Healthier people with biomedical and digital health

The development process of new medicines costs a lot of time and money. This can be improved with predictive tools and models in the pre-clinical stage and by a better design of clinical trials. At TNO, we develop translational models, measurement and modelling tools that help drug and nutritional food developers to more effectively investigate the effectiveness and safety of their products. Among other things, this reduces the failure rate, and therefore the cost, of medicines at a later stage of development.

In order to predict why someone becomes ill, we investigate how the disease originates and develops. We can detect the selection of metabolic and immune diseases earlier and predict how they will develop for a given individual. For this we use knowledge about biomarkers. These are indicators that can be measured in the blood, for example. We also use insights into disease mechanisms and into people's behaviour.

Every individual is unique. Whether an overweight person develops diabetes and other complications depends on factors such as lifestyle, environment, metabolism and genetics. It is not simply ‘one size fits all’. A particular intervention that works for one patient may not work for another. With our broad and in-depth biomedical knowledge, we help companies and healthcare professionals to develop personalised interventions in the field of metabolic and immune health.

We are constantly looking for new insights and technologies to use animal testing as effectively as possible and to refine, reduce and, where possible, replace it. The animal models we use are developed in such a way that they mimic human disease processes demonstrably well. In addition, we use non-animal models, such as computer models and organ-on-a-chip technology. This allows us to improve and accelerate the development of new products or to test them directly on humans . For all preclinical models, it is essential for us to know which part of human physiology is correctly imitated and which is not.

People today collect a lot of health data with apps that record heart rate, daily activity or diet, for example. This data is valuable for both users and scientists. We use this data to develop models and advisory systems, for example. We support and advise patients and healthy individuals on their health and lifestyle. Users themselves indicate who may use which data for what purposes. Transparency leads to trust, and secure data storage is crucial.

Within Digital Health, we work with 3 programme lines:

1. Sense: acquisition, safe storage and management of data
2. Reason: modelling and interpreting data
3. Act: interact with individuals with apps, develop interventions to change behaviour

For all 3 lines and the development of tools, there are 4 principles (pdf) of importance:

1. Digital Health's collection, analysis and advice must be personal. Because everyone has different genetics, history and preferences. And everyone has different needs to stay healthy.
2. In order to move from data to advice, valid and reliable health and disease prediction models are needed. To do this, we integrate large health databases and domain knowledge with artificial intelligence (AI) and other modelling techniques.
3. Effective access to own data and connections with public data are crucial to the development of effective models.
4. Data must be very well protected and secured.