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Ma C, Gurkan-Cavusoglu E. A comprehensive review of computational cell cycle models in guiding cancer treatment strategies. NPJ Syst Biol Appl 2024; 10:71. [PMID: 38969664 PMCID: PMC11226463 DOI: 10.1038/s41540-024-00397-7] [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: 01/26/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
This article reviews the current knowledge and recent advancements in computational modeling of the cell cycle. It offers a comparative analysis of various modeling paradigms, highlighting their unique strengths, limitations, and applications. Specifically, the article compares deterministic and stochastic models, single-cell versus population models, and mechanistic versus abstract models. This detailed analysis helps determine the most suitable modeling framework for various research needs. Additionally, the discussion extends to the utilization of these computational models to illuminate cell cycle dynamics, with a particular focus on cell cycle viability, crosstalk with signaling pathways, tumor microenvironment, DNA replication, and repair mechanisms, underscoring their critical roles in tumor progression and the optimization of cancer therapies. By applying these models to crucial aspects of cancer therapy planning for better outcomes, including drug efficacy quantification, drug discovery, drug resistance analysis, and dose optimization, the review highlights the significant potential of computational insights in enhancing the precision and effectiveness of cancer treatments. This emphasis on the intricate relationship between computational modeling and therapeutic strategy development underscores the pivotal role of advanced modeling techniques in navigating the complexities of cell cycle dynamics and their implications for cancer therapy.
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Affiliation(s)
- Chenhui Ma
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA.
| | - Evren Gurkan-Cavusoglu
- Department of Electrical, Computer and Systems Engineering, Case Western Reserve University, Cleveland, OH, USA
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Manuel AM, Gottlieb A, Freeman L, Zhao Z. Montelukast as a repurposable additive drug for standard-efficacy multiple sclerosis treatment: Emulating clinical trials with retrospective administrative health claims data. Mult Scler 2024; 30:696-706. [PMID: 38660773 PMCID: PMC11073911 DOI: 10.1177/13524585241240398] [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] [Indexed: 04/26/2024]
Abstract
BACKGROUND Effective and safe treatment options for multiple sclerosis (MS) are still needed. Montelukast, a leukotriene receptor antagonist (LTRA) currently indicated for asthma or allergic rhinitis, may provide an additional therapeutic approach. OBJECTIVE The study aimed to evaluate the effects of montelukast on the relapses of people with MS (pwMS). METHODS In this retrospective case-control study, two independent longitudinal claims datasets were used to emulate randomized clinical trials (RCTs). We identified pwMS aged 18-65 years, on MS disease-modifying therapies concomitantly, in de-identified claims from Optum's Clinformatics® Data Mart (CDM) and IQVIA PharMetrics® Plus for Academics. Cases included 483 pwMS on montelukast and with medication adherence in CDM and 208 in PharMetrics Plus for Academics. We randomly sampled controls from 35,330 pwMS without montelukast prescriptions in CDM and 10,128 in PharMetrics Plus for Academics. Relapses were measured over a 2-year period through inpatient hospitalization and corticosteroid claims. A doubly robust causal inference model estimated the effects of montelukast, adjusting for confounders and censored patients. RESULTS pwMS treated with montelukast demonstrated a statistically significant 23.6% reduction in relapses compared to non-users in 67.3% of emulated RCTs. CONCLUSION Real-world evidence suggested that montelukast reduces MS relapses, warranting future clinical trials and further research on LTRAs' potential mechanism in MS.
