Although several studies have revealed the association between rosuvastatin pharmacokinetics and the ABCG2 421C>A (rs2231142) polymorphism, most studies were conducted with small sample sizes, making it challenging to apply the findings clinically. Therefore, the purpose of this study is to perform a meta-analysis of the relationship between the ABCG2 421C>A polymorphism and rosuvastatin pharmacokinetics. We searched three electronic databases, EMBASE, PubMed, and Web of Science, using search terms related to ABCG2 gene polymorphisms and rosuvastatin. In addition, we reviewed studies published before 12 August 2021, to examine the relationship between the ABCG2 421C>A polymorphism and rosuvastatin pharmacokinetics. To examine the magnitude of the association, the log geometric mean difference (lnGM) and 95% confidence intervals (CIs) were calculated and interpreted as the antilogarithm of a natural logarithm (e^lnGM). The meta-analysis was performed using Review Manager (version 5.4) and R Studio (version 4.0.2). Subgroup analysis was performed according to race and the types of mean values. Among the 318 identified studies, a total of 8 studies involving 423 patients is included in this meta-analysis. The A allele carriers of ABCG2 421C>A showed 1.5 times higher in both AUC_0-∞ (lnGM = 0.43; 95% CI = 0.35–0.50; p < 0.00001) and C_max (lnGM = 0.42; 95% CI = 0.33–0.51; p < 0.00001) than non-carriers, while there was no significant difference in T_max and half-life. There was no significance in the pharmacokinetic parameters of the subgroups using either ethnicity or mean values. This meta-analysis demonstrates that subjects carrying the A allele of ABCG2 421C>A show significantly increased AUC_0-∞ and C_max values compared to subjects with the CC genotype. Therefore, information about ABCG2 genotypes might be useful for individualized rosuvastatin therapy.

Erlotinib is a drug used for the treatment of non-small cell lung cancer (NSCLC) and pancreatic cancer. Severe hepatotoxicity was observed in 4% to 31% of patients receiving erlotinib treatment prompting delay or termination of treatment. Only a few factors related to hepatotoxicity of erlotinib have been reported. No study has investigated the role of concomitant medications and erlotinib-induced hepatotoxicity. The aim of this study was to investigate the association between erlotinib-induced hepatotoxicity and various factors including concomitant medications in patients with NSCLC and pancreatic cancer. From January 2014 to June 2017, a retrospective study was conducted in patients with NSCLC and pancreatic cancer, who were treated with erlotinib. Various data were reviewed, including sex, age, body weight, height, body surface area (BSA), underlying disease, Eastern Cooperative Oncology Group (ECOG) Performance Status (PS), smoking history, erlotinib dose, EGFR mutation, and concomitant drugs. The incidence of grade 2 or higher hepatotoxicity in the study group of patients was 17.2%. Multivariate analysis showed a 2.7-fold increase in hepatotoxicity with the concomitant use of CYP3A4 inducers. In NSCLC patients, co-administration of H2-antagonist/PPI increased hepatotoxicity 3.5-fold. Among the demographic factors, liver metastasis and age ≥ 65 years were significant risk factors in all study patients and NSCLC patients, respectively; the attributable risks for liver metastasis and age were 46.3% and 71.8%, respectively. Subgroup analysis using pancreatic cancer patients yielded marginally significant results with CYP3A4 inducers and erlotinib-induced hepatotoxicity. Liver metastasis and CYP3A4 inducers also shortened time to hepatotoxicity 2.1 and 2.3-fold, respectively. Our study showed that concomitant use of CYP3A4 inducers and H2-antagonist/PPI, liver metastasis, and age ≥ 65 were associated with erlotinib-induced hepatotoxicity. Thus, close monitoring of liver function is recommended, especially in patients using CYP3A4 inducers and anti-acid secreting agents.

