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  • 1
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    AlzGPS: a genome-wide positioning systems platform to catalyze multi-omics for Alzheimer’s drug discovery
    Springer Science and Business Media LLC ; 2021
    In:  Alzheimer's Research & Therapy Vol. 13, No. 1 ( 2021-12)
    Details
    In: Alzheimer's Research & Therapy, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2021-12)
    Abstract: Recent DNA/RNA sequencing and other multi-omics technologies have advanced the understanding of the biology and pathophysiology of AD, yet there is still a lack of disease-modifying treatments for AD. A new approach to integration of the genome, transcriptome, proteome, and human interactome in the drug discovery and development process is essential for this endeavor. Methods In this study, we developed AlzGPS ( G enome-wide P ositioning S ystems platform for Alz heimer’s Drug Discovery, https://alzgps.lerner.ccf.org ), a comprehensive systems biology tool to enable searching, visualizing, and analyzing multi-omics, various types of heterogeneous biological networks, and clinical databases for target identification and development of effective prevention and treatment for AD. Results Via AlzGPS: (1) we curated more than 100 AD multi-omics data sets capturing DNA, RNA, protein, and small molecule profiles underlying AD pathogenesis (e.g., early vs. late stage and tau or amyloid endophenotype); (2) we constructed endophenotype disease modules by incorporating multi-omics findings and human protein-protein interactome networks; (3) we provided possible treatment information from ~ 3000 FDA approved/investigational drugs for AD using state-of-the-art network proximity analyses; (4) we curated nearly 300 literature references for high-confidence drug candidates; (5) we included information from over 1000 AD clinical trials noting drug’s mechanisms-of-action and primary drug targets, and linking them to our integrated multi-omics view for targets and network analysis results for the drugs; (6) we implemented a highly interactive web interface for database browsing and network visualization. Conclusions Network visualization enabled by AlzGPS includes brain-specific neighborhood networks for genes-of-interest, endophenotype disease module networks for omics-of-interest, and mechanism-of-action networks for drugs targeting disease modules. By virtue of combining systems pharmacology and network-based integrative analysis of multi-omics data, AlzGPS offers actionable systems biology tools for accelerating therapeutic development in AD.
    Type of Medium: Online Resource
    ISSN: 1758-9193
    URL: Article
    DOI: 10.1186/s13195-020-00760-w
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
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  • 2
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    Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer’s disease
    Springer Science and Business Media LLC ; 2022
    In:  Alzheimer's Research & Therapy Vol. 14, No. 1 ( 2022-12)
    Details
    In: Alzheimer's Research & Therapy, Springer Science and Business Media LLC, Vol. 14, No. 1 ( 2022-12)
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 1758-9193
    URL: Article
    DOI: 10.1186/s13195-021-00951-z
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
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  • 3
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    Multimodal single-cell/nucleus RNA sequencing data analysis uncovers molecular networks between disease-associated microglia and astrocytes with implications for drug repurposing in Alzheimer's disease
    Cold Spring Harbor Laboratory ; 2021
    In:  Genome Research Vol. 31, No. 10 ( 2021-10), p. 1900-1912
    Details
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 31, No. 10 ( 2021-10), p. 1900-1912
    Abstract: Because disease-associated microglia (DAM) and disease-associated astrocytes (DAA) are involved in the pathophysiology of Alzheimer's disease (AD), we systematically identified molecular networks between DAM and DAA to uncover novel therapeutic targets for AD. Specifically, we develop a network-based methodology that leverages single-cell/nucleus RNA sequencing data from both transgenic mouse models and AD patient brains, as well as drug-target network, metabolite-enzyme associations, the human protein–protein interactome, and large-scale longitudinal patient data. Through this approach, we find both common and unique gene network regulators between DAM (i.e., PAK1 , MAPK14 , and CSF1R ) and DAA (i.e., NFKB1 , FOS , and JUN ) that are significantly enriched by neuro-inflammatory pathways and well-known genetic variants (i.e., BIN1 ). We identify shared immune pathways between DAM and DAA, including Th17 cell differentiation and chemokine signaling. Last, integrative metabolite-enzyme network analyses suggest that fatty acids and amino acids may trigger molecular alterations in DAM and DAA. Combining network-based prediction and retrospective case-control observations with 7.2 million individuals, we identify that usage of fluticasone (an approved glucocorticoid receptor agonist) is significantly associated with a reduced incidence of AD (hazard ratio [HR] = 0.86, 95% confidence interval [CI] 0.83–0.89, P 〈 1.0 × 10 −8 ). Propensity score–stratified cohort studies reveal that usage of mometasone (a stronger glucocorticoid receptor agonist) is significantly associated with a decreased risk of AD (HR = 0.74, 95% CI 0.68–0.81, P 〈 1.0 × 10 −8 ) compared to fluticasone after adjusting age, gender, and disease comorbidities. In summary, we present a network-based, multimodal methodology for single-cell/nucleus genomics-informed drug discovery and have identified fluticasone and mometasone as potential treatments in AD.
