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321 results for “Drug response”
Data from : Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations
<p>This dataset contains representative 1D 1H NMR spectra and data used in the manuscript " Metabolic footprint of Vero E6 cells highlights the key metabolic routes associated with SARS-CoV-2 infection and response to drug combinations " . </p><p> </p><p>The present study used Nuclear Magnetic Resonance-based metabolic footprinting to characterize the secreted cellular metabolite levels (exometabolomes) of Vero E6 cells in response to SARS-CoV-2 infection and to two candidate drugs (Remdesivir, RDV and Azithromycin, AZI). </p><p> </p><p><strong>Supplementary File 1.zip = </strong>Representative 1D 1H NMR profiles of examined VE6 esometabolomes, </p><p> </p><p><strong>Supplementary File 2.xlsx</strong> = Average Mean ± Standard Deviations of NMR relative quantified data (integrals, a.u.) from examined VE6 esometabolomes. </p><p> </p><p><strong>Supplementary File 3.csv = </strong>p–values and associated False Discover Rate (FDR) derived from univariate ANOVA with Fischer's LDS post-hoc test comparisons carried out on NMR relative quantified data.</p><p> </p><p><strong>List of Supplementary Files derived from Metabolite Set Enrichment Analysis (MSEA) : </strong></p><p> </p><p><strong>Supplementary File 4.csv </strong>= Tabular Results from MSEA performed on VE6+ VE6- comparison.</p><p><strong>Supplementary File 5.csv </strong>= Tabular Results from MSEA performed on VE6+ RDV vs. VE6+ comparison.</p><p><strong>Supplementary File 6.csv </strong>= Tabular Results from MSEA performed on VE6+ AZI vs. VE6+ comparison.</p><p><strong>Supplementary File 7.csv</strong> = Tabular Results from MSEA performed on VE6+ R+A vs. VE6+ comparison.</p><p> </p>
Interpretable AI for drug response prediction
<p>This dataset contains input data needed to run the models evaluated in our study "Interpretable deep learning architectures for improving drug response prediction: myth or reality?". The data required to run each model is organized into sub-folders, with the folder name being the corresponding model name. The data in 'model_agnostic_data' is shared by all models. For details regarding each data file, please refer to the documentation ('input_data_doc') included. The code for this study is available at <a href="https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP">https://github.com/Emad-COMBINE-lab/InterpretableAI_for_DRP</a></p> <p> </p>
Dataset of Raman spectroscopy responses for over-the-counter drugs in Paraguay, including acetylsalicylic acid, paracetamol, and ibuprofen.
<p>Spectra of three over-the-counter pharmaceuticals—acetylsalicylic acid, paracetamol, and ibuprofen—were collected at the Faculty of Exact and Natural Sciences of the National University of Asuncion, with the aim of creating a dataset that serves as a reference for Raman responses from different drug manufacturers. This dataset will also provide the scientific community with data that can be used for multivariate analysis and model training.</p> <p>In the data collection phase, spectra were obtained using a Raman spectroscopy system (iRaman 785s model from BWTEK) equipped with a 785 nm excitation laser. Samples were collected from diverse sales points such as pharmacies, shopping centers, and street vendors. Each spectrum was captured at 50% laser power with a measurement time of 1 second and an accumulation of 10 spectra over a range of 150 to 3200 cm-1. This method preserved the integrity of the raw data, which includes a common column for Raman shifts and additional columns for intensities and labels, detailing the activation modes in the Raman spectrum.</p> <p>The data is structured into specific xlsx files for each drug, such as "Paracetamol.xlsx", "acetylsalicylic-acid .xlsx", and "Ibuprofen .xlsx", each containing 50 spectra categorized by the type of pharmaceutical but not by brand. Brand-specific categorization is detailed in separate files like "Paracetamol-trademark .xlsx", where samples are classified using codes such as "Par-A" for different brands. This organization aids the scientific community in using clustering methods to analyze the spectral data and differentiate pharmaceutical brands based on their excipients or binders, with consistent codes across different drugs suggesting common manufacturers for various medications.</p> <p> </p>
Dataset and Data treatment for Data mining Raman Microspectroscopic Responses of Cells to Drugs in Vitro using Multivariate Curve Resolution-Alternating Least Squares
<p><strong>Matlab scripts for the simulation and treatment of Raman datasets obtained from time dependent experiments Using MCR-ALS.</strong></p> <p> </p> <p><strong>- SIMULATED DATA: </strong>Simulated data is obtained by adding spectra of artificially generated responses (weighted considering artificially generated time profiles) to an experimental cell spectrum (Initial component) Three Different Scenarios are generated. </p> <p>Spectral and time profiles are obtained from here: </p> <p> </p> <p><strong>- EXPERIMENTAL DATA: </strong>DOX dataset obtained from here</p> <p>https://doi.org/10.1002/jbio.201800328</p> <p> </p> <p> </p> <p><strong>- DATA ANALYSIS INSTRUCTIONS</strong></p> <p>Run <em>datatreatment.m</em></p>
Supplementary Data: Data Imbalance in Drug Response Prediction: Multi-Objective Optimization Approach in Deep Learning Setting
Open the record for dataset details and reuse information.