This study aimed to investigate the influence of CYP2C9 genetic polymorphisms on the pharmacokinetics of losartan and its active metabolite, E-3174, through a systematic review and meta-analysis. Eight studies published before March 2021 were included in this study. We used PubMed, the Cochrane Library, EMBASE, and Web of Science, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data analysis was conducted through Review Manager (RevMan), version 5.3, and R software. We found that healthy volunteers with CYP2C9*2 or *3 carriers had higher area under the curve (AUC_0-∞) of losartan (mean difference (MD) 0.17 $\mathsf{\mu }\mathrm{g}·\mathrm{h}/\mathrm{mL}$; 95% confidence intervals (CI): 0.04, 0.29) and lower AUC_0-∞ of E-3174 (MD −0.35 $\mathsf{\mu }\mathrm{g}·\mathrm{h}/\mathrm{mL}$; 95% CI: −0.62, −0.08) than those with CYP2C9*1/*1. Subjects with CYP2C9*2 or *3 carriers showed lower maximum concentration (C_max) of E-3174 than those with CYP2C9*1/*1 (MD −0.13 $\mathsf{\mu }\mathrm{g}/\mathrm{mL}$; 95% CI: −0.17, −0.09). For half-life, subjects with CYP2C9*2 or *3 carriers had longer half-lives of losartan and E-3174 than those with CYP2C9*1/*1 (MD 0.47 h; 95% CI: 0.32, 0.61 and MD 0.68 h; 95% CI: 0.44, 0.92, respectively). This meta-analysis suggests that the pharmacokinetics of losartan and E-3174 are associated with the CYP2C9 polymorphisms

Background: Thiazolidinediones (TZDs) are a type of oral drug that are utilized for the treatment of type 2 diabetes mellitus (T2DM). They function by acting as agonists for a nuclear transcription factor known as peroxisome proliferator-activated receptor-gamma (PPAR-γ). TZDs, such as pioglitazone and rosiglitazone, help enhance the regulation of metabolism in individuals with T2DM by improving their sensitivity to insulin. Previous studies have suggested a relationship between the therapeutic efficacy of TZDs and the PPARG Pro12Ala polymorphism (C > G, rs1801282). However, the small sample sizes of these studies may limit their applicability in clinical settings. To address this limitation, we conducted a meta-analysis assessing the influence of the PPARG Pro12Ala polymorphism on the responsiveness of TZDs. Method: We registered our study protocol with PROSPERO, number CRD42022354577. We conducted a comprehensive search of the PubMed, Web of Science, and Embase databases, including studies published up to August 2022. We examined studies investigating the association between the PPARG Pro12Ala polymorphism and metabolic parameters such as hemoglobin A_1C (HbA_1C), fasting plasma glucose (FPG), triglyceride (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and total cholesterol (TC). The mean difference (MD) and 95% confidence intervals (CIs) between pre- and post-drug administration were evaluated. The quality of the studies included in the meta-analysis was assessed by using the Newcastle–Ottawa Scale (NOS) tool for cohort studies. Heterogeneity across studies was assessed by using the I² value. An I² value greater than 50% indicated substantial heterogeneity, and a random-effects model was used for meta-analysis. If the I² value was below 50%, a fixed-effects model was employed instead. Both Begg’s rank correlation test and Egger’s regression test were performed to detect publication bias, using R Studio software. Results: Our meta-analysis incorporated 6 studies with 777 patients for blood glucose levels and 5 studies with 747 patients for lipid levels. The included studies were published between 2003 and 2016, with the majority involving Asian populations. Five of the six studies utilized pioglitazone, while the remaining study employed rosiglitazone. The quality scores, as assessed with the NOS, ranged from 8 to 9. Patients carrying the G allele exhibited a significantly greater reduction in HbA_1C (MD = −0.3; 95% CI = −0.55 to −0.05; p = 0.02) and FPG (MD = −10.91; 95% CI = −19.82 to −2.01; p = 0.02) levels compared to those with the CC genotype. Furthermore, individuals with the G allele experienced a significantly larger decrease in TG levels than those with the CC genotype (MD = −26.88; 95% CI = −41.30 to −12.46; p = 0.0003). No statistically significant differences were observed in LDL (MD = 6.69; 95% CI = −0.90 to 14.29; p = 0.08), HDL (MD = 0.31; 95% CI = −1.62 to 2.23; p = 0.75), and TC (MD = 6.4; 95% CI = −0.05 to 12.84; p = 0.05) levels. No evidence of publication bias was detected based on Begg’s test and Egger’s test results. Conclusions: This meta-analysis reveals that patients with the Ala12 variant in the PPARG Pro12Ala polymorphism are more likely to exhibit positive responses to TZD treatment in terms of HbA_1C, FPG, and TG levels compared to those with the Pro12/Pro12 genotype. These findings suggest that genotyping the PPARG Pro12Ala in diabetic patients may be advantageous for devising personalized treatment strategies, particularly for identifying individuals who are likely to respond favorably to TZDs.

