Antiplatelet medications are a critical component to manage cardiovascular diseases (CVD), which is a prevalent affliction in the US. While monitoring platelet therapy is of increasing importance for the identification of hypo- or hyper-responsive patients, there is not a clear picture of the medical value of tailored antiplatelet therapy using currently available platelet function tests. Currently, US FDA cleared assays of platelet function designed to be near the patient measure platelet adhesion and aggregation. Stasys Medical is developing a system based on patented platelet contraction sensors to assay platelet contraction force as a biomarker for platelet dysfunction. In the current project, the Company will build on preliminary data to demonstrate that the force biomarker is sensitive to platelet inhibition via multiple pathways. Specifically, aims are designed to 1) correlate the platelet contractile force measurements to inhibition of platelet activation and adhesion pathways, and 2) benchmark the force assay to standard platelet function tests. The go/no-go criteria for moving to Phase II is demonstration that platelet force is sensitive to specific inhibitors and that the assay can identify platelet dysfunction currently missed by existing standard platelet function assays. In the Phase II project we will leverage data generated in the current project to develop an algorithm that can automatically identify antiplatelet medications. Clinically, this will be a valuable tool to identify medication non-responders. Taken together, the project will be used to support the Company?s 510(k) submission to FDA with claims directed at evaluating platelet function to assess clinical conditions, such as bleeding risk, associated with the use of antiplatelet drugs, and during and following cardiovascular surgery.
Antiplatelet medications are a critical component to manage cardiovascular diseases (CVD), which is a prevalent affliction in the US. In the Phase I project, Stasys Medical will build on preliminary data to show that the platelet contraction force biomarker reflects platelet function as proof-of-concept that the force assay can identify platelet dysfunction missed by existing assays. The Phase II project will leverage Phase I results to develop an algorithm using machine learning techniques that can automatically identify antiplatelet medications to identify medication non-responders.
