.)
| | - Bothaina H. Alansi
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA; (J.H.); (S.W.B.); (B.H.A.); (B.B.)
| | - Benjamin Barlock
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA; (J.H.); (S.W.B.); (B.H.A.); (B.B.)
| | - Keykavous Parang
- Center for Targeted Drug Delivery, Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Harry and Diane Rinker Health Science Campus, Irvine, CA 92618, USA;
| | - Richard Thompson
- Neurological Disorder Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar; (K.E.S.); (K.O.)
| | - Khalid Ouararhni
- Neurological Disorder Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar; (K.E.S.); (K.O.)
| | - Nasser H. Zawia
- Department of Biomedical and Pharmaceutical Sciences, College of Pharmacy, University of Rhode Island, Kingston, RI 02881, USA; (J.H.); (S.W.B.); (B.H.A.); (B.B.)
- Neurological Disorder Research Center, Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar; (K.E.S.); (K.O.)
- George and Anne Ryan Institute for Neuroscience, University of Rhode Island, Kingston, RI 02881, USA
- Interdisciplinary Neuroscience Program, University of Rhode Island, Kingston, RI 02881, USA
- Biological and Biomedical Sciences Division, College of Health & Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation, Doha 34110, Qatar
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Wei H, Wu C, Yuan Y, Lai L. Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer's disease. Front Aging Neurosci 2023; 15:1249682. [PMID: 37799623 PMCID: PMC10548137 DOI: 10.3389/fnagi.2023.1249682] [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: 06/29/2023] [Accepted: 08/30/2023] [Indexed: 10/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD. Methods In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets. Results Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD. Conclusion This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.
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Affiliation(s)
- Huangwei Wei
- Department of Neurology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Chunle Wu
- Department of Blood Transfusion, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yulin Yuan
- Department of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Lichuan Lai
- Department of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Chakkittukandiyil A, Chakraborty S, Kothandan R, Rymbai E, Muthu SK, Vasu S, Sajini DV, Sugumar D, Mohammad ZB, Jayaram S, Rajagopal K, Ramachandran V, Selvaraj D. Side effects based network construction and drug repositioning of ropinirole as a potential molecule for Alzheimer's disease: an in-silico, in-vitro, and in-vivo study. J Biomol Struct Dyn 2023:1-15. [PMID: 37723871 DOI: 10.1080/07391102.2023.2258968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 09/08/2023] [Indexed: 09/20/2023]
Abstract
Alzheimer's disease (AD) is the leading cause of dementia in older adults. Drug repositioning is a process of finding new therapeutic applications for existing drugs. One of the methods in drug repositioning is to use the side-effect profile of a drug to identify a new therapeutic indication. The drugs with similar side-effects may act on similar biological targets and could affect the same biochemical process. In this study, we explored the Food and Drug Administration-approved drugs using PROMISCUOUS database to find those that have adverse effects profile comparable with the ligands being studied or used to treat AD. Here, we found that the ropinirole, a dopamine receptor agonist, shared a maximum number of side-effects with the drugs proven beneficial for treating AD. Furthermore, molecular modelling demonstrated that ropinirole exhibited strong binding affinity (-9.313 kcal/mol) and best ligand efficiency (0.49) with sigma-1 receptor. Here, we observed that the quaternary amino group of ropinirole is essential for binding with sigma-1 receptor. Molecular dynamic simulation indicated that the movement of the carboxy-terminal helices (α4/α5) could play a major role in the receptor's physiological functions. The neurotoxicity induced by Aβ25-35 in SH-SY5Y cells was reduced by ropinirole at concentrations 10, 30, and 50 µM. The effect on spatial learning and memory was examined in mice with Aβ25-35 induced memory deficit using the radial arm maze. Ropinirole (10 and 20 mg/kg) significantly improved the short and long-term memories in the radial arm maze test. Our results suggest that ropinirole has the potential to be repositioned for AD treatment.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Amritha Chakkittukandiyil
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Saurav Chakraborty
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Ram Kothandan
- Bioinformatics Laboratory, Department of Biotechnology, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India
| | - Emdormi Rymbai
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Santhosh Kumar Muthu
- Department of Biochemistry, Kongunadu Arts and Science College, GN Mills, Coimbatore, Tamil Nadu, India
