Effects of Dose, Age, Sex, Body Weight, and Smoking on Plasma Concentrations of Olanzapine and N-desmethyl Olanzapine in Inpatients With Schizophrenia

An, Huimei; Fan, Hongzhen; Chen, Song; Qi, Siyuan; Ma, Botao; Shi, Jing; Wang, Zhiren; Yang, Fude

doi:10.1097/jcp.0000000000001390

Cited by 7 publications

(12 citation statements)

References 32 publications

(87 reference statements)

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“… Deng et al (2020) have reported that patients younger than 60 years had higher C (OLZ) than patients older than 60 years. A previous study by An et al (2021) also revealed that advanced age is related to a lower C (OLZ). However, another prior study arrived at the opposite conclusions.…”

Section: Discussionmentioning

confidence: 74%

“…Third, some features are known to have effects on the C (OLZ). For example, smokers have been reported to exhibit significantly lower steady-state plasma concentrations of OLZ and N-desmethyl-olanzapine, and clear OLZ 55% faster than non-smokers/past smokers ( Bigos et al, 2008 ; An et al, 2021 ). However, smoking history was excluded in the optimal feature subset of our model, owing to the small size of samples.…”

Section: Discussionmentioning

confidence: 99%

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An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

Zhu

Xiao

et al. 2022

Front. Pharmacol.

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Background and Aim: Therapeutic drug monitoring (TDM) has evolved over the years as an important tool for personalized medicine. Nevertheless, some limitations are associated with traditional TDM. Emerging data-driven model forecasting [e.g., through machine learning (ML)-based approaches] has been used for individualized therapy. This study proposes an interpretable stacking-based ML framework to predict concentrations in real time after olanzapine (OLZ) treatment.Methods: The TDM-OLZ dataset, consisting of 2,142 OLZ measurements and 472 features, was formed by collecting electronic health records during the TDM of 927 patients who had received OLZ treatment. We compared the performance of ML algorithms by using 10-fold cross-validation and the mean absolute error (MAE). The optimal subset of features was analyzed by a random forest-based sequential forward feature selection method in the context of the top five heterogeneous regressors as base models to develop a stacked ensemble regressor, which was then optimized via the grid search method. Its predictions were explained by using local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDPs).Results: A state-of-the-art stacking ensemble learning framework that integrates optimized extra trees, XGBoost, random forest, bagging, and gradient-boosting regressors was developed for nine selected features [i.e., daily dose (OLZ), gender_male, age, valproic acid_yes, ALT, K, BW, MONO#, and time of blood sampling after first administration]. It outperformed other base regressors that were considered, with an MAE of 0.064, R-square value of 0.5355, mean squared error of 0.0089, mean relative error of 13%, and ideal rate (the percentages of predicted TDM within ± 30% of actual TDM) of 63.40%. Predictions at the individual level were illustrated by LIME plots, whereas the global interpretation of associations between features and outcomes was illustrated by PDPs.Conclusion: This study highlights the feasibility of the real-time estimation of drug concentrations by using stacking-based ML strategies without losing interpretability, thus facilitating model-informed precision dosing.

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Section: Discussionmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 99%

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

Zhu

Xiao

et al. 2022

Front. Pharmacol.

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“…18 Dose-adjusted serum concentrations were significantly higher in female patients than in male patients. 27,28 The influence of sex on olanzapine concentration may be associated with differences of body weight, liver blood flow, fat distribution or expression of enzyme and transporter. 29 In addition, CYP1A2 is the main metabolic enzyme of olanzapine in vivo.…”

Section: Discussionmentioning

confidence: 99%

“…Deng et al found that normalized concentration and dose‐normalized concentration in male patients aged 10 to 90 years were significantly lower than those in female patients 18 . Dose‐adjusted serum concentrations were significantly higher in female patients than in male patients 27,28 . The influence of sex on olanzapine concentration may be associated with differences of body weight, liver blood flow, fat distribution or expression of enzyme and transporter 29 .…”

