ideasIdea
Every resistance mechanism found in a patient must be rebuilt in the laboratory
When doctors discover how a tumour escaped a drug, that finding usually stops at a paper. Recreating it in a model gives everyone a system to test the next drug against.
Clinically observed resistance mechanisms (mutations, bypass activation, lineage switch) are frequently reported but rarely converted into a distributed, isogenic model. A standing reverse-translation facility would engineer each reported mechanism into relevant backgrounds, verify the resistance phenotype, and distribute the lines openly, creating a growing panel that drug developers must test new candidates against.
Hypothesis
A public panel of clinically derived resistance models identifies cross-resistance for a substantial fraction of next-generation agents before they enter trials, and correctly anticipates their clinical failure settings.
Rationale
Next-generation inhibitors are often developed against laboratory-derived resistance that does not match what patients actually develop; a clinically anchored panel aligns discovery with reality.
What would test it
Build 50 clinically derived resistance models for three drug classes, profile ten clinical-stage successor agents against them blinded, and compare with subsequent trial results.
Maturity
speculative
Who has to act
research
Cost to try
Medium ($1M to $50M)
Years to first evidence
5
Bottlenecks it attacks
- Preclinical models that do not predict people · Nine in ten cancer drugs that work in mice fail in humans. Our models are the reason.
- Acquired resistance to every therapy · Nearly every targeted therapy stops working within months to a few years as the tumour adapts.