{"entity":{"id":"idea-data-in-silico-trials-calibrated","kind":"idea","name":"In silico trials to prioritise combinations, scored against later real trials","aka":[],"tldr":"Simulate trials of drug combinations in populations of virtual patients to decide which real trials to run, and keep score of how often the simulations were right.","summary":"The number of possible combinations far exceeds trial capacity. Simulated trials using mechanistic and machine-learned models of virtual patient populations could rank combinations, but their predictive value is unknown. The proposal is a scored programme: simulations are registered with predicted effect sizes for combinations entering real phase 2 or 3 trials, and outcomes are compared as trials read out, building a public track record that determines how much weight simulation gets in portfolio decisions.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021)","url":"https://doi.org/10.1038/s41591-021-01312-x"}],"tags":[],"related":["idea-data-digital-twin-predict-then-observe","idea-data-open-cell-foundation-model"],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation","b-combination-space","b-trial-design"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Simulation rankings will correlate positively with real trial outcomes for at least some drug classes, allowing a measurable reduction in failed phase 3 combination trials when used to filter candidates.","rationale":"Regulators already accept in silico evidence for device testing and some pharmacokinetic questions; the missing element for efficacy is a track record, which only forward scoring can build.","test":"Register predictions for 50 ongoing combination trials; compare with outcomes as they read out over four years; publish the correlation and calibration.","maturity":"speculative","actor":"research","cost":"medium","horizonYears":4},"route":"/ideas/idea-data-in-silico-trials-calibrated/","neighbours":{"idea":[{"id":"idea-data-open-cell-foundation-model","kind":"idea","name":"An open foundation model of the cancer cell trained on perturbation data","route":"/ideas/idea-data-open-cell-foundation-model/"},{"id":"idea-data-digital-twin-predict-then-observe","kind":"idea","name":"Digital twins for treatment selection, validated by predicting before observing","route":"/ideas/idea-data-digital-twin-predict-then-observe/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-combination-space","kind":"bottleneck","name":"Too many combinations to test","route":"/bottlenecks/b-combination-space/"},{"id":"b-trial-design","kind":"bottleneck","name":"Trial design, endpoints and cost","route":"/bottlenecks/b-trial-design/"}],"paper":[{"id":"paper-wu-nat-med","kind":"paper","name":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","route":"/key-papers/paper-wu-nat-med/"}]}}