OnCo
ideasIdea

Score every preclinical model by how often it predicted the clinical result

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.

Hypothesis
Report cards will show at least two-fold differences in positive predictive value between model classes within the same indication, and this information will change model choice in subsequent grant applications.
Rationale
Systematic reviews in stroke and neuroscience showed that animal model results predicted clinical results poorly; oncology has never computed the equivalent at scale despite having the most trials.
What would test it
Link 300 drug-indication pairs with published preclinical data to trial outcomes; compute predictive values by model class; publish and update annually.
Maturity
speculative
Who has to act
data
Cost to try
Small (under $1M)
Years to first evidence
3
Bottlenecks it attacks

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