Abstract
Risperidone is an efficacious second-generation antipsychotic (SGA) to treat a wide spectrum of psychiatric diseases, whereas its active moiety (risperidone and 9-hydroxyrisperidone) concentration without a therapeutic reference range may increase the risk of adverse drug reactions. We aimed to establish a prediction model of risperidone active moiety concentration in the next therapeutic drug monitoring (TDM) based on the initial TDM information using machine learning methods. A total of 983 patients treated with risperidone between May 2017 and May 2018 in Beijing Anding Hospital were collected as the data set. Sixteen predictors (the initial TDM value, dosage, age, WBC, PLT, BUN, weight, BMI, prolactin, ALT, MECT, Cr, AST, Ccr, TDM interval, and RBC) were screened from 26 variables through univariate analysis (p < 0.05) and XGBoost (importance score >0). Ten algorithms (XGBoost, LightGBM, CatBoost, AdaBoost, Random Forest, support vector machine, lasso regression, ridge regression, linear regression, and k-nearest neighbor) compared the model performance, and ultimately, XGBoost was chosen to establish the prediction model. A cohort of 210 patients treated with risperidone between March 1, 2019, and May 31, 2019, in Beijing Anding Hospital was used to validate the model. Finally, the prediction model was evaluated, obtaining R2 (0.512 in test cohort; 0.374 in validation cohort), MAE (10.97 in test cohort; 12.07 in validation cohort), MSE (198.55 in test cohort; 324.15 in validation cohort), RMSE (14.09 in test cohort; 18.00 in validation cohort), and accuracy of the predicted TDM within ±30% of the actual TDM (54.82% in test cohort; 60.95% in validation cohort). The prediction model has promising performance to facilitate rational risperidone regimen on an individualized level and provide reference for other antipsychotic drugs' risk prediction.
Highlights
As one of the representative atypical antipsychotic drugs, risperidone is a benzisoxazole derivative with potent serotonin (5-hydroxytryptamine) antagonism and moderate dopamine (D2) receptor-blocking effects [1, 2]
A retrospective analysis of patients with risperidone treatment between May 2017 and May 2018 in Beijing Anding Hospital was performed in this study, and all clinical and demographic data were collected from the electronic health records
population pharmacokinetic (PopPK) models were commonly performed under restricted conditions, and its parameters were generally affected by the wide inter-individual differences and the interactions of concomitant medications [24, 25]
Summary
As one of the representative atypical antipsychotic drugs, risperidone is a benzisoxazole derivative with potent serotonin (5-hydroxytryptamine) antagonism and moderate dopamine (D2) receptor-blocking effects [1, 2]. According to the Food and Drug Administration and European Medicines Agency, risperidone has various indications, such as irritability associated with autistic disorder, manic episodes associated with bipolar disorder, and persistent aggression in patients with mental retardation or Alzheimer’s dementia [3, 7]. It shows efficacy in treating disruptive behavior, tic, and attention-deficit/ hyperactivity disorders [8]. The combined risperidone and 9-hydroxyrisperidone serum concentration is termed as “active moiety,” which displays association with adverse drug reactions (ADRs) and clinical treatment effects [10, 11]. In accordance with the Arbeitsgemeinschaft für Neuropsychopharmakologieund Pharmakopsychiatrie (AGNP) consensus guidelines, the recommended therapeutic reference range is 20∼60 ng/ml for the risperidone active moiety [4, 9]
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