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Affiliation(s)
- Astrid M Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX
| | - Assaf Gottlieb
- Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX
| | - Leorah Freeman
- Neurology Department, Dell Medical School, The University of Texas at Austin, TX
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, TX
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Yang R, Fu Y, Zhang Q, Zhang L. GCNGAT: Drug-disease association prediction based on graph convolution neural network and graph attention network. Artif Intell Med 2024; 150:102805. [PMID: 38553169 DOI: 10.1016/j.artmed.2024.102805] [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: 03/31/2023] [Revised: 01/22/2024] [Accepted: 02/08/2024] [Indexed: 04/02/2024]
Abstract
Predicting drug-disease associations can contribute to discovering new therapeutic potentials of drugs, and providing important association information for new drug research and development. Many existing drug-disease association prediction methods have not distinguished relevant background information for the same drug targeted to different diseases. Therefore, this paper proposes a drug-disease association prediction model based on graph convolutional network and graph attention network (GCNGAT) to reposition marketed drugs under the distinguishment of background information. Firstly, in order to obtain initial drug-disease information, a drug-disease heterogeneous graph structure is constructed based on all known drug-disease associations. Secondly, based on the heterogeneous graph structure, the corresponding subgraphs of each group of drug-disease association pairs are extracted to distinguish different background information for the same drug from different diseases. Finally, a model combining Graph neural network with global Average pooling (GnnAp) is designed to predict potential drug-disease associations by learning drug-disease interaction feature representations. The experimental results show that adding subgraph extraction can effectively improve the prediction performance of the model, and the graph representation learning module can fully extract the deep features of drug-disease. Using the 5-fold cross-validation, the proposed model (GCNGAT) achieves AUC (Area Under the receiver operating characteristic Curve) values of 0.9182 and 0.9417 on the PREDICT dataset and CDataset dataset, respectively. Compared with other predictors on the same dataset (PREDICT dataset), GCNGAT outperforms the existing best-performing model (PSGCN), with a 1.58% increase in the AUC value. It is anticipated that this model can provide experimental reference for drug repositioning and further promote the drug research and development process.
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Affiliation(s)
- Runtao Yang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Yao Fu
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
| | - Qian Zhang
- Heze Institute of Science and Technology Information, Heze, 274000, China.
| | - Lina Zhang
- School of Mechanical, Electrical and Information Engineering, Shandong University at Weihai, 264209, China.
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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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Kim JB, Kim Y, Kim SJ, Ha TY, Kim DK, Kim DW, So M, Kim SH, Woo HG, Yoon D, Park SM. Integration of National Health Insurance claims data and animal models reveals fexofenadine as a promising repurposed drug for Parkinson's disease. J Neuroinflammation 2024; 21:53. [PMID: 38383441 PMCID: PMC10880337 DOI: 10.1186/s12974-024-03041-7] [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: 11/13/2023] [Accepted: 02/12/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) is a common and costly progressive neurodegenerative disease of unclear etiology. A disease-modifying approach that can directly stop or slow its progression remains a major unmet need in the treatment of PD. A clinical pharmacology-based drug repositioning strategy is a useful approach for identifying new drugs for PD. METHODS We analyzed claims data obtained from the National Health Insurance Service (NHIS), which covers a significant portion of the South Korean population, to investigate the association between antihistamines, a class of drugs commonly used to treat allergic symptoms by blocking H1 receptor, and PD in a real-world setting. Additionally, we validated this model using various animal models of PD such as the 6-hydroxydopmaine (6-OHDA), α-synuclein preformed fibrils (PFF) injection, and Caenorhabditis elegans (C. elegans) models. Finally, whole transcriptome data and Ingenuity Pathway Analysis (IPA) were used to elucidate drug mechanism pathways. RESULTS We identified fexofenadine as the most promising candidate using National Health Insurance claims data in the real world. In several animal models, including the 6-OHDA, PFF injection, and C. elegans models, fexofenadine ameliorated PD-related pathologies. RNA-seq analysis and the subsequent experiments suggested that fexofenadine is effective in PD via inhibition of peripheral immune cell infiltration into the brain. CONCLUSION Fexofenadine shows promise for the treatment of PD, identified through clinical data and validated in diverse animal models. This combined clinical and preclinical approach offers valuable insights for developing novel PD therapeutics.
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Affiliation(s)
- Jae-Bong Kim
- Department of Pharmacology, Ajou University School of Medicine, 164, Worldcup-Ro, Yeongtong-Gu, Suwon, 16499, Korea
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
- Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea
| | - Yujeong Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | - Soo-Jeong Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Tae-Young Ha
- Department of Pharmacology, Ajou University School of Medicine, 164, Worldcup-Ro, Yeongtong-Gu, Suwon, 16499, Korea
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
- Neuroscience Research Institute, Gachon University, Incheon, Korea
| | - Dong-Kyu Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
| | - Dong Won Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea
| | | | - Seung Ho Kim
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
- Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | - Hyun Goo Woo
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea
- Department of Physiology, Ajou University School of Medicine, Suwon, Korea
| | - Dukyong Yoon
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Korea.
| | - Sang Myun Park
- Department of Pharmacology, Ajou University School of Medicine, 164, Worldcup-Ro, Yeongtong-Gu, Suwon, 16499, Korea.