Ephedrine, the main active ingredient of mahuang, may lead to weight loss; however, it can also induce cardiovascular side effects. As ephedrine use remains controversial, this study aimed to systematically review previous studies on ephedrine-containing products and perform meta-analysis of the existing evidence on weight, blood pressure (BP), heart rate, and lipid change effects of ephedrine-containing products. We searched for placebo-controlled randomized studies in PubMed, Web of Science, and EMBASE until July 2021 using the following search terms: (ephedr* OR mahuang) AND (“weight loss” OR obes* OR overweight). Mean differences (MDs) and 95% confidence intervals (CIs) were calculated to evaluate the effects of ephedrine-containing products on weight, BP, heart rate, and lipid profiles. A total of 10 articles were included. Compared with the placebo group, the ephedrine-containing product group was associated with greater weight loss, with an MD of −1.97 kg (95% CI: −2.38, −1.57). In the ephedrine-containing product group, the mean heart rate was 5.76 beats/min higher than in the placebo group (95% CI: 3.42, 8.10), whereas intergroup differences in systolic and diastolic BP were not statistically significant. The ephedrine-containing product group had a significantly higher mean high-density lipoprotein cholesterol level (MD: 2.74 mg/dL; 95% CI: 0.94, 4.55), lower mean low-density lipoprotein cholesterol level (MD: −5.98 mg/dL; 95% CI: −10.97, −0.99), and lower mean triglyceride level (MD: −11.25 mg/dL; 95% CI: −21.83, −0.68) than the placebo group. Compared with placebo, the ephedrine-containing products showed better effects on weight loss and lipid profiles, whereas they caused increased heart rate. The ephedrine-containing products may be beneficial to obese or overweight patients; however, close monitoring is needed, especially heart rate monitoring.

Vancomycin-associated acute kidney injury (AKI) remains a major challenge for patients and clinicians. This study aimed to construct a risk scoring system for vancomycin-associated AKI. We retrospectively reviewed medical records of patients who underwent therapeutic drug monitoring for vancomycin from June 2018 to July 2019. We selected possible risk factors for AKI by univariate and multivariable logistic regression analyses and developed a scoring system for vancomycin-associated AKI. Machine learning methods were utilized to predict risk factors for the occurrence of AKI. The incidence of vancomycin-associated AKI was 31.7% among 104 patients included in this study. A bodyweight ≤60 kg (two points), a Charlson comorbidity index ≥3 (two points), a vancomycin trough serum level >15 μg/ml (one point), and concomitant use of ≥6 nephrotoxic agents (two points) were included to construct a risk scoring system based on the coefficient from the logistic regression model. The area under the receiver operating characteristic curve (AUROC) (mean, 95% confidence interval (CI)) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)-linear kernel, and SVM-radial kernel models was 0.735 (0.638–0.833), 0.737 (0.638–0.835), 0.721 (0.610–0.833), 0.739 (0.648–0.829), and 0.733 (0.640–0.826), respectively. For total scores of 0–1, 2–3, 4–5, 6–7, the risk of vancomycin-associated AKI was 5, 25, 45, and 65%, respectively. Our scoring system can be applied to clinical settings in which several nephrotoxic agents are used along with vancomycin therapy.