Tumor microenvironment (TME) normalization improves efficacy by increasing anticancer nanocarrier delivery by restoring transvascular pressure gradients that induce convection. However, transport depends on TME biophysics, normalization dose, and nanocarrier size. With increased understanding, we could use computation to personalize normalization amount and nanocarrier size. Here, we use deterministic global dynamic optimization with novel bounding routines to validate mechanistic models against in vivo data. We find that normalization with dexamethasone increases the maximum transvascular convection rate of nanocarriers by 48-fold, the tumor volume fraction with convection by 61%, and the total amount of convection by 360%. Nonetheless, 22% of the tumor still lacks convection. These findings underscore both the effectiveness and limits of normalization. Using artificial neural network surrogate modeling, we demonstrate the feasibility of rapidly determining the dexamethasone dose and nanocarrier size to maximize accumulation. Thus, this digital testbed quantifies transport and performs therapy design.

中文翻译：

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肿瘤微环境正常化的最佳治疗设计

肿瘤微环境 (TME) 正常化通过恢复诱导对流的经血管压力梯度来增加抗癌纳米载体的递送，从而提高疗效。然而，运输取决于 TME 生物物理学、标准化剂量和纳米载体尺寸。随着理解的深入，我们可以使用计算来个性化归一化量和纳米载体尺寸。在这里，我们使用具有新颖边界例程的确定性全局动态优化来验证针对体内的机械模型数据。我们发现用地塞米松标准化后，纳米载体的最大经血管对流率增加了 48 倍，对流的肿瘤体积分数增加了 61%，对流总量增加了 360%。尽管如此，22% 的肿瘤仍然缺乏对流。这些发现强调了标准化的有效性和局限性。使用人工神经网络替代模型，我们证明了快速确定地塞米松剂量和纳米载体大小以最大化积累的可行性。因此，这个数字测试台可以量化运输并执行治疗设计。