The temptation is to use the most advanced technology available. A more effective approach is to first define the specific biological question and then select the simplest possible model that can answer it, thus avoiding premature and unnecessary over-engineering.
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.
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.
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.
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.