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Dataset results
213 results for “drug screening”
Antihypertensive drug metabolite screening toy dataset for MS/MS application
<p><strong>Objectives :</strong></p> <p>Detect and visualize antihypertensive drug metabolites in untargeted metabolomics experiments</p> <p><strong>Cohort :</strong></p> <p>6/26 patients on antihypertensive therapy</p> <p><strong>Mass spectrometer :</strong></p> <p>Thermo Q-Exactive coupled to pHILIC chromatography using data dependent analysis (DDA) MS/MS gas-phase experiments</p>
Screening for side effects of COVID-19 drug candidates on cardiovascular development -RAW DATA qPCR RESULTS
<p>Raw Data relating to Figures 4 and Supplemental Figures S7 and S8 of the article </p> <p><strong>Screening for side effects of COVID-19 drug candidates on cardiovascular development </strong></p> <p>Alexander Ernst<sup>1#</sup>, Indre Piragyte<sup>1,2#</sup>, Ayisha Marwa MP<sup>1,2</sup>, Ngoc Dung Le<sup>3</sup>, Denis Grandgirard<sup>3</sup>, Stephen L. Leib<sup>3</sup>, Andrew Oates<sup>4</sup>, Nadia Mercader<sup>1,2,5</sup></p> <p> </p> <p> </p> <p> </p> <p><sup>1</sup> Institute of Anatomy, University of Bern, Switzerland</p> <p><sup>2</sup> Department for Biomedical Research DBMR, University of Bern, Switzerland</p> <p><sup>3</sup> Institute for Infectious Diseases, University of Bern, Switzerland</p> <p><sup>4 </sup>School of Life Sciences, École polytechnique fédérale de Lausanne, Switzerland</p> <p><sup>5</sup> Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain</p> <p># shared first-authorship</p>
Dataset for: Image-based screen capturing misfolding status of Niemann-Pick type C1 identifies potential candidates for chaperone drugs
<p>Niemann-Pick disease type C is a rare, fatal neurodegenerative disorder characterized by massive intracellular accumulation of cholesterol. In most cases, loss-of-function mutations in NPC1 gene that encodes for a lysosomal cholesterol transporter NPC1 are responsible for the disease, and more than half of the mutations are considered to interfere with biogenesis or folding of the protein. Previously we have identified a series of oxysterol derivatives and phenanthridine-6-one derivatives as pharmacological chaperones, small molecules that rescue folding defective phenotypes of a mutated NPC1, and opened an avenue to develop chaperone therapy for Niemann-Pick disease type C. Here, we established an improved image-based screen for NPC1 chaperones and performed drug-repurposing screening to identify some azole antifungals, including itraconazole and posaconazole, and a kinase inhibitor lapatinib as probable pharmacological chaperones. Photo-crosslinking probes of the compounds allowed us to detect direct binding of itraconazole to a representative folding-defective mutant, NPC1-I1061T. Competitive photo-crosslinking experiments suggested that oxysterol-based chaperones and itraconazole share the same or nearby binding site(s), and sensitivity of the crosslinking to P691S mutation on the sterol-sensing domain supported currently proposed hypothesis that their binding sites are located near the domain. Although the azoles were less effective in reducing cholesterol accumulation than the oxysterol-derived chaperone or an HDAC inhibitor LBH-589, our findings should offer new starting points for developing better pharmacological chaperones for NPC1 through medicinal chemistry efforts.</p>
ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery
<p>All of the individual docking data and ESSENCE-Dock consensus results for 21 diverse DUD-E targets as presented in the paper "ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery".</p> <p>The data is sorted per DUD-E target. It contains the prepared data that was used for the docking calculations (in the Undocked directory), as well as our docking results. Finally, our ESSENCE-Dock Consensus results are included as well</p> <p>Docking calculations were performed using:</p> <ul> <li><a href="https://github.com/bio-hpc/metascreener">Metascreener (V1.1)</a> (Gnina and LeadFinder Calculations; prefix VS_GN_ and VS_LF_ respectively)</li> <li><a href="https://github.com/Jnelen/DiffDockHPC/tree/DiffDockHPCv1.0">DiffDockHPC (v1.0)</a> (DiffDock calculations; prefix VS_DD_ )</li> </ul> <p>The consensus calculations were performed using ESSENCE-Dock, available via <a href="https://github.com/bio-hpc/metascreener">Metascreener </a>as well.</p> <p>The whole methodology and all of the details are described in the ESSENCE-Dock paper: <a href="https://doi.org/10.1021/acs.jcim.3c01982">https://doi.org/10.1021/acs.jcim.3c01982</a></p> <p><strong>Paper Abstract</strong></p> <p>Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose, however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches, which combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking.<br>In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening, while also being designed for use in high-performance computing (HPC) environments.</p>
