Abstract
A new method for parameter optimization of pharmacokinetics based on an artificial immune network named PKAIN is proposed. To improve local searching ability of the artificial immune network, a partition-based concurrent simplex mutation is developed. By means of evolution of network cells in the PKAIN artificial immune network, an optimal set of parameters of a given pharmacokinetic model is obtained. The Laplace transform is applied to the pharmacokinetic differential equations of remifentanil and its major metabolite, remifentanil acid. The PKAIN method is used to optimize parameters of the derived compartment models. Experimental results show that the two-compartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.
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Yang X M, Yang K N, Zhang F L. Clinical application guide of pharmacokinetics[M]. Bei**g: Military Medicine Science Press, 2004, 391–405 (in Chinese).
Zhang J W, Pei X B. Feathering method for processing absorption and elimination rate constants of mono-compartment drugs administered with repeated doses of nonparenteral administration[J]. Acta Universitatis Medicinalis Anhui, 2004, 39(1):34–36 (in Chinese).
Jang J S R, Sun C T, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence[M]. NJ, USA: Prentice Hall, 1997, 112–137.
Nelder A, Mead R. A simplex method for function optimization[J]. Computation J, 1965, 7(12):308–313.
Jeffrey C L, James A R, Margaret HW, Paul EW. Convergence properties of the Nelder-Mead simplex method in low dimensions[J]. Society for Industrial and Applied Mathematics, 1998, 9(1):112–147.
Liu L B, Zhang G W, **e Z C. Pharmacokinetics parameter calculation based on improved simplex method[J]. Journal of Zhejiang University of Science and Technology, 2007, 19(1):4–6 (in Chinese).
de Castro L N, Timmis J. Artificial immune systems as a novel soft computing paradigm[J]. Soft Computing, 2003, 7(8):526–544.
Chun J S, Hahn S Y. A study on comparison of optimization performances between immune algorithm and other heuristic algorithms[J]. IEEE Transactions on Magnetics, 1998, 34(5):2972–2975.
**e K G, Zeng X H, Li C Y, et al. Comparative analysis between immune algorithm and other random searching algorithms[J]. Journal of Chongqing University, 2003, 26(11):43–47 (in Chinese).
Yuan S F, Chu F L. Fault diagnosis based on support vector machines with parameter optimization by artificial immunization algorithm[J]. Mechanical System and Signal Processing, 2007, 21(3):1318–1330.
Ma X L, Liu F, Jiao L C. Parameters optimization of synergetic neural network based on immunity clonal algorithm[J]. J Infrared Millim Wave, 2007, 26(1):38–42 (in Chinese).
He H, Qian F. Parameters setting of PID controller based on adaptive immune evolutionary algorithms[J]. Computer Applications, 2002, 27(5):1175–1176, 1196 (in Chinese).
de Castro L N, Timmis J. An artificial immune network for multimodal function optimization[C]. In: Proceedings of IEEE Congress on Evolutionary Computation (CEC’02), Honolulu, HI, USA: IEEEE Service Center, 2004, 1:674–699.
de Franca F O, Von Zuben F J, de Castro L N. An artificial immune network for multimodal function optimization on dynamic environments[C]. In: Proc of the GECCO conf, Washington DC: ACM Press, 2005, 289–296.
Wang G J. Pharmacokinetics[M]. Bei**g: Chemical Industry Press, 2005, 88–120 (in Chinese).
Huang X J, **e H T, Sun R Y. Laplace transform and its applications in the pharmacokinetics[J]. Chin J Clin Pharmocol Ther, 2001, 6(1):59–63 (in Chinese).
Ada G L, Nossal G J V. The clonal selection theory[J]. Scientific American, 1987, 257(2):50–57.
Jerne N K. Towards a network theory of the immune system[J]. Annals of Immunology, Ser C, 1974, 125(1/2):373–389.
Huang X H, Shi J, Li J, et al. Pharmacokinetic pharmacodynamic modeling and simulation: concepts and basic principles(2)[J]. Chinese Journal of Clinical Pharmacology and Therapeutics, 2007, 12(3):334–341.
de Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle[J]. IEEE Trans on Evol Comp, 2002, 6(3):239–251.
Renders J, Bersini H. Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways[C]. In: Proceedings of the First IEEE Conference on Evolutionary Computation, Orlando Florida: IEEE Press, 1994, 312–317.
Yen J, Liao J, Randolph D, Lee B. A Hybrid approach to modeling metabolic systems using a genetic algorithms and the simplex method[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1998, 28(2):173–191.
Yu J J, Sun S D, Wang J Q. Simplex immune hybrid algorithm and its application to multi-dimensional no-protruding function optimization[J]. Mechanical Science and Technology, 2007, 26(3):296–303 (in Chinese).
de Castro L N, Von Zuben F J. aiNet: an artificial immune network for data analysis[M]. Abbass H A, Saker R A, Newton C S (eds). Data Mining: A Heuristic Approach, USA: Idea Group Publishing, 2001, 231–259.
Westmoreland C L, Hoke J F, Sebel P S, et al. Pharmacokinetics of remifentanil (GI87084B) and its major metabolite (GR90291) in patients undergoing elective surgery[J]. Anesthesiology, 1993, 79(5):893–903.
Pitsiu M, Wilmer A, Bodenham A, et al. Pharmacokinetics of remifentanil and its major metabolite, remifentanil acid, in ICU patients with renal impairment[J]. British Journal of naesthesia, 2004, 92(4):493–503.
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Communicated by ZHANG Lu-kun
Project supported by Health Department of Jiangsu Province (No. P200512)
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Liu, L., Zhou, Sd., Lu, Hw. et al. Parameter optimization of pharmacokinetics based on artificial immune network. Appl. Math. Mech.-Engl. Ed. 29, 549–558 (2008). https://doi.org/10.1007/s10483-008-0414-7
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DOI: https://doi.org/10.1007/s10483-008-0414-7