Galgeuntang (GGT), a traditional herbal medicine, is widely co-administered with acetaminophen (AAP) for treatment of the common cold, but this combination has not been the subject of investigation. Therefore, we investigated the herb–drug interaction between GGT and AAP by population pharmacokinetics (PKs) modeling and simulation studies. To quantify PK parameters and identify drug interactions, an open label, three-treatment, three-period, one-sequence (AAP alone, GGT alone, and AAP and GGT in combination) clinical trial involving 12 male healthy volunteers was conducted. Ephedrine (EPD), the only GGT component detected, was identified using a one-compartment model. The PKs of AAP were described well by a one-compartment model and exhibited two-phase absorption (rapid followed by slow) and first-order elimination. The model showed that EPD significantly influenced the PKs of AAP. The simulation results showed that at an AAP dose of 1000 mg × 4 times daily, the area under the concentration versus time curve of AAP increased by 16.4% in the presence of GGT compared to AAP only. In conclusion, the PKs of AAP were affected by co-administration of GGT. Therefore, when AAP is combined with GGT, adverse effects related to overdose of AAP could be induced possibly.

Sample sizes for single-period clinical trials, including pharmacokinetic studies, are statistically determined by within-subject variability (WSV). However, it is difficult to determine WSV without replicate-designed clinical trial data, and statisticians typically estimate optimal sample sizes using total variability, not WSV. We have developed an efficient population-based method to predict WSV accurately with single-period clinical trial data and demonstrate method performance with eperisone. We simulated 1000 virtual pharmacokinetic clinical trial datasets based on single-period and dense sampling studies, with various study sizes and levels of WSV and interindividual variabilities (IIVs). The estimated residual variability (RV) resulting from population pharmacokinetic methods were compared with WSV values. In addition, 3 × 3 bioequivalence results of eperisone were used to evaluate method performance with a real clinical dataset. With WSV of 40% or less, regardless of IIV magnitude, RV was well approximated by WSV for sample sizes greater than 18 subjects. RV was underestimated at WSV of 50% or greater, even with datasets having low IIV and numerous subjects. Using the eperisone dataset, RV was 44% to 48%, close to the true value of 50%. In conclusion, the estimated RV accurately predicted WSV in single-period studies, validating this method for sample size estimation in clinical trials.