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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.
Shares CRISPR functional genomics, Functional (ex vivo) drug testing, Drug resistance (primary and acquired), Acquired resistance to every therapy.
Shares Estimation of clinical trial success rates and related parameters, Functional (ex vivo) drug testing, Lab models that fail to predict what happens in patients.
Shares Estimation of clinical trial success rates and related parameters, CRISPR functional genomics, Lab models that fail to predict what happens in patients.
Shares Estimation of clinical trial success rates and related parameters, Functional (ex vivo) drug testing, Lab models that fail to predict what happens in patients.
Shares CRISPR functional genomics, Lab models that fail to predict what happens in patients, Drug resistance (primary and acquired).
Shares Estimation of clinical trial success rates and related parameters, Lab models that fail to predict what happens in patients, Acquired resistance to every therapy.
Shares Estimation of clinical trial success rates and related parameters, Functional (ex vivo) drug testing, Lab models that fail to predict what happens in patients.
Shares Estimation of clinical trial success rates and related parameters, CRISPR functional genomics, Lab models that fail to predict what happens in patients.