Stent thrombosis remains an infrequent but significant complication following percutaneous coronary intervention. Preclinical models to rapidly screen and validate therapeutic compounds for efficacy are lacking. Herein, we describe a reproducible, high throughput and cost-effective method to evaluate candidate therapeutics and devices for either treatment or propensity to develop stent thrombosis in an in vitro bench-top model. Increasing degree of stent malapposition (0.00 mm, 0.10 mm, 0.25 mm and 0.50 mm) was associated with increasing thrombosis and luminal area occlusion (4.1 ± 0.5%, 6.3 ± 0.5%, 19.7 ± 4.5%, and 92.6 ± 7.4%, p < 0.0001, respectively). Differences in stent design in the form of bare-metal, drug-eluting, and bioresorbable vascular scaffolds demonstrated differences in stent thrombus burden (14.7 ± 3.8% vs. 20.5 ± 3.1% vs. 86.8 ± 5.3%, p < 0.01, respectively). Finally, thrombus burden was significantly reduced when healthy blood samples were incubated with Heparin, ASA/Ticagrelor (DAPT), and Heparin+DAPT compared to control (DMSO) at 4.1 ± 0.6%, 6.9 ± 1.7%, 4.5 ± 1.2%, and 12.1 ± 1.8%, respectively (p < 0.01). The reported model produces high throughput reproducible thrombosis results across a spectrum of antithrombotic agents, stent design, and degrees of apposition. Importantly, performance recapitulates clinical observations of antiplatelet/antithrombotic regimens as well as device and deployment characteristics. Accordingly, this model may serve as a screening tool for candidate therapies in preclinical evaluation.
Background Adenosine is a ubiquitous regulatory molecule known to modulate signaling in many cells and processes vital to vascular homeostasis. While studies of adenosine receptors have dominated research in the field, quantification of adenosine systemically and locally remains limited owing largely to technical restrictions. Given the potential clinical implications of adenosine biology, there is a need for adequately powered studies examining the role of plasma adenosine in vascular health. We sought to describe the analytical and biological factors that affect quantification of adenosine in humans in a large, real‐world cohort of patients undergoing evaluation for coronary artery disease. Methods and Results Between November 2016 and April 2018, we assessed 1141 patients undergoing angiography for evaluation of coronary artery disease. High‐performance liquid chromatography was used for quantification of plasma adenosine concentration, yielding an analytical coefficient of variance (CV a ) of 3.2%, intra‐subject variance (CV i ) 35.8% and inter‐subject variance (CV g ) 56.7%. Traditional cardiovascular risk factors, medications, and clinical presentation had no significant impact on adenosine levels. Conversely, increasing age ( P =0.027) and the presence of obstructive coronary artery disease ( P =0.026) were associated with lower adenosine levels. Adjusted multivariable analysis supported only age being inversely associated with adenosine levels ( P =0.039). Conclusions Plasma adenosine is not significantly impacted by traditional cardiovascular risk factors; however, advancing age and presence of obstructive coronary artery disease may be associated with lower adenosine levels. The degree of intra‐ and inter‐subject variance of adenosine has important implications for biomarker use as a prognosticator of cardiovascular outcomes and as an end point in clinical studies.
Introduction and objective: Target lesion failure continues to limit the efficacy of percutaneous coronary intervention despite advancements in stent design and medical therapy. Identification of biomarkers to risk stratify patients after percutaneous coronary intervention has the potential to focus therapies on cohorts with increased benefits. Plasminogen activator inhibitor-1 has been identified as a candidate biomarker. Herein, we evaluate biological variables which impact plasminogen activator inhibitor-1 levels and analytical characteristics which impact its utility as a biomarker in humans. Methods: Plasma plasminogen activator inhibitor-1 was measured in 689 patients undergoing coronary angiography. Plasminogen activator inhibitor-1 levels were measured. Clinical and procedural characteristics were collected in a prospective registry. Results: Plasma plasminogen activator inhibitor-1 analytical ( CV a = 4.1%), intra-individual ( CV i = 44.0%) and inter-individual ( CV g = 118.6%) variations with reference change value of 122.3% were calculated. Plasminogen activator inhibitor-1 levels were elevated in patients with cardiovascular risk factors, including type 2 diabetes, pre-diabetes, smokers, obesity, hypertension, and daytime variation in procedure and blood draw. Conclusion: Variation in plasma plasminogen activator inhibitor-1 levels is influenced by multiple biological and procedural characteristics. The performance of plasma plasminogen activator inhibitor-1 is consistent with biomarkers in clinical use (N-terminal pro-B-type natriuretic peptide and C-reactive protein) and its applicability is promising.
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