Work

Development of autologous in vitro vascular models for use in preclinical biotherapeutic development

Year: 2026
Type: article
Abstract: Following previous failures to predict drug-induced adverse immune reactions in clinical trials, for example in cases with preclinical species differences or poorly indicative in vitro assays, there has been an emphasis on developing improved preclinical hazard identification tools. Concurrently, there is a regulatory agency-backed responsibility to reduce reliance on preclinical animal models, highlighted by the Food and Drug Administration (FDA) Modernization Act 2.0 and the FDA's 2025 announcement to phase out animal testing for specific compounds. Traditional in vitro cytokine release assays utilize plastic-based formats of antibody presentation to blood cell fractions, and, although biologically simple to run, they do not accurately recapitulate in vivo blood vessel physiology. Including endothelial cells improves physiological relevance by representing the internal vascular wall, enabling cell-cell interactions, compound presentation, and cellular responses from endothelial cells alongside blood cells. Here, endothelial cells outgrown from healthy donors were cocultured with their blood cells to model the immune response to compounds. Building on existing endothelial assays cocultured with blood cell fractions, we established the model using whole blood as an alternative format. We then transferred both formats from 2-dimensional (2D) 96-well plates into a 3D microfluidics system, further mimicking the dynamics and structural microenvironment of a blood vessel. We used these human vasculature models to recapitulate the expected cytokine response to existing compounds and highlight the additional preclinical safety end points that can be investigated by using a 3D vessel, such as vascular leak. This proof-of-concept study demonstrates foundations for a scalable, physiologically relevant method for preclinical testing while reducing reliance on animal models.
Language: English
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W7128084666
FWCI:
Cites: 48
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