Company: IMRX
Filing Date: 2025-03-20
Form Type: 10-K
Source: 0001790340-25-000042
Chunk: 48

Company: Immuneering Corp
Filing Date: 2025-03-20
Form: 10-K
Item: Item 1
Chunk 48
---
 for Cancer Research and other conferences demonstrating how cell lines fail to capture the heterogeneity of patient tumors, and our discovery team’s experience in the 3D tumor modeling field has also highlighted the limitations of 2D in vitro data. Moreover, working closely with several FDA-approved drugs, we have found that transcriptomic data was most frequently and dynamically linked to drug activity. Thus, our core insights are derived from transcriptomic data (RNA), while some of our competitor’s platforms may focus on sequencing data (DNA), imaging data from phenotypic screens and/or literature. DCT is focused on identifying novel targets or novel ways to modulate existing targets, with the goal of generating novel therapeutics with improved clinical activity. We have not in-licensed external drugs and we do not focus on “drug repurposing” activities. Our pipeline is composed of programs with potentially novel pharmacological effects.

Biological Mechanisms of Response

We also identify novel biology by applying translational bioinformatics to analyze the biological mechanisms of response of existing therapies. This may include comparing the transcriptional profiles induced by a drug at different timepoints in order to highlight biological feedback loops that we then seek to counteract.

32

Novel Chemistry

Fluency

We developed Fluency, an easy-to-use AI-based tool, to allow for the rapid screening of large compound libraries for potential binders to a protein target of interest. Fluency can be run with any compound library, including libraries containing millions of compounds. It identifies the most attractive drug candidates within a library by making ranked predictions of binding affinity for all compounds. It also makes predictions about the target binding location for all compounds, which allows us to filter the library for drug candidates that are the most likely to affect a specific region of interest on the desired target. Fluency accelerates our drug development process by allowing us to go from millions of potential compounds down to what Fluency selects as the best hundred drug candidates within a single workday. This allows us to quickly advance only those select candidates to medicinal chemistry and experimental validation (as depicted below), increasing our capital efficiency. Knowledge of the 3D structure of the protein target of interest is not required, which expands the applicability of Fluency to include targets with poorly defined or non-existent 3D structures.

Fluency Accelerates Our Drug Discovery

To illustrate both the ease of use, as well as the power of Fluency to identify promising drug candidates, we constructed a test screen of Tukysa® (tucatinib), a recently