106th Edition – November 2020

Patient education project goes global with Japan national platform

Former IMI patient education project EUPATI is going global with the launch of a national platform in Japan, and the translation into Japanese of its toolbox. The move highlights EUPATI’s role as a global pioneer of patient education.

EUPATI has already established national platforms in over 20 European countries. Now, the creation of the ‘Patient and Public Involvement Consortium in Japan’ (PPI Japan) marks the first EUPATI national platform outside Europe. The PPI Japan story began in 2016, when Satoshi Miki, Vice-President of UCB Japan, learnt about EUPATI.

‘I was astonished and stunned with its objective and the system that had been already well advanced and up & running in Europe,’ said Mr Miki, who is now a board member of PPI Japan. ‘I felt strongly that patient involvement should be made available in Japan.’

Simply copying and pasting EUPATI was not an option. ‘The background and relationship of patients and medicine as well as cultural aspects in Japan are so different from those in Europe,’ explained Mr Miki. One of PPI Japan’s first tasks was to translate the EUPATI toolbox into Japanese – this went live at the beginning of the autumn. Most of the content of the toolbox is relevant to patients anywhere in the world. However, the PPI Japan team is creating new texts on regulatory systems, pharmacovigilance and health technology assessment (HTA) to reflect how these things work in Japan.

Looking to the future, PPI Japan’s priorities are to continue its collaboration with EUPATI, introduce the EUPATI tools in Japan, and support the involvement of patients and the public in medicines development.

Find out more

• Read the article in full
• Visit the Japanese language version of the EUPATI Toolbox
• Visit the EUPATI website and the EUPATI national platforms

Breathy, tense, tight, flat: a person’s voice can hint at depression, and AI can pick it up

When Slovenian artist Sanela Jahić was working on a thought-provoking installation about how art and creativity can be turned into dispassionate data points, she came across a scientific paper by RADAR-CNS scientist Dr Nick Cummins. The paper suggests that depression can affect the way people speak – the voices of those affected sound breathy, tense or tight. Dr Cummins and fellow researcher Judith Dineley from the University of Augsburg provided Jahić with their insights into how depression affects speech and how machine learning can be used to analyse this.

The result is PATAKA, a visual and auditory journey named after a vocal exercise used by speech pathologists – how someone says ‘PATAKA’ can offer clues as to their mental state. As part of the exhibition, parrots were trained to repeat the PATAKA sequence, and the cacophony was combined with data visualisations to make a point about what our own voices can tell machines about us.

Although doctors can use speech in a subjective way to aid diagnosis, the researchers hope that applying artificial intelligence to the field will contribute to making diagnoses more objective. RADAR-CNS is using wearable tech and smartphones to detect and validate biosignatures not only for depression but also epilepsy and multiple sclerosis. The research covers other biosignatures, including heart rate, physical and social activity, facial muscle movements and EEG signals. Dr Cummins and Dr Dineley extract the meaningful information from these raw signals recorded during clinical trials before aggregating them for further analysis.

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The WHO’s COVID-19 clinical trial is ‘adaptive’. What does that mean?

The record for the fastest vaccine ever developed is about four years from lab to patient. The new coronavirus vaccines are set to smash this record. In some cases, it will be thanks to the use of adaptive trial platforms, which are trials that run studies of multiple drugs at the same time and ‘pool’ the placebo group.

While adaptive trial design is extremely good at reducing drug development times, it is also very complex. That’s why in 2019, IMI launched EU-PEARL to help provide the evidence and support for those who want to design their own adaptive trials, with the ultimate aim of getting medicines more quickly to market.  

EU-PEARL’s partners include some of the world’s foremost experts in adaptive platform trials. These experts were consulted by the World Health Organisation during the design phase of the Solidarity trial, a study to test multiple COVID-19 treatment, diagnostic and prevention options, including vaccines. New treatment groups can be added at any time, where necessary, and candidate compounds that don’t work can be dropped.