    Type of Medium: Online Resource
    ISSN: 1088-9051 , 1549-5469
    URL: Article
    DOI: 10.1101/gr.272484.120
    RVK:
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    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2021
    detail.hit.zdb_id: 1483456-X
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  • 4
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    Interpretable deep learning translation of GWAS and multi-omics findings to identify pathobiology and drug repurposing in Alzheimer’s disease
    Elsevier BV ; 2022
    In:  Cell Reports Vol. 41, No. 9 ( 2022-11), p. 111717-
    Details
    In: Cell Reports, Elsevier BV, Vol. 41, No. 9 ( 2022-11), p. 111717-
    Type of Medium: Online Resource
    ISSN: 2211-1247
    URL: Article
    DOI: 10.1016/j.celrep.2022.111717
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 2649101-1
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  • 5
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    The Alzheimer’s Cell Atlas (TACA): A single‐cell type transcriptomics and network pathobiology map for target identification and drug repurposing in Alzheimer’s disease and related dementia
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S4 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S4 ( 2022-12)
    Abstract: Recent advances in massive single‐cell/nucleus (sc/sn) transcriptomic data have great potential for identification of cell‐type specific Alzheimer’s disease (AD) pathobiology, as well as target discovery for drug repurposing. Method We designed and implemented a multimodal omics analytic framework for the processing of sc/sn transcriptomic datasets, analyses of gene expression, pathobiology, cell‐cell communications with results presentation, interpretation, and translation. We performed an exhaustive search of cell type‐specific gene/protein network modules and cell‐cell interaction networks using our recently compiled ligand‐receptor and the human protein‐protein interactome networks. Furthermore, we developed a web portal with comprehensive visualization tools, such as cell and gene expression viewers, volcano plot and protein‐protein interaction network for differentially expressed genes (DEGs), ligand‐receptor interaction network for cell‐cell interactions, and heatmap for drug perturbation profiles cell type‐specific DEGs. Result We created The Alzheimer’s Cell Atlas (TACA, available at https://taca.lerner.ccf.org ). In this endeavor, we compiled an AD brain cell atlas consisting of more than one million cells/nuclei from 20 datasets, covering major brain regions (cortex, hippocampus, cerebellum, etc.) and cell types (astrocytes, microglia, neurons, oligodendrocytes, etc.). We also conducted over 1,000 DE comparisons of these datasets to reveal cell‐type specific gene expression alterations. Major comparison types are (i) case vs healthy control; (ii) sex‐specific differential expression, (iii) genotype‐driven DE (i.e., APOE4/4 vs. APOE3/3; TREM2 R47H vs. common variants) analysis; and (iv) others. In addition, each comparison was followed by human protein‐protein interactome network module analysis, pathway enrichment analysis, and gene‐set enrichment analysis. For drug screening, we conducted gene set enrichment analysis for all comparisons with over 700,000 drug perturbation profiles connecting more than 12,000 human genes and 13,000 drugs/compounds. A total of over 300 analyses of cell‐cell interactions against 6,000 experimentally validated ligand‐receptor interactions were also conducted. We then generated summaries for the genes (for target identification) and drugs (for drug repurposing) from all analyses in sex‐specific and cell type‐specific manners. Conclusion We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for therapeutic discovery.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S4
