Wednesday, April 2, 2025
4/2/2025
The NIH SPAN Coordinating Center - We propose to continue a novel, adaptive, secured system for parallel testing of promising interventions designed to improve outcome after ischemic stroke when compared to reperfusion alone, the Stroke Preclinical Assessment Network. SPAN will screen and select highly promising candidate interventions for possible study in StrokeNet. The applicant PI and research team—the SPAN Coordinating Center (CC)—created and implemented novel solutions to several key barriers impeding successful pre-clinical network implementation. The result is a highly successful, multi-site network for testing putative cerebroprotectants in animal stroke models that include equal numbers of females and males and co-morbidities such as aging, hypertension and hyperglycemia. We invented novel methods of case enrollment and tracking; centralized randomization; centralized drug preparation/masking/bottling; group concealment from the surgeons; centralized, blinded behavioral assessment; and centralized, blinded evaluation of MRI scans. Each aspect of SPAN went through a thorough, organized process: literature review, rounds of debate and re-review, and finally decisions were made and documented. Using this process, SPAN developed SOPs concerning the choice of animal models, surgical methods, behavior assessments, assessor training and certification. SPAN uses a Multi-Arm Multi- Stage design and state-of-the-art experimental rigor to successfully reduce or eliminate common sources of bias. Stroke is administered and interventions are provided in blind fashion. Outcome data (behavioral tests and MRI scans) are uploaded by the SPAN Testing Laboratories to a central repository, and then randomly assigned by the CC to independent raters at other labs. We, the SPAN CC, coordinate, network communication via daily contact with the sites; weekly enrollment update reports; monthly Steering Committee meetings; annual investigator meetings; and semi-annual site visits. To improve reproducibility across all labs, we devised training sessions and certification tests for all surgeons and behavioral raters targeted to specific tasks, e.g., surgery, recording behavior testing, corner test rating, etc. These training and certification tools will facilitate rapid, rigorous addition of new Testing Labs in SPAN 2.0. Throughout SPAN 1.0 in fact, all SOPs, protocols, and infrastructure were designed to facilitate rapid, simple addition of new sites and easy transition to SPAN 2.0 with minimal down-time. Presently, we are devising innovative enhancements for the next funding cycle: we are training a machine learning algorithm to score behavior videos to allow rapid, reproducible scoring using a digital pipeline; we are developing a blood clot/thrombolysis model to allow testing in the presence of thrombolytics. This present application, if funded, will allow the SPAN CC to continue to improve and advance preclinical development by implementing critical technical innovations, including novel assessment tools using machine learning. SPAN 2.0 will continue our track record of successful enrollment and technical innovation, providing a model for pre-clinical networks in other disease areas.
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