基于新型饱和速率细胞周期模型的每日抗癌药物给药非线性模型预测控制

Nonlinear model predictive control for dosing daily anticancer agents using a novel saturating-rate cell-cycle model.

作者信息

Florian Jeffry A, Eiseman Julie L, Parker Robert S

机构信息

Department of Chemical and Petroleum Engineering, University of Pittsburgh School of Engineering, Pittsburgh, PA, USA.

出版信息

Comput Biol Med. 2008 Mar;38(3):339-47. doi: 10.1016/j.compbiomed.2007.12.003. Epub 2008 Jan 28.

DOI:10.1016/j.compbiomed.2007.12.003

PMID:18222419

Abstract

A nonlinear model predictive control (NMPC) algorithm was developed to dose the chemotherapeutic agent tamoxifen based on a novel saturating-rate, cell-cycle model (SCM). Using daily tumor measurements, the algorithm decreased tumor volume along a specified reference trajectory in simulated animals over 4 months. In mismatch case studies, controllers based on the Gompertz model (GM) yielded equivalent total drug delivered and elapsed time to t(99%) reference step convergence to those obtained using the SCM, though this performance was dependent on the cell-cycle phase of drug effect. Overall, the NMPC algorithm is suitable for dosing chemotherapeutics with regular administration schedules and may be adapted for regularly administered chemotherapeutics other than tamoxifen.

摘要

开发了一种非线性模型预测控制（NMPC）算法，用于基于一种新型的饱和速率细胞周期模型（SCM）对化疗药物他莫昔芬进行给药。利用每日肿瘤测量数据，该算法在4个月的模拟动物实验中，使肿瘤体积沿着指定的参考轨迹减小。在失配案例研究中，基于Gompertz模型（GM）的控制器在达到t(99%)参考步收敛时，所输送的总药物量和经过时间与使用SCM时相当，不过这种性能取决于药物作用的细胞周期阶段。总体而言，NMPC算法适用于按常规给药方案进行化疗药物给药，并且可能适用于除他莫昔芬之外的其他常规给药的化疗药物。