Research Objective
Responsible Translational Methodologies
This Research Objective seeks to develop Responsible Translational Methodologies for biomedicine. Our goals include: robust reproducible research; transparent open access to research results and data; validated incentives and key performance indicators (KPIs); education; and a patient-centered translational culture at the BIH, with outreach to Charité, the translational ecosystem in Germany and beyond.
Translational effectiveness is notoriously difficult to measure because it often takes years or even decades to move from initial discovery to clinical application in patients. Several BIH initiatives are therefore studying translational research in order to identify meaningful early markers of translational effectiveness.
The Responsible Translational Methodologies Research Objective will develop strong support structures to benefit the community at the BIH, the Charité, and beyond. By identifying promising research projects to embed in an efficient translational framework, our goal is to increase translational success. Beyond the BIH research projects, the translational framework should serve as a blueprint for national and international partners, including public-private partnerships. To strengthen preclinical robustness and foster close connections with the Computational and Functional Precision Medicine Research Objective, this Research Objective will make patient data available to a large research community, thus enabling the development and application of tools that directly benefit patients. We will create added value for patients and researchers by creating FAIR health data spaces and developing advancing modeling and artificial intelligence (AI) for clinical use.
This impact is generated through targeted activities that will strengthen translational effectiveness via researchers’ discoveries, patient involvement, and contributions from additional stakeholders:
Activities
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Translational Metrology
Despite significant progress in health research, translation of scientific discoveries to the clinic has been slower than expected. New strategies and support are needed to fully measure and use the translational potential of scientific discoveries.
Find out more in our Strategic Research Program 2023-2027.
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Preclinical Evidence
The robustness of evidence from preclinical studies is pivotal for the successful translation of novel treatments and interventions. Thus we strengthen preclinical evidence generation and its link to clinical contexts.
Find out more in our Strategic Research Program 2023-2027.
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FAIR Health Data
An alternative approach to leverage health data to improve health is to apply artificial intelligence (AI) for precision medicine. This requires the tailoring of modeling and data science algorithms to clinical use, and the consideration of ethical, legal, and societal issues. The following processes must be optimized: the acquisition of clinical data; the interfaces of workflows to extract knowledge; and structured phased pathways to validate and translate computational diagnostics and interventions.
Find out more in our Strategic Research Program 2023-2027.
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Clinical Translation
We develop frameworks, tools, and pipelines to fully exploit the comprehensive, robust, and FAIR (see 1.3) patient data generated by molecular, clinical, and digital phenotyping. We thus accelerate the translation of biomedical advances to the clinic.
Find out more in our Strategic Research Program 2023-2027.
