Remedial dosing recommendations for delayed or missed doses of valproic acid in patients with epilepsy based on Monte Carlo simulations

Population pharmacokinetics and dosing optimization of perampanel in children with epilepsy: A real-world study

OBJECTIVE

The purposes of this study were to explore the pharmacokinetics of perampanel (PER) in children with epilepsy, identify factors that contribute to pharmacokinetic variations among subjects, evaluate the connection between PER exposure and clinical outcome, and establish an evidence-based approach for tailoring individualized antiepileptic treatment in this specific population.

METHODS

In this prospective study, PER plasma concentrations and genetic information on metabolic enzymes were obtained from 194 patients younger than 18 years. The disposition kinetics of PER in pediatric patients following oral dosing were characterized using nonlinear mixed effect models. The effective range for the plasma concentration of PER was determined by assessing the efficacy and safety of PER treatment and analyzing the relationship between drug exposure and clinical response. Monte Carlo simulations were then performed to evaluate and optimize the current dosing regimens.

RESULTS

The pharmacokinetic profile of PER was adequately described by a one-compartment model with first-order absorption and elimination. Body weight, total bilirubin level, and concomitant oxcarbazepine were found to have significant influences on PER pharmacokinetics. Model estimates of apparent clearance and volume of distribution were .016 ± .009 L/h/kg and 1.47 ± .78 L/kg, respectively. The effective range predicted from plasma concentration data in responders was 215-862 μg/L. Dosing scenarios stratified according to essential covariates were proposed through simulation analysis.

SIGNIFICANCE

In this study, we captured the pharmacokinetic/pharmacodynamic characteristics of PER in pediatric epilepsy patients through analysis of real-world data and adopted a pharmacometric approach to support an individualized dosing strategy for PER in this specific population.

Managing delayed or missed pregabalin doses in patients with focal epilepsy: a Monte Carlo simulation study

BACKGROUND

Delayed or missed doses are inevitable in epilepsy pharmacotherapy. The current remedial measures recommended by the United States Food and Drug Administration (FDA) for non-adherence are generic and lack clinical evidence.

AIM

To assess remedial strategies for delayed or missed pregabalin doses in patients with epilepsy using Monte Carlo simulations.

METHOD

Monte Carlo simulations were performed using a published population pharmacokinetic model for pregabalin. The applicability of five proposed remedial regimens as well as FDA recommendations was evaluated by simulating various poor adherence scenarios in eight populations, including those with renal dysfunction.

RESULTS

All proposed remedial strategies were associated with delay duration and renal function. When delays are relatively short, an immediate regular dose is advised. The cut-off time points for taking the regular dose as a remedial regimen were 1, 2, 4, and 12 h for patients with mild renal impairment and normal renal function, moderate renal impairment, severe renal impairment, and end-stage renal disease, respectively. However, when delay aligns closely with a dosing interval, a regular dose combined with a partial dose proves effective. Generally, supplementing 1.3-fold the regular dose at the next scheduled time adequately compensates for the missed dose.

CONCLUSION

Model-based simulations provided quantitative evidence for the effectiveness and feasibility of remedial strategies for missed or delayed pregabalin doses.

Escitalopram population pharmacokinetics and remedial strategies based on CYP2C19 phenotype

BACKGROUND AND PURPOSE

CYP2C19 is a key factor influencing escitalopram (SCIT) exposure. However, different studies reported various results. This study aims to develop a population pharmacokinetic (popPK) model characterizes the disposition of SCIT in the Chinese population. Based on the popPK model, the study simulates non-adherence scenarios and proposes remedial strategies to facilitate SCIT personalized therapy.

METHODS

Nonlinear mixed-effects modeling using data from two Chinese bioequivalence studies was employed. Monte-Carlo simulation was used to explore non-adherence scenarios and propose remedial strategies based on the proportion of time within the therapeutic window.

RESULTS

Results showed that a one-compartment model with transit absorption and linear elimination described the data well, CYP2C19 phenotypes and weight were identified as significant covariates impacting SCIT exposure. Patients were recommended to take the entire delayed dose immediately if the delay time was no >12 h, followed by the regular regimen at the next scheduled time. When there is one or two doses missed, taking a double dose immediately was recommended to the CYP2C19 intermediate and extensive population, and a 1.5-fold dose was recommended to the CYP2C19 poor metabolizers with the consideration of adverse effects.

