In preclinical drug development, choosing the right biological model is the most critical initial decision. Selecting an inappropriate model, such as the wrong PDX or organoid line, guarantees the research program will fail as it will be designed to answer the wrong question from the outset.

The push away from animal models is a technical necessity, not just an ethical one. Advanced therapeutics like T-cell engagers and multispecific antibodies depend on human-specific biological pathways. These mechanisms are not accurately reproduced in animal models, rendering them ineffective for testing these new drug classes.

Traditional 2D cell cultures can be misleading. Advanced 3D models, by reconstituting the tumor microenvironment with stromal cells, can uncover mechanisms of drug resistance (e.g., to ADCs) that are completely invisible in simpler systems, providing more clinically relevant data.

Radiopharmaceuticals can use the same molecular scaffold for diagnosing a tumor with one radionuclide and treating it with another. This "theranostic" strategy improves patient stratification and accelerates the transition from diagnosis to effective therapy.

Only 5% of investigational cancer drugs reach the market due to the gap between lab models and human biology. Dr. Saav Solanki highlights organoids, which use real patient tissue, as a key translational model to improve the predictive accuracy of preclinical research and increase the low success rate.

An individual tumor can have hundreds of unique mutations, making it impossible to predict treatment response from a single genetic marker. This molecular chaos necessitates functional tests that measure a drug's actual effect on the patient's cells to determine the best therapy.

While personalized cancer vaccines require extracting and processing a patient's tumor, Create Medicines' in vivo approach is entirely off-the-shelf. By delivering the programming directly into the body, they enable the patient's own immune system to do the complex, personalized work of attacking the cancer itself.

Unlike using genetically identical mice, Gordian tests therapies in large, genetically varied animals. This variation mimics human patient diversity, helping identify drugs that are effective across different biological profiles and addressing patient heterogeneity, a primary cause of clinical trial failure.

The next frontier in preclinical research involves feeding multi-omics and spatial data from complex 3D cell models into AI algorithms. This synergy will enable a crucial shift from merely observing biological phenomena to accurately predicting therapeutic outcomes and patient responses.