Explore the words cloud of the HTS MALDI-TOF MDD project. It provides you a very rough idea of what is the project "HTS MALDI-TOF MDD" about.
The following table provides information about the project.
UNIVERSITY OF NEWCASTLE UPON TYNE
|Coordinator Country||United Kingdom [UK]|
|Total cost||212˙933 €|
|EC max contribution||212˙933 € (100%)|
1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
|Duration (year-month-day)||from 2020-04-01 to 2022-03-31|
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|1||UNIVERSITY OF NEWCASTLE UPON TYNE||UK (NEWCASTLE UPON TYNE)||coordinator||212˙933.00|
Discovery for drug targets is a key step within the process of drug development, and is the corner stone in the pharmaceutical industry. This is mostly achieved by high-throughput screening (HTS) approaches in which a large number of chemical substances are assayed for a specific effect or activity in diverse areas of biology. Mass spectrometry (MS) has become a widely adopted tool in this field as it offers the possibility to simultaneously track molecules in a label-free manner, provides excellent signal to noise, reproducibility, assay precision, and a significantly reduced reagent cost when compared to fluorescence-based assays. Matrix-assisted laser/desorption ionisation time of flight (MALDI-TOF) is the most validated surface ionisation method for HTS approaches, but reported applications of this technology have been limited to in vitro assays with simple readouts and to peptide/protein-centric activity assays. To date, comprehensive and unbiased metabolomics based HTS approaches for cellular assays with MALDI-TOF have not been explored. The objective of this fellowship is to set up a MALDI-TOF based cellular assay for HTS metabolomics drug discovery in order to identify a set of metabolites to screen which will enable us to unbiasly and comprehensibly measure, in a HT manner, how drugs affect different metabolic pathways while simultaneously mapping the metabolic profile of the cell. First, I will develop a robust protocol for the detection, identification, and quantification of ~50 small metabolites for cellular assays. Then, I will apply this protocol to track metabolites in a pulmonary fibrosis cellular model, as well as in fibrotic lung tissue sections using MS imaging. The goal is to compare a disease model with the control to see how drugs are affecting these metabolites, and to see if identified biomarkers can be used to identify disease in tissue samples. As a final validation step, I will implement this platform in Boehringer Ingelheim in Germany.
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