Browsing by Subject "Drug repurposing"

Now showing 1 - 6 of 6
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    Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease
    (BMC, 2022-01-10) Fang, Jiansong; Zhang, Pengyue; Wang, Quan; Chiang, Chien‑Wei; Zhou, Yadi; Hou, Yuan; Xu, Jielin; Chen, Rui; Zhang, Bin; Lewis, Stephen J.; Leverenz, James B.; Pieper, Andrew A.; Li, Bingshan; Li, Lang; Cummings, Jeffrey; Cheng, Feixiong; Biostatistics and Health Data Science, School of Medicine
    Background: Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. Methods: To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Results: Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. Conclusions: In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.
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    Computational biology approaches in drug repurposing and gene essentiality screening
    (2016-06-20) Philips, Santosh; Li, Lang; Liu, Yunlong; Liu, Xiaowen; Skaar, Todd C.; Janga, Sarath C.
    The rapid innovations in biotechnology have led to an exponential growth of data and electronically accessible scientific literature. In this enormous scientific data, knowledge can be exploited, and novel discoveries can be made. In my dissertation, I have focused on the novel molecular mechanism and therapeutic discoveries from big data for complex diseases. It is very evident today that complex diseases have many factors including genetics and environmental effects. The discovery of these factors is challenging and critical in personalized medicine. The increasing cost and time to develop new drugs poses a new challenge in effectively treating complex diseases. In this dissertation, we want to demonstrate that the use of existing data and literature as a potential resource for discovering novel therapies and in repositioning existing drugs. The key to identifying novel knowledge is in integrating information from decades of research across the different scientific disciplines to uncover interactions that are not explicitly stated. This puts critical information at the fingertips of researchers and clinicians who can take advantage of this newly acquired knowledge to make informed decisions. This dissertation utilizes computational biology methods to identify and integrate existing scientific data and literature resources in the discovery of novel molecular targets and drugs that can be repurposed. In chapters 1 of my dissertation, I extensively sifted through scientific literature and identified a novel interaction between Vitamin A and CYP19A1 that could lead to a potential increase in the production of estrogens. Further in chapter 2 by exploring a microarray dataset from an estradiol gene sensitivity study I was able to identify a potential novel anti-estrogenic indication for the commonly used urinary analgesic, phenazopyridine. Both discoveries were experimentally validated in the laboratory. In chapter 3 of my dissertation, through the use of a manually curated corpus and machine learning algorithms, I identified and extracted genes that are essential for cell survival. These results brighten the reality that novel knowledge with potential clinical applications can be discovered from existing data and literature by integrating information across various scientific disciplines.
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    GAD1 Upregulation Programs Aggressive Features of Cancer Cell Metabolism in the Brain Metastatic Microenvironment
    (American Association for Cancer Research, 2017-06-01) Schnepp, Patricia M.; Lee, Dennis D.; Guldner, Ian H.; O'Tighearnaigh, Treasa K.; Howe, Erin N.; Palakurthi, Bhavana; Eckert, Kaitlyn E.; Toni, Tiffany A.; Ashfeld, Brandon L.; Zhang, Siyuan; Medicine, School of Medicine
    The impact of altered amino acid metabolism on cancer progression is not fully understood. We hypothesized that a metabolic transcriptome shift during metastatic evolution is crucial for brain metastasis. Here, we report a powerful impact in this setting caused by epigenetic upregulation of glutamate decarboxylase 1 (GAD1), a regulator of the GABA neurotransmitter metabolic pathway. In cell-based culture and brain metastasis models, we found that downregulation of the DNA methyltransferase DNMT1 induced by the brain microenvironment-derived clusterin resulted in decreased GAD1 promoter methylation and subsequent upregulation of GAD1 expression in brain metastatic tumor cells. In a system to dynamically visualize cellular metabolic responses mediated by GAD1, we monitored the cytosolic NADH:NAD+ equilibrium in tumor cells. Reducing GAD1 in metastatic cells by primary glia cell coculture abolished the capacity of metastatic cells to utilize extracellular glutamine, leading to cytosolic accumulation of NADH and increased oxidative status. Similarly, genetic or pharmacologic disruption of the GABA metabolic pathway decreased the incidence of brain metastasis in vivo Taken together, our results show how epigenetic changes in GAD1 expression alter local glutamate metabolism in the brain metastatic microenvironment, contributing to a metabolic adaption that facilitates metastasis outgrowth in that setting.