    DOI: 10.1002/alz.066855
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2201940-6
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  • 6
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    The Alzheimer's Cell Atlas (TACA): A single‐cell molecular map for translational therapeutics accelerator in Alzheimer's disease
    Wiley ; 2022
    In:  Alzheimer's & Dementia: Translational Research & Clinical Interventions Vol. 8, No. 1 ( 2022-01)
    Details
    In: Alzheimer's & Dementia: Translational Research & Clinical Interventions, Wiley, Vol. 8, No. 1 ( 2022-01)
    Abstract: Recent advances in generating massive single‐cell/nucleus transcriptomic data have shown great potential for facilitating the identification of cell type–specific Alzheimer's disease (AD) pathobiology and drug‐target discovery for therapeutic development. Methods We developed The Alzheimer's Cell Atlas (TACA) by compiling an AD brain cell atlas consisting of over 1.1 million cells/nuclei across 26 data sets, covering major brain regions (hippocampus, cerebellum, prefrontal cortex, and so on) and cell types (astrocyte, microglia, neuron, oligodendrocytes, and so on). We conducted nearly 1400 differential expression comparisons to identify cell type–specific molecular alterations (e.g., case vs healthy control, sex‐specific, apolipoprotein E ( APOE ) ε4/ε4, and TREM2 mutations). Each comparison was followed by protein‐protein interaction module detection, functional enrichment analysis, and omics‐informed target and drug (over 700,000 perturbation profiles) screening. Over 400 cell‐cell interaction analyses using 6000 ligand‐receptor interactions were conducted to identify the cell‐cell communication networks in AD. Results All results are integrated into TACA ( https://taca.lerner.ccf.org/ ), a new web portal with cell type–specific, abundant transcriptomic information, and 12 interactive visualization tools for AD. Discussion We envision that TACA will be a highly valuable resource for both basic and translational research in AD, as it provides abundant information for AD pathobiology and actionable systems biology tools for drug discovery. Highlights We compiled an Alzheimer's disease (AD) brain cell atlas consisting of more than 1.1 million cells/nuclei transcriptomes from 26 data sets, covering major brain regions (cortex, hippocampus, cerebellum) and cell types (e.g., neuron, oligodendrocyte, astrocyte, and microglia). We conducted over 1400 differential expression (DE) comparisons to identify cell type–specific gene expression alterations. Major comparison types are (1) AD versus healthy control; (2) sex‐specific DE, (3) genotype‐driven DE (i.e., apolipoprotein E [ APOE ] ε4/ε4 vs APOE ε3/ε3; TREM2 R47H vs common variants) analysis; and (4) others. Each comparison was further followed by (1) human protein‐protein interactome network module analysis, (2) pathway enrichment analysis, and (3) gene‐set enrichment analysis. For drug screening, we conducted gene set enrichment analysis for all the comparisons with over 700,000 drug perturbation profiles connecting more than 10,000 human genes and 13,000 drugs/compounds. A total of over 400 analyses of cell‐cell interactions against 6000 experimentally validated ligand‐receptor interactions were conducted to reveal the disease‐relevant cell‐cell communications in AD.
