突变选择窗口（Mutant selection windows，MSWs）是一种模型，用于预测抗药性和设计最佳剂量策略，已经长期被用于感染性疾病。典型的MSW模型将一个时间内两个亚型进行比较：耐药和敏感。相反，具有N等位基因的适应度景观模型，将基因型映射到适应度，允许同时对N个基因型进行比较，但不编码连续的药物反应数据。在临床环境中，可能有一系列的药物浓度选择不同的基因型。因此，需要一个更强大的病原体对治疗的反应模型，以预测抗性和设计新的治疗方法。适应度景观模型可以模拟基因型与环境互作，通过编码基因型特异的剂量-反应数据，可以同时进行多个MSW比较。通过比较剂量-反应曲线，可以可视化在哪些药物浓度下一个基因型被选择而不是另一个基因型。在这项研究中，我们展示了N-等位基因适应度景观如何允许N*2^(N-1)个独特的MSW比较。在空间药物扩散模型中，我们演示了适应度景观如何揭示空间异质的MSW，从而将MSW模型扩展到更准确地反映药物抗性基因型的选择。此外，我们发现MSW的空间结构在基于代理的模型中塑造了药物抗性的演变。我们的工作凸显了在进化医学中考虑剂量依赖性适应度景观的重要性和实用性。 感染性疾病和癌症中的药物抗性是导致死亡的主要驱动因素。在接受治疗时，肿瘤或感染病灶的细胞群体可能会发展出即使使用先前有效药物之后仍能生长的能力。研究人员假设这些疾病群体的空间组织可能有助于药物抗性的产生。在这项工作中，我们分析了药物浓度的空间梯度对药物抗性演化的影响。我们考虑了一个几十年来称为突变选择窗口（MSW）的模型，描述了选择对药物耐性细胞的药物浓度范围。我们展示了如何通过连续的剂量-反应数据来扩展该模型，该数据描述了不同类型的细胞对药物的响应方式，从而提高了MSW预测进化能力。这项工作有助于我们了解细胞的空间组织，例如肿瘤内血管的结构如何促进药物抗性的产生。将来，我们可以使用这些方法优化药物剂量以预防产生抗性，或者利用已知的药物抗性细胞的脆弱性。

Mutant selection windows (MSWs), the range of drug concentrations that select for drug-resistant mutants, have long been used as a model for predicting drug resistance and designing optimal dosing strategies in infectious disease. The canonical MSW model offers comparisons between two subtypes at a time: drug-sensitive and drug-resistant. In contrast, the fitness landscape model with N alleles, which maps genotype to fitness, allows comparisons between N genotypes simultaneously, but does not encode continuous drug response data. In clinical settings, there may be a wide range of drug concentrations selecting for a variety of genotypes. Therefore, there is a need for a more robust model of the pathogen response to therapy to predict resistance and design new therapeutic approaches. Fitness seascapes, which model genotype-by-environment interactions, permit multiple MSW comparisons simultaneously by encoding genotype-specific dose-response data. By comparing dose-response curves, one can visualize the range of drug concentrations where one genotype is selected over another. In this work, we show how N -allele fitness seascapes allow for N ∗ 2 N- 1 unique MSW comparisons. In spatial drug diffusion models, we demonstrate how fitness seascapes reveal spatially heterogeneous MSWs, extending the MSW model to more accurately reflect the selection fo drug resistant genotypes. Furthermore, we find that the spatial structure of MSWs shapes the evolution of drug resistance in an agent-based model. Our work highlights the importance and utility of considering dose-dependent fitness seascapes in evolutionary medicine.Drug resistance in infectious disease and cancer is a major driver of mortality. While undergoing treatment, the population of cells in a tumor or infection may evolve the ability to grow despite the use of previously effective drugs. Researchers hypothesize that the spatial organization of these disease populations may contribute to drug resistance. In this work, we analyze how spatial gradients of drug concentration impact the evolution of drug resistance. We consider a decades-old model called the mutant selection window (MSW), which describes the drug concentration range that selects for drug-resistant cells. We show how extending this model with continuous dose-response data, which describes how different types of cells respond to drug, improves the ability of MSWs to predict evolution. This work helps us understand how the spatial organization of cells, such as the organization of blood vessels within a tumor, may promote drug resistance. In the future, we may use these methods to optimize drug dosing to prevent resistance or leverage known vulnerabilities of drug-resistant cells.