Table 1.

Scientific priorities from the NINDS-sponsored workshop “Translational Stroke Research: Vision and Opportunities”.

	Recommendations (1–3 years)
Preclinical Outcome Measures	Development of preclinical outcome measures that align with phase II human outcome measures, including cognitive and other outcome measures, with a reasonable therapeutic window. Different approaches and outcomes may be needed based on the type of study (discovery vs. confirmatory). Identify reliable biomarkers.
Experimental animal models	Improve the understanding of how experimental animal models are similar or different in recapitulating human stroke. More research is needed to validate preclinical targets to make sure they are applicable for the human population. Moreover, many features associated with human stroke are not adequately recapitulated in experimental studies. Adequate animal models for age, co-morbid human conditions, atrial fibrillation, hypoperfusion, transient ischemic attacks, white matter damage, or chronic stroke with persistent deficits need additional development and emphasis. Endpoints should depend on the particular question being addressed. It may be valuable to use different models and different outcomes.
Preclinical Standards and Common Data Elements	Develop best preclinical practices guidance, small animal imaging guidelines and preclinical CDEs for stroke, for clinical, radiological, molecular and other forms of measurement. Preclinical stroke researchers should agree on a set of common data elements for reporting of their results. Minimum quality criteria (standards) should be established for exploratory (discovery) and confirmatory studies (prevention of bias, statistics, etc.).
Collaboration between preclinical and clinical scientists	Incentivize Team Science between clinical and preclinical investigators, via T32 funding for trainees, or a Clinical and Translational Science Awards-based mechanism, or through hands-on approach specialized training courses.
Multi-center network approach for late preclinical Testing of promising therapies	Preclinical multi-laboratory trials of a putative treatment may be valuable before investing in a clinical trial. Such multi-centered preclinical testing is complex and should include, at a minimum, an agreed upon protocol that all labs would follow; rigorous good laboratory practices; a centralized randomization and data center; on-site source verification of data, and early validation of targets in humans. Such approach should only be considered for a highly promising therapy that is under consideration for late phase human trials. Incentives and requirements for participation should be discussed.
Independent Replication	Preclinical results should be independently replicated before moving into clinical testing. Specifics of such a requirement need to be worked out (definition of successful replication, strength of evidence, degree of inter-lab standardization, multi-site, academia vs. Contract Research Organization, etc.).
Publish/report negative data	Publication or public use dataset of negative findings is critical for moving translational stroke research forward. This could be a requirement for all NIH-funded preclinical stroke research.
	Recommendations (4–5 years)
Clinical Outcome Measures	Develop new and better outcome measures for Phase II and Phase III clinical trials. Clinical trials traditional endpoints are widely used and well validated. However, new predictive and standardized endpoints that also incorporate cognitive outcomes and recovery of function are needed. A workshop specifically targeting endpoints, such as the workshop held during the NINDS tPA for acute stroke trial that resulted in the global outcomes method, is desirable.
Stroke Repositories and Biobanks	Create data repositories and include stroke in biobanks.
Interaction with vascular contributions to dementia and other fields	Interaction and collaboration with the field of small vessel disease/vascular dementia/cerebral amyloid angiopathy and vascular contributions to cognitive impairment and dementia. Consider systemic effects, not just the brain (e.g., cardiology, immunology).
Standardization and monitoring of preclinical studies	Confidence in results from preclinical studies must be underpinned by verification. Specifics of such monitoring/auditing need to be defined (random inspection, peer versus institutional, responsibility of funders and journals, funding, etc.).