Scheduling anticancer drug administration over 24h may critically impact treatment success in a patient-specific manner. Here, we address personalization of treatment timing using a novel mathematical model of irinotecan cellular pharmacokinetics and –dynamics linked to a representation of the core clock and predicted treatment toxicity in a colorectal cancer (CRC) cellular model. The mathematical model is fitted to three different scenarios, mouse liver, where the drug metabolism mainly occurs, and two human colorectal cancer cell lines representing an in vitro experimental system for human colorectal cancer progression. Our model successfully recapitulated quantitative circadian datasets of mRNA and protein expression together with timing-dependent irinotecan cytotoxicity data. The model also discriminates time-dependent toxicity between the different cells, suggesting that treatment can be optimized in a time-dependent manner according to the cellular clock. Our results suggest that the time-dependent degradation of the protein mediating irinotecan activation, as well as an oscillation in the death rate play an important role in the timing of drug toxicity. In the future, this model can be used to support personalized treatment scheduling by predicting drug toxicity based on the patient’s gene expression profile.

在 24 小时内安排抗癌药物给药可能会以患者特定的方式严重影响治疗成功。在这里，我们使用伊立替康细胞药代动力学和动力学的新数学模型来解决治疗时间的个性化问题，该模型与结肠直肠癌 (CRC) 细胞模型中的核心时钟和预测治疗毒性相关联。该数学模型适用于三种不同的情况，主要发生药物代谢的小鼠肝脏，以及代表体外的两种人类结直肠癌细胞系。人类结直肠癌进展的实验系统。我们的模型成功地概括了 mRNA 和蛋白质表达的定量昼夜节律数据集以及时间依赖性伊立替康细胞毒性数据。该模型还区分了不同细胞之间的时间依赖性毒性，表明可以根据细胞时钟以时间依赖性方式优化治疗。我们的结果表明，介导伊立替康活化的蛋白质的时间依赖性降解以及死亡率的波动在药物毒性的时间安排中起着重要作用。未来，该模型可用于通过基于患者基因表达谱预测药物毒性来支持个性化治疗安排。