Opponent Shaping for Antibody Development
<|reference_start|>Opponent Shaping for Antibody Development: Anti-viral therapies are typically designed to target only the current strains of a virus. Game theoretically, this corresponds to a short-sighted, or myopic, response. However, therapy-induced selective pressures act on viruses to drive the emergence of mutated strains, against which initial therapies have reduced efficacy. Building on a computational model of binding between antibodies and viral antigens (the Absolut! framework), we design and implement a genetic simulation of viral evolutionary escape. Crucially, this allows our antibody optimisation algorithm to consider and influence the entire escape curve of the virus, i.e. to guide (or "shape") the viral evolution. This is inspired by opponent shaping which, in general-sum learning, accounts for the adaptation of the co-player rather than playing a myopic best response. Hence we call the optimised antibodies shapers. Within our simulations, we demonstrate that our shapers target both current and simulated future viral variants, outperforming the antibodies chosen in a myopic way. Furthermore, we show that shapers exert specific evolutionary pressure on the virus compared to myopic antibodies. Altogether, shapers modify the evolutionary trajectories of viral strains and minimise the viral escape compared to their myopic counterparts. While this is a simplified model, we hope that our proposed paradigm will facilitate the discovery of better long-lived vaccines and antibody therapies in the future, enabled by rapid advancements in the capabilities of simulation tools. Our code is available at https://github.com/olakalisz/antibody-shapers.<|reference_end|>
@article{towers2024opponent,
    title={Opponent Shaping for Antibody Development},
    author={Sebastian Towers and Aleksandra Kalisz, Philippe A. Robert, Alicia
  Higueruelo, Francesca Vianello, Ming-Han Chloe Tsai, Harrison Steel, Jakob N.
  Foerster},
    journal={arXiv preprint arXiv:2409.10588},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10588},
    primaryClass={q-bio.PE cs.AI cs.GT cs.MA}
}