Notice of Interim Results

RNS Number : 9786A

e-Therapeutics plc

17 September 2018

 

E-THERAPEUTICS PLC

("e-therapeutics" or the "Company")

NOTICE OF INTERIM RESULTS

Oxford, UK, 17 September 2018: e-therapeutics plc (AIM: ETX), the network-driven computational drug discovery company, will announce its interim results for the half-year ended 31 July 2018 on 4 October 2018.

For further information, please contact:

e-therapeutics plc Ray Barlow, Chief Executive Officer Steve Medlicott, Finance Director	Tel: [phone] www.etherapeutics.co.uk
Numis Securities Limited Michael Meade/Freddie Barnfield (Nominated Adviser) James Black (Corporate Broking)	Tel: [phone] www.numis.com
FTI Consulting Simon Conway/Brett Pollard	Tel: [phone] [e-mail]

About e-therapeutics

We are an Oxford, UK-based company with a unique and powerful computer-based drug discovery platform and a specialised approach to network biology.

Our novel network-driven methodology allows us to discover new and better drugs in a more efficient and effective way.

We use our highly productive drug Discovery Engine to develop our own IP-protected, preclinical drug discovery programmes which will be of interest to partners looking to acquire or in-license novel and differentiated assets. We are currently developing two programmes in immuno-oncology and have a number of partner-ready projects in areas such as fibrosis and tumour microenvironment.

Because of our novel network-driven drug discovery ("NDD") approach, we believe there is potential to enter into several different types of collaborative partnerships with biotech, pharma and other technology companies to create sustainable mutual value.

About Network-Driven Drug Discovery ("NDD")

e-Therapeutics' proprietary NDD platform comprises a suite of powerful computational tools to augment and interrogate the vast amount of biological information currently available in both public and private databases.

Our NDD platform is founded on sophisticated network science and employs techniques such as machine learning, artificial intelligence and state-of-the-art data analysis tools. Using our biological expertise, we can create and analyse network models of disease to identify likely proteins that could effectively be disrupted to treat the disease.

We believe that our network-driven approach more realistically reflects the true complexity of disease, with its multiple and often interconnected cellular pathways. By modelling and analysing disease networks and considering the pattern of connections between proteins, and not just single pathways, we more efficiently select the very best drug-like compounds for screening and for subsequent medicinal chemistry and pre-clinical testing. With our novel methodology, significant numbers of active molecules can be identified and tested quickly. Our approach is highly productive and consistently generates hits that have been progressed into potent, selective and novel drug molecules.

Our overall aim is to discover more efficacious drugs more effectively. By using more biologically realistic, cell and tissue-based assays we can choose and design compounds that are more likely to translate into better, more clinically efficacious drugs.

 

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