Advancing Drug Testing Assays for Atherosclerosis Treatment

Atherosclerotic cardiovascular disease is the number one cause of death globally. Despite years of research, one major bottleneck remains: A lack of relevant laboratory models to test new therapies targeting a key player in the disease. The smooth muscle cells (SMCs).

Although more than half of all cells in atherosclerotic plaques originate from SMCs, their complex behavior in diseased arteries is still poorly captured in current lab models. The SIF project aims to change that by developing new, disease-relevant SMC assays with complementary strengths, including one that can be widely adopted by both academic and industrial researchers.

“Smooth muscle cells are crucially involved in the development of human atherosclerosis, but the lack of in vitro assays that accurately model their behaviour in diseased arteries is a great obstacle for developing therapies,” explains Professor Jacob Fog Bentzon, a principal investigator in SIF and former ODIN grantee for the THOR project.
“Our hope is that SIF will overcome these barriers and support the development of new therapies targeting rogue smooth muscle cells.”

To develop better models, SIF uses spatial transcriptomics to guide assay development. Spatial transciptomics is a technology that reveals which genes are active in individual cells while preserving their spatial organization within tissue.

The involvement of company partners is highlighted in the description below.

Step-by-Step Strategy:

1. SIF-MAP: Mapping SMCs in human plaques:
   Novo Nordisk will conduct spatial transcriptomic analyses of 20 human atherosclerotic plaques, providing detailed maps of SMC transcriptomes in the diseased vessel wall.
2. SIF-REFINE: Refining SMC phenotypes in a new assay:
   Insights from SIF-MAP will guide the optimization of culture conditions to achieve disease-relevant SMC phenotypes for a new assay.
3. SIF-SPHERE: Modelling atherosclerosis in 3D spheres:
   To maximize transferability and scalability, the SIF team will use a 3D spheroid assay with commercial coronary SMCs to model SMC behaviour during atherosclerosis development.
4. SIF-APPLY: Scaling for drug screening:
   AstraZeneca will adapt the spheroid-based assay SIF-SPHERE for high-throughput drug screening, enabling the testing of large compound libraries. SIF-REFINE will be implemented as a calidation tool to confirm cadidate targets from initial SIF-SPHERE screenings.

A key ambition of SIF is to ensure that its tools are not just scientifically robust, but scalable and usable across research environments. This includes academic labs, hospital research units, and industry partners developing future drugs for atherosclerosis.

“The SIF project is committed to developing disease-relevant smooth muscle cell assays to bridge the gap between research and drug discovery. Beyond our immediate goals, I hope the data generated and assays developed through SIF will help catalyze future efforts to translate the ever-increasing biological knowledge of atherosclerosis into clinical applications. SIF greatly benefits from ODIN’s IP-free model, which reduces the administrative work required to enable collaboration between academia and industry partners while promoting open data sharing with the public.” says Head of SIF Lasse Bach Steffensen.

Lars Melholt Rasmussen, who is a professor at Odense University Hospiral add: “SIF unites a highly complementary team with diverse skills and resources across hospitals, bringing together the necessary expertise to achieve our goals.”

The SIF project is built on strong collaboration across academia and industry. The two company partners in the project, Novo Nordisk and AstraZeneca, contribute not only with technologies and resources but also with strategic insights into how these tools can benefit future drug development.

“SIF provides a unique opportunity to understand smooth muscle cell behaviour in its native environment and translate that knowledge into better in vitro models. We’re excited to contribute with spatial transcriptomics and scientific input that can improve disease relevance in early discovery.” says Dimitra Aravani, Research Associate at Novo Nordisk.

“The SIF collaboration allows us to apply advanced cellular models in atherosclerosis drug discovery. By combining AstraZeneca’s screening capabilities with the biological insight from SIF, we hope to uncover new and more effective therapeutic targets.” says Qing-Dong Wang, Chief Scientist at AstraZeneca.

• Lasse Bach Steffensen (University of Southern Denmark)
  Project coordinator with expertise in smooth muscle cell biology and atherosclerosis, Lasse leads efforts around the artery biobank and tissue sample preparation for transcriptomic analysis.
• Jacob Fog Bentzon (Aarhus University)
  Former ODIN grantee, Jacob has in depth knowledge of the open approach to university-industry collaboration in ODIN. Jacob will drive data interpretation and assay development based on his in depth knowledge of atherosclerosis, smooth muscle cell function, single-cell transcriptomics. Jacob’s lab will provide key bioinformatics support.
• Lars Melholt Rasmussen (Odense University Hospital)
  Working at a hospital, Lars will co-manage the artery biobank and provide access to clinically relevant samples. His expertise lies within clinical biochemistry, cardiovascular disease.
• Novo Nordisk (Dimitra Aravani, Anna Uryga, Charles Pyke, Michael Nyberg)
  The key roles of the Novo Nordisk team headed by Dimitra Aravani is to conduct spatial transcriptomics.
• AstraZeneca (Qing-Dong Wang, Kenny Hansson, Ceri Michelle Wiggins)
  The AstraZeneca team headed by Qing-Dong Wang is responsible for scaling up the SIF-SPHERE assay and leading high-throughput drug screening efforts.

True to the ODIN principles, SIF will share data, protocols, and assay formats openly, accelerating progress across both academic research and therapeutic development. Whether you’re a researcher focused on basic biology or a company screening for new cardiovascular drugs, the tools developed through SIF aim to bring clinical relevance into the lab.

You can read more about the SIF project right here