Company: IMRX
Filing Date: 2025-03-20
Form Type: 10-K
Source: 0001790340-25-000042
Chunk: 50

Company: Immuneering Corp
Filing Date: 2025-03-20
Form: 10-K
Item: Item 1
Chunk 50
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 differ between cell types, and we leverage this to modulate targets in such a way that certain cell types will be more impacted than others. Our platform enables us to assess the signaling dynamics of product candidates, which we believe allows us to optimize the chemistry of our product candidate programs to achieve broad therapeutic activity against diseased cells while sparing healthy normal cells. Modulation of these signaling networks impacts cell fate decisions in many cell types, including cancerous cells. Our computational biology expertise enables us to analyze transcriptomic data that closely reflects spatiotemporal dynamics of biological signaling networks.

Proprietary Translational Planning

Humanized 3D Tumor Models.

In oncology, we are deeply experienced in advanced, humanized 3D-based tumor growth models, which based on peer reviewed research by members of our team and others, more accurately predict drug response in animal models, and we believe in patients, compared to standard models. Unlike in vitro approaches, the 3D tumor growth models reflect the complexity of tumor biology given their alignment with the TME. Thus, we believe our deep expertise in 3D tumor models enables us to more accurately stratify patients likely to benefit from our potential product candidates. 

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3D-TGA Background and Utility

Although two-dimensional ("2D") tissue culture has dominated cell-based preclinical oncology research for over 70 years, successful translation and regulatory approval of new oncology drugs are amongst the lowest of all therapeutic areas. Innovations in cell-based platform technologies, such as matrix-based humanized 3D Tumor Growth Assay ("3D-TGA") tumor models, offer the promise to enrich preclinical drug discovery and improve overall clinical translational success. Our company and leadership have a strong history in research and translation of drug response using patient-derived tumor models (e.g., patient-derived xenografts and 3D ex vivo models) and translational bioinformatics using public and internal patient data. Our team has built on previous ex vivo tumor modeling experience and patient-alignment model selection approaches to identify models that better reflect molecular profiles of target patient populations. We believe this effort will help improve translational success of our pipeline and help ensure that established 3D-TGA models perform well as a core platform for innovative oncology drug R&D. By leveraging over two decades of humanized modeling expertise, our translational and discovery teams have built, characterized, and interrogated a large set of 3D-TGA tumor models from commonly used human tumor cell lines (i.e., currently