Adjusting doses of sunitinib according to BSA and ABCB1 polymorphism in Asian mRCC patients may be recommended for sufficient attainment of a target TTL of sunitinib and its metabolite.

BackgroundAlthough alterations in the methionine metabolism cycle (MMC) have been associated with vascular complications of diabetes, there have not been consistent results about the levels of methionine and homocysteine in type 2 diabetes mellitus (T2DM). The aim of the current study was to predict changes in plasma methionine and homocysteine concentrations after simulated consumption of methionine-rich foods, following the development of a mathematical model for MMC in Zucker Diabetic Fatty (ZDF) rats, as a representative T2DM animal model.MethodThe model building and simulation were performed using NONMEM® (ver. 7.3.0) assisted by Perl-Speaks-NONMEM (PsN, ver. 4.3.0). Model parameters were derived using first-order conditional estimation method with interactions permitted among the parameters (FOCE-INTER). NCA was conducted using Phoenix (ver. 6.4.0). For all tests, we considered a P-value < 0.05 to reflect statistical significance.ResultsOur model featured seven compartments that considered all parts of the cycle by applying non-linear mixed effects model. Conversion of S-adenosyl-L-homocysteine (SAH) to homocysteine increased and the metabolism of homocysteine was reduced under diabetic conditions, and consequently homocysteine accumulated in the elimination phase.Using our model, we performed simulations to compare the changes in plasma methionine and homocysteine concentrations between ZDF and normal rats, by multiple administrations of the methionine-rich diet of 1 mmol/kg, daily for 60 days. The levels of methionine and homocysteine were elevated approximately two- and three-fold, respectively, in ZDF rats, while there were no changes observed in the normal control rats.ConclusionThese results can be interpreted to mean that both methionine and homocysteine will accumulate in patients with T2DM, who regularly consume high-methionine foods.

Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug–drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration–time curve (AUC) and Cmax of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and Cmax of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.

Pharmacogenomics, which is defined as the study of changes in the properties of DNA and RNA associated with drug response, enables the prediction of the efficacy and adverse effects of drugs based on patients’ specific genetic mutations. For the safe and effective use of drugs, it is important that pharmacogenomic information is easily accessible to clinical experts and patients. Therefore, we examined the pharmacogenomic information provided on drug labels in Korea, Europe, Japan, and the United States (US). The selection of drugs that include pharmacogenomic information was based on the drug list that includes genetic information from the Korea Ministry of Food and Drug Safety (MFDS) and US Food and Drug Administration (FDA) websites. Drug labels were retrieved from the sites of MFDS, FDA, European Medicines Agency, and Japanese Pharmaceuticals and Medical Devices Agency. Drugs were classified as per the Anatomical Therapeutic Chemical code, and the biomarkers, labeling sections, and necessity of genetic tests were determined. In total, 348 drugs were selected from 380 drugs with available pharmacogenomic information in Korea and the US after applying the inclusion and exclusion criteria. Of these drugs, 137, 324, 169, and 126 were with pharmacogenomics information in Korea, the US, Europe, and Japan, respectively. The most commonly represented drug class was antineoplastic and immunomodulating agents. Regarding the classification as per the mentioned biomarkers, the cytochrome P450 enzyme was the most frequently mentioned information, and the targeted anticancer drugs most commonly required genetic biomarker testing. The reasons for differences in drug labeling information based on country include differences in mutant alleles according to ethnicity, frequencies at which drug lists are updated, and pharmacogenomics-related guidelines. Clinical experts must continuously strive to identify and report mutations that can explain drug efficacy or side effects for safe drug use.