30 research outputs found
Synchronously diagnosed eosinophilic granuloma and Hodgkin's disease in a 12-year-old boy: a case report
Influence of Proton Pump Inhibitors on Mycophenolic Acid Pharmacokinetics in Patients With Renal Transplantation and the Relationship With Cytochrome 2C19 Gene Polymorphism.
A single oral dose of a polyglucosamine influences the bioavailability of [9-14C]-Oleic acid in adult female Göttingen minipigs
Classification and analysis of a large collection of <i>in vivo</i> bioassay descriptions
<div><p>Testing potential drug treatments in animal disease models is a decisive step of all preclinical drug discovery programs. Yet, despite the importance of such experiments for translational medicine, there have been relatively few efforts to comprehensively and consistently analyze the data produced by <i>in vivo</i> bioassays. This is partly due to their complexity and lack of accepted reporting standards—publicly available animal screening data are only accessible in unstructured free-text format, which hinders computational analysis. In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening-related assays in rats and mice. We retrieve our dataset from ChEMBL—an open-source literature-based database focused on preclinical drug discovery. We show that <i>in vivo</i> assay descriptions can be effectively mined for relevant information, including experimental factors that might influence the outcome and reproducibility of animal research: genetic strains, experimental treatments, and phenotypic readouts used in the experiments. We further systematize extracted information using unsupervised language model (Word2Vec), which learns semantic similarities between terms and phrases, allowing identification of related animal models and classification of entire assay descriptions. In addition, we show that random forest models trained on features generated by Word2Vec can predict the class of drugs tested in different <i>in vivo</i> assays with high accuracy. Finally, we combine information mined from text with curated annotations stored in ChEMBL to investigate the patterns of usage of different animal models across a range of experiments, drug classes, and disease areas.</p></div