Wan-Su Park

Pharmacokinetic-pharmacodynamic analysis to evaluate the effect of moxifloxacin on QT interval prolongation in healthy Korean male subjects

A single 400 mg dose of moxifloxacin has been the standard positive control for thorough QT (TQT) studies. However, it is not clearly known whether a 400 mg dose is also applicable to TQT studies in Asian subjects, including Koreans. Thus, we aimed to develop a pharmacokinetic (PK)-pharmacodynamic (PD) model for moxifloxacin, to evaluate the time course of its effect on QT intervals in Koreans. Data from three TQT studies of 33 healthy male Korean subjects who received 400 and 800 mg of moxifloxacin and placebo (water) were used. Twelve-lead electrocardiograms were taken for 2 consecutive days: 1 day to record diurnal changes and the next day to record moxifloxacin or placebo effects. Peripheral blood samples were also obtained for PK analysis. The PK-PD data obtained were analyzed using a nonlinear mixed-effects method (NONMEM ver. 7.2). A two-compartment linear model with first-order absorption provided the best description of moxifloxacin PK. Individualized QT interval correction, by heart rate, was performed by a power model, and the circadian variation of QT intervals was described by two mixed-effect cosine functions. The effect of moxifloxacin on QT interval prolongation was well explained by the nonlinear dose-response (Emax) model, and the effect by 800 mg was only slightly greater than that of 400 mg. Although Koreans appeared to be more sensitive to moxifloxacin-induced QT prolongation than were Caucasians, the PK-PD model developed suggests that a 400 mg dose of moxifloxacin is also applicable to QT studies in Korean subjects.

Physiologically-based pharmacokinetic predictions of intestinal BCRP-mediated drug interactions of rosuvastatin in Koreans

It was recently reported that the Cmax and AUC of rosuvastatin increases when it is coadministered with telmisartan and cyclosporine. Rosuvastatin is known to be a substrate of OATP1B1, OATP1B3, NTCP, and BCRP transporters. The aim of this study was to explore the mechanism of the interactions between rosuvastatin and two perpetrators, telmisartan and cyclosporine. Published (cyclosporine) or newly developed (telmisartan) PBPK models were used to this end. The rosuvastatin model in Simcyp (version 15)s drug library was modified to reflect racial differences in rosuvastatin exposure. In the telmisartan–rosuvastatin case, simulated rosuvastatin CmaxI/Cmax and AUCI/AUC (with/without telmisartan) ratios were 1.92 and 1.14, respectively, and the Tmax changed from 3.35 h to 1.40 h with coadministration of telmisartan, which were consistent with the aforementioned report (CmaxI/Cmax: 2.01, AUCI/AUC:1.18, Tmax: 5 h → 0.75 h). In the next case of cyclosporine–rosuvastatin, the simulated rosuvastatin CmaxI/Cmax and AUCI/AUC (with/without cyclosporine) ratios were 3.29 and 1.30, respectively. The decrease in the CLint,BCRP,intestine of rosuvastatin by telmisartan and cyclosporine in the PBPK model was pivotal to reproducing this finding in Simcyp. Our PBPK model demonstrated that the major causes of increase in rosuvastatin exposure are mediated by intestinal BCRP (rosuvastatin–telmisartan interaction) or by both of BCRP and OATP1B1/3 (rosuvastatin–cyclosporine interaction).

Drug–drug interaction of microdose and regular-dose omeprazole with a CYP2C19 inhibitor and inducer

Purpose A microdose drug–drug interaction (DDI) study may be a valuable tool for anticipating drug interaction at therapeutic doses. This study aimed to compare the magnitude of DDIs at microdoses and regular doses to explore the applicability of a microdose DDI study. Patients and methods Six healthy male volunteer subjects were enrolled into each DDI study of omeprazole (victim) and known perpetrators: fluconazole (inhibitor) and rifampin (inducer). For both studies, the microdose (100 μg, cold compound) and the regular dose (20 mg) of omeprazole were given at days 0 and 1, respectively. On days 2–9, the inhibitor or inducer was given daily, and the microdose and regular dose of omeprazole were repeated at days 8 and 9, respectively. Full omeprazole pharmacokinetic samplings were performed at days 0, 1, 8, and 9 of both studies for noncompartmental analysis. Results The magnitude of the DDI, the geometric mean ratios (with perpetrator/omeprazole only) of maximum concentration (Cmax) and area under the curve to the last measurement (AUCt) of the microdose and the regular dose were compared. The geometric mean ratios in the inhibition study were: 2.17 (micro) and 2.68 (regular) for Cmax, and 4.07 (micro), 4.33 (regular) for AUCt. For the induction study, they were 0.26 (micro) and 0.21 (regular) for Cmax, and 0.16 (micro) and 0.15 (regular) for AUCt. There were no significant statistical differences in the magnitudes of DDIs between microdose and regular-dose conditions, regardless of induction or inhibition. Conclusion Our results may be used as partial evidence that microdose DDI studies may replace regular-dose studies, or at least be used for DDI-screening purposes.

