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.
Unlike the unified US system, running a multi-country clinical trial in Europe is a bureaucratic nightmare. A single trial can require three slightly different protocols for Switzerland, the UK, and Spain, for example, creating significant delays, costs, and complexity for investigators.
The traditional drug-centric trial model is failing. The next evolution is trials designed to validate the *decision-making process* itself, using platforms to assign the best therapy to heterogeneous patient groups, rather than testing one drug on a narrow population.
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.
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.
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.
With over 5,000 oncology drugs in development and a 9-out-of-10 failure rate, the current model of running large, sequential clinical trials is not viable. New diagnostic platforms are essential to select drugs and patient populations more intelligently and much earlier in the process.
A funding paradox exists where capital-efficient medical service platforms struggle to raise funds while high-risk, cash-intensive therapeutic companies secure large rounds. This is because investors understand the traditional drug development model but are unclear on how to value a medical service.
Despite billions invested over 20 years in targeted and genome-based therapies, the real-world benefit to cancer patients has been minimal, helping only a small fraction of the population. This highlights a profound gap and the urgent need for new paradigms like functional precision oncology.