    Type of Medium: Online Resource
    ISSN: 2352-8737 , 2352-8737
    URL: Issue
    URL: Article
    DOI: 10.1002/trc2.v8.1
    DOI: 10.1002/trc2.12350
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 7
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    A Network‐based Deep Learning Framework Translates GWAS and Multi‐Omics Findings to Pathobiology and Drug Repurposing for Alzheimer’s Disease
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S4 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S4 ( 2022-12)
    Abstract: Human genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer’s disease (AD). However, translating human genetic and genomic findings (i.e., genome‐wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge. Today, artificial intelligence and deep learning approaches that identify new risk genes and drug targets from human genome sequencing findings are enabling a more complete mechanistic understanding of disease biology. This is facilitating more rapid development of targeted therapeutic interventions for AD. Method We presented a net work t opology‐based deep learning framework to identify disease‐ a ssociated g enes (NETTAG). NETTAG integrates multi‐genomics data and the human protein‐protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leveraged non‐coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone‐QTLs, transcription factor binding‐QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The fundamental premise of NETTAG is that disease risk genes exhibit distinct functional characteristics compared to non‐risk genes, and can therefore be distinguished by their aggregated genomic features under the human protein interactome. Result Applying NETTAG to the latest AD GWAS data, we identified 156 putative AD‐risk genes (i.e., APOE , BIN1 , GSK3B , MARK4 , and PICALM ). We showed that predicted risk genes are: 1) significantly enriched in AD‐related pathobiological pathways, 2) more likely to be differentially expressed in transcriptomes and proteomes of AD brains, and 3) enriched in druggable targets with approved medicines. Specifically, we showed that NETTAG‐predicted genes (e.g., MEF2D , CPLX2 , KLF4 , ACTL6B , P2RX7 and etc.) are differentially expressed in AD‐associated microglia and astrocytes from single‐nuclei RNA‐sequencing data of human postmortem brains with varying degrees of AD neurobiology. Via network‐based prediction, we identified multiple repurposable drug candidates (i.e., choline, deferoxamine and ibudilast) for potential treatment of AD. Conclusion Our findings suggest that understanding human pathobiology and therapeutic development could benefit from network‐based deep learning methodology that utilizes GWAS findings with multimodal genomic analyses.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S4
    DOI: 10.1002/alz.066647
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 8
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    Network medicine‐based multimodal omics analytic framework identifies immunometabolism endophenotypes underlying sex difference of Alzheimer’s disease
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S3 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S3 ( 2022-12)
    Abstract: Although Alzheimer’s disease (AD) is sexually dimorphic in prevalence, incidence, symptomology, and neuropathology, the molecular mechanisms underlying these sex differences are not well understood. Method We presented a multimodal omics analytic framework to inspect sex differences in susceptibility to inflammation, cellular metabolism, and disease pathophysiology of AD. Specifically, we investigated the interplay between cellular metabolism and immune responses, termed the “immunometabolism endophenotype,” by unique integration of transcriptomics (single‐cell/nuclei RNA‐seq), proteomics (inflammatory biomarker expression), and metabolomics from the AD knowledge portal, the Alzheimer’s Disease Metabolomics Consortium, and the Cleveland Alzheimer's Disease Research Center (CADRC). Result We identified sex‐specific, elevated pro‐inflammatory gene/protein expression in blood (including myeloid‐derived suppressor cells [MDSCs]) and cerebrospinal fluid of phenotypically characterized subjects by AD biomarkers of A myloid deposition, pathologic T au, and N eurodegeneration (ATN). We also identified sex‐specific microglial gene signatures in AD patient brains from single‐cell/nuclei RNA‐seq (sc/snRNA‐seq) data analyses. We further found that women lost gene expression of innate and adaptive pathways (i.e., NF‐kappa B signaling, Toll‐like receptor signaling, and HIF signaling) in microglia compared to men with amyloid and tau neuropathology. However, women have elevated expression of innate and adaptive pathways (i.e., IL‐17 signaling and antigen processing and presentation) in excitatory neurons compared to men with tau neuropathology. Integrative sc/snRNA‐seq data analyses showed sex‐specific ligand‐receptor interactions among microglia, astrocytes, and neurons. We also found that women with AD have elevated metabolites of glycerophospholipids and sphingolipids compared to men with AD. A microglia‐specific male‐biased metabolite‐enzyme network is enriched in immune pathways of Type I and II IFN signaling and fructose, mannose, and HIF‐1 metabolic pathways. By contrast, the female‐biased metabolite‐enzyme network is significantly enriched in glycerophospholipids. These findings were further validated by the female‐biased expression of pro‐inflammatory cytokines (i.e., IFNγ, GM‐CSF, IL‐8, and IL‐15) from ATN‐characterized cohorts from our CADRC. Conclusion This study establishes proof‐of‐concept of sex‐specific immune responses, cellular metabolism, and microglial immunometabolism underlying sex differences in AD.