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Data-driven precision medicine ecosystem

Background

‚ÄčSecondary use of data

Personal data is increasingly collected and used in the context of digitalized services. Consequently, the amount of personal data stored in information systems is increasing exponentially. Besides the primary purpose, collected personal data is used for so called secondary purposes, such as for monitoring the quality of provided services, in the development of new services or products and for scientific research. The Finnish infrastructure related to secondary use of health data is depicted below. The figure shows data sources on the left and data users on the right. In between, centralized data storages and services support collection of data and making it available for data users.

 Finnish health and social services data exploitation infrastructure

 

In general, secondary use is permitted under certain conditions by the existing privacy legislation, in particular the General Data Protection Regulation (GDPR).  Specific legislation addressing the use of health data in the context of bionbanks has been implemented in some countries, including Finland, which is also preparing additional national legislation addressing secondary use of health and social services data.

Precision medicine in healthcare

In healthcare, exploitation of data enables precision medicine, referring to customization of healthcare, with medical decisions, treatments, practices, or products tailored to the individual patient. This is expected to be cost effective as interventions can be targeted to those individuals, who really benefit from them. Precision medicine is often based on genetic, molecular and cellular analysis of the individual and involves techniques such as molecular diagnostics, genetic sequencing, imaging and data analytics. Precision medicine is increasingly data-driven by integrating multi-omics data, clinical data and other real-world data influencing the health or the disease state of the  individual.

Precision medicine is expected to have a remarkable role especially in cancer care as tumours have particular genetic signatures correlating with their response to treatments. Genetic tests can help to decide which treatments a patient's tumour is most likely to respond to, sparing the patient from receiving treatments that are not likely to help. Benefits are foreseen also in other clinical domains. Metabolomics of certain drugs has been shown to depend on certain gene variants, which means that information of these variants can be used when selecting the drug to be used in a particular case. Furthermore, the personal risk of many common diseases, including many cancers and cardiovascular diseases as well as autoimmune diseases, depends on the genome. Knowing these risks through genetic tests is helpful in identifying and motivating individuals to preventive interventions.

Precision medicine in pharma

For the pharmaceutical product development data-driven precision medicine opens new opportunities in several phases of drug development. In the preclinical phase genome and other individual level data provides understanding of disease mechanisms and helps in identifying the drug targets and molecules.  During the product development healthcare data resources can be used for identifying and stratifying subjects to clinical trials. For pharmaceutical products in the markets Real World Evidence (RWE) studies are needed to monitor the efficacy of pharma products. 

Personalized food

Companies in food and nutrition industry are focusing R&D efforts towards new products with positive health effects. There is an increasing interest towards working together with employers in order to manage health risks of employees. Such activities include targeted interventions for risk groups and combining the elements of nutrition and physical exercise. The understanding of personalized effects of food is of high importance and has motivated several research activities. The future is foreseen where optimum diet for an individual can be constructed by using individual-level data, including genome and microbiome data. Especially, the effect of microbiome on health and the possibility of modifying the microbiome with the help of food are currently under extensive research.

Artificial intelligence

Data-driven precision medicine applications in healthcare and pharma domains are based on sophisticated analysis of data and increasingly exploiting Artificial Intelligence (AI) technologies. Examples in healthcare include advanced data-driven Clinical Decision Support systems (CDSS), automation of customer service processes by chatbots and support of self-care by automatic feedback to patients.     

FinnGen project

The recently started FinnGen project is expected to have a big positive impact on precision medicine in growing a national reserve of genome data and in developing the required infrastructure and competences needed to exploit it. In the framework of the project bio samples of 500 000 Finns will be collected and combined  with clinical registry data. The data and samples will be made available to biobank research during and after the project.