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Optimal therapy design with tumor microenvironment normalization
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DOI：10.1002/aic.17747.png)
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摘要
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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.
Keyword:
deterministic global dynamic optimization
machine learning surrogate
mass transport
nanomedicine
therapy design
tumor microenvironment
期刊
IF:
4
论文数:
1.1W
被引数:
2.9W
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引用论文
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