Self-driving laboratories that run the cancer biology hypothesis loop autonomously
Robotic labs guided by AI that design experiments on tumour models, run them, read the results and design the next ones, around the clock, with every result published openly.
Autonomous laboratories exist in chemistry and materials science, and cloud labs and robotic organoid culture are emerging in biology. Cancer biology is limited by slow, poorly reproducible manual experimentation. The proposal is a network of self-driving cancer labs: automated organoid and cell line culture, perturbation, imaging and sequencing readouts, active-learning experiment selection against defined questions (resistance mechanisms, combination synergy, dependency mapping), and automatic public deposition of raw data and protocols.
- Preclinical models that do not predict people · Nine in ten cancer drugs that work in mice fail in humans. Our models are the reason.
- Preclinical results do not reproduce · Fewer than half of landmark cancer biology findings reproduce when someone else tries.
- The valley of death between lab and product · Most academic discoveries die before anyone tests them in people because nobody funds the middle step.
Pages like this
not linked directly; found by shared links- TechnologyPhenom-2 and Recursion OS
Shares Recursion Pharmaceuticals, AI-driven drug & target discovery, CRISPR functional genomics.
- IdeaA virtual cancer cell that predicts what a drug will do before you test it
Shares Recursion Pharmaceuticals, AI-driven drug & target discovery, CRISPR functional genomics, Preclinical models that do not predict people.
- IdeaScore every model system on how well it predicted real trial results
Shares AI-driven drug & target discovery, Patient-derived organoids, Preclinical results do not reproduce, Preclinical models that do not predict people.
- IdeaAn independent replication institute that re-tests key preclinical cancer findings before trials
Shares Patient-derived organoids, Preclinical results do not reproduce, The valley of death between lab and product.
- InstitutionCancer Center at Illinois
Shares AI-driven drug & target discovery, The valley of death between lab and product, Preclinical models that do not predict people.
- InstitutionThe Jackson Laboratory Cancer Center
Shares CRISPR functional genomics, Preclinical results do not reproduce, Preclinical models that do not predict people.
- Key paperReproducibility Project: Cancer Biology found that landmark preclinical results mostly shrank or vanished on replication
Shares Patient-derived organoids, Preclinical results do not reproduce, The valley of death between lab and product, Preclinical models that do not predict people.
- IdeaAn open model of every cancer cell state, built from perturbation atlases
Shares CRISPR functional genomics, Patient-derived organoids, Preclinical results do not reproduce, Preclinical models that do not predict people.