What to Do About Missed Doses? A Retrospective Study of Olanzapine in the Elderly

Xiao, Tao; Wang, Zhanzhang; Li, Guanlie; Huang, Shanqing; Zhu, Xiuqing; Liu, Shujing; Li, Xin; Hu, Jinqing; Shang, Dewei; Wen, Yuguan

doi:10.2147/dddt.s316110

Cited by 9 publications

(10 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A PopPK model has been created to increase the possibility of meeting suitable pharmacokinetic/pharmacodynamic targets due to the limited therapeutic index of voriconazole and the relatively large systematic interindividual variability ( Chen et al, 2019 ). On the other hand, due to the tendency of order patients to miss doses of medication, they developed a strategy to correct for missed doses through establishing a PopPK model and simulating ( Xiao et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective: To establish a population pharmacokinetic model in Chinese psychiatric patients to characterize escitalopram pharmacokinetic profile to identify factors influencing drug exposure, and through simulation to compare the results with the established therapeutic reference range.Methods: Demographic information, dosing regimen, CYP2C19 genotype, concomitant medications, and liver and kidney function indicators were retrospectively collected for inpatients taking escitalopram with therapeutic drug monitoring from 2018 to 2021. Nonlinear mixed-effects modeling was used to model the pharmacokinetic characteristics of escitalopram. Goodness-of-fit plots, bootstrapping, and normalized prediction distribution errors were used to evaluate the model. Simulation for different dosing regimens was based on the final estimations.Results: The study comprised 106 patients and 337 measurements of serum sample. A structural model with one compartment with first-order absorption and elimination described the data adequately. The population-estimated apparent volume of distribution and apparent clearance were 815 and 16.3 L/h, respectively. Age and CYP2C19 phenotype had a significant effect on the apparent clearance (CL/F). CL/F of escitalopram decreased with increased age, and CL/F of poor metabolizer patients was significantly lower than in extensive and immediate metabolizer patients. The final model-based simulation showed that the daily dose of adolescents with poor metabolizer might be as high as 15 mg or 20 mg and referring to the therapeutic range for adults may result in overdose and a high risk of adverse effects in older patients.Conclusion: A population pharmacokinetics model of escitalopram was successfully created for the Chinese population. Depending on the age of the patients, CYP2C19 genotype and serum drug concentrations throughout treatment are required for adequate individualization of dosing regimens. When developing a regimen for older patients, especially those who are poor metabolizers, vigilance is required.

show abstract

Section: Discussionmentioning

confidence: 99%

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The serum concentration of OLZ was determined by using a previously reported analytical method developed by our laboratory, where the calibration curve was linear over a wide range of concentrations of 2–200 ng/ml ( Xiao et al, 2021 ). In particular, we replicated the static values across time, excluded OLZ concentrations below the lower limit of quantification, and assumed that TDM measurements of other medications were zero if they had not been co-administrated.…”

Section: Methodsmentioning

confidence: 99%

“…The large inter-individual pharmacokinetic variations in OLZ, which are influenced by genetic and environmental factors ( Arnaiz et al, 2021 ), make TDM an essential part of personalizing OLZ treatment in any age group, especially the pediatric population (high-level recommendation to use TDM according to guidelines of the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie consensus) ( Hiemke et al, 2018 ). Several studies, using popPK and multiple regression analyses, have been conducted to reveal the sources of the pharmacokinetic variability of OLZ by integrating various covariates (e.g., age, gender, body weight, and concomitant medications) ( Deng et al, 2020 ; Xiao et al, 2021 ; Zang et al, 2021 ). All of these studies involved low-dimensional data, with a small sample size and few patient-related variables.…”

Section: Introductionmentioning

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The trough concentration was collected during the elimination phase. Even if there were errors in the serum sampling time, the trough concentrations’ changes were not significant ( Xiao et al, 2021 ). The absorption rate constant (Ka) was set to previously published values of 0.908/h due to the absence of serum samples throughout the absorption phase ( Venkatakrishnan and Obach, 2005 ; Nishimura et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Pharmacokinetics of immediate and sustained-release formulations of paroxetine: Population pharmacokinetic approach to guide paroxetine personalized therapy in chinese psychotic patients

Huang

Xiao

et al. 2022

Front. Pharmacol.

Self Cite

View full text Add to dashboard Cite

Paroxetine is one of the most potent selective serotonin reuptake inhibitors (SSRIs) approved for treating depression, panic disorder, and obsessive-compulsive disorder. There is evidence linking genetic polymorphisms and nonlinear metabolism to the Paroxetine’s pharmacokinetic (PK) variability. The purpose of the present study was to develop a population PK (PPK) model of paroxetine in Chinese patients, which was used to define the paroxetine’s PK parameters and quantify the effect of clinical and baseline demographic factors on these PK characteristics. The study included 184 inpatients with psychosis (103 females and 81 males), with a total of 372 serum concentrations of paroxetine for PPK analyses. The total daily dosage ranged from 20 to 75 mg. One compartment model could fit the PKs characterize of paroxetine. Covariate analysis revealed that dose, formulation, and sex had a significant effect on the PK parameters of paroxetine; however, there was no evident genetic influence of CYP2D6 enzymes on paroxetine concentrations in Chinese patients. The study determined that the population’s apparent distribution volume (V/F) and apparent clearance (CL/F), respectively, were 8850 and 21.2 L/h. The CL/F decreased 1-2-fold for each 10 mg dose increase, whereas the different formulations caused a decrease in V/F of 66.6%. Sex was found to affect bioavailability (F), which decreased F by 47.5%. Females had higher F values than males. This PPK model described data from patients with psychosis who received paroxetine immediate-release tablets (IR-T) and/or sustained-release tablets (SR-T). Paroxetine trough concentrations and relative bioavailability were different between formulations and sex. The altered serum concentrations of paroxetine resulting from individual variants and additive effects need to be considered, to optimize the dosage regimen for individual patients.

show abstract

What to Do About Missed Doses? A Retrospective Study of Olanzapine in the Elderly

Cited by 9 publications

References 30 publications

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age

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

Pharmacokinetics of immediate and sustained-release formulations of paroxetine: Population pharmacokinetic approach to guide paroxetine personalized therapy in chinese psychotic patients

Contact Info

Product

Resources

About