LIMITATION

All samples were derived from the homogenized Chinese healthy population for model building, which may pose certain constraints on the ability to identify significant covariates, such as age.

CONCLUSION

The study highlights the importance of considering patient characteristics for personalized medication and offers a unique perspective on utilizing the popPK repository in precision dosing.

Remedial Dosing Recommendations for Sirolimus Delayed or Missed Dosages Caused by Poor Medication Compliance in Pediatric Tuberous Sclerosis Complex Patients

BACKGROUND

Delayed or missed dosages caused by poor medication compliance significantly affected the treatment of diseases in children.

AIMS

The present study aimed to investigate the influence of delayed or missed dosages on sirolimus pharmacokinetics (PK) in pediatric tuberous sclerosis complex (TSC) patients and to recommend remedial dosages for nonadherent patients.

METHODS

A published sirolimus population PK model in pediatric TSC patients was used to assess the influence of different nonadherence scenarios and recommend optimally remedial dosages based on Monte Carlo simulation. Thirteen nonadherent scenarios were simulated in this study, including delayed 2h, 4 h, 6 h, 8 h, 10 h, 12 h, 14 h, 16 h, 18 h, 20 h, 22 h, 23.5 h, and missed one dosage. Remedial dosing strategies contained 10-200% of scheduled dosages. The optimal remedial dosage was that with the maximum probability of returning the individual therapeutic range.

RESULTS

For delayed or missed sirolimus dosages in pediatric TSC patients, when the delayed time was 0-8 h, 8-10 h, 10-18 h, 18-22.7 h, 22.7-24 h, 70%, 60%, 40%, 30%, 20% scheduled dosages were recommended to take immediately. When one dosage was missed, 120% of scheduled dosages were recommended at the next dose.

CONCLUSION

It was the first time to recommend remedial dosages for delayed or missed sirolimus therapy caused by poor medication compliance in pediatric TSC patients based on Monte Carlo simulation. Meanwhile, the present study provided a potential solution for delayed or missed dosages in clinical practice.

Handling Delayed or Missed Dose of Antiseizure Medications: A Model-Informed Individual Remedial Dosing

BACKGROUND AND OBJECTIVES

Delayed or missed antiseizure medications (ASMs) doses are common during long-term or lifelong antiepilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used ASMs.

METHODS

To explore remedial dosing regimens, Monte Carlo simulation was used based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients' demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were used to perform Monte Carlo simulation and recommend individual remedial regimens.

RESULTS

The recommended remedial regimens were highly correlated with delayed time, scheduled dosing interval, and half-life of the ASM. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affects the clearance of the ASM, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built.

DISCUSSION

Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of ASMs in pediatric and adult patients with epilepsy.

The effect of nonadherence on phenobarbital concentrations and recommendations on the replacement dose using Monte Carlo simulation

OBJECTIVES

To determine the impacts of missed phenobarbital (PB) doses on its pharmacokinetics and to investigate the appropriate replacement dosing scheme for various PB missed dose scenarios.

METHODS

Monte Carlo simulations were performed using parameters from the selected population pharmacokinetic study. The impacts of missed PB dose and the proper replacement dosing scheme were assessed based on the percent deviation of simulated concentrations outside the reference range from the full adherence scenario.

RESULTS

The impact of missed PB dose on its concentrations depended on the daily dose. The replacement with a respective regular dose and one and a half regular dose was appropriate for the one and two missed doses scenarios for patients receiving PB monotherapy. For patients receiving PB with valproic acid or phenytoin, the same replacement scheme was still appropriate. The results also indicated that weight did not influence the proper replacement dosing scheme.

CONCLUSIONS

The impacts of missed PB doses on its pharmacokinetics were identified and the proper replacement dosing schemes for different missed dose scenarios were proposed. These schemes should be implemented based on the clinician's justification of the patient's seizure control.

Lithium replacement dose recommendations using Monte Carlo simulations

OBJECTIVES

Missed medication doses are a common clinical problem, and cause consternation when prescribing lithium because its plasma levels must be kept within a narrow therapeutic window. Therefore, this study set out to determine the potential impact of missed lithium doses on its pharmacokinetics, and to explore the optimal compensatory dosing scheme. This is difficult to determine clinically and in research because of ethical constraints and therefore we modelled the effects using simulations.

