PKAIN: an artificial immune network for parameter optimization in pharmacokinetics

Abstract

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.

An improved optimization algorithm of the three-compartment model with spillover and partial volume corrections for dynamic FDG PET images of small animal heartsin vivo

“…Antibody with lower excellence was suppressed, which is an effective measure to preserve diversity in the solution. Following step 7 (see flow chart, figure 2), the clonal selection procedure selected 10 antibodies with higher fitness and another 10 with higher excellence as parent generation, and then clone, selection, and crossover and mutation algorithm (Liu et al 2009) applied to the remaining antibodies in order to generate offspring antibodies. Due to the nature of clone and reproduction of AIS algorithm, the feature of better antibody was inherited and propagated to next generations.…”