Sensyne Health announces collaboration with the University of Oxford to provide software for remote symptom data collection and analytics for a Phase II clinical trial in care homes of adalimumab to prevent respiratory failure due to COVID-19
· Sensyne’s software for remote symptom data collection and analytics will support researchers in academia and the pharmaceutical industry to undertake clinical trials during the pandemic, particularly within the community environment
· Collaboration with the University of Oxford for the AVID-CC Phase II clinical trial in care homes of the anti-TNF drug adalimumab funded by the Wellcome Trust
· Further agreements to use Sensyne’s capabilities in other clinical trials are in advanced discussions
Oxford, U.K. 30 September 2020: Sensyne Health plc (LSE: SENS) (“Sensyne” or the “Company” or the “Group”), the UK Clinical AI company, today announces a collaboration with the University of Oxford in which Sensyne will provide software for remote data collection and analytics for the AVID-CC Phase II clinical trial of the anti-TNF drug adalimumab to prevent respiratory failure due to COVID-19. The trial is funded by the COVID-19 Therapeutics Accelerator, an initiative set up by the Wellcome Trust, the Bill and Melinda Gates Foundation and Mastercard, with support from an array of public and philanthropic donors.
The purpose of the trial is to assess the effectiveness of Adalimumab in preventing progression to respiratory failure or death due to COVID-19 infection. A particular feature of this trial, supported by Sensyne’s software, is the remote collection of data on symptoms, vital signs and activities of daily living from clinical trial subjects in care homes, followed by a phase of exploratory data analysis using Sensyne’s machine learning algorithms.
The software is based on Sensyne’s CVm-Health™ software application for tracking COVID-19 symptoms launched earlier this year. The use of CVm-Health will allow clinical trial participants in care homes to record their symptoms and vital signs and enable trial investigators to access such data remotely. The ‘Good Neighbour’ aspect of CVm-Health, whereby a carer, with consent, can enter data on behalf of an individual, is an important feature that facilitates clinical trials in the community care environment. Under the trial protocol, and with the consent of participants, anonymised data recorded by the application during the trial will be analysed using machine learning algorithms developed by Sensyne.
Sensyne is in advanced discussions for the use of its remote symptom and vital sign data collection software and analytics expertise by other clinical researchers seeking to undertake drug trials during the COVID-19 pandemic, particularly trials within community settings such as care homes. Further announcements will be made as such discussions reach agreement.
Lord (Paul) Drayson PhD, CEO of Sensyne Health, said:
“Sensyne is at the forefront of developing advanced remote monitoring software and clinical AI tools that enable clinical research to be undertaken during the new circumstances caused by the COVID-19 pandemic. We are delighted to be working with the University of Oxford on this important new trial of Adalimumab, pioneering new clinical research techniques in the community environment.”
Professor Duncan Richards FRCP, Climax Professor of Clinical Therapeutics and Director of the Oxford Clinical Trials Research Unit (OCTRU), formerly Head of Clinical Pharmacology and Experimental Medicine for GSK (until 2019), who is the Chief Investigator for the AVID-CC trial, said:
"We have been impressed by Sensyne Health’s digital health and clinical AI capabilities. We are excited by the opportunity that that flexible electronic diaries for remote data collection and analytics, such as CVm-Health for patients and carers to contribute to information collected in clinical trials. This pandemic has highlighted the need to re-examine the ways in which we collect data. We are delighted to be working with Sensyne and are excited by the potential for unique insights that may come from exploratory analyses of the anonymised clinical trial data using their proprietary machine learning algorithms”