METHODS

Monte Carlo simulations were used to simulate lithium concentrations under different missed dose scenarios. For patients with normal renal function, the optimal replacement dosing scheme was selected based on the lowest percentage of deviation from the full adherence scenario. However, for patients with renal impairment the appropriate dosing schedule was selected based on the lowest number of simulated concentrations above the upper range of 1.2 mEq/L.

RESULTS

The impact of a missed lithium dose depended on its daily dose. The higher the daily dose, the higher the deviation from full adherence. In patients with normal renal function, replacement with a regular dose was most appropriate. But in patients with renal impairment, replacement with a partial dose appeared to be most suitable.

CONCLUSIONS

This study has enabled insights into the optimal suitable lithium replacement dosing schemes for patients with normal renal function and renal impairment. These proposed schemes can be used cautiously in clinical practice in conjunction with clinician judgment and can also be used as a basis for future clinical research.

Machine learning advances the integration of covariates in population pharmacokinetic models: Valproic acid as an example

Background and Aim: Many studies associated with the combination of machine learning (ML) and pharmacometrics have appeared in recent years. ML can be used as an initial step for fast screening of covariates in population pharmacokinetic (popPK) models. The present study aimed to integrate covariates derived from different popPK models using ML.

Methods: Two published popPK models of valproic acid (VPA) in Chinese epileptic patients were used, where the population parameters were influenced by some covariates. Based on the covariates and a one-compartment model that describes the pharmacokinetics of VPA, a dataset was constructed using Monte Carlo simulation, to develop an XGBoost model to estimate the steady-state concentrations (Css) of VPA. We utilized SHapley Additive exPlanation (SHAP) values to interpret the prediction model, and calculated estimates of VPA exposure in four assumed scenarios involving different combinations of CYP2C19 genotypes and co-administered antiepileptic drugs. To develop an easy-to-use model in the clinic, we built a simplified model by using CYP2C19 genotypes and some noninvasive clinical parameters, and omitting several features that were infrequently measured or whose clinically available values were inaccurate, and verified it on our independent external dataset.

Results: After data preprocessing, the finally generated combined dataset was divided into a derivation cohort and a validation cohort (8:2). The XGBoost model was developed in the derivation cohort and yielded excellent performance in the validation cohort with a mean absolute error of 2.4 mg/L, root-mean-squared error of 3.3 mg/L, mean relative error of 0%, and percentages within ±20% of actual values of 98.85%. The SHAP analysis revealed that daily dose, time, CYP2C19*2 and/or *3 variants, albumin, body weight, single dose, and CYP2C19*1*1 genotype were the top seven confounding factors influencing the Css of VPA. Under the simulated dosage regimen of 500 mg/bid, the VPA exposure in patients who had CYP2C19*2 and/or *3 variants and no carbamazepine, phenytoin, or phenobarbital treatment, was approximately 1.74-fold compared to those with CYP2C19*1/*1 genotype and co-administered carbamazepine + phenytoin + phenobarbital. The feasibility of the simplified model was fully illustrated by its performance in our external dataset.

Conclusion: This study highlighted the bridging role of ML in big data and pharmacometrics, by integrating covariates derived from different popPK models.

Simulations of topiramate dosage recommendations for poor compliance events

PURPOSE

To determine the influences of one or two consecutive missed topiramate (TPM) doses on TPM pharmacokinetics and to suggest the proper TPM replacement dosing schemes using Monte Carlo simulations.

METHODS

Monte Carlo simulations were performed for various replacement dosing schemes using the parameters from the published population pharmacokinetic models. The lowest percentage of deviation of simulated concentrations outside the reference range of 5-20 mg/L from the compliance scenario for each replacement dosing scheme was used as a criterion for choosing the proper replacement dosing scheme.

RESULTS

For the one missed dose, the replacement with an immediate regular dose and a partial dose resulted in the lowest and highest percentages of concentration below 5 mg/L, respectively. While the opposite results were observed for the upper bound of the reference range (20 mg/L). For the two consecutive missed doses, the replacement with one and a half-missed doses resulted in a lower percentage of deviation of concentrations below 5 mg/L from the compliance scenario than the replacement with one regular dose.

CONCLUSIONS

For the one missed dose, taking an immediate regular dose might be suitable for patients who require higher TPM levels, while for patients who require lower TPM levels, an immediate partial dose could be used. For the two consecutive missed doses, an immediate one and a half regular dose might be suitable. However, these results were merely based on simulations; thus, they should be used alongside the clinician's justification based on seizure control.