Though the adaptive trial concept has existed for years, there is still lack of uniformity and understanding as to what an adaptive platform means and how it works in practice. ‘The process of designing and running these trials is incredibly complex,’ says EU-PEARL project lead Ann Van Dessel of Johnson & Johnson. ‘To support researchers who decide to take on the task, we are putting in place the tools that are necessary to make sure they succeed. The project will set up a new entity whose job it will be to help facilitate these very complex new studies, and to maintain the tools and guidelines developed during the project.’

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Can computers learn to think like chemists? IMI’s MELLODDY project thinks so

Machine learning promises to make drug discovery faster, better and cheaper, but it requires access to vast datasets of molecules and their properties. While every pharma company can apply machine learning algorithms to their own data, the true power of this technology comes from combining the (usually ultra-confidential) datasets of several companies to fuel the algorithms.

The MELLODDY project is working on using machine learning to make the most of the combined power of these highly valuable datasets without sharing them, exposing them, or even moving them from where they’re housed.

Because companies’ data is too valuable to risk sharing, MELLODDY is applying a technique called federated learning: this allows datasets to remain behind their firewall, stored independently from each other. With this method, algorithms go back and forth between subsets of each company’s data and the central server, which prevents anyone from knowing which company’s data adds to the central model. This exposes the algorithm to a much wider range of data than any one company has in-house. All this is done while keeping sensitive data safely ensconced within each company’s own infrastructure.

Earlier this year, MELLODDY announced that it had managed to carry out the first successful federated learning run using this new predictive modelling platform.

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Interested in FAIR data? Join our FAIRplus project’s events!

IMI’s FAIRplus project is working is delivering guidelines and tools to make it easier to ensure life sciences data is ‘FAIR’, i.e. findable, accessible, interoperable, reusable. Two upcoming online events provide an opportunity to learn more about this important subject.

FAIRplus webinar: What is the value of FAIR data?

9 December 2020, 10.00-12.00 CET

Using real life examples and use cases, this event will showcase the value of FAIR data in pharmaceutical research and make the case for policy changes in relation to research data management and research funding. It is aimed at funders and policy-makers responsible for data management policies, research managers and data scientists in industry, SMEs and academia.

• Registration

2nd FAIRplus SME and Innovation Forum

13 January 2021, 10.30-14.45 CET

The second FAIRplus Innovation and SME Forum is designed for companies working with life science data, companies planning to provide FAIRification services and anyone interested in FAIRification of their data. During the event, the project will share experience from FAIRification of datasets from selected IMI projects and explore different business models based on FAIR data expertise.

• Registration

Finally, a reliable way to track drug side effects

Once clinical trials are over and medicines have been approved for sale, they still need to be monitored for safety. The WEB-RADR project created a mobile app for smartphone users to report side effects of medicines directly to the regulatory authorities. The app was first rolled out in three European countries (UK, Netherlands, Croatia).

In Europe, systems for reporting side effects are well established. However, in some low-and middle-income countries there are no electronic systems in place and adverse effects are reported via paper forms. The WHO recognised the potential of the WEB-RADR app to fill this gap, and a generic multi-country version of the app, the Med Safety App was created for wider adoption. The Med Safety App has been rolled out in 12 countries to date, from far afield as Pakistan to Nigeria and Armenia.

Some of the Med Safety App functionalities include the ability to have multiple languages including local dialects, use the app without internet connection, partially create reports and save them for completing later, and have tailored logos and branding. The future for the WEB-RADR project will be less about rolling out apps and more about developing additional functionality. The project has received extra funding to adapt its code to allow reporting on medical devices, which will become increasingly important as digital health devices become more commonly used.

The UK regulator, the MHRA, has recently funded adaptation of the platform to help with the reporting of medicines used to treat coronavirus symptoms in the UK. Such functionality has been made available to Med Safety App countries and a number of countries have implemented the COVID-19 reporting form. The MHRA are discussing how to further develop this functionality to support the reporting of adverse effects following immunisation, known by the acronym AEFI, which will be crucial because these campaigns will use brand new vaccines on a massive scale.

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