Model-informed prediction of antiviral drug combination synergy from sparse data using complementary mechanistic and...

Optimization of antiviral drug combinations remains challenging because exhaustive measurement of dose-response spaces is experimentally impractical. Although Bliss-based pharmacodynamic models are commonly used to describe combination responses, their empirical nature limits biological interpretation.

To address these limitations, we developed a model-informed framework to reconstruct antiviral combination response surfaces from sparse diagonal observations by combining a Bliss-based pharmacodynamic model with a mechanistic viral dynamics model.

Both models inferred unmeasured response regions and quantified synergy relative to Bliss independence through an interaction parameter. The mechanistic viral dynamics model reconstructed response surfaces more accurately than the Bliss-based model while retaining a biologically interpretable structure linked to viral infectivity and production.

These results provide a practical modeling strategy for antiviral combination optimization under limited-data experimental settings.