Browsing by Subject "Drug repurposing"
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Item 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 MedicineBackground: 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.Item 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.Item 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 MedicineThe 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.Item Health position paper and redox perspectives - Bench to bedside transition for pharmacological regulation of NRF2 in noncommunicable diseases(Elsevier, 2025) Cuadrado, Antonio; Cazalla, Eduardo; Bach, Anders; Bathish, Boushra; Naidu, Sharadha Dayalan; DeNicola, Gina M.; Dinkova-Kostova, Albena T.; Fernández-Ginés, Raquel; Grochot-Przeczek, Anna; Hayes, John D.; Kensler, Thomas W.; León, Rafael; Liby, Karen T.; López, Manuela G.; Manda, Gina; Shivakumar, Akshatha Kalavathi; Hakomäki, Henriikka; Moerland, Jessica A.; Motohashi, Hozumi; Rojo, Ana I.; Sykiotis, Gerasimos P.; Taguchi, Keiko; Valverde, Ángela M.; Yamamoto, Masayuki; Levonen, Anna-Liisa; Medicine, School of MedicineNuclear factor erythroid 2-related factor 2 (NRF2) is a redox-activated transcription factor regulating cellular defense against oxidative stress, thereby playing a pivotal role in maintaining cellular homeostasis. Its dysregulation is implicated in the progression of a wide array of human diseases, making NRF2 a compelling target for therapeutic interventions. However, challenges persist in drug discovery and safe targeting of NRF2, as unresolved questions remain especially regarding its context-specific role in diseases and off-target effects. This comprehensive review discusses the dualistic role of NRF2 in disease pathophysiology, covering its protective and/or destructive roles in autoimmune, respiratory, cardiovascular, and metabolic diseases, as well as diseases of the digestive system and cancer. Additionally, we also review the development of drugs that either activate or inhibit NRF2, discuss main barriers in translating NRF2-based therapies from bench to bedside, and consider the ways to monitor NRF2 activation in vivo.Item 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 MedicineRepurposing 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.Item Integrating Interpretable Machine Learning and Multi-omics Systems Biology for Personalized Biomarker Discovery and Drug Repurposing in Alzheimer’s Disease(bioRxiv, 2025-03-28) Mottaqi, Mohammadsadeq; Zhang, Pengyue; Xie, Lei; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public HealthBackground: Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial molecular variability across different brain regions and individuals, hindering therapeutic development. This study introduces PRISM-ML, an interpretable machine learning (ML) framework integrating multiomics data to uncover patient-specific biomarkers, subtissue-level pathology, and drug repurposing opportunities. Methods: We harmonized transcriptomic and genomic data of three independent brain studies containing 2105 post-mortem brain samples (1363 AD, 742 controls) across nine tissues. A Random Forest classifier with SHapley Additive exPlanations (SHAP) identified patient-level biomarkers. Clustering further delineated each tissue into subtissues, and network analysis revealed critical "bottleneck" (hub) genes. Finally, a knowledge graph-based screening identified multi-target drug candidates, and a real-world pharmacoepidemiologic study evaluated their clinical relevance. Results: We uncovered 36 molecularly distinct subtissues, each defined by a set of associated unique biomarkers and genetic drivers. Through network analysis of gene-gene interactions networks, we highlighted 262 bottleneck genes enriched in synaptic, cytoskeletal, and membrane-associated processes. Knowledge graph queries identified six FDA-approved drugs predicted to target multiple bottleneck genes and AD-relevant pathways simultaneously. One candidate, promethazine, demonstrated an association with reduced AD incidence in a large healthcare dataset of over 364000 individuals (hazard ratios ≤ 0.43; p < 0.001). These findings underscore the potential for multi-target approaches, reveal connections between AD and cardiovascular pathways, and offer novel insights into the heterogeneous biology of AD. Conclusions: PRISM-ML bridges interpretable ML with multi-omics and systems biology to decode AD heterogeneity, revealing region-specific mechanisms and repurposable therapeutics. The validation of promethazine in real-world data underscores the clinical relevance of multi-target strategies, paving the way for more personalized treatments in AD and other complex disorders.Item Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer’s Disease(medRxiv, 2025-01-09) Liu, Shiwei; Cho, Min Young; Huang, Yen-Ning; Park, Tamina; Chaudhuri, Soumilee; Jacobson Rosewood, Thea; Bice, Paula J.; Chung, Dongjun; Bennett, David A.; Ertekin-Taner, Nilüfer; Saykin, Andrew J.; Nho, Kwangsik; Radiology and Imaging Sciences, School of MedicineBackground: Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level. Methods: To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD. Results: We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD. Conclusions: We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.Item 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 MedicineThe 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.Item Single-cell Approach to Repurposing of Drugs for Alzheimer’s Disease(2023-05) Peyton, Madeline Elizabeth; Johnson, Travis S.; Zhang, Jie; Zhang, PengyueBackground: 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.