Shaped, not Stirred: Co-evolving Combination Therapies to Prevent Viral Escape

Combination therapies are the gold standard for treating rapidly evolving viruses like HIV or Influenza, where single-drug approaches quickly lose efficacy due to viral adaptation. However, current therapy design is fundamentally *myopic*, and optimises for immediate efficacy against present viral strains without anticipating how therapeutic pressure will influence future viral evolution. We introduce **Co-ADIOS** (**Co**operative **A**ntibody **D**esign v**i**a **O**pponent **S**haping), a framework that designs antibody combination therapies, or *cocktails*, that steer viral evolution toward weaker variants. We formulate the interaction between antibody cocktails and the virus as a *Stackelberg Evolutionary Game*, where antibodies act as *leaders* that shape the fitness landscape to steer the adaptive trajectory of the virus (the *evolving follower*). In the game, viral evolutionary dynamics are governed by a *biophysically-grounded lattice simulator* that accounts for binding energies and evolutionary plausibility. We evaluate Co-ADIOS on the Influenza H5 virus in two clinically relevant scenarios: (1) designing de novo antibody cocktails, and (2) co-designing a partner antibody to complement an established therapeutic. We demonstrate that Co-ADIOS antibodies outperform myopic baselines by using cooperative strategies which achieve stronger shaping over viral evolution, at a modest cost in efficacy against the current strain. Finally, our analysis shows that the designed cocktails leave the virus far fewer single mutations worth taking, and that their two antibodies bind a shared site while covering different escape mutations, offering physically explainable insights for the design of effective real-world viral therapies.