# In silico trials to choose the dose before the first patient

Source: https://onco.cc/ideas/idea-bio1-in-silico-trials-dose/  
OnCo record `idea-bio1-in-silico-trials-dose` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Simulating thousands of virtual patients on a computer can suggest which dose and schedule to test, so fewer real patients receive doses that are too high or too low.

## Summary

Quantitative systems pharmacology and mechanistic tumour growth models, calibrated on prior trial data, can simulate exposure-response across virtual populations. Regulators already accept model-informed drug development for paediatric extrapolation and some dosing decisions. Project Optimus requires dose optimisation; simulation could narrow the candidate schedules before the randomised dose-comparison stage, saving patients and time.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: For agents where a model-informed schedule was proposed prospectively, the simulation-selected dose matches the eventually recommended phase 2 dose more often than the traditional maximum tolerated dose approach.
- Rationale: Most oncology drugs were historically dosed too high; the exposure-response and toxicity data needed for simulation usually exist by end of phase 1 but are analysed informally.
- Proposed test: Retrospective blinded simulation of 20 agents with known optimised doses, then prospective use in three phase 1 programmes with the model prediction locked before dose expansion.
- Maturity: speculative
- Actor: regulator

## Sources

- Bottleneck evidence (Lab models that fail to predict what happens in patients): Wong, Siah & Lo, Estimation of clinical trial success rates (Biostatistics 2019): https://doi.org/10.1093/biostatistics/kxx069

## Connected records

- technologies: [AI-driven drug & target discovery](https://onco.cc/technologies/ai-drug-design/)
- companies: [Insilico Medicine](https://onco.cc/companies/insilico-medicine/)
- bottlenecks: [Lab models that fail to predict what happens in patients](https://onco.cc/bottlenecks/b-preclinical-models/), [Wrong doses](https://onco.cc/bottlenecks/b-dose-optimisation/)
- key papers: [Estimation of clinical trial success rates and related parameters](https://onco.cc/key-papers/paper-wong-biostatistics/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/), [Drug discovery roadmap: screening in mice → maps of dependency → designing in silico](https://onco.cc/roadmaps/drug-discovery-roadmap/)

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