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

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.

Temporal relationship between levetiracetam nonadherence and breakthrough seizures in a preclinical model of temporal lobe epilepsy

OBJECTIVE

Poor medication adherence remains a concern for individuals managing their epilepsy with antiseizure medicines (ASMs); however, ethical concerns around withholding medication make it impossible to study the causal relationship between missed doses and seizures in patients. Previous preclinical studies from our group suggest that mechanistically distinct ASMs have varying degrees of forgiveness when a dose is missed. However, with only a few ASMs studied in the context of nonadherence, we sought to expand on previous work to understand the relationship between levetiracetam (LEV) nonadherence and breakthrough seizures.

METHODS

Chronic oral dosing was initiated in rats with established epilepsy via our automated medication-in-food delivery system coupled to 24/7 video-electroencephalographic recording. Baseline seizure burden was established for 4 weeks before enrolling subjects into a 4-week treatment period with LEV in a 100% fully adherent (75 mg/kg four times daily) or 50% variably adherent paradigm. The temporal relationship between missed doses and breakthrough seizures was correlated with LEV plasma and brain concentrations in separate cohorts of animals.

RESULTS

Full adherence to LEV significantly improved seizure control by 50% in half of the animals. Poor adherence worsened seizure frequency by 85%, with most rats having more severe seizures that formed in clusters following missed doses. LEV concentrations remained below therapeutic levels (<10 μg/mL) in nonadherent animals, with brain and plasma levels directly correlating with the degree of adherence in a 24-h period. Missed doses of LEV immediately increased the risk of breakthrough seizures; however, this risk was significantly reduced with improved adherence in a 24-h period.

SIGNIFICANCE

These findings enhance our understanding of ASM nonadherence in preclinical models, highlighting that the timing of missed doses and their impact on seizures may vary between different ASMs. Notably, LEV demonstrates a robust pharmacokinetic reliance on missed doses leading to breakthrough seizures.

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.

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.