IMI launches final IMI2 Calls for proposals

7th July 2020

 The Innovative Medicines Initiative (IMI) is launching the last Calls for proposals under the IMI2 programme, with topics on tackling cancer through artificial intelligence, antimicrobial resistance, neurodegenerative diseases, rare disease diagnosis, the return of clinical trial data to participants, and patient adherence.

IMI will contribute a total of EUR 59 million to the projects funded under the Calls; these funds come from Horizon 2020 and will support the participation in the projects of organisations such as universities, small and medium-sized enterprises, and patient groups. EFPIA companies and IMI Associated Partners will contribute EUR 47 million, mostly as ‘in kind’ contributions (e.g. staff time, access to equipment, etc.).

Pierre Meulien, IMI Executive Director commented: ‘These final IMI2 Calls showcase the areas where IMI is best placed to make a difference. The topics on antimicrobial resistance, neurodegenerative disease and rare diseases demonstrate our commitment to addressing unmet medical needs that are too complex for any single organisation to solve alone. Our topics on clinical trial data and patient adherence are aligned with our principle of putting patients at the centre of our work. And the topics on artificial intelligence contribute our goal of linking up with other sectors active in health research.’

IMI2 – Call 23 is a standard, two-stage Call for proposals with the following topics:

Returning clinical trial data to participants – Clinical trials and studies generate vast amounts of high quality data, yet it is rarely returned to the people taking part in the trial. The aim of this topic is to develop a prototype process to return clinical trial data to study participants, taking into account legal, ethical and data protection issues. Patients would then be able to include the data in their health record where it would help them and their clinicians in decision making. They could also contribute it to additional studies, e.g. through ‘patient-powered research’, which is particularly relevant for rare diseases where treatments and data are scarce.

Modelling the impact of monoclonal antibodies and vaccines on the antimicrobial resistance (AMR) – Monoclonal antibodies (mAb) and vaccines could help to tackle AMR, but assessing different mAbs and vaccines and their potential impacts on AMR is far from easy. The goal of this topic is to develop a mathematical model capable of realistically assessing which vaccines and mAbs have the best chance of reducing AMR and the associated health and economic impacts. The model will take into account the concerns of different stakeholders (e.g. the industry and public health bodies), and will be made publicly accessible. This topic is part of IMI’s AMR Accelerator programme.

A platform to advance neurodegenerative disease biomarker research – Researchers worldwide have amassed a wealth of biological samples and data that could be used to advance research into biological markers for neurodegenerative diseases such as Alzheimer’s and Parkinson’s diseases. This topic aims to create a platform that will make it possible for researchers to see what samples and data are available and access them for further research. The hope is that by opening up these samples and data, researchers will be able to discover new biomarkers that will ultimately help to diagnose patients, monitor their disease, and select the most appropriate treatment.

Using artificial intelligence (AI) to select the best cancer treatment – Advances in research mean that for many cancers, there are more treatment options than ever. Physicians face a growing number of potential therapeutic options, each of which needs to be understood and adopted effectively to ensure each patient receives the right treatment. The goal of this topic is to develop a treatment decision support tool with AI technologies to support decision making and research for cancer. The topic focuses on breast, lung and prostate cancer as there are large numbers of patients, a high unmet medical need, and a rapidly evolving treatment environment. However, it should be possible to apply the project outputs to other cancers afterwards.

Towards faster diagnosis for rare diseases – There are some 5 000 to 8 000 rare diseases and between them, they affect up to 36 million people in the EU alone. Yet despite ongoing research, fewer than 10 % of patients receive any treatment and just 1 % have a treatment specifically approved for their condition. One of many challenges facing rare disease patients is getting a diagnosis; this takes an average of 8 years. Diagnosing patients faster would allow them to be followed up by the right medical team and get any treatments that are available. The aim of this topic is to speed up the path to diagnosis. Its strategy is based around two key elements. Firstly, the genetic screening of new-born babies, as around three quarters of rare diseases (excluding rare cancers) are genetic in origin and a majority of rare disease patients are children. Secondly, the development of artificial intelligence algorithms to identify rare disease patients via electronic health records (EHRs). Ultimately, the topic should enable the development of a broad rare disease ‘symptom checker’ to help undiagnosed rare disease patients to find their way to a diagnosis.

Understanding patient adherence to treatment – Around half of patients do not take or follow treatments as prescribed, and this causes an estimated 200 000 deaths annually in the EU. Currently, we lack a comprehensive understanding of all of the factors that influence patients’ decisions regarding treatment adherence. This topic aims to deliver a behavioural model that will enable a full understanding of patient adherence, as well as tools to enable the development of solutions that address patients’ needs and improve adherence rates.

IMI is also launching IMI2 – Call 22. The goal of this single-stage Call is to provide additional support to certain existing IMI2 projects to allow them to build on their achievements and maximise the impacts of their work.

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