Project description:ADRs are immune mediated skin reactions of diverse severity and etiology. The patho-mechanisms are however not well understood. We used a gene expression array for the comparison of the gene expression profile of 2 cutaneous adverse drug reactions (MPR and AGEP) to normal skin.
Project description:Severe cutaneous adverse reactions (SCAR) are rare but life-threatening drug reactions mediated by human leukocyte antigen (HLA) class I-restricted CD8+ T-cells. To obtain an unbiased assessment of SCAR cellular immunopathogenesis, we performed single-cell (sc) transcriptome, surface proteome, and TCR sequencing (5' scRNA-TCR-CITE-seq, 10x Genomics) on unaffected skin, affected skin, and blister fluid from diverse SCAR patients.
Project description:Search for SNPs associated with the pharmacogenomic profile of Benzidazole adverse reactions in Chagas Disease Homo sapiens patients.
Project description:Drug-induced cis-regulatory elements in human hepatocytes affect molecular phenotypes associated with drug efficacy and adverse reactions
Project description:G protein-coupled receptors are important drug targets that engage and activate signaling transducers in multiple cellular compartments. Delineating therapeutic signaling from signaling associated with adverse events is an important step towards rational drug design. The glucagon-like peptide-1 receptor (GLP-1R) is a validated target for the treatment of diabetes and obesity, but drugs that target this receptor are a frequent cause of adverse events. Using recently developed biosensors, we explored the ability of GLP-1R to activate 15 pathways in 4 cellular compartments and demonstrate that modifications aimed at improving the therapeutic potential of GLP-1R agonists greatly influence compound efficacy, potency and safety in a pathway- and compartment-selective manner. These findings, together with comparative structure analysis, time-lapse microscopy and phosphoproteomics, reveal unique signaling signatures for GLP-1R agonists at the level of receptor conformation, functional selectivity and location bias, thus associating signaling neighborhoods with functionally distinct cellular outcomes and clinical consequences.
Project description:The model predicts the putative adverse drug reactions (ADR) of a molecule, using the SIDER database (MoleculeNet) that contains pairs of marketed drugs and their described ADRs.This model has been trained using the GROVER transformer
Model Type: Predicitive machine learning model.
Model Relevance: Predicts adverse drug reactions (ADR) of a molecule
Model Encoded by: Amna Ali (Ersilia)
Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam
Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos77w8
Project description:Hypersensitivity reactions to medications constitute a growing problem in the clinical practice. In order to study the molecular basis underlying the pathogenesis of non-immediate hypersensitivity reactions to drugs, we characterized the gene expression profiles of PBMCs isolated from patients during the acute phase and upon resolution of the clinical symptoms using a cDNA array technology. Eighty five genes were found to be differentially expressed during the acute phase of drug-induced delayed hypersensitivity reactions. Furthermore, ninety two genes with distinct expression patterns during the acute phase of severe and benign diseases were identified. Keywords: Comparison between disease and healty status