Weighted target interval stochastic control methods with global optimization and their applications in individualizing therapy

被引:2
|
作者
Ji, Shaolin
Zeng, Yingzhi
Wu, Ping
Lee, Edmund Jon Deoon
机构
[1] Natl Univ Singapore, Fac Med, Dept Pharmacol, Singapore 119260, Singapore
[2] Inst High Performance Comp, Singapore 117528, Singapore
关键词
target interval stochastic control; weighting function; Bayesian estimation; dosage regimen; global optimization;
D O I
10.1007/s10928-007-9054-4
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Several improvements on the target interval stochastic control (TISC) method are addressed for individualizing therapy. In particular, a global optimization control strategy is implemented to obtain the optimal dosage regimen, and weighting functions are introduced to balance the drug efficacy and the risk of toxicity. Since general guidance is often lacking in the determination of a weighting function, we introduce a systematic approach, i.e., the standard reference gamble method of medical decision theory, for the determination of the weighting function. The population model for the individualization of theophylline therapy reported by D'Argenio and Katz is applied in this research. The present method of the integration of weighting functions and global optimal strategy offer an effective and safe means to balance the drug efficacy and risk of toxicity. In addition, it also achieves better accuracy than the existing TISC method which uses a local optimal strategy.
引用
收藏
页码:433 / 449
页数:17
相关论文
共 50 条
  • [1] Weighted target interval stochastic control methods with global optimization and their applications in individualizing therapy
    Shaolin Ji
    Yingzhi Zeng
    Ping Wu
    Edmund Jon Deoon Lee
    Journal of Pharmacokinetics and Pharmacodynamics, 2007, 34 : 433 - 449
  • [2] Global Optimization in Systems Biology: Stochastic Methods and Their Applications
    Balsa-Canto, Eva
    Banga, J. R.
    Egea, J. A.
    Fernandez-Villaverde, A.
    de Hijas-Liste, G. M.
    ADVANCES IN SYSTEMS BIOLOGY, 2012, 736 : 409 - 424
  • [3] Interval methods for global optimization
    Wolfe, MA
    APPLIED MATHEMATICS AND COMPUTATION, 1996, 75 (2-3) : 179 - 206
  • [4] Stochastic methods for practical global optimization
    J of Global Optim, 4 (433-444):
  • [5] Stochastic Methods for Practical Global Optimization
    Zelda B. Zabinsky
    Journal of Global Optimization, 1998, 13 : 433 - 444
  • [6] CONCURRENT STOCHASTIC METHODS FOR GLOBAL OPTIMIZATION
    BYRD, RH
    DERT, CL
    KAN, AHGR
    SCHNABEL, RB
    MATHEMATICAL PROGRAMMING, 1990, 46 (01) : 1 - 29
  • [7] Stochastic methods for practical global optimization
    Zabinsky, ZB
    JOURNAL OF GLOBAL OPTIMIZATION, 1998, 13 (04) : 433 - 444
  • [8] MINORANT METHODS OF STOCHASTIC GLOBAL OPTIMIZATION
    Norkin, V. I.
    Onishchenko, B. O.
    CYBERNETICS AND SYSTEMS ANALYSIS, 2005, 41 (02) : 203 - 214
  • [9] New interval methods for constrained global optimization
    Markót, MC
    Fernández, J
    Casado, LG
    Csendes, T
    MATHEMATICAL PROGRAMMING, 2006, 106 (02) : 287 - 318
  • [10] Convergence speed of interval methods for global optimization
    Csallner, A.E.
    Csendes, T.
    Computers & Mathematics with Applications, 1996, 31 (4-5):