Industry partnerships are crucial for more than just funding. Collaborating with pharmaceutical companies provides translation-focused questions that guide the design of advanced cell models, ensuring they are predictive, scalable, and compatible with real-world development workflows.

Simple cell viability screens fail to identify powerful drug combinations where each component is ineffective on its own. AI can predict these synergies, but only if trained on mechanistic data that reveals how cells rewire their internal pathways in response to a drug.

Advanced 3D neural models that include resident immune cells (microglia) can detect very brief but intense immune responses to gene therapy vectors. These transient responses, previously missed in other models, mirror observations in patients, highlighting the predictive power of complex systems.

An innovative strategy for solid tumors involves using bispecific T-cell engagers to target the tumor stroma—the protective fibrotic tissue surrounding the tumor. This novel approach aims to first eliminate this physical barrier, making the cancer cells themselves more vulnerable to subsequent immune attack.

A major challenge in phenotypic drug screening is determining a compound's mechanism of action. AI models can analyze the complex visual data of cellular condensates after drug treatment, extracting maximal information to understand how the drug is actually working inside the cell.

The same cancer-driving mutation behaves differently depending on the cell's internal "wiring." For example, a drug targeting a mutation works in melanoma but induces resistance in colorectal cancer due to a bypass pathway. This cellular context is why genetic data alone is insufficient.

The progress of AI in predicting cancer treatment is stalled not by algorithms, but by the data used to train them. Relying solely on static genetic data is insufficient. The critical missing piece is functional, contextual data showing how patient cells actually respond to drugs.

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.

A 3D model is considered "advanced" when it's a bioactive system recreating a tissue's microenvironment. It's not just about three-dimensional growth; cells must both influence and be influenced by their surroundings, including architecture, diffusion gradients, and mechanical cues, to be truly representative.