Company: IMRX
Filing Date: 2025-03-20
Form Type: 10-K
Source: 0001790340-25-000042
Chunk: 51

Company: Immuneering Corp
Filing Date: 2025-03-20
Form: 10-K
Item: Item 1
Chunk 51
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 at over 130 tumor models). The cohort of 3D-TGA tumor models span at least 12 major tumor types, including: breast, colorectal, lung, pancreas, melanoma, ovary, liver, stomach, prostate, neuroblastoma, rhabdomyosarcoma, and thyroid. Over time, additional 3D-TGA models may be developed to add new indications or expand existing indications to better test emerging discovery or translational hypotheses. A large, proprietary 3D-TGA collection ensures that new drug candidates, identified as active in one cohort, could be quickly tested for broader tumor activity in additional tumor types. The established collection of 3D-TGA tumor models can also be used to perform novel drug screens and develop novel chemical entities (NCE’s) that demonstrate drug activity.

Prioritize Indications and Identify Sensitive Subpopulations

We are able to leverage bioinformatics to analyze genomic data from large patient databases of primary tumor data to identify specific indications where first line patients are likely to have characteristics that align with our more reflective humanized models and identify biological mechanisms and biomarkers that enable us to identify subpopulations that are more likely to be sensitive based on their similarity to our translational approaches.

Analyze Clinical Data

We have developed proprietary software that we believe enables us to view, analyze, and interpret clinical data in new and more robust ways.

Our Platform and its Role in the IMM-1-104 Program

Our platform played a key role in creating the most important characteristics of our lead product candidate, IMM-1-104. In the early stages of the program, insights from human data were used to identify transcriptional profiles we aimed to counteract. DCT and our analysis of mechanisms of existing drugs led us to identify what we believe to be novel biology, specifically new ways to drug an existing target, to highlight the goal of counteracting a biologic feedback loop. Novel chemistry was generated to counteract the feedback loop, and the PK was tuned to generate optimal signaling dynamics (deep but cyclic interruptions of the pathway) as confirmed for translational profiling. Our proprietary translational planning has involved profiling IMM-1-104 in a large number of 3D models to identify the types of cancer (and biomarkers of subsets when needed) that we believe will have the highest probability of success in the clinic. Together, these insights enabled us to demonstrate in an in vitro model that a drug with feedback loop resistance combined with a short half-life was able to move toward in vivo