Effect of Nonadherence on Levetiracetam Pharmacokinetics and Remedial Dose Recommendations Using Monte Carlo Simulations

BACKGROUND AND OBJECTIVE

Nonadherence to levetiracetam (LEV) use can result in subtherapeutic concentrations and increase the risk of the occurrence of seizures. The impact of missing LEV doses on its pharmacokinetics and evidence of the appropriate remedial dose is lacking. This study has determined the influence of missed LEV doses on its pharmacokinetics and has explored the appropriate remedial dosage regimens.

METHODS

Monte Carlo simulation was used to assess the impacts of different remedial dosage regimens on LEV concentrations. Simulated LEV concentrations outside the individual therapeutic range were calculated for the compliance scenario and for each of the remedial dosage regimens. The percentage of deviation from the full compliance scenario was also calculated. The regimen with the lowest percentage of deviation was considered the most appropriate.

RESULTS

The suitable LEV remedial dose varied across the delay times. For one missed dose, a remedial regimen with a regular dose followed by the usual dose was suitable for a delay time of less than 6 h, while a replacement with a regular dose followed by a partial dose appeared to be appropriate for a delay time of 6 h and longer. This was justified based on the concerns of LEV toxicity when the remedial dose is close to the next scheduled dose. For two consecutive missed doses, a remedial dose with one and a half of the regular dose was suitable if the gap between that and the next dose was greater than 6 h.

CONCLUSIONS

The appropriate remedial dosage regimen for one and two consecutive missed doses of LEV have been proposed. These remedial regimens, however, should be applied with clinicians' judgment based on the clinical status of the patients.

The effect of nonadherence on phenobarbital concentrations and recommendations on the replacement dose using Monte Carlo simulation

OBJECTIVES

To determine the impacts of missed phenobarbital (PB) doses on its pharmacokinetics and to investigate the appropriate replacement dosing scheme for various PB missed dose scenarios.

METHODS

Monte Carlo simulations were performed using parameters from the selected population pharmacokinetic study. The impacts of missed PB dose and the proper replacement dosing scheme were assessed based on the percent deviation of simulated concentrations outside the reference range from the full adherence scenario.

RESULTS

The impact of missed PB dose on its concentrations depended on the daily dose. The replacement with a respective regular dose and one and a half regular dose was appropriate for the one and two missed doses scenarios for patients receiving PB monotherapy. For patients receiving PB with valproic acid or phenytoin, the same replacement scheme was still appropriate. The results also indicated that weight did not influence the proper replacement dosing scheme.

CONCLUSIONS

The impacts of missed PB doses on its pharmacokinetics were identified and the proper replacement dosing schemes for different missed dose scenarios were proposed. These schemes should be implemented based on the clinician's justification of the patient's seizure control.

How to handle a delayed or missed dose of edoxaban in patients with non-valvular atrial fibrillation? A model-informed remedial strategy

AIM

Edoxaban is a non-vitamin K antagonist oral anticoagulant (NOAC) widely used for the long-term prevention of stroke in patients with non-valvular atrial fibrillation (NVAF). Adherence to NOAC therapy has been unsatisfactory and decreases over time. Remedial strategies are currently used to address the non-adherence events. Current recommendations, however, are generic and not well supported by evidence. The aim of this study was to explore appropriate remedial dosing regimens for non-adherent edoxaban-treated NVAF patients through the Monte Carlo simulation.

METHODS

Six regimens were compared with the current recommendations of the European Heart Rhythm Association (EHRA) guide based on total deviation time. Both edoxaban plasma concentration and intrinsic Factor Xa activity were considered. Monte Carlo simulations were performed using RxODE based on a published population pharmacokinetics/pharmacodynamics (PK/PD) model.

RESULTS

The proposed remedial strategies were different than the EHRA recommendations and were related to the delay time. However, it was found that the missed dose can be administered immediately if the delay time is within 11 h. When the delay is between 12 and 19 h, a half dose followed by a regular dosing schedule is recommended. When the delay time exceeds 19 h, a full dose followed by a half dose is preferred.

CONCLUSION

PK/PD modelling and simulation are effective in developing and evaluating the remedial strategies of edoxaban, which can help maximise its therapeutic effect.