| | - Soumya Vasu
- Department of Pharmaceutical Chemistry, Sri Ramachandra Institute of Higher Education & Research, Porur, Chennai, Tamil Nadu, India
| | - Deepak Vasudevan Sajini
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Deepa Sugumar
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Zubair Baba Mohammad
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Saravanan Jayaram
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Kalirajan Rajagopal
- Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Vadivelan Ramachandran
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
| | - Divakar Selvaraj
- Department of Pharmacology, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Ooty, Nilgiris, Tamil Nadu, India
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Umar AK, Roy D, Abdalla M, Modafer Y, Al-Hoshani N, Yu H, Zothantluanga JH. In-silico screening of Acacia pennata and Bridelia retusa reveals pinocembrin-7-O-β-D-glucopyranoside as a promising β-lactamase inhibitor to combat antibiotic resistance. J Biomol Struct Dyn 2023:1-13. [PMID: 37587843 DOI: 10.1080/07391102.2023.2248272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023]
Abstract
The β-lactamase of Pseudomonas aeruginosa is known to degrade β-lactam antibiotics such as penicillins, cephalosporins, monobactams, and carbapenems. With the discovery of an extended-spectrum β-lactamase in a clinical isolate of P. aeruginosa, the bacterium has become multi-drug resistant. In this study, we aim to identify new β-lactamase inhibitors by virtually screening a total of 43 phytocompounds from two Indian medicinal plants. In the molecular docking studies, pinocembrin-7-O-β-D-glucopyranoside (P7G) (-9.6 kcal/mol) from Acacia pennata and ellagic acid (EA) (-9.2 kcal/mol) from Bridelia retusa had lower binding energy than moxalactam (-8.4 kcal/mol). P7G and EA formed 5 (Ser62, Asn125, Asn163, Thr209, and Ser230) and 4 (Lys65, Ser123, Asn125, and Glu159) conventional hydrogens bonds with the active site residues. 100 ns MD simulations revealed that moxalactam and P7G (but not EA) were able to form a stable complex. The binding free energy calculations further revealed that P7G (-59.6526 kcal/mol) formed the most stable complex with β-lactamase when compared to moxalactam (-46.5669 kcal/mol) and EA (-28.4505 kcal/mol). The HOMO-LUMO and other DFT parameters support the stability and chemical reactivity of P7G at the active site of β-lactamase. P7G passed all the toxicity tests and bioavailability tests indicating that it possesses drug-likeness. Among the studied compounds, we identified P7G of A. pennata as the most promising phytocompound to combat antibiotic resistance by potentially inhibiting the β-lactamase of P. aeruginosa.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abd Kakhar Umar
- Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy, Universitas Padjadjaran, Sumedang, Indonesia
| | - Dhritiman Roy
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, Assam, India
| | - Mohnad Abdalla
- Pediatric Research Institute, Children's Hospital Affiliated to Shandong University, Jinan, China
| | - Yosra Modafer
- Department of Biology, Faculty of Science, Jazan University, Jazan, Saudi Arabia
| | - Nawal Al-Hoshani
- Department of Biology, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Han Yu
- Pediatric Research Institute, Children's Hospital Affiliated to Shandong University, Jinan, China
- Department of Computational Biology, School of Life Sciences, Fudan University, Shanghai, China
| | - James H Zothantluanga
- Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University, Dibrugarh, Assam, India
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Nariya MK, Mills CE, Sorger PK, Sokolov A. Paired evaluation of machine-learning models characterizes effects of confounders and outliers. PATTERNS (NEW YORK, N.Y.) 2023; 4:100791. [PMID: 37602225 PMCID: PMC10435952 DOI: 10.1016/j.patter.2023.100791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/27/2023] [Accepted: 06/08/2023] [Indexed: 08/22/2023]
Abstract
The true accuracy of a machine-learning model is a population-level statistic that cannot be observed directly. In practice, predictor performance is estimated against one or more test datasets, and the accuracy of this estimate strongly depends on how well the test sets represent all possible unseen datasets. Here we describe paired evaluation as a simple, robust approach for evaluating performance of machine-learning models in small-sample biological and clinical studies. We use the method to evaluate predictors of drug response in breast cancer cell lines and of disease severity in patients with Alzheimer's disease, demonstrating that the choice of test data can cause estimates of performance to vary by as much as 20%. We show that paired evaluation makes it possible to identify outliers, improve the accuracy of performance estimates in the presence of known confounders, and assign statistical significance when comparing machine-learning models.
