文章：

利用数学建模和人工智能来改善癌症治疗的递送和疗效

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer

原文发布日期：2025-02-19

DOI: 10.1038/s41568-025-00796-w

类型: Review Article

开放获取: 否

英文摘要：

Mathematical modelling has proven to be a valuable tool in predicting the delivery and efficacy of molecular, antibody-based, nano and cellular therapy in solid tumours. Mathematical models based on our understanding of the biological processes at subcellular, cellular and tissue level are known as mechanistic models that, in turn, are divided into continuous and discrete models. Continuous models are further divided into lumped parameter models — for describing the temporal distribution of medicine in tumours and normal organs — and distributed parameter models — for studying the spatiotemporal distribution of therapy in tumours. Discrete models capture interactions at the cellular and subcellular levels. Collectively, these models are useful for optimizing the delivery and efficacy of molecular, nanoscale and cellular therapy in tumours by incorporating the biological characteristics of tumours, the physicochemical properties of drugs, the interactions among drugs, cancer cells and various components of the tumour microenvironment, and for enabling patient-specific predictions when combined with medical imaging. Artificial intelligence-based methods, such as machine learning, have ushered in a new era in oncology. These data-driven approaches complement mechanistic models and have immense potential for improving cancer detection, treatment and drug discovery. Here we review these diverse approaches and suggest ways to combine mechanistic and artificial intelligence-based models to further improve patient treatment outcomes.

摘要翻译： 

数学建模已被证明是预测实体瘤中分子疗法、抗体疗法、纳米疗法及细胞疗法的递送与疗效的重要工具。基于我们对亚细胞、细胞和组织层面生物过程理解而建立的数学模型称为机理模型，这类模型可进一步分为连续模型与离散模型。连续模型又可划分为集总参数模型（用于描述药物在肿瘤和正常器官中的时间分布）和分布参数模型（用于研究治疗手段在肿瘤中的时空分布）。离散模型则主要捕捉细胞和亚细胞层面的相互作用。总体而言，这些模型通过整合肿瘤的生物学特征、药物的理化特性、药物与癌细胞及肿瘤微环境各组分间的相互作用，并结合医学影像实现患者特异性预测，有助于优化分子、纳米尺度和细胞疗法在肿瘤中的递送与疗效。基于人工智能的方法（如机器学习）已引领肿瘤学进入新时代。这些数据驱动的方法与机理模型形成互补，在癌症检测、治疗及药物研发方面具有巨大潜力。本文回顾了这些多元化方法，并提出了将机理模型与人工智能模型相结合以进一步提升患者治疗效果的可行路径。

原文链接：

Using mathematical modelling and AI to improve delivery and efficacy of therapies in cancer