Cancer-specific association between Tau (MAPT) and cellular pathways, clinical outcome, and drug response
<p>To bring new evidence that Tau represents a key protein in cancer, we present an <em>in silico</em> pan-cancer analysis of <em>MAPT</em> transcriptomic profile in over 10000 clinical samples and over 1300 pre-clinical samples provided by the TCGA and the DEPMAP datasets respectively.</p>
Data from: Anthelmintic drugs modulate the acute phase immune response but not the microbiota in wild Song Sparrows
<p>Co-infection with microparasites (e.g., bacteria) and macroparasites (e.g., helminths) is often the natural state for wild animals. Despite evidence that gut helminths can bias immune responses away from inflammatory processes, few field studies have examined the role that helminths, or their potential interactions with internal microbial communities, play in modulating immunity in free-living, wild birds. Here, we used anthelmintic drugs to treat wild Song Sparrows (<em>Melospiza melodia</em>) for helminth infections and measured markers of systemic inflammation (heterothermia and locomotor activity) in response to an immune challenge with lipopolysaccharide (LPS), a cell wall component of gram-negative bacteria. Using birds from a population that previously showed high helminth prevalence, we monitored skin temperature and activity remotely using automated radio telemetry. We also collected cloacal swabs to determine whether drug treatment was associated with changes in the cloacal microbiota, and whether cloacal microbial community structure was associated with the severity of birds' immune responses. Because helminths can reduce the severity of inflammatory immune responses in other species, we predicted that in comparison with untreated control birds, anthelmintic-treated birds would be more lethargic and display higher fevers when challenged with LPS. Consistent with these predictions, anthelmintic-treated birds expressed higher fevers in response to immune challenge. However, all LPS-challenged birds decreased locomotor activity to a similar degree, regardless of anthelmintic treatment. Although several individual indicator bacterial taxa were strongly associated with anthelmintic treatment, this treatment did not alter overall bacterial alpha- and beta- diversity. Similarly, we did not find evidence that bacterial community diversity influenced the severity of immune responses to LPS. These results suggest that under field conditions, natural helminth infection can reduce the severity of songbirds' thermoregulatory responses (fever) during an immune challenge, without major impacts on internal microbial communities or behavioral responses to infection.</p>
Predicting transcriptional responses to novel chemical perturbations using deep generative model for drug discovery
<p>Understanding transcriptional responses to chemical perturbations is central to drug discovery, but exhaustive experimental screening of diseasecompound combinations is unfeasible. To overcome this limitation, here we introduce PRnet, a perturbation-conditioned deep generative model that predicts transcriptional responses to novel chemical perturbations that have never experimentally perturbed at bulk and single-cell levels. Evaluations indicate that PRnet outperforms alternative methods in predicting responses across novel compounds, pathways, and cell lines. PRnet enables gene-level response interpretation and in-silico drug screening for diseases based on gene signatures. PRnet further identifies and experimentally validates novel compound candidates against small cell lung cancer and colorectal cancer. Lastly, PRnet generates a large-scale integration atlas of perturbation profiles, covering 88 cell lines, 52 tissues, and various compound libraries. PRnet provides a robust and scalable candidate recommendation workflow and successfully recommends drug candidates for 233 diseases. Overall, PRnet is an effective and valuable tool for gene-based therapeutics screening.</p>
Predicting cancer prognosis and drug response from the tumor microbiome
<p>Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression data, can predict cancer prognosis and drug response to some extent – microbial abundances are significantly less predictive of prognosis than gene expression, although remarkably, similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone.</p>
A systematic assessment of deep learning methods for drug response prediction: from in-vitro to clinical application
<p>https://github.com/LihongLab/Suppl-data-Benchmark</p> <p>## GDSC dataset</p> <p>**Table S3.** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S4.** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S5.** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S6.** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**Table S7.** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**Table S8.** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**Table S9.** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**Table S10.** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**Table S11.** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p>
