Platinum-based chemotherapy regimens have been proven to be effective in various cancers; however, considerable toxicities may develop and can even lead to treatment discontinuation. Diverse factors may influence adverse treatment events, with pharmacogenetic variations being one prime example. Polymorphisms within the glutathione S-transferase pi 1 (GSTP1) gene may especially alter enzyme activity and, consequently, various toxicities in patients receiving platinum-based chemotherapy. Due to a lack of consistency in the degree of elevated complication risk, we performed a systematic literature review and meta-analysis to determine the level of platinum-associated toxicity in patients with the GSTP1 rs1695 polymorphism. We conducted a systematic search for eligible studies published before January 2022 from PubMed, Web of Science, and EMBASE based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between the rs1695 polymorphism and various toxicities. Ten eligible studies met the inclusion criteria. The pooled ORs for hematological toxicity and neutropenia in the patients with the variant (G) allele were 1.7- and 2.6-times higher than those with the AA genotype (95% CI 1.06–2.73 and 1.07–6.35), respectively. In contrast, the rs1695 polymorphism resulted in a 44% reduced gastrointestinal toxicity compared to wild-type homozygotes. Our study found that the GSTP1 rs1695 polymorphism was significantly correlated with platinum-induced toxicities. The study also revealed that rs1695 expression exhibited tissue-specific patterns and thus yielded opposite effects in different tissues. A personalized chemotherapy treatment based on these polymorphisms may be considered for cancer patients in the future.

Background: Fluoropyrimidine is widely used owing to its clinical efficacy, however, patients with dihydropyrimidine dehydrogenase (DPD) deficiency can experience fluoropyrimidine-associated toxicity. The dihydropyrimidine dehydrogenase (DPYD) gene encodes DPD, and studies suggest that DPYD polymorphisms can result in DPD deficiency. Since there is not a complete consistency of how much the risk of complication is elevated, we aimed to conduct a systematic literature review and a meta-analysis to provide the risk of fluoropyrimidine-associated toxicity in patients with DPYD rs1801160 polymorphism. Methods: We searched for qualifying studies published before October 2021 from PubMed, Web of Science, and EMBASE based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to evaluate the strength of the association between rs1801160 polymorphism and toxicities. A sensitivity analysis using the leave-one-out method was performed on the overall toxicity. Results: The pooled OR for overall toxicity in the patients with A allele was elevated 1.73 times higher than those with the GG genotype (95% CI 1.44–2.07). Sensitivity analysis yielded similar results, showing the robustness of the result. Subjects with variants showed a 2.37-fold increased hematological toxicity (95% CI 1.48–3.81); especially a 1.87-fold increased neutropenia compared to patients with wildtype (95% CI 1.49–2.34). Patients with A allele revealed 1.22 times higher gastrointestinal toxicity compared to those with GG genotype (95% CI 0.93–1.61), and among gastrointestinal toxicity, the risk of diarrhea was elevated 1.43 times higher in those with variants than patients with wildtype (95% CI 1.12–1.83). Conclusions: rs1801160 polymorphism is associated with elevated fluoropyrimidine-associated toxicity. Therefore, rs1801160 can be a potential candidate for DPD deficiency screening prior to fluoropyrimidine-based regimen.

Background: In March 2020, the US Food and Drug Administration decided that the dangers related to neuropsychiatric events (NPEs) of montelukast, one of the leukotriene modifying agents (LTMAs), should be communicated through ‘boxed warning’. In case of NPEs, the prevalence has been the highest in elderly people. Because the characteristics of the elderly such as old age itself can act as risk factors. Therefore, an investigation on safety of LTMAs related to NPEs in elderly using LTMAs is needed. Method: A nested case-control study using an elderly sample cohort from the Korean National Health Insurance Service database was used. The asthma cohort included asthma patients newly diagnosed between 2003 and 2013. Within the asthma cohort, the case group was defined as patients who were diagnosed with NPEs. Among patients who had never been diagnosed with NPEs, the control group was selected by matching 1:1 by propensity score. Patients who were prescribed LTMAs for 1 year prior to index date were defined as the exposure group. The logistic regression model was used to measure the effect of LTMAs on NPEs. Results: We identified 141,165 patients with newly diagnosed asthma, and selected 31,992 patients per each case and control group. Exposure to LTMAs significantly increased the risk of overall NPEs about in comparison with the absence of exposure (crude odds ratio [OR] 1.58, 95% CI 1.50–1.68). After adjusting for confounding factors, the overall NPEs risk increased (adjusted OR, 1.67, 95% CI 1.58–1.78). Conclusion: This study suggests that elderly asthma patients prescribed LTMAs had a higher risk of NPEs than patients who were not treated with LTMAs. Therefore, clinicians should be aware of the potential risks of LTMAs.

Introduction Various subtypes of severe acute respiratory syndrome coronavirus 2 and variations among immune systems in different ethnicities need to be considered to understand the outcomes of coronavirus disease 2019 (COVID-19). This study aimed to provide evidence for the association between the use of antidepressants and the severity of COVID-19. Methods We used the National Health Information Data-COVID database. Patients with one or more prescriptions of any antidepressant were selected as the exposure group. Detailed analyses were performed to determine the type of medication associated with the prognosis. Results The use of selective serotonin reuptake inhibitors (SSRIs) was associated with a lower risk of severe outcomes of COVID-19, whereas the use of tricyclic antidepressants (TCAs) increased the risk of poor prognosis of COVID-19. Detailed analyses showed that escitalopram was significantly associated with better clinical outcomes, and nortriptyline was linked to more severe COