The US Food and Drug Administration (FDA) guidance has recommended several model-based predictions to determine potential drug-drug interactions (DDIs) mediated by cytochrome P450 (CYP) induction. In particular, the ratio of substrate area under the plasma concentration-time curve (AUCR) under and not under the effect of inducers is predicted by the Michaelis-Menten (MM) model, where the MM constant (K m ) of a drug is implicitly assumed to be sufficiently higher than the concentration of CYP enzymes that metabolize the drug (E T ) in both the liver and small intestine. Furthermore, the fraction absorbed from gut lumen (F a ) is also assumed to be one because F a is usually unknown. Here, we found that such assumptions lead to serious errors in predictions of AUCR. To resolve this, we propose a new framework to predict AUCR. Specifically, F a was re-estimated from experimental permeability values rather than assuming it to be one. Importantly, we used the total quasi-steady-state approximation to derive a new equation, which is valid regardless of the relationship between K m and E T , unlike the MM model. Thus, our framework becomes much more accurate than the original FDA equation, especially for drugs with high affinities, such as midazolam or strong inducers, such as rifampicin, so that the ratio between K m and E T becomes low (i.e., the MM model is invalid). Our work greatly improves the prediction of clinical DDIs, which is critical to preventing drug toxicity and failure.Cytochrome P450 (CYP) is the most important superfamily of enzymes, playing a crucial role in the metabolic clearance of an enormous number of compounds in humans. Approximately 70-80% of marketed drugs are metabolized by this superfamily of enzymes, especially CYP1A2, CYP2C, CYP2D6, and CYP3A4. 1 Such CYP enzymes can be induced by xenobiotic substances, including drugs, resulting in the increased metabolic activity of these enzymes. For instance, rifampicin, a prototype CYP3A4 enzyme inducer, enhances the metabolic process of midazolam, a probe substrate of CYP3A4, decreasing its plasma concentration and thus its therapeutic efficacy. 2 Therefore, the induction of CYP enzymes is one of the major reasons for clinical drug-drug interactions (DDIs).

Rivaroxaban (RIV) is commonly prescribed with carbamazepine or phenytoin (CBZ/PHT) in post-stroke seizure or post-stroke epilepsy patients. Although adverse events have been reported in several previous studies when they are coadministered, there are no studies of the interactions between these drugs. Therefore, our study was conducted to solve this lack of information. The potential effects of CBZ/PHT were investigated by comparing the pharmacokinetic (PK) and pharmacodynamic (PD) parameters of RIV between the control group (RIV alone) and the test groups (RIV administered with CBZ/PHT) in rats using the noncompartmental analysis (NCA) and the compartmental model approach. The NCA results indicate that AUCt of RIV decreased by 57.9% or 89.7% and Cmax of RIV decreased by 43.3% or 70.0% after administration of CBZ/PHT, respectively. In addition, both CBZ and PHT generally reduced the effects of RIV on the prothrombin times of the blood samples. PK profiles of RIV were most properly described by a two-compartment disposition model with a mixed first- and zero-order absorption kinetics and a first-order elimination kinetics. The compartmental model approach showed that a 211% or 1030% increase in CL/F of RIV and a 33.9% or 43.4% increase in D2 of RIV were observed in the test groups by the effects of CBZ/PHT, respectively. In conclusion, CBZ and PHT significantly reduced RIV exposure and therefore reduced the therapeutic effects of RIV. Consequently, this might result in adverse events due to insufficient RIV concentration to attain its therapeutic effects. Further studies are needed to validate this finding.

Propafenone (PPF) is a class 1C antiarrhythmic agent mainly metabolized by cytochrome (CYP) 2D6, CYP1A2, and CYP3A4. Previous studies have shown that CYP2D6 polymorphism influences the pharmacokinetics (PK) of PPF. However, the small sample sizes of PK studies can lead to less precise estimates of the PK parameters. Thus, this meta-analysis was performed to merge all current PK studies of PPF to determine the effects of the CYP2D6 phenotype more accurately on the PPF PK profile. We searched electronic databases for published studies to investigate the association between the PPF PK and CYP2D6 phenotype. Four PK-related outcomes were included: area under the time–concentration curve (AUC), maximum concentration (Cmax), apparent clearance (CL/F), and half-life (t1/2). A total of five studies were included in this meta-analysis (n = 56). Analyses were performed to compare PK parameters between poor metabolizers (PMs) versus extensive metabolizers (EMs). PPF has a non-linear pharmacokinetics; therefore, analyses were performed according to dose (300 mg and 400 mg). At 300 mg, the AUC mean (95% CI), Cmax, and t1/2 of PPF in PMs were 15.9 (12.5–19.2) µg·h/mL, 1.10 (0.796–1.40) µg/mL, and 12.8 (11.3–14.3) h, respectively; these values were 2.4-, 11.2-, and 4.7-fold higher than those in the EM group, respectively. At 400 mg, a comparison was performed between S- and R-enantiomers. The CL/F was approximately 1.4-fold higher for the R-form compared with the S-form, which was a significant difference. This study demonstrated that CYP2D6 metabolizer status could significantly affect the PPF PK profile. Adjusting the dose of PPF according to CYP2D6 phenotype would help to avoid adverse effects and ensure treatment efficacy.

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.

In healthcare situations, time-to-event (TTE) data are common outcomes. A parametric approach is often employed to handle TTE data because it is possible to easily visualize different scenarios via simulation. Not all pharmacometricians are familiar with the use of non-linear mixed effects models (NONMEMs) to deal with TTE data. Therefore, this tutorial simply explains how to analyze TTE data using NONMEM. We show how to write the code and evaluate the model. We also provide an example of a hands-on model for training.