EPSRC & MRC Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick, Coventry, UK.
North Wales Cancer Centre, Ysbyty Gwynedd, Betsi Cadwaladr University Health Board, Bangor, UK.
PLoS Comput Biol. 2020 Jan 27;16(1):e1007218. doi: 10.1371/journal.pcbi.1007218. eCollection 2020 Jan.
Precision medicine requires accurate technologies for drug administration and proper systems pharmacology approaches for patient data analysis. Here, plasma pharmacokinetics (PK) data of the OPTILIV trial in which cancer patients received oxaliplatin, 5-fluorouracil and irinotecan via chronomodulated schedules delivered by an infusion pump into the hepatic artery were mathematically investigated. A pump-to-patient model was designed in order to accurately represent the drug solution dynamics from the pump to the patient blood. It was connected to semi-mechanistic PK models to analyse inter-patient variability in PK parameters. Large time delays of up to 1h41 between the actual pump start and the time of drug detection in patient blood was predicted by the model and confirmed by PK data. Sudden delivery spike in the patient artery due to glucose rinse after drug administration accounted for up to 10.7% of the total drug dose. New model-guided delivery profiles were designed to precisely lead to the drug exposure intended by clinicians. Next, the complete mathematical framework achieved a very good fit to individual time-concentration PK profiles and concluded that inter-subject differences in PK parameters was the lowest for irinotecan, intermediate for oxaliplatin and the largest for 5-fluorouracil. Clustering patients according to their PK parameter values revealed patient subgroups for each drug in which inter-patient variability was largely decreased compared to that in the total population. This study provides a complete mathematical framework to optimize drug infusion pumps and inform on inter-patient PK variability, a step towards precise and personalized cancer chronotherapy.
精准医学需要精确的药物给药技术和适当的系统药理学方法来分析患者数据。在这里，我们对 OPTILIV 试验的血浆药代动力学（PK）数据进行了数学研究，该试验中癌症患者通过输注泵以时间调控的方案经肝动脉接受奥沙利铂、5-氟尿嘧啶和伊立替康治疗。设计了一个泵到患者的模型，以准确地代表从泵到患者血液中的药物溶液动力学。它与半机械 PK 模型连接，以分析 PK 参数的个体间变异性。模型预测并经 PK 数据证实，实际泵启动与患者血液中药物检测之间存在长达 1 小时 41 分钟的大时间延迟。由于给药后葡萄糖冲洗，患者动脉中的药物突然输送峰值高达总药物剂量的 10.7%。新的模型指导的输送方案旨在精确达到临床医生预期的药物暴露。接下来，完整的数学框架很好地拟合了个体时间浓度 PK 曲线，并得出结论，伊立替康的 PK 参数个体间差异最小，奥沙利铂居中，5-氟尿嘧啶最大。根据 PK 参数值对患者进行聚类，揭示了每种药物的患者亚组，与总人群相比，这些亚组的个体间变异性大大降低。这项研究提供了一个完整的数学框架来优化药物输注泵，并告知个体间 PK 变异性，这是实现精确和个性化癌症时间治疗的一步。
