Box-Behnken Response Surface Method Combined with Backpropagation Neural Network to Optimize Black Pepper Navel Patch Formulation
Objective To optimize the formulation of a black pepper navel patch by using the Box-Behnken response surface method combined with the backpropagation neural network algorithm.Methods On the basis of pre-experimental data,the sin-gle factor experiment was carried out with drug loading,penetration enhancer content,and the mass ratio of PEG6000 to silica gel as influencing factors.We determined the content of piperine through high-performance liquid chromatography in combina-tion with the invitro transdermal test,and assigned weights to the peak area of piperine,the total peak area of the fingerprint, and the formability,peel-off residue,and matrix filling of the umbilical patch,constructing a comprehensive evaluation index.On the basis of the single-factor experiment,the navel patch formulation was optimized using the Box-Behnken response surface method combined with the backpropagation neural network algorithm.Results The optimal black pepper navel patch formula-tion was:drug loading,7％;azone,4.5％;the mass ratio of PEG6000 to silica gel,43∶1.Conclusion The black pepper navel patch formulation is reasonable,which can provide a basis for the future pharmacodynamic study of patches.
Black pepperUmbilical patchBox-Behnken response surface methodBackpropagation neural network
万方数据
维普