Section: Discussionmentioning

confidence: 99%

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance

Xiao

Liu

et al. 2022

Clinical Pharmacy Therapeu

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What Is Known and Objective: Olanzapine is an atypical antipsychotic drug used for mental disorders. There are limited studies providing sufficient pharmacokinetic data, thus the variability of concentrations of olanzapine used in Chinese paediatric patients aged 10 to 17 years remains to be evaluated.Methods: Therapeutic drug monitoring data were collected from 151 paediatric patients aged 10 to 17 years who received olanzapine. The model was developed with a NONMEM software program. The final model validation and evaluation were assessed by bootstrap, diagnostic scatter plots, and normalized prediction distribution error (NPDE). Regimens of different dosages were simulated to reach the target concentration levels of 20 ng/ml, by using the final model with typical parameters. Results:The one-compartment model was considered the best fit for the data. Typical estimates of the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) in the final model were 0.142 h À1 , 15.4 L/h, and 322 L, respectively. Sex and concomitant valproate (VPA) were included as significant predictors of olanzapine clearance, which was described by the following equation:Results of Monte-Carlo simulation suggested that male paediatric patients with concomitant VPA were advised to take no less than 15 mg per day of olanzapine orally, and in female paediatric patients with concomitant VPA, a dosing regimen of 10 mg may be sufficient to achieve the therapeutic range of olanzapine.What Is New and Conclusion: Our results identified concomitant valproate and sex as significant covariates in olanzapine population pharmacokinetics. Our model may be a useful tool for recommending dosage adjustments for physicians. The pharmacokinetics of olanzapine in patients aged 10 to 17 years was generally similar to that of adults and the elderly.

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“…The steady-state plasma concentrations of OLA and DMO were measured by the high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method, which was detailed in our previous study ( 17 ). Briefly, plasma samples of 100 μl were extracted with 300 μl acetonitrile (containing 15 ng/ml d 8 -olanzapine) by agitation for 30 s and centrifuged for 10 min, and then 150 μl of supernatant was injected into the HPLC-MS/MS system.…”

Section: Methodsmentioning

confidence: 99%

Relationship Between Plasma Olanzapine and N-Desmethyl-Olanzapine Concentration and Metabolic Parameters in Patients With Schizophrenia

Fan

Yun

et al. 2022

Front. Psychiatry

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ObjectivesThe aim of the present study was to investigate a potential relationship between metabolic parameters and steady-state plasma concentrations of olanzapine (OLA) and its metabolite, 4-N'-desmethyl-olanzapine (DMO) in patients with schizophrenia taking therapeutic doses.MethodsA total of 352 inpatients, diagnosed with schizophrenia according to the DSM-V criteria and treated with OLA, were investigated. The plasma concentrations of OLA and DMO were measured by high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS). Fasting blood samples were measured for insulin, glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), C-reactive protein (CRP) and homocysteine, and differences in these parameters were investigated in relation to plasma concentrations of OLA and DMO.ResultsLower plasma DMO concentrations were associated with higher glucose and TG levels and homeostasis model assessment of insulin resistance (HOMA-IR), while higher plasma OLA concentrations were associated with higher CRP and homocysteine levels in the OLA-treated patients with schizophrenia.ConclusionThese results demonstrate that OLA and its metabolite DMO may have different effects on OLA-induced metabolic abnormalities. DMO might have a counteracting effects on glucose-insulin homeostasis and lipid metabolic abnormalities, which suggests that regular measure of various metabolic parameters and drug monitoring on both OLA and DMO are recommended in OLA-treated patients with schizophrenia.

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Effects of Dose, Age, Sex, Body Weight, and Smoking on Plasma Concentrations of Olanzapine and N-desmethyl Olanzapine in Inpatients With Schizophrenia

Cited by 7 publications

References 32 publications

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance

Relationship Between Plasma Olanzapine and N-Desmethyl-Olanzapine Concentration and Metabolic Parameters in Patients With Schizophrenia

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