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Affiliation(s)
- Maulik K. Nariya
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin E. Mills
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Artem Sokolov
- Laboratory of Systems Pharmacology, Harvard Program in Therapeutic Science, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
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Song N, Sun S, Chen K, Wang Y, Wang H, Meng J, Guo M, Zhang XD, Zhang R. Emerging nanotechnology for Alzheimer's disease: From detection to treatment. J Control Release 2023; 360:392-417. [PMID: 37414222 DOI: 10.1016/j.jconrel.2023.07.004] [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: 03/03/2023] [Revised: 06/15/2023] [Accepted: 07/03/2023] [Indexed: 07/08/2023]
Abstract
Alzheimer's disease (AD), one of the most common chronic neurodegenerative diseases, is characterized by memory impairment, synaptic dysfunction, and character mutations. The pathological features of AD are Aβ accumulation, tau protein enrichment, oxidative stress, and immune inflammation. Since the pathogenesis of AD is complicated and ambiguous, it is still challenging to achieve early detection and timely treatment of AD. Due to the unique physical, electrical, magnetic, and optical properties of nanoparticles (NPs), nanotechnology has shown great potential for detecting and treating AD. This review provides an overview of the latest developments in AD detection via nanotechnology based on NPs with electrochemical sensing, optical sensing, and imaging techniques. Meanwhile, we highlight the important advances in nanotechnology-based AD treatment through targeting disease biomarkers, stem-cell therapy and immunotherapy. Furthermore, we summarize the current challenges and present a promising prospect for nanotechnology-based AD diagnosis and intervention.
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Affiliation(s)
- Nan Song
- Department of Physics, School of Science, Tianjin Chengjian University, Tianjin 300384, China
| | - Si Sun
- Department of Physics and Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, School of Sciences, Tianjin University, Tianjin 300350, China
| | - Ke Chen
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Yang Wang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Hao Wang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
| | - Jian Meng
- The First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China
| | - Meili Guo
- Department of Physics, School of Science, Tianjin Chengjian University, Tianjin 300384, China.
| | - Xiao-Dong Zhang
- Department of Physics and Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, School of Sciences, Tianjin University, Tianjin 300350, China; Tianjin Key Laboratory of Brain Science and Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.
| | - Ruiping Zhang
- The First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
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Gao Z, Winhusen TJ, Gorenflo M, Ghitza UE, Davis PB, Kaelber DC, Xu R. Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses. Addiction 2023; 118:1307-1319. [PMID: 36792381 PMCID: PMC10631254 DOI: 10.1111/add.16168] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/25/2023] [Indexed: 02/17/2023]
Abstract
BACKGROUND AND AIMS Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment. DESIGN Our drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD. SETTING The study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases. PARTICIPANTS A total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching. MEASUREMENTS EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription. FINDINGS Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence. CONCLUSIONS Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.
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Affiliation(s)
- Zhenxiang Gao
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - T. John Winhusen
- Center for Addiction Research, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Maria Gorenflo
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Udi E. Ghitza
- Center for the Clinical Trials Network (CCTN), National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Pamela B. Davis
- Center for Community Health Integration, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - David C. Kaelber
- Center for Clinical Informatics Research and Education, The Metro Health System, Cleveland, OH, USA
| | - Rong Xu
- Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
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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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42
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Hu E, Li TS, Wineinger NE, Su AI. Association study between drug prescriptions and Alzheimer's disease claims in a commercial insurance database. Alzheimers Res Ther 2023; 15:118. [PMID: 37355615 PMCID: PMC10290352 DOI: 10.1186/s13195-023-01255-0] [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: 08/01/2022] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
In the ongoing effort to discover treatments for Alzheimer's disease (AD), there has been considerable focus on investigating the use of repurposed drug candidates. Mining of electronic health record data has the potential to identify novel correlated effects between commonly used drugs and AD. In this study, claims from members with commercial health insurance coverage were analyzed to determine the correlation between the use of various drugs on AD incidence and claim frequency. We found that, within the insured population, several medications for psychotic and mental illnesses were associated with higher disease incidence and frequency, while, to a lesser extent, antibiotics and anti-inflammatory drugs were associated with lower AD incidence rates. The observations thus provide a general overview of the prescription and claim relationships between various drug types and Alzheimer's disease, with insights into which drugs have possible implications on resulting AD diagnosis.
