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For each type of laboratory model, keep a public record of how often its predictions came true in patients, so that researchers know which models to trust for which question.
Model predictivity is asserted, not measured. Linking preclinical efficacy claims (from publications and investigational new drug packages) to subsequent clinical outcomes would yield per-model, per-indication predictive values: for instance how often cell-line xenograft regression preceded objective responses in the same indication. Failures are essential to this calculation, which is why they must be recorded.
Shares Cancer Models (PDCM Finder) & HCMI, Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares A machine-readable taxonomy of why cancer drugs fail, Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Cancer Models (PDCM Finder) & HCMI, Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Cancer Models (PDCM Finder) & HCMI, Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Cancer Models (PDCM Finder) & HCMI, Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients.
Shares A machine-readable taxonomy of why cancer drugs fail, Compliance with results reporting at ClinicalTrials.gov, Failures are hidden.
Shares Patient-derived xenografts, Patient-derived organoids, Lab models that fail to predict what happens in patients, Failures are hidden.