Nomograms based on clinical factors to predict abnormal metabolism of psychotropic drugs.

Abstract

Interindividual variability in drug metabolism serves a critical role in the occurrence of adverse drug reactions. Factors such as age, sex, body mass index (BMI) and liver and renal function can influence the metabolism of antipsychotic medications. To the best of our knowledge, however, clinical prediction models based on these factors for estimating drug-metabolizing capacity have not yet been developed. Between January 2022 and September 2023, 185 adult patients (aged ≥18 years) who did not have cancer and were not critically ill, with or without comorbidities such diabetes, hypertension and liver and kidney diseases, who underwent pharmacogenetic testing at The First Hospital of Jilin University (Changchun, China) were enrolled. Clinical data were collected, and the participants were divided into training and validation cohorts. Logistic regression was performed to identify significant risk factors, which were incorporated into multivariable models to construct nomograms predicting psychotropic drug metabolism. A total of eight clinical indicators (BMI, hypertension, alkaline phosphatase, aspartate aminotransferase, cholinesterase, albumin to globulin ratio, urea, and uric acid) were significantly associated with psychotropic drug metabolism (all P<0.05). Based on these indicators, along with age and sex, prediction models for psychotropic drug metabolism were developed. The areas under the receiver operating characteristic curves for haloperidol, olanzapine, paroxetine, mirtazapine/venlafaxine and oxazepam/lorazepam in the validation dataset were 0.767, 0.767, 0.705, 0.740 and 0.789, respectively, indicating the models had moderate diagnostic efficiency. Nomograms were constructed to demonstrate the contribution of each indicator to drug metabolism capacity. To the best of our knowledge, the present study is the first to develop predictive models for psychotropic drug metabolism. These models offer clinicians practical tools to identify patients with impaired drug-metabolizing capacity, thereby enabling more precise and personalized medication management.