- Center for Convergence Research of Neurological Disorders, Ajou University School of Medicine, Suwon, Korea.
- Neuroscience Graduate Program, Department of Biomedical Sciences, Ajou University School of Medicine, Suwon, Korea.
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Visan AI, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life (Basel) 2024; 14:233. [PMID: 38398742 PMCID: PMC10890405 DOI: 10.3390/life14020233] [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: 01/09/2024] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Drug development is expensive, time-consuming, and has a high failure rate. In recent years, artificial intelligence (AI) has emerged as a transformative tool in drug discovery, offering innovative solutions to complex challenges in the pharmaceutical industry. This manuscript covers the multifaceted role of AI in drug discovery, encompassing AI-assisted drug delivery design, the discovery of new drugs, and the development of novel AI techniques. We explore various AI methodologies, including machine learning and deep learning, and their applications in target identification, virtual screening, and drug design. This paper also discusses the historical development of AI in medicine, emphasizing its profound impact on healthcare. Furthermore, it addresses AI's role in the repositioning of existing drugs and the identification of drug combinations, underscoring its potential in revolutionizing drug delivery systems. The manuscript provides a comprehensive overview of the AI programs and platforms currently used in drug discovery, illustrating the technological advancements and future directions of this field. This study not only presents the current state of AI in drug discovery but also anticipates its future trajectory, highlighting the challenges and opportunities that lie ahead.
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Affiliation(s)
| | - Irina Negut
- National Institute for Lasers, Plasma and Radiation Physics, 409 Atomistilor Street, 077125 Magurele, Ilfov, Romania;
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Papanastasiou G, Yang G, Fotiadis DI, Dikaios N, Wang C, Huda A, Sobolevsky L, Raasch J, Perez E, Sidhu G, Palumbo D. Large-scale deep learning analysis to identify adult patients at risk for combined and common variable immunodeficiencies. COMMUNICATIONS MEDICINE 2023; 3:189. [PMID: 38123736 PMCID: PMC10733406 DOI: 10.1038/s43856-023-00412-8] [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: 02/17/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Primary immunodeficiency (PI) is a group of heterogeneous disorders resulting from immune system defects. Over 70% of PI is undiagnosed, leading to increased mortality, co-morbidity and healthcare costs. Among PI disorders, combined immunodeficiencies (CID) are characterized by complex immune defects. Common variable immunodeficiency (CVID) is among the most common types of PI. In light of available treatments, it is critical to identify adult patients at risk for CID and CVID, before the development of serious morbidity and mortality. METHODS We developed a deep learning-based method (named "TabMLPNet") to analyze clinical history from nationally representative medical claims from electronic health records (Optum® data, covering all US), evaluated in the setting of identifying CID/CVID in adults. Further, we revealed the most important CID/CVID-associated antecedent phenotype combinations. Four large cohorts were generated: a total of 47,660 PI cases and (1:1 matched) controls. RESULTS The sensitivity/specificity of TabMLPNet modeling ranges from 0.82-0.88/0.82-0.85 across cohorts. Distinctive combinations of antecedent phenotypes associated with CID/CVID are identified, consisting of respiratory infections/conditions, genetic anomalies, cardiac defects, autoimmune diseases, blood disorders and malignancies, which can possibly be useful to systematize the identification of CID and CVID. CONCLUSIONS We demonstrated an accurate method in terms of CID and CVID detection evaluated on large-scale medical claims data. Our predictive scheme can potentially lead to the development of new clinical insights and expanded guidelines for identification of adult patients at risk for CID and CVID as well as be used to improve patient outcomes on population level.
