ArticleJournal of microbiological methods2016
Development of a web-based tool for automated processing and cataloging of a unique combinatorial drug screen.
Article in Journal of microbiological methods, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.Metallomics : integrated biometal science · 2019Article
- 8-Hydroxyquinolines Are Boosting Agents of Copper-Related Toxicity in Mycobacterium tuberculosis.Antimicrobial agents and chemotherapy · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Facing totally resistant bacteria, traditional drug discovery efforts have proven to be of limited use in replenishing our depleted arsenal of therapeutic antibiotics. Recently, the natural anti-bacterial properties of metal ions in synergy with metal-coordinating ligands have shown potential for generating new molecule candidates with potential therapeutic downstream applications. We recently developed a novel combinatorial screening approach to identify compounds with copper-dependent anti-bacterial properties. Through a parallel screening technique, the assay distinguishes between copper-dependent and independent activities against Mycobacterium tuberculosis with hits being defined as compounds with copper-dependent activities. These activities must then be linked to a compound master list to process and analyze the data and to identify the hit molecules, a labor intensive and mistake-prone analysis. Here, we describe a software program built to automate this analysis in order to streamline our workflow significantly. We conducted a small, 1440 compound screen against M. tuberculosis and used it as an example framework to build and optimize the software. Though specifically adapted to our own needs, it can be readily expanded for any small- to medium-throughput screening effort, parallel or conventional. Further, by virtue of the underlying Linux server, it can be easily adapted for chemoinformatic analysis of screens through packages such as OpenBabel. Overall, this setup represents an easy-to-use solution for streamlining processing and analysis of biological screening data, as well as offering a scaffold for ready functionality expansion.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.