Correlation between drug response and driver gene mutations
<p>Raw results of paper 'Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers'</p>
A systematic assessment of deep learning methods for drug response prediction: From in vitro to clinical applications
<p>## GDSC dataset</p> <p>**GDSC_EXP.csv** GDSC gene expression profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**GDSC_MUT.csv** GDSC gene mutation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**GDSC_CNV.csv** GDSC copy number variation profiles for 966 cancer cell lines, where each column represents a cell line in the form of its name and tissue collection site, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**GDSC_DR.csv** GDSC drug response data for 966 cancer cell lines and 282 drugs in the form of the natural logarithm of the IC50 readout. The first column shows the cell line name and tissue collection site, the second column shows the drug name, and the third column shows the drug response readout.</p> <p> </p> <p>**GDSC_DrugAnnotation.csv** GDSC annotations for 282 drugs include drug name, PubChem CID, PubChem canonical SMILES, Rdkit canonical SMILES, Target Pathway, standard deviation, bimodality coefficient and density coverage.</p> <p>## TCGA dataset</p> <p>**TCGA_EXP.csv** TCGA gene expression profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p> </p> <p>**TCGA_MUT.csv** TCGA gene mutation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The wild type is coded as 1 and the wild type as 0.</p> <p> </p> <p>**TCGA_CNV.csv** TCGA copy number variation profiles, where each column represents a patient in the form of TCGA patient ID, and each row represents a gene in the form of the HGNC symbol. The copy-neutral is coded as 0 and the deletion or amplification as 1.</p> <p> </p> <p>**TCGA_DR.csv** TCGA clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, the fourth column shows the cancer type, and the last column shows the clinical label as responder or non-responder.</p> <p>## PMID17185464 (Bortezomib) dataset</p> <p>**PMID17185464_EXP.csv** Bortezomib clinical trial gene expression profiles, where each column represents a patient in the form of patient ID, and each row represents a gene in the form of the HGNC symbol.</p> <p>**PMID17185464_DR.csv** Bortezomib clinical trial clinical response data. The first column shows the TCGA patient ID, the second column shows the drug name, the third column shows the clinical response category, and the last column shows the clinical label as responder or non-responder (NR: Non-responder, R: Responder).</p>
Multi-Omic Approach Associates Blood Methylome with Bronchodilator Drug Response in Pediatric Asthma: Summary statistics
<p>We conducted an epigenome-wide association study of bronchodilator drug response (BDR) in a discovery and validation design. The discovery phase was focused on 221 African American children with asthma. The association between DNA methylation and BDR was conducted using the limma package correcting for age, sex, ancestry, and tissue heterogeneity. Summary statistics include the output from toptable limma function and CpG annotation (based on Illumina EPIC Manifest file v 1.0 B4) organized in the following columns:</p> <ul> <li>Probe: Probe ID</li> <li>Chr: chromosome</li> <li>Pos: genomic position based on GRCh37/hg19</li> <li>Gene: Gene annotation based on Illumina EPIC Manifest file v 1.0 B4</li> <li>logFC: estimate of the log2-fold-change corresponding to the effect or contrast</li> <li>SE: standard error</li> <li>AveExpr: average log2-expression for the probe over all arrays and channels</li> <li>t: moderated t-statistic</li> <li>P.value: raw p-value</li> <li>FDR: adjusted p-value by false discovery rate</li> <li>B: log-odds that the gene is differentially expressed</li> <li>Problem: Flagged potentially problematic probes</li> </ul>
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>
Variability in Response to Non-steroidal Anti-inflammatory Drugs
ClinicalTrials.gov study NCT02502006. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Phase 3 Study of GSK548470 in Patients With Compensated Chronic Hepatitis B With Poor Response to Other Drugs
ClinicalTrials.gov study NCT01475851. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Study of Abatacept (BMS-188667) in Subjects With Active Rheumatoid Arthritis on Background Non-biologic DMARDS (Disease Modifying Antirheumatic Drugs) Who Have an Inadequate Response to Anti-TNF Thera
ClinicalTrials.gov study NCT00124982. IPD Sharing: Not stated. Countries: 10. Publications: 5.
INCB028050 Compared to Background Therapy in Patients With Active Rheumatoid Arthritis (RA) With Inadequate Response to Disease Modifying Anti-Rheumatic Drugs
ClinicalTrials.gov study NCT00902486. IPD Sharing: Not stated. Countries: 2. Publications: 3.
Best African American Response to Asthma Drugs
ClinicalTrials.gov study NCT01967173. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Differential Responses to Drugs and Sweet Tastes
ClinicalTrials.gov study NCT03810703. IPD Sharing: NO. Countries: 1. Publications: 21.
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Allen Brain Atlas
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