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 twocompartment model is sufficient for the pharmacokinetic study of remifentanil acid for patients with mild degree of renal impairment.

The PKAIN algorithm is an artificial immune network, which has been designed to optimize parameters of linear pharmacokinetic models in our previous work. In this paper, the algorithm is modified to optimise parameters of nonlinear pharmacokinetic models. To evaluate parameters, the numerical inverse Laplace method is adopted to calculate drug concentrations of the dynamic system. The initial solutions of pharmacokinetic parameters are generated randomly by the PKAIN algorithm in a given solution space. Memory cells to be used in the search of global optimal parameters are generated. The optimal mechanism of the algorithm is based on artificial immune network principles and simplex mutation. In addition, a distributed version of the PKAIN algorithm is proposed to improve its efficiency.

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