Profiling LNP and blood elements interactions

The National Heart, Lung, and Blood Institute awarded the University of New Haven, Incorporated $496,520 on August 15, 2026, under the Cardiovascular Diseases Research program (CFDA 93.837) to profile lipid nanoparticle and blood element interactions under physiologically relevant flow conditions and develop a predictive computational framework for nanoparticle-blood binding.

The project addresses the gap between nanoparticles' therapeutic potential for targeted drug delivery and their unpredictable interactions with blood components—platelets, leukocytes, erythrocytes, and plasma proteins. Research activities include formulating and characterizing a diverse library of lipid-based nanoparticles varying in size, charge, PEGylation, and ligand chemistry; quantifying nanoparticle-blood interactions in a custom-designed whole-blood flow loop to capture dynamic binding effects under shear conditions; and developing and validating a random forest regression model that predicts nanoparticle-blood binding profiles from formulation parameters. The work will generate the first experimentally validated predictive model of nanoparticle-blood binding to advance rational nanomedicine design and inform safer therapeutic development.

The project is classified as a Project Grant, the assistance type supporting research at primarily undergraduate institutions. Performance occurs at West Haven, Connecticut, with a period of performance from August 15, 2026, through April 30, 2029. The award will provide immersive research experience in nanomedicine and computational modeling for undergraduate and graduate students at the University of New Haven.

Abstract

Systemically administered nanoparticles (NPS) offer transformative potential for targeted drug delivery, yet their unpredictable interactions with blood components-platelets, leukocytes, erythrocytes, and plasma proteins-limit clinical translation. This project addresses a critical GAP by systematically linking NP formulation parameters to blood element binding under physiologically relevant flow conditions and developing a predictive computational framework for NP-blood interactions. Aim 1 will formulate and characterize a diverse library of lipid-based NPS varying in size, charge, pegylation, and ligand chemistry to determine how physicochemical properties influence binding to blood elements. Aim 2 will quantify NP-blood interactions in a custom-designed whole- blood flow loop to capture dynamic binding effects under shear conditions relevant to human circulation. Aim 3 will develop and validate a random forest regression model that predicts NP-blood binding profiles from formulation parameters, integrating experimental data with machine learning to guide future NP design. Innovation and significance. This study uniquely integrates nanomaterial formulation, blood biology, and data-driven modeling to reveal the mechanistic and predictive rules governing NP-blood interactions. By generating the first experimentally validated predictive model of NP-blood binding, the project will advance rational nanomedicine design and inform safer, more effective therapeutic development. Training and institutional impact. Conducted at the university of new haven, a primarily undergraduate institution, this R15 project will provide immersive research experience in nanomedicine and computational modeling for undergraduate and graduate students. Trainees will gain skills in nanoparticle synthesis, blood biology, flow cytometry, imaging, and machine learning, fostering a new generation of biomedical engineers prepared for translational research careers. Long-term impact. The outcomes will establish a foundational framework for designing blood- compatible nanoparticles and support future NIH R01 or collaborative studies focused on predictive nanotherapeutics. The project aligns with NIH's mission to promote health through innovative research and stem workforce development at undergraduate institutions.