Loading
Drugs fail for distinct reasons: wrong target, drug never reached it, unacceptable toxicity, unselected population or poor trial design. A shared machine-readable taxonomy applied to every discontinued oncology programme in public pipeline databases would show where the system breaks, as AstraZeneca and Pfizer's own attrition analyses did.
Analyses of attrition (for example those published by AstraZeneca and Pfizer on their own pipelines) show that failure reasons are learnable but rarely recorded consistently. A shared taxonomy (target biology, exposure, safety, efficacy in unselected population, biomarker failure, design, commercial) applied to every discontinued oncology programme in public pipeline databases would enable system-level diagnosis and comparison across sponsors and decades.
Shares Public post-mortem reports when a cancer drug programme is stopped, Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Public post-mortem reports when a cancer drug programme is stopped, The valley of death between lab and product, Failures are hidden.
Shares Score every preclinical model by how often it predicted the clinical result, Failures are hidden.
Shares Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares A target de-risking index that counts failures as well as successes, Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.