Paradigm Shift to Overcome Clinical Trial Failures

Despite remarkable advancements in oncology, only about 5% of anticancer drugs that enter clinical trials ultimately receive final approval. Notably, over half of late-stage failures in Phase 3 trials are due to a lack of efficacy. This high failure rate is largely attributed to insufficient consideration of the actual tumor biology of patients or overly broad patient population selection. Indivumed Therapeutics is addressing these issues by establishing a patient-centric, precision oncology R&D model.

Combining the nRavel Platform with Ultra-Short Cold Ischemia Time

Indivumed possesses a standardized biobank as a core asset, preserving samples within 10 minutes of removal of cancer tissue from the body, before any alterations occur. By drastically reducing cold ischemia time, it preserves the natural state and microenvironment of the tumor, maximizing the reliability of target discovery. This unique biological material is analyzed using its proprietary AI platform, nRavel, which integrates hundreds of clinical and tumor genomic datasets. By combining actual human cell-based biological data, it goes beyond simple virtual predictions, significantly reducing errors in early target validation.

Multi-Omics-Based Precision Targeting and Modality Validation

The company performs multi-omics analysis, encompassing DNA, RNA, proteins, and post-translational modifications. This allows for a multifaceted evaluation of cancer cell signaling pathways and matching the most suitable drug modality, such as antibody-drug conjugates (ADCs) or bispecific antibodies. Colorectal cancer (CRC) exhibits significant genetic heterogeneity, making treatment difficult with a single biomarker. Indivumed creates patient-specific, multi-dimensional profiles to identify precise targets. This provides a powerful tool for selecting the most responsive patient population during clinical trial design.

AI and Patient-Derived Models: A Virtuous R&D Loop

To overcome the limitations of incomplete animal models, Indivumed utilizes 3D cell models (organoids) and patient-derived cell lines from its biobank for target validation. This validation data is fed back into the nRavel AI algorithm, enhancing its accuracy and creating a 'virtuous loop.' This approach reduces the time required for anticancer target discovery and drug compatibility validation from several years to just a few months. This innovative R&D model significantly improves the economics of new drug development and will be a major milestone in increasing the clinical success rate of anticancer drugs.