Towards standardization guidelines for in silico approaches in personalized medicine

Abstract

Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.

Keywords: in silico modelling; data integration; personalized medicine; reproducibility; standards.

Figure 1:

A typical workflow for personalized medicine. A personalized medicine approach to improve patient health typically starts with identifying which aspect of health that is to be addressed and modelled, for example prediction of disease, prediction of severity of a particular disease or prediction of response to treatment. The next step is typically identification of relevant data sources, which can be of many different types, as illustrated. These data often have to be harmonized, a task that is made easier whether common data standards have been used. Once this has been completed, modelling to predict the clinically relevant state takes place. Usually the models then have to be validated in an independent setting. Once that has been completed, the models can be used in a clinical environment to help improve patient health. Picture source (licence free): www.pixabay.com.