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S3
    DOI: 10.1002/alz.066649
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 9
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    Sildenafil (Viagra) is a candidate drug for Alzheimer’s disease
    Wiley ; 2022
    In:  Alzheimer's & Dementia Vol. 18, No. S10 ( 2022-12)
    Details
    In: Alzheimer's & Dementia, Wiley, Vol. 18, No. S10 ( 2022-12)
    Abstract: The hypothesis that accumulation of amyloid‐beta protein and consequent tau pathology represents the main cause of Alzheimer’s disease (AD) has dominated the field for more than 2 decades; yet, anti‐tau or anti‐amyloid selective drug discovery approaches have lack of clinical benefits for AD patients. We posited that systematic identification of underlying AD‐related endophenotype modules shared by amyloid and tau may provide a foundation for generating predictive models to characterize pathogenesis and therapeutic development for AD. Method In this study, we developed an endophenotype network‐based methodology for in silico drug repurposing in AD. Specifically, we proposed an integrated network‐based framework by incorporating systems pharmacology and network medicine strategies to identify candidate drugs for AD. We further evaluated the drug‐AD outcomes by analyzing 7.23 million U.S. commercially insured individuals and investigated the drug’s mechanism‐of‐action using AD patient‐induced pluripotent stem cells (iPSC)‐derived neuron models. Result Using an endophenotype disease module‐based methodology for Alzheimer’s disease (AD) drug repurposing, we identified sildenafil as a potential disease risk modifier. Based on retrospective case‐control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25‐0.39, P 〈 1.0´10 −8 ). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho‐tau expression in AD patient iPSC‐derived neurons, supporting mechanistically its potential beneficial effect in Alzheimer’s disease. Conclusion We identified sildenafil (Viagra) as potential treatment of AD; yet, the association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.
    Type of Medium: Online Resource
    ISSN: 1552-5260 , 1552-5279
    URL: Issue
    URL: Article
    DOI: 10.1002/alz.v18.S10
    DOI: 10.1002/alz.062169
    Language: English
    Publisher: Wiley
    Publication Date: 2022
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  • 10
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    Alzheimer's disease drug development pipeline: 2023
    Wiley ; 2023
    In:  Alzheimer's & Dementia: Translational Research & Clinical Interventions Vol. 9, No. 2 ( 2023-04)
    Details
    In: Alzheimer's & Dementia: Translational Research & Clinical Interventions, Wiley, Vol. 9, No. 2 ( 2023-04)
    Abstract: Drugs that prevent the onset, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. Methods We searched ClinicalTrials.gov for all current Phase 1, 2 and 3 clinical trials for AD and mild cognitive impairment (MCI) attributed to AD. We created an automated computational database platform to search, archive, organize, and analyze the derived data. The Common Alzheimer's Disease Research Ontology (CADRO) was used to identify treatment targets and drug mechanisms. Results On the index date of January 1, 2023, there were 187 trials assessing 141 unique treatments for AD. Phase 3 included 36 agents in 55 trials; 87 agents were in 99 Phase 2 trials; and Phase 1 had 31 agents in 33 trials. Disease‐modifying therapies were the most common drugs comprising 79% of drugs in trials. Twenty‐eight percent of candidate therapies are repurposed agents. Populating all current Phase 1, 2, and 3 trials will require 57,465 participants. Discussion The AD drug development pipeline is advancing agents directed at a variety of target processes. HIGHLIGHTS There are currently 187 trials assessing 141 drugs for the treatment of Alzheimer's disease (AD). Drugs in the AD pipeline address a variety of pathological processes. More than 57,000 participants will be required to populate all currently registered trials.
    Type of Medium: Online Resource
    ISSN: 2352-8737 , 2352-8737
    URL: Issue
    URL: Article
    DOI: 10.1002/trc2.v9.2
    DOI: 10.1002/trc2.12385
    Language: English
    Publisher: Wiley
    Publication Date: 2023
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