How e-therapeutics can enable novel treatments
Fibrosis is a common feature of a number of devastating disease for which there are very few treatment options. Network-Driven Drug Discovery (NDD) is a unique and productive way to rapidly identify novel SMEs that could be used treat different forms of this complex disease. By using NDD and GAINs we also generate “actionable insights” that could enable novel treatments to be uncovered. Please see the presentation below and feel free to write to us at firstname.lastname@example.org.
IPF is a progressive and fatal lung disease for which there is no known cause or cure. Excessive fibrous connective tissue and extracellular matrix (“ECM”) components, such as collagen and fibronectin, build up in the lungs, making it more difficult for oxygen to pass from the lungs to the body. This irreversibly reduces lung volume and capacity, usually resulting in death due to respiratory failure. The median survival of patients with IPF is only two to three years.
Fibrosis has complex biology, unknown disease mechanisms and limited therapeutic options - need we could address with our NDD approach.
The limited therapeutic options currently available slow disease progression but do not prevent death.
We have successfully applied our in silico NDD platform to identify small molecules across different areas of disease.
Our NDD approach aims at developing anti-fibrotic agents by targeting the networks of interactions underpinning the disease.
We start by using data to drive models of complex cellular mechanisms involved in the disease processes we are aiming to disrupt.
Our approach is both knowledge-driven and data-driven.
We research and use known biology, such as proteins and pathways identified to affect disease.
We also use omics data generated from both diseased and healthy tissue e.g.
- the gene expression profile of human IPF vs normal control lung tissue
- the differential methylation pattern of human IPF vs normal control lung tissue
- the proteomics profile of IPF vs normal lung tissue
Network construction has been undertaken based on both multiomics data and known molecular mechanisms. Focusing on systems-level approaches is beneficial for IPF, and fibrotic diseases in general, as fibrosis is a multicomponent disease and a deep understanding of the molecular mechanisms is lacking.
Using our proprietary NDD platform for network analysis, we can identify potential active compounds with desirable physico-chemical characteristics for in vitro testing. We analyse the networks that we have constructed to ultimately yield a list of pre-selected compounds ideal for phenotypic screening.
These compounds have been selected based on an assessment of how their biological footprint impacts network integrity.
Our network analysis has returned both novel and known compounds, showing that our approach works.