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Affiliation(s)
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Dimitris I Fotiadis
- Department of Biomedical Research, Institute of Molecular Biology and Biotechnology, FORTH, Ioannina, Greece
- Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, Ioannina, Greece
| | | | - Chengjia Wang
- School of Mathematical and Computer Sciences, Heriot Watt, Edinburgh, UK
- Edinburgh Centre for Robotics, Edinburgh, UK
| | | | | | | | - Elena Perez
- Allergy Associates of the Palm Beaches, North Palm Beach, FL, USA
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Rassy E, Andre F. A forgotten dimension of big data in drug repositioning. Eur J Cancer 2023; 192:113277. [PMID: 37647850 DOI: 10.1016/j.ejca.2023.113277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023]
Affiliation(s)
- Elie Rassy
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; CESP, INSERM U1018, Université Paris-Saclay, Villejuif, France.
| | - Fabrice Andre
- Department of Medical Oncology, Gustave Roussy, University Paris-Saclay, Villejuif, France; Gustave Roussy, INSERM U981, Université Paris-Saclay, Villejuif, France
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Grabowska ME, Huang A, Wen Z, Li B, Wei WQ. Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review. Front Pharmacol 2023; 14:1257700. [PMID: 37745051 PMCID: PMC10512468 DOI: 10.3389/fphar.2023.1257700] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.
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Affiliation(s)
- Monika E. Grabowska
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Annabelle Huang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhexing Wen
- Departments of Psychiatry and Behavioral Sciences, Cell Biology, and Neurology, Emory University School of Medicine, Atlanta, GA, United States
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
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Xu T, Zhao J, Xiong M. Graphical Learning and Causal Inference for Drug Repurposing. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.29.23293346. [PMID: 37577650 PMCID: PMC10418581 DOI: 10.1101/2023.07.29.23293346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Gene expression profiles that connect drug perturbations, disease gene expression signatures, and clinical data are important for discovering potential drug repurposing indications. However, the current approach to gene expression reversal has several limitations. First, most methods focus on validating the reversal expression of individual genes. Second, there is a lack of causal approaches for identifying drug repurposing candidates. Third, few methods for passing and summarizing information on a graph have been used for drug repurposing analysis, with classical network propagation and gene set enrichment analysis being the most common. Fourth, there is a lack of graph-valued association analysis, with current approaches using real-valued association analysis one gene at a time to reverse abnormal gene expressions to normal gene expressions. To overcome these limitations, we propose a novel causal inference and graph neural network (GNN)-based framework for identifying drug repurposing candidates. We formulated a causal network as a continuous constrained optimization problem and developed a new algorithm for reconstructing large-scale causal networks of up to 1,000 nodes. We conducted large-scale simulations that demonstrated good false positive and false negative rates. To aggregate and summarize information on both nodes and structure from the spatial domain of the causal network, we used directed acyclic graph neural networks (DAGNN). We also developed a new method for graph regression in which both dependent and independent variables are graphs. We used graph regression to measure the degree to which drugs reverse altered gene expressions of disease to normal levels and to select potential drug repurposing candidates. To illustrate the application of our proposed methods for drug repurposing, we applied them to phase I and II L1000 connectivity map perturbational profiles from the Broad Institute LINCS, which consist of gene-expression profiles for thousands of perturbagens at a variety of time points, doses, and cell lines, as well as disease gene expression data under-expressed and over-expressed in response to SARS-CoV-2.
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Affiliation(s)
- Tao Xu
- Department of Epidemiology, University of Florida, Gainesville, FL 32611, USA
| | - Jinying Zhao
- Department of Epidemiology, University of Florida, Gainesville, FL 32611, USA
| | - Momiao Xiong
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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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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Deplanque D, Fetro C, Ferry A, Lechat P, Beghyn T, Bernard C, Bernasconi A, Bienayme H, Cougoule C, Del Bano J, Demiot C, Lebrun-Vignes B. Repositionnement des médicaments : de la découverte d’un effet pharmacologique utile à la mise à disposition du traitement pour le patient. Therapie 2023; 78:1-9. [PMID: 36564262 DOI: 10.1016/j.therap.2022.12.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Dominique Deplanque
- Université Lille, Inserm, CHU Lille, centre d'investigation clinique (CIC) 1403, 59000 Lille, France.