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Affiliation(s)
- Eric Hu
- Integrative Structural and Computational Biology, Scripps Research Institute, 10550 North Torrey Pines Rd, La Jolla, CA 92037 USA
| | - Tong Shu Li
- Integrative Structural and Computational Biology, Scripps Research Institute, 10550 North Torrey Pines Rd, La Jolla, CA 92037 USA
| | | | - Andrew I. Su
- Integrative Structural and Computational Biology, Scripps Research Institute, 10550 North Torrey Pines Rd, La Jolla, CA 92037 USA
- Present Address: Scripps Research Translational Institute, La Jolla, CA 92037 USA
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43
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Smer-Barreto V, Quintanilla A, Elliott RJR, Dawson JC, Sun J, Campa VM, Lorente-Macías Á, Unciti-Broceta A, Carragher NO, Acosta JC, Oyarzún DA. Discovery of senolytics using machine learning. Nat Commun 2023; 14:3445. [PMID: 37301862 PMCID: PMC10257182 DOI: 10.1038/s41467-023-39120-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 05/31/2023] [Indexed: 06/12/2023] Open
Abstract
Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.
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Affiliation(s)
- Vanessa Smer-Barreto
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK.
| | - Andrea Quintanilla
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
| | - Richard J R Elliott
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - John C Dawson
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Jiugeng Sun
- School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK
| | - Víctor M Campa
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
| | - Álvaro Lorente-Macías
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Asier Unciti-Broceta
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
| | - Juan Carlos Acosta
- Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK.
- Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain.
| | - Diego A Oyarzún
- School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK.
- School of Biological Sciences, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK.
- The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK.
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44
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Niu Y, Lin P. Advances of computer-aided drug design (CADD) in the development of anti-Azheimer's-disease drugs. Drug Discov Today 2023:103665. [PMID: 37302540 DOI: 10.1016/j.drudis.2023.103665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/04/2023] [Accepted: 06/06/2023] [Indexed: 06/13/2023]
Abstract
Alzheimer's disease (AD) is a degenerative disease of the nervous system that progressively destroys memory and thinking skills. Currently there is no treatment to prevent or cure AD; targeting the direct cause of neuronal degeneration would constitute a rational strategy and hopefully offer better options for the treatment of AD. This paper first summarizes the physiological and pathological pathogenesis of AD and then discusses the representative drug candidates for targeted therapy of AD and their binding mode with their targets. Finally, the applications of computer-aided drug design in discovering anti-AD drugs are reviewed. Teaser.
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Affiliation(s)
- Yuzhen Niu
- Weifang University of Science and Technology, Weifang, 262700, China
| | - Ping Lin
- Weifang University of Science and Technology, Weifang, 262700, China; Institute of modern physics, Chinese Academy of Science, Lanzhou 730000, China.
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45
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Aleksandrova Y, Munkuev A, Mozhaitsev E, Suslov E, Tsypyshev D, Chaprov K, Begunov R, Volcho K, Salakhutdinov N, Neganova M. Elaboration of the Effective Multi-Target Therapeutic Platform for the Treatment of Alzheimer's Disease Based on Novel Monoterpene-Derived Hydroxamic Acids. Int J Mol Sci 2023; 24:ijms24119743. [PMID: 37298694 DOI: 10.3390/ijms24119743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/12/2023] Open
Abstract
Novel monoterpene-based hydroxamic acids of two structural types were synthesized for the first time. The first type consisted of compounds with a hydroxamate group directly bound to acyclic, monocyclic and bicyclic monoterpene scaffolds. The second type included hydroxamic acids connected with the monoterpene moiety through aliphatic (hexa/heptamethylene) or aromatic linkers. An in vitro analysis of biological activity demonstrated that some of these molecules had powerful HDAC6 inhibitory activity, with the presence of a linker area in the structure of compounds playing a key role. In particular, it was found that hydroxamic acids containing a hexa- and heptamethylene linker and (-)-perill fragment in the Cap group exhibit excellent inhibitory activity against HDAC6 with IC50 in the submicromolar range from 0.56 ± 0.01 µM to 0.74 ± 0.02 µM. The results of the study of antiradical activity demonstrated the presence of moderate ability for some hydroxamic acids to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2ROO• radicals. The correlation coefficient between the DPPH radical scavenging activity and oxygen radical absorbance capacity (ORAC) value was R2 = 0.8400. In addition, compounds with an aromatic linker based on para-substituted cinnamic acids, having a monocyclic para-menthene skeleton as a Cap group, 35a, 38a, 35b and 38b, demonstrated a significant ability to suppress the aggregation of the pathological β-amyloid peptide 1-42. The 35a lead compound with a promising profile of biological activity, discovered in the in vitro experiments, demonstrated neuroprotective effects on in vivo models of Alzheimer's disease using 5xFAD transgenic mice. Together, the results obtained demonstrate a potential strategy for the use of monoterpene-derived hydroxamic acids for treatment of various aspects of Alzheimer's disease.
