用对抗塑造设计抗体,让疗法主动引导病毒进化方向
ADIOS: Antibody Development via Opponent Shaping
- 采用内外层优化框架,让抗体设计考虑病毒的演化响应
- 新抗体不仅能抗现有毒株,还能抑制强毒变异株出现
- 适合研发长效疫苗与抗耐药治疗,可推广至癌症等领域
抗病毒疗法通常仅针对当前病毒株,具有短视性。但治疗带来的选择压力会催生新毒株,使原有疗法失效。这带来新机遇:疗法可同时防御并主动影响病毒演化。为此提出ADIOS方法——一种元学习框架,外层为抗体设计,内层模拟病毒适应性演化。通过该框架,抗体不仅对潜在未来变异株具备鲁棒性,还能‘塑造’演化路径,引导产生较弱毒株。基于Absolut!框架构建的病毒演化模拟器验证了其有效性:相比传统短视抗体,ADIOS设计的抗体能更有效地应对当前及未来毒株;且能改变病毒演化轨迹分布,减少强毒株比例。该范式有望推动长效疫苗和抗体疗法开发,并拓展至抗微生物耐药、癌症治疗等演化对抗场景。代码已开源。
原文摘要 · Abstract (English)
Anti-viral therapies are typically designed to target only the current strains of a virus, a myopic response. However, therapy-induced selective pressures drive the emergence of new viral strains, against which the original myopic therapies are no longer effective. This evolutionary response presents an opportunity: our therapies could both defend against and actively influence viral evolution. This motivates our method ADIOS: Antibody Development vIa Opponent Shaping. ADIOS is a meta-learning framework where the process of antibody therapy design, the outer loop, accounts for the virus's adaptive response, the inner loop. With ADIOS, antibodies are not only robust against potential future variants, they also influence, i.e., shape, which future variants emerge. In line with the opponent shaping literature, we refer to our optimised antibodies as shapers. To demonstrate the value of ADIOS, we build a viral evolution simulator using the Absolut! framework, in which shapers successfully target both current and future viral variants, outperforming myopic antibodies. Furthermore, we show that shapers modify the distribution over viral evolutionary trajectories to result in weaker variants. We believe that our ADIOS paradigm will facilitate the discovery of long-lived vaccines and antibody therapies while also generalising to other domains. Specifically, domains such as antimicrobial resistance, cancer treatment, and others with evolutionarily adaptive opponents. Our code is available at https://github.com/olakalisz/adios.
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