popTK — trial outcomes from preclinical data
A hierarchical PK/PD model with only three free parameters per drug that recovered four phase III trials and predicted biomarker-dependent benefit.
Problem
More than half of phase III oncology trials fail for lack of efficacy. Preclinical metrics like xenograft tumour-growth inhibition don't translate cleanly to clinical endpoints such as progression-free survival, and most translation models either over-fit a handful of in-vitro endpoints or carry too many free parameters to be identifiable. The goal: predict whether a drug combination will deliver a clinically meaningful PFS benefit before committing to a trial — using only preclinical data.
Approach
I built a hierarchical model that represents drug sensitivity as a continuous latent variable — log-normally distributed across patients and across cells within a tumour — capturing both between-subject and intratumoral heterogeneity with only three free parameters per drug. Tumour growth is computed by integrating a Hill-type growth-rate function over that sensitivity distribution using quasi-Monte-Carlo (Sobol) quadrature; parameters are fit by differential evolution. Crucially, combination efficacy is predicted from monotherapy parameters alone under a Loewe dose-additivity assumption — without fitting to any combination data.
Clinical outcomes were assessed with proper survival analysis — Kaplan-Meier, Cox proportional-hazards, and the log-rank test — with bootstrap confidence intervals and Metropolis-Hastings sampling to confirm parameter identifiability, so the small parameter count is a tested property rather than an assumption.
Result
Across 22 drug combinations in 6 tumour types, simulated and observed PFS were largely statistically indistinguishable. Parameterised from preclinical data alone, the model then recovered four historical phase III trials — MONALEESA-7 (matching both arms, log-rank p = 0.978 and 0.893), COLUMBUS, BR.21, and SOLAR-1 — and correctly predicted biomarker-dependent benefit, fitting separate PIK3CA-mutant and wild-type subsets to show that only mutant patients derive meaningful benefit. As few as ~12 xenograft models proved sufficient for confident predictions in the settings tested.