# In silico trials to prioritise combinations, scored against later real trials

Source: https://onco.cc/ideas/idea-data-in-silico-trials-calibrated/  
OnCo record `idea-data-in-silico-trials-calibrated` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

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.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- 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.
- Proposed 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

## Sources

- 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): https://doi.org/10.1038/s41591-021-01312-x

## Connected records

- ideas: [An open foundation model of the cancer cell trained on perturbation data](https://onco.cc/ideas/idea-data-open-cell-foundation-model/), [Digital twins for treatment selection, validated by predicting before observing](https://onco.cc/ideas/idea-data-digital-twin-predict-then-observe/)
- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Too many combinations to test](https://onco.cc/bottlenecks/b-combination-space/), [Trial design, endpoints and cost](https://onco.cc/bottlenecks/b-trial-design/)
- key papers: [How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals](https://onco.cc/key-papers/paper-wu-nat-med/)

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