| | | | | | - Philippe Lechat
- Université Paris-cité, service de pharmacologie et toxicologie, hôpital européen Georges-Pompidou, 75015 Paris, France; Agence générale des équipements et des produits de santé (AGEPS), Assistance publique-Hôpitaux de Paris, 75005 Paris, France
| | - Terence Beghyn
- APTEEUS SAS, campus Institut Pasteur, 59000 Lille, France
| | - Claude Bernard
- Agence générale des équipements et des produits de santé (AGEPS), Assistance publique-Hôpitaux de Paris, 75005 Paris, France
| | | | | | - Céline Cougoule
- Institut de pharmacologie et de biologie structurale (IPBS), université de Toulouse, CNRS, université Toulouse III - Paul-Sabatier (UPS), 31400 Toulouse, France
| | - Joanie Del Bano
- Aix-Marseille université, AP-HM, Inserm, DHUNE, Inst Neurosci Syst, service de pharmacologie clinique et pharmacovigilance, Thelonius Mind, 13000 Marseille, France
| | - Claire Demiot
- UR 20218-NeurIT, faculties of medicine and pharmacy, university of Limoges, 87025 Limoges, France
| | - Bénédicte Lebrun-Vignes
- Service de pharmacologie et centre régional de pharmacovigilance, hôpital Pitié-Salpêtrière, groupe hospitalier, AP-HP, Sorbonne université, 75013 Paris, France
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Deplanque D, Fetro C, Ferry A, Lechat P, Beghyn T, Bernard C, Bernasconi A, Bienayme H, Cougoule C, Del Bano J, Demiot C, Lebrun-Vignes B. Drug repurposing: From the discovery of a useful pharmacological effect to making the treatment available to the patient. Therapie 2023; 78:10-18. [PMID: 36528417 DOI: 10.1016/j.therap.2022.11.009] [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: 11/07/2022] [Accepted: 11/21/2022] [Indexed: 12/07/2022]
Abstract
The repurposing of a medicine already on the market to a new indication could be an opportunity to respond rapidly to a therapeutic need not yet covered, particularly in the context of rare and neglected diseases, or health emergencies. However, at each stage, difficulties may arise that will prevent the repurposed drug from being provided to patients. Beyond fortuity or a systematic strategy to detect a useful pharmacological effect, the implementation of the preclinical and clinical stages is sometimes complicated by the difficulty of accessing the molecule and its pharmaceutical data. Furthermore, relevant clinical results will not always be sufficient to ensure that a marketing authorisation is obtained or that patients receive satisfactory care. In addition to describing these various obstacles, the round table provided an opportunity to put forward recommendations for overcoming them, in particular the creation of a public-private partnership structure with sufficient funding to be able to offer individualised support for projects up to and including the marketing application.
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Affiliation(s)
- Dominique Deplanque
- Université Lille, Inserm, CHU Lille, centre d'investigation clinique (CIC) 1403, 59000 Lille, France.
| | | | | | - Philippe Lechat
- Université Paris-cité, service de pharmacologie et toxicologie, hôpital européen Georges-Pompidou, 75015 Paris, France; Agence générale des équipements et des produits de santé (AGEPS), Assistance publique-Hôpitaux de Paris, 75005 Paris, France
| | - Terence Beghyn
- APTEEUS SAS, campus Institut Pasteur, 59000 Lille, France
| | - Claude Bernard
- Agence générale des équipements et des produits de santé (AGEPS), Assistance publique-Hôpitaux de Paris, 75005 Paris, France
| | | | | | - Céline Cougoule
- Institut de pharmacologie et de biologie structurale (IPBS), université de Toulouse, CNRS, université Toulouse III - Paul-Sabatier (UPS), 31400 Toulouse, France
| | - Joanie Del Bano
- Aix-Marseille université, AP-HM, Inserm, DHUNE, Inst Neurosci Syst, service de pharmacologie clinique et pharmacovigilance, Thelonius Mind, 13000 Marseille, France
| | - Claire Demiot
- UR 20218-NeurIT, faculties of medicine and pharmacy, university of Limoges, 87025 Limoges, France
| | - Bénédicte Lebrun-Vignes
- Service de pharmacologie et centre régional de pharmacovigilance, hôpital Pitié-Salpêtrière, groupe hospitalier, AP-HP, Sorbonne université, 75013 Paris, France
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Drug Repurposing at the Interface of Melanoma Immunotherapy and Autoimmune Disease. Pharmaceutics 2022; 15:pharmaceutics15010083. [PMID: 36678712 PMCID: PMC9865219 DOI: 10.3390/pharmaceutics15010083] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/06/2022] [Accepted: 12/21/2022] [Indexed: 12/29/2022] Open
Abstract
Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.
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