Metabolomics-based phenotypic screens for evaluation of drug synergy via DIMS
<p>Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery. Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens relied on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope tracer direct infusion mass spectrometry that informs a novel algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening integrating isotope enriched metabolomics data and computational data mining on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.</p>
Screening Rafflesia and Sapria Metabolites Using a Bioinformatics Approach to Assess Their Potential as Drugs
<p>This dataset contains molecular docking results read using LigPlus, referred in the text:</p> <p>Wicaksono et al. (2022) Screening Rafflesia and Sapria Metabolites Using a Bioinformatics Approach to Assess Their Potential as Drugs. Philippine Journal of Science.</p> <p>As Appendix II.</p>
Fig. 1 Screening flow, testing 1600 FDA compounds against S in Assessment of FDA-approved drugs against StrongyloideS rAtti in vitro and in vivo to identify potentially active drugs against strongyloidiasis
Fig. 1 Screening flow, testing 1600 FDA compounds against S. ratti
Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses
<p>Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed <em>Trellis</em> — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy.</p> <p> </p> <p>This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis</p>
The Culture of Advanced or Recurrent Ovarian Cancer Organoids and Drug Screening
ClinicalTrials.gov study NCT05290961. IPD Sharing: NO. Countries: 1. Publications: 14.
Validation of a Community Pharmacy-Based Prescription Drug Monitoring Program Risk Screening Tool
ClinicalTrials.gov study NCT03936985. IPD Sharing: NO. Countries: 1. Publications: 2.
The Culture of Advanced/Recurrent/Metastatic Colorectal Cancer Organoids and Drug Screening
ClinicalTrials.gov study NCT05304741. IPD Sharing: NO. Countries: 1. Publications: 7.
Dataset for: Image-based screen capturing misfolding status of Niemann-Pick type C1 identifies potential candidates for chaperone drugs
Open the record for dataset details and reuse information.
Computer-Aided Drug Design (CADD): To Screen Potential Antibiotics Against Klebsiella Pneumoniae Beta-Lactamase Enzyme
Open the record for dataset details and reuse information.
Fig. 1 in Design, synthesis and screening of a drug discovery library based on an Eremophila-derived serrulatane scaffold
Fig. 1. Chemical structures of the targeted NP diterpenoid scaffolds (1–2), methylated products (3–4) and the amide library (5–16).
Fig. 4 in Design, synthesis and screening of a drug discovery library based on an Eremophila-derived serrulatane scaffold
Fig. 4. Representative flow cytometry examples of GFP expression (i.e., HIV production) in J-Lat 10.6 cells in the absence of stimulation (A), treatment with 50 nM PMA (B) and PMA plus 100 μM compound 2 (C). Dose response profiles (D) of compounds on GFP expression induced by 50 nM PMA in J-Lat 10.6 cells. Data denote mean ± s.d. from three independent experiments.
Fig. 3 in Design, synthesis and screening of a drug discovery library based on an Eremophila-derived serrulatane scaffold
Fig. 3. Microscopic images of the skinny (Skn) phenotype in L4s induced by compound 3 compared with the wild-type (WT) phenotype control (cultured in culture medium + 0.25% DMSO); the bottom panels enlarged from the dashed/ boxed areas in the upper panels.
Screening of Healthy Volunteers for Investigational Antimalarial Drugs, Malaria Vaccines, and Controlled Human Malaria Challenge
ClinicalTrials.gov study NCT02639299. IPD Sharing: NO. Countries: 1. Publications: 1.
Patient-derived Organoids Drug Screen in Pancreatic Cancer
ClinicalTrials.gov study NCT05351983. IPD Sharing: NO. Countries: 1. Publications: 8.
Personalized Targeted Glioblastoma Therapies by ex Vivo Drug Screening
ClinicalTrials.gov study NCT06512311. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Selecting Chemotherapy With High-throughput Drug Screen Assay Using Patient Derived Organoids in Patients With Refractory Solid Tumours (SCORE)
ClinicalTrials.gov study NCT04279509. IPD Sharing: NO. Countries: 1. Publications: 2.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.