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

Background

Schizophrenia is characterized by a high disease burden. Olanzapine is a common drug used in antipsychotic medication. Little is known about the population pharmacokinetics of olanzapine in elderly patients. Missed doses are a common and unavoidable issue during the treatment of psychiatric diseases, especially in elderly patients. This study aimed to identify what an elderly person should do if doses are inadvertently missed.

Methods

Data were collected from 140 elderly psychiatric patients (aged ≥65 years) who received olanzapine therapy. Olanzapine concentrations were determined by high pressure liquid chromatographic tandem mass spectrometry (HPLC-MS/MS) and a population-based approach was used to quantify the characteristics of elderly patients. A non-linear mixed-effects model was used for data analysis. Simulations based on the final model were applied to predict situations involving a single missed dose or three consecutive missed doses under several remedial regimens.

Results

A total of 474 samples from 140 elderly patients were included in the therapeutic drug monitoring (TDM) data analysis. A one-compartment model, with no significant covariates, was developed to describe the population pharmacokinetics of olanzapine in elderly patients. The population predicted systematic clearance (CL/F) and volumes of distribution (V/F) were 18 L/h and 785 L, respectively. The simulation demonstrated that in a missed dose situation, elderly patients should take the regular dose immediately; the refill dose used at the second remedial time point depends on the length of the time delay.

Conclusion

Here, we used a simulation to provide a remedial regimen for missed doses of olanzapine in the elderly population. Our simulation can provide valuable suggestions for individualized therapy in elderly patients.

How to handle the delayed or missed dose of rivaroxaban in patients with non-valvular atrial fibrillation: model-informed remedial dosing

BACKGROUND

Rivaroxaban is an oral anticoagulant widely used for stroke prevention in patients with non-valvular atrial fibrillation (NVAF). During long-term anticoagulant therapy, delayed or missed doses are common. This study aimed to explore appropriate remedial dosing regimens for non-adherent rivaroxaban-treated patients.

METHODS

Monte Carlo simulation based on a previously established rivaroxaban population pharmacokinetic/pharmacodynamic (PK/PD) model for patients with NVAF was employed to design remedial dosing regimens. The proposed regimens were compared with remedial strategies in the European Heart Rhythm Association (EHRA) guide by assessing deviation time in terms of drug concentration, factor Xa activity, and prothrombin time.

RESULTS

The proposed remedial dosing regimens were dependent on delay duration. The missed dose should be taken immediately when the delay does not exceed 6 h; a half dose is advisable when the delay is between 6 and 20 h. A missed dose should be skipped if less than 4 h remains before the next dose. The proposed regimens resulted in shorter deviation time than that of the EHRA guide.

CONCLUSION

PK/PD modeling and simulation provide valid evidence on the remedial dosing regimen of rivaroxaban, which could help to minimize the risk of bleeding and thromboembolism.

Case Report: Predicting the Range of Lamotrigine Concentration Using Pharmacokinetic Models Based on Monte Carlo Simulation: A Case Study of Antiepileptic Drug-Related Leukopenia

Lamotrigine (LTG), a wide-spectrum antiepileptic drug, is frequently associated with cutaneous side-effects, whereas hematological side-effects such as leukopenia have rarely been reported for it. We report the case of a 15-year-old Chinese female epileptic patient weighing 60 kg who developed combined asymptomatic leukopenia after receiving concomitant therapy with LTG and valproate acid (VPA). In this case report, antiepileptic drug-related leukopenia may have occurred in definite relation to an increase in LTG concentration and reversed with the discontinuation of VPA. Monte Carlo (MC) simulations were performed to estimate the steady-state serum concentrations (C_ss) of LTG for different dosing regimens in adolescent Chinese epileptic patients weighing the same as the patient considered in the case study, based on pharmacokinetic (PK) models published in past research. Adjustments to the dosage of LTG for the patient were analyzed to illustrate the application of MC simulations and verify the results. The predicted LTG concentrations within a prediction interval between the 10th and 90th percentiles that represented 80% of the simulated populations, could adequately capture the measured LTG concentrations of the patient, indicating that MC simulations are a useful tool for estimating drug concentrations. Clinicians may benefit from the timely probabilistic predictions of the range of drug concentration based on an MC simulation that considers a large sample of virtual patients. The case considered here highlights the importance of therapeutic drug monitoring (TDM) and implementing model-informed precision dosing in the course of a patient’s individualized treatment to minimize adverse reactions.