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    Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases
    (Frontiers Media, 2021-07-28) Challa, Anup P.; Zaleski, Nicole M.; Jerome, Rebecca N.; Lavieri, Robert R.; Shirey-Rice, Jana K.; Barnado, April; Lindsell, Christopher J.; Aronoff, David M.; Crofford, Leslie J.; Harris, Raymond C.; Ikizler, T. Alp; Mayer, Ingrid A.; Holroyd, Kenneth J.; Pulley, Jill M.; Medicine, School of Medicine
    Repurposing is an increasingly attractive method within the field of drug development for its efficiency at identifying new therapeutic opportunities among approved drugs at greatly reduced cost and time of more traditional methods. Repurposing has generated significant interest in the realm of rare disease treatment as an innovative strategy for finding ways to manage these complex conditions. The selection of which agents should be tested in which conditions is currently informed by both human and machine discovery, yet the appropriate balance between these approaches, including the role of artificial intelligence (AI), remains a significant topic of discussion in drug discovery for rare diseases and other conditions. Our drug repurposing team at Vanderbilt University Medical Center synergizes machine learning techniques like phenome-wide association study-a powerful regression method for generating hypotheses about new indications for an approved drug-with the knowledge and creativity of scientific, legal, and clinical domain experts. While our computational approaches generate drug repurposing hits with a high probability of success in a clinical trial, human knowledge remains essential for the hypothesis creation, interpretation, "go-no go" decisions with which machines continue to struggle. Here, we reflect on our experience synergizing AI and human knowledge toward realizable patient outcomes, providing case studies from our portfolio that inform how we balance human knowledge and machine intelligence for drug repurposing in rare disease.
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    NHLBI-CMREF Workshop Report on Pulmonary Vascular Disease Classification: JACC State-of-the-Art Review
    (Elsevier, 2021) Oldham, William M.; Hemnes, Anna R.; Aldred, Micheala A.; Barnard, John; Brittain, Evan L.; Chan, Stephen Y.; Cheng, Feixiong; Cho, Michael H.; Desai, Ankit A.; Garcia, Joe G.N.; Geraci, Mark W.; Ghiassian, Susan D.; Hall, Kathryn T.; Horn, Evelyn M.; Jain, Mohit; Kelly, Rachel S.; Leopold, Jane A.; Lindstrom, Sara; Modena, Brian D.; Nichols, William C.; Rhodes, Christopher J.; Sun, Wei; Sweatt, Andrew J.; Vanderpool, Rebecca R.; Wilkins, Martin R.; Wilmot, Beth; Zamanian, Roham T.; Fessel, Joshua P.; Aggarwal, Neil R.; Loscalzo, Joseph; Xiao, Lei; Medicine, School of Medicine
    The National Heart, Lung, and Blood Institute and the Cardiovascular Medical Research and Education Fund held a workshop on the application of pulmonary vascular disease omics data to the understanding, prevention, and treatment of pulmonary vascular disease. Experts in pulmonary vascular disease, omics, and data analytics met to identify knowledge gaps and formulate ideas for future research priorities in pulmonary vascular disease in line with National Heart, Lung, and Blood Institute Strategic Vision goals. The group identified opportunities to develop analytic approaches to multiomic datasets, to identify molecular pathways in pulmonary vascular disease pathobiology, and to link novel phenotypes to meaningful clinical outcomes. The committee suggested support for interdisciplinary research teams to develop and validate analytic methods, a national effort to coordinate biosamples and data, a consortium of preclinical investigators to expedite target evaluation and drug development, longitudinal assessment of molecular biomarkers in clinical trials, and a task force to develop a master clinical trials protocol for pulmonary vascular disease.
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    Single-cell Approach to Repurposing of Drugs for Alzheimer’s Disease
    (2023-05) Peyton, Madeline Elizabeth; Johnson, Travis S.; Zhang, Jie; Zhang, Pengyue
    Background: Alzheimer’s disease (AD) is the third leading cause of death for the older demographic in the United States, just after heart disease and cancer. However, unlike heart disease and cancer, the death rates for AD are increasing. Despite extensive research, the cause or origin of AD remains unclear and there is no existing cure. However, with the improvement of single-cell RNA-sequencing (scRNA-seq) technologies and drug repurposing tools, we can further our knowledge of AD and its pathogenesis. Method: Our primary aim was to identify repurposable drug and compound candidates for AD treatment and identify significant cell types and signaling pathways using two scRNA-seq datasets from cortex samples of AD patients and controls. To achieve this aim, we generated differential gene expression profiles, calculated log fold-changes, and estimated standard errors to make pairwise comparisons between the diseased and healthy samples. We used the 21,304 drugs/compounds with response gene expression profiles in 98 cell lines from the LINCS L1000 project to detect consistent differentially expressed genes (DEGs), that were either i) up-regulated in cells of diseased samples and down-regulated in cells with treatment, or ii) down-regulated in cells from diseased samples but up-regulated in cells with treatment. To evaluate these identified drugs, we compared the p-value, false discovery rate (FDR) and A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) drug score for each cell type. We further annotated and assessed doublet cell types within the Grubman et al. dataset using cell type proportions. Result: The analysis provided several potential therapeutic treatments for AD and its target genes and pathways as well as important cell type interactions. Notably, we identified an interaction between endothelial cells and microglia, and further identified drug candidates to target this interaction. Conclusion: We identified repurposable drugs/compounds candidates in each dataset which were also identified in literature. We further identified doublet cell type interactions of interest and drugs that target this interaction.