Objective  To construct a genetic algorithm back propagation (GA-BP) artificial neural network model for predicting the blood concentration of aripiprazole (APZ) and its metabolite dehydro-aripiprazole (DAPZ), and to provide a concentration prediction model for patients who need to adjust the dose of APZ or cannot monitor APZ blood concentration.

Methods  Blood drug concentration data were collected retrespectively from 174 patients who regularly took APZ in Xinjiang Uygur Autonomous Region People's Hospital from July 2021 to August 2022. Relevant variables were extracted, and GA-BP artificial neural network prediction model was constructed by Matlab R2018a programming software combined with deep learning network to predict blood drug concentration of APZ+DAPZ.

Results  The GA-BP artificial neural network prediction model showed that compared with the measured results, the average prediction error and the average absolute error of the 35 samples in the verification group were -0.092 6 and 0.689 5, respectively. The 35 prediction errors were all less than 15%, and the probability of less than 15% was 100%. The correlation coefficient between the predicted value and the measured value was 0.997, and the predicted result was ideal.

Conclusion  GA-BP artificial neural network prediction model can be used to predict the blood concentration of APZ+DAPZ and for individual drug administration of APZ.

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