Multimorbidity and polypharmacy increase the risk of adverse drug reactions and can accelerate cognitive decline. Therefore, the main goal of the research group is to investigate effects of different drug treatments in view of parameters especially relevant in elderly patients such as cognitive performance, falls, and use of health care services. Drugs that have been approved so far to enhance cognition in dementia patients cannot provide stable disease-modification or cure. Thus, it is highly interesting to detect interventions which are able to prevent or at least delay the onset of cognitive decline and dementia in the elderly population. In this way drug repositioning can also be achieved. The aim is to assess efficiency and safety of pharmacotherapy. Furthermore, risk constellations in view of different treatments with multiple drugs should be detected. Thus, pharmacoepidemiological analyses contribute to drug safety and provide a basis for further decisions in health care policy.

The group is analyzing data from multiple sources, including primary and secondary data. Primary data comprise data from longitudinal cohort studies. Detailed documentation of sociodemographic parameters, cognitive performance, medical history and medication allow comprehensive pharmacoepidemiological analyses. Incidence, progress and predictors of cognitive decline and dementia are identified. Inclusion of data on genetic variability can provide hints for a structured personalized therapy. Other sources of information are claims data from public health insurance, so-called routine data. These data offer pharmacoepidemiological research with large longitudinal samples on a broad population-based level. The evaluation of different kinds of epidemiological databases, including primary and secondary data, allows combining data from applied clinical research and claims data in order to examine the benefit/risk ratio of medicinal products.

Real4Reg aims to develop, optimise, and implement artificial intelligence methods for real-world data (RWD) analyses in regulatory decision-making and health technology (HTA) assessment along the product lifecycle. Real4Reg will employ use cases for the development, optimisation, and implementation of artificial intelligence and machine learning methods for RWD analyses in regulatory decision-making and HTA. All selected use cases have practical relevance along the product life cycle. The findings will inform training activities on good practice examples and will be implemented in existing and emerging guidelines for both health regulatory authorities and HTA bodies across Europe. Real4Reg supports better decision-making about medicines and ultimately benefits patients’ health. More information about the project and the consortium partners are available on the project homepage.