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Affiliation(s)
- Yulia Aleksandrova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severnij Pr. 1, Chernogolovka 142432, Russia
| | - Aldar Munkuev
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Evgenii Mozhaitsev
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Evgenii Suslov
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Dmitry Tsypyshev
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Kirill Chaprov
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severnij Pr. 1, Chernogolovka 142432, Russia
| | - Roman Begunov
- Biology and Ecology Faculty of P. G. Demidov Yaroslavl State University, Matrosova Ave., 9, Yaroslavl 150003, Russia
| | - Konstantin Volcho
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Nariman Salakhutdinov
- Department of Medicinal Chemistry, N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Siberian Branch, Russian Academy of Sciences, Lavrentiev Ave., 9, Novosibirsk 630090, Russia
| | - Margarita Neganova
- Institute of Physiologically Active Compounds at Federal Research Center of Problems of Chemical Physics and Medicinal Chemistry, Russian Academy of Sciences, Severnij Pr. 1, Chernogolovka 142432, Russia
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46
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Melchiorri D, Merlo S, Micallef B, Borg JJ, Dráfi F. Alzheimer's disease and neuroinflammation: will new drugs in clinical trials pave the way to a multi-target therapy? Front Pharmacol 2023; 14:1196413. [PMID: 37332353 PMCID: PMC10272781 DOI: 10.3389/fphar.2023.1196413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/02/2023] [Indexed: 06/20/2023] Open
Abstract
Despite extensive research, no disease-modifying therapeutic option, able to prevent, cure or halt the progression of Alzheimer's disease [AD], is currently available. AD, a devastating neurodegenerative pathology leading to dementia and death, is characterized by two pathological hallmarks, the extracellular deposits of amyloid beta (Aβ) and the intraneuronal deposits of neurofibrillary tangles (NFTs) consisting of altered hyperphosphorylated tau protein. Both have been widely studied and pharmacologically targeted for many years, without significant therapeutic results. In 2022, positive data on two monoclonal antibodies targeting Aβ, donanemab and lecanemab, followed by the 2023 FDA accelerated approval of lecanemab and the publication of the final results of the phase III Clarity AD study, have strengthened the hypothesis of a causal role of Aβ in the pathogenesis of AD. However, the magnitude of the clinical effect elicited by the two drugs is limited, suggesting that additional pathological mechanisms may contribute to the disease. Cumulative studies have shown inflammation as one of the main contributors to the pathogenesis of AD, leading to the recognition of a specific role of neuroinflammation synergic with the Aβ and NFTs cascades. The present review provides an overview of the investigational drugs targeting neuroinflammation that are currently in clinical trials. Moreover, their mechanisms of action, their positioning in the pathological cascade of events that occur in the brain throughout AD disease and their potential benefit/limitation in the therapeutic strategy in AD are discussed and highlighted as well. In addition, the latest patent requests for inflammation-targeting therapeutics to be developed in AD will also be discussed.
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Affiliation(s)
- Daniela Melchiorri
- Department of Physiology and Pharmacology, Sapienza University, Rome, Italy
| | - Sara Merlo
- Department of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, Catania, Italy
| | | | - John-Joseph Borg
- Malta Medicines Authority, San Ġwann, Malta
- School of Pharmacy, Department of Biology, University of Tor Vergata, Rome, Italy
| | - František Dráfi
- Institute of Experimental Pharmacology and Toxicology, Centre of Experimental Medicine SAS Bratislava, Bratislava, Slovakia
- State Institute for Drug Control, Bratislava, Slovakia
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47
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Pang W, Hu F. C9ORF72 suppresses JAK-STAT mediated inflammation. iScience 2023; 26:106579. [PMID: 37250330 PMCID: PMC10214391 DOI: 10.1016/j.isci.2023.106579] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/31/2023] [Accepted: 03/31/2023] [Indexed: 05/31/2023] Open
Abstract
Hexanucleotide repeat expansion in the gene C9ORF72 is a leading cause of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD). C9ORF72 deficiency leads to severe inflammatory phenotypes in mice, but exactly how C9ORF72 regulates inflammation remains to be fully elucidated. Here, we report that loss of C9ORF72 leads to the hyperactivation of the JAK-STAT pathway and an increase in the protein levels of STING, a transmembrane adaptor protein involved in immune signaling in response to cytosolic DNA. Treatment with a JAK inhibitor rescues the enhanced inflammatory phenotypes caused by C9ORF72 deficiency in cell culture and mice. Furthermore, we showed that the ablation of C9ORF72 results in compromised lysosome integrity, which could contribute to the activation of the JAK/STAT-dependent inflammatory responses. In summary, our study identifies a mechanism by which C9ORF72 regulates inflammation, which might facilitate therapeutic development for ALS/FTLD with C9ORF72 mutations.
