A pharmacokinetic simulation study to assess the performance of a sparse blood sampling approach to quantify early drug exposure

Abstract

Estimating early exposure of drugs used for the treatment of emergent conditions is challenging because blood sampling to measure concentrations is difficult. The objective of this work was to evaluate predictive performance of two early concentrations and prior pharmacokinetic (PK) information for estimating early exposure. The performance of a modeling approach was compared with a noncompartmental analysis (NCA). A simulation study was performed using literature-based models for phenytoin (PHT), levetiracetam (LEV), and valproic acid (VPA). These models were used to simulate rich concentration-time profiles from 0 to 2 h. Profiles without residual unexplained variability (RUV) were used to obtain the true partial area under the curve (pAUC) until 2 h after the start of drug infusion. From the profiles with the RUV, two concentrations per patient were randomly selected. These concentrations were analyzed under a population model to obtain individual population PK (PopPK) pAUCs. The NCA pAUCs were calculated using a linear trapezoidal rule. Percent prediction errors (PPEs) for the PopPK pAUCs and NCA pAUCs were calculated. A PPE within ±20% of the true value was considered a success and the number of successes was obtained for 100 simulated datasets. For PHT, LEV, and VPA, respectively, the median value of the success statistics obtained using the PopPK approach of 81%, 92%, and 88% were significantly higher than the 72%, 80%, and 67% using the NCA approach (p < 0.05; Mann-Whitney U test). This study provides a means by which early exposure can be estimated with good precision from two concentrations and a PopPK approach. It can be applied to other settings in which early exposures are of interest.

Trial registration: ClinicalTrials.gov NCT01960075.

Conflict of interest statement

All authors declared no competing interests for this work.

© 2021 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.

Figures

FIGURE 1

Schematic of the methodology used. AUC, area under the concentration‐time curve from 0 to 2 h after the start of study drug infusion; NCA, non‐compartmental analysis; PK, pharmacokinetic; RUV, residual unexplained variability

FIGURE 2

Distribution of percent of successes per dataset for phenytoin using noncompartmental analysis (left) and population pharmacokinetic analysis (right). The red line indicates the median percent of successes per dataset

FIGURE 3

Distribution of percent of successes per dataset for levetiracetam using noncompartmental analysis (left) and population pharmacokinetic analysis (right). The red line indicates the median percent of successes per dataset

FIGURE 4

Distribution of percent of successes per dataset for valproic acid using noncompartmental analysis (left) and population pharmacokinetic analysis (right). The red line indicates the median percent of successes per dataset