A shape-constrained regression and wild bootstrap framework for reproducible drug synergy testing

Abstract High-throughput drug combination screens require methods to identify synergistic pairs, yet widely used synergy scores lack statistical inference and can fail when parametric dose–response fits do not converge. We present SIR (Synergy via Isotonic Regression), a nonparametric framework that defines interaction as deviation from a monotone-additive null, fit by 2D isotonic regression. A degrees-of-freedom-corrected wild bootstrap yields calibrated p-values for each dose–response matrix. On DrugCombDB, SIR interaction surfaces achieve higher replicate concordance (median correlation 0.91 across 1,839 replicate pairs) than all baselines (0.53–0.74), while avoiding Loewe’s 20.9% and ZIP’s 3.6% failure rates. The fitted surface also predicts missing wells (median holdout RMSE 0.040). By replacing heuristic scores with calibrated effect sizes and p-values, SIR enables principled hit calling and error-rate control in large screens. Competing Interest Statement The authors have declared no competing interest. Footnotes Substantial revision of the writing of the manuscript. Method renamed to SIR (Synergy via Isotonic Regression).