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Affiliation(s)
- Weilun Pang
- Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
| | - Fenghua Hu
- Department of Molecular Biology and Genetics, Weill Institute for Cell and Molecular Biology, Cornell University, Ithaca, NY 14853, USA
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48
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Bu Y, Gao R, Zhang B, Zhang L, Sun D. CoGT: Ensemble Machine Learning Method and Its Application on JAK Inhibitor Discovery. ACS OMEGA 2023; 8:13232-13242. [PMID: 37065046 PMCID: PMC10099439 DOI: 10.1021/acsomega.3c00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 03/16/2023] [Indexed: 06/19/2023]
Abstract
The discovery of new drug candidates to inhibit an intended target is a complex and resource-consuming process. A machine learning (ML) method for predicting drug-target interactions (DTI) is a potential solution to improve the efficiency. However, traditional ML approaches have limitations in accuracy. In this study, we developed a novel ensemble model CoGT for DTI prediction using multilayer perceptron (MLP), which integrated graph-based models to extract non-Euclidean molecular structures and large pretrained models, specifically chemBERTa, to process simplified molecular input line entry systems (SMILES). The performance of CoGT was evaluated using compounds inhibiting four Janus kinases (JAKs). Results showed that the large pretrained model, chemBERTa, was better than other conventional ML models in predicting DTI across multiple evaluation metrics, while the graph neural network (GNN) was effective for prediction on imbalanced data sets. To take full advantage of the strengths of these different models, we developed an ensemble model, CoGT, which outperformed other individual ML models in predicting compounds' inhibition on different isoforms of JAKs. Our data suggest that the ensemble model CoGT has the potential to accelerate the process of drug discovery.
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Affiliation(s)
- Yingzi Bu
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ruoxi Gao
- Department
of Electrical Engineering and Computer Science, University of MichiganAnn Arbor, Michigan 48109, United States
| | - Bohan Zhang
- School
of Information, University of MichiganAnn Arbor, Michigan 48109, United States
| | - Luchen Zhang
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Duxin Sun
- Department
of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, Michigan 48109, United States
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49
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Li H, Zou L, Kowah JAH, He D, Liu Z, Ding X, Wen H, Wang L, Yuan M, Liu X. A compact review of progress and prospects of deep learning in drug discovery. J Mol Model 2023; 29:117. [PMID: 36976427 DOI: 10.1007/s00894-023-05492-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Accepted: 02/27/2023] [Indexed: 03/29/2023]
Abstract
BACKGROUND Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.
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Affiliation(s)
- Huijun Li
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Lin Zou
- College of Medicine, Guangxi University, Nanning, 530004, China
| | | | - Dongqiong He
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Zifan Liu
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xuejie Ding
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Hao Wen
- College of Chemistry and Chemical Engineering, Guangxi University, Nanning, 530004, China
| | - Lisheng Wang
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Mingqing Yuan
- College of Medicine, Guangxi University, Nanning, 530004, China
| | - Xu Liu
- College of Medicine, Guangxi University, Nanning, 530004, China.
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50
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Alamro H, Thafar MA, Albaradei S, Gojobori T, Essack M, Gao X. Exploiting machine learning models to identify novel Alzheimer’s disease biomarkers and potential targets. Sci Rep 2023; 13:4979. [PMID: 36973386 PMCID: PMC10043000 DOI: 10.1038/s41598-023-30904-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 03/03/2023] [Indexed: 03/29/2023] Open
Abstract
AbstractWe still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.
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