Company: IMRX
Filing Date: 2025-03-20
Form Type: 10-K
Source: 0001790340-25-000042
Chunk: 47

Company: Immuneering Corp
Filing Date: 2025-03-20
Form: 10-K
Item: Item 1
Chunk 47
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DCT is able to rapidly compare disease state signatures against vast numbers of target signatures. DCT ranks signatures resulting from the modulation of specific targets by the extent to which they oppose disease signatures (as depicted below). Unlike some algorithms or artificial intelligence ("AI") approaches, the results originating from DCT are designed to be interpretable from a computational and biological perspective. This platform uses gene expression from patient datasets and does not rely on literature. Together with the target, DCT provides a specific list of testable genes associated with the target of interest, relevant drug concentrations and temporal dynamic information driving the result. Thus, we believe DCT can identify new targets and readily detect dynamic relevant biology relating to modulating a target in a better way.

A summary workflow for DCT’s novel target identification can be described as follows:

•Carefully curated and quality-controlled human transcriptomic data representing a specific aspect of disease, or Input 1, is input and vectorized for processing (as depicted below).

•A carefully curated and quality-controlled library of gene expression signals associated with perturbing specific targets at specific time points and concentrations, or Input 2, is input and vectorized for processing (as depicted below). This library can potentially include clustered regularly interspaced short palindromic repeats ("CRISPR"), RNA interference, tool compounds, screening library compounds and existing drugs.

•The strength of disease signal cancellation is measured between Input 1 and every target signature in Input 2.

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Disease Cancelling Technology Summary Workflow for Target Identification

A second filtration step selects target candidates for which multiple biological pathways are restored in the proper direction compared to the disease signal. DCT includes a method to compute a per pathway contribution to disease canceling in terms of percent contribution to overall disease reversal for cases when a specific pathway is particularly relevant. DCT is designed to have many capabilities in addition to identifying novel targets or novel ways to drug existing targets. To enable rapid translation to experimental validation, DCT can suggest ideal concentrations, temporal dynamics and marker genes to monitor. DCT is also capable of predicting target combinations for a given disease or an ideal target for combination with an existing therapy. For expanded utility, DCT has a graphical user interface that enables our biologists to interact with, sort, modify, query and run results along with producing visualizations of results.

We believe DCT has several advantages over other target identification technologies. The platform uses patient data as a starting point, rather than artificial 2D in vitro models. We have presented data at American Association