Use of a Target-Mediated Drug Disposition Model to Predict the Human Pharmacokinetics and Target Occupancy of GC1118, an Anti-epidermal Growth Factor Receptor Antibody.

GC1118 is an anti‐epidermal growth factor receptor (EGFR) monoclonal antibody that is currently under clinical development. In this study, the pharmacokinetics (PK) of GC1118 were modelled in monkeys to predict human PK and receptor occupancy (RO) profiles. The serum concentrations of GC1118 and its comparator (cetuximab) were assessed in monkeys with a non‐compartmental analysis and a target‐mediated drug disposition (TMDD) model after intravenous infusion (3–25 mg/kg) of these drugs. The scaling exponent of the EGFR synthesis rate was determined using a sensitivity analysis. The human cetuximab exposures were simulated by applying different exponents (0.7–1.0) for the EGFR synthesis rate in the allometric monkey PK model. Simulated Cmax and area under the curve values therein were compared with those previously reported in the literature to find the best exponent for the EGFR synthesis rate in human beings. The TMDD model appropriately described the monkey PK profile, which showed a decrease in clearance (CL; 1.2–0.4 ml/hr/kg) as the dose increased. The exponents for CL (0.75) and volume of distribution (Vd; 1.0) were used for the allometric scaling to predict human PK. The allometric coefficient for the EGFR synthesis rate chosen by the sensitivity analysis was 0.85, and the RO profiles that could not be measured experimentally were estimated based on the predicted concentrations of the total target and the drug–target complex. Our monkey TMDD model successfully predicts human PK and RO profiles of GC1118 and can be used to determine the appropriate dose for a first‐in‐human study investigating this drug.

Comparative Pharmacokinetic Analysis of Thiamine and Its Phosphorylated Metabolites Administered as Multivitamin Preparations

PURPOSE Fursultiamine and benfotiamine are lipophilic thiamine derivatives used as oral sources of thiamine. Although there are many publications on the pharmacokinetic (PK) properties of thiamine-containing products, no direct comparisons between these agents . We aimed to compare the PK profiles of these lipophilic thiamine derivatives and to compare the extent of the increase in bioavailability to that of naïve thiamine. METHODS Two randomized, single-dose, 2-way crossover, full PK studies were conducted in healthy Korean male subjects (n = 24 per group). Among the test compounds, fursultiamine was compared with benfotiamine (reference A in study A) and thiamine nitrate (reference B in study B). All formulations were multivitamin preparations containing the test or reference formulation as the major thiamine source. In study A, the plasma and hemolysate concentrations of thiamine and its metabolites were measured, while only the plasma thiamine concentration was assayed in study B. FINDINGS The systemic thiamine exposure of the test compound was slightly greater than that of reference A, based on the geometric mean ratio (%) of the AUClast value for plasma (116.6%) and hemolysate (137.5%). The thiamine diphosphate (TDP) distribution between plasma and hemolysate showed clear differences according to the formulations, in that more TDP was present in the hemolysate when thiamine was given as the test formulation. The AUClast value of plasma thiamine showed a >300% increase when thiamine was given as the test formulation in study B. The summed total exposure to thiamine (thiamine + TDP in both plasma and hemolysate) observed as a point estimate after the administration of fursultiamine was slightly greater than that with benfotiamine; however, the 90% CI was within the conventional bioequivalence range. IMPLICATIONS These findings support clear benefits of lipophilic thiamine derivatives in the absorption of thiamine in healthy volunteers. Clinical Research Information Service identifiers: KCT0001419 (study A), KCT0001628 (study B).

Exposure–response model for sibutramine and placebo: suggestion for application to long-term weight-control drug development

No wholly successful weight-control drugs have been developed to date, despite the tremendous demand. We present an exposure–response model of sibutramine mesylate that can be applied during clinical development of other weight-control drugs. Additionally, we provide a model-based evaluation of sibutramine efficacy. Data from a double-blind, randomized, placebo-controlled, multicenter study were used (N=120). Subjects in the treatment arm were initially given 8.37 mg sibutramine base daily, and those who lost <2 kg after 4 weeks’ treatment were escalated to 12.55 mg. The duration of treatment was 24 weeks. Drug concentration and body weight were measured predose and at 4 weeks, 8 weeks, and 24 weeks after treatment initiation. Exposure and response to sibutramine, including the placebo effect, were modeled using NONMEM 7.2. An asymptotic model approaching the final body weight was chosen to describe the time course of weight loss. Extent of weight loss was described successfully using a sigmoidal exposure–response relationship of the drug with a constant placebo effect in each individual. The placebo effect was influenced by subjects’ sex and baseline body mass index. Maximal weight loss was predicted to occur around 1 year after treatment initiation. The difference in mean weight loss between the sibutramine (daily 12.55 mg) and placebo groups was predicted to be 4.5% in a simulation of 1 year of treatment, with considerable overlap of prediction intervals. Our exposure–response model, which included the placebo effect, is the first example of a quantitative model that can be used to predict the efficacy of weight-control drugs. Similar approaches can help decision-making during clinical development of novel weight-loss drugs.