OnCo
ideasIdea

In silico trials to choose the dose before the first patient

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.

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.

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.
What would test it
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
Who has to act
regulator
Cost to try
Small (under $1M)
Years to first evidence
4
Bottlenecks it attacks

Connected

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Pages like this

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technologies

1
AI-driven drug & target discovery

companies

1
Insilico Medicine

bottlenecks

2
Preclinical models that do not predict peopleWrong doses