Ontology highlight
ABSTRACT:
PROVIDER: PRJNA691557 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. In this study, we profile 80 FDA-approved and clinically tested drugs in neural cell cultures, with the goal of producing a ranked list of possible repurposing candidates.
2021-01-14 | GSE164788 | GEO
Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.
2020-01-22 | GSE129141 | GEO
Project description:Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance
| PRJNA839943 | ENA
Project description:High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed a multi-omics approach for uncovering MoAs through an interpretable machine learning model of the effects of compounds on transcriptomic, epigenomic, metabolomic, and proteomic data. We applied this approach to examine compounds with beneficial effects in models of Huntington’s disease, finding common MoAs for previously unrelated compounds that were not predicted based on similarities in the compounds’ structures, connectivity scores, or binding targets. We experimentally validated two such disease-relevant MoAs, autophagy activation and bioenergetics manipulation. This interpretable machine learning approach can be used to find and evaluate MoAs in future drug development efforts.
2020-01-22 | GSE129143 | GEO
Project description:Spinal muscular atrophy (SMA) is a neuromuscular disorder caused by loss of survival motor neuron (SMN) protein. While SMN restoration therapies are beneficial, they are not a cure. We aimed to identify novel treatments to alleviate muscle pathology combining transcriptomics, proteomics and perturbational datasets. This revealed potential drug candidates for repurposing in SMA. One of the candidates, harmine, was further investigated in cell and animal models, improving multiple disease phenotypes, including SMN expression and lifespan. Our work highlights the potential of multiple and parallel data driven approaches for the development of novel treatments for use in combination with SMN restoration therapies.
2020-05-14 | GSE150517 | GEO
Project description:Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease. Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC50 = 0.4 uM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms. Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals. We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.
2022-12-13 | GSE179709 | GEO
Project description:Alzheimer's disease (AD) drug discovery has rarely been addressed in the context of aging even though sporadic AD accounts for 99% of the cases. Phenotypic screens based upon old age-associated brain toxicities were used to develop the potent AD drug candidates CMS121 and J147. The aim of this project was to investigate whether these two different AD drug candidates prevented the progression of dementia in SAMP8 mice when administered at advanced stages of disease, and whether they shared common modes of action. These transcriptomic data are part of an integrative multi-omics approach that also investigated protein expression, metabolite levels as well as cognition. In addition, in order to further investigate the effect of the drugs in in vitro neuronal cultures, rat primary neurons were treated with the compounds and the transcriptome sequenced.
2019-11-26 | GSE101112 | GEO
Project description:Discovery of small molecules that correct gene networks dysregulated in human disease may allow identification of therapies that treat disease at its fundamental basis by leveraging mechanism-based data. Here, we report the first broad gene network-based drug screen, which led to discovery of a drug candidate that effectively treats aortic valve disease in an animal model. We previously reported haploinsufficiency of NOTCH1 (N1) as a genetic cause of human aortic valve thickening and calcification, the third most common form of heart disease, and described the resulting gene network dysregulation in human induced pluripotent stem cell (iPSC)-derived endothelial cells (ECs). We exposed isogenic N1+/+ or N1+/– iPSC-derived ECs to each of 1595 small molecules or control and developed a machine learning approach that accurately distinguished WT or N1-haploinsufficient cells based on expression of 119 genes assayed by targeted RNA-sequencing. 9 small molecules corrected the gene network of N1+/– ECs sufficiently to be classified as WT. Among hits tested in vivo, the estrogen receptor-related alpha inverse agonist XCT790 significantly reduced aortic valve thickening, calcification, and stenosis in N1-haploinsufficient mice with shortened telomeres, which model the range of age-dependent cardiac disease observed in humans. This strategy, made feasible by human iPSC technology, next generation sequencing approaches, and machine learning, may represent a more effective path for drug discovery compared to conventional screening approaches.
2021-01-01 | GSE125299 | GEO
Project description:The human liver cytosol stability model is used for predicting the stability of a drug in the cytosol of human liver cells, which is beneficial for identifying potential drug candidates early during the drug discovery process. If a drug compound is quickly absorbed, it may not reach the intended target in the body or become toxic. On the other hand, if a drug compound is too stable, it could accumulate and cause detrimental effects. The authors use an NCATS dataset of 1450 compounds screened in vitro in mouse and human cytosol fractions. Compounds were classified as stable (half-life > 30min) or unstable (half-life ≤ 30 min). Note that authors report the dataset was biased towards stable compounds.
Model Type: Machine learning model.
Model Relevance: Predicts probability of a compound stability due to liver cells metabolism.
Model Encoded by: Pauline (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos9yy1
2024-04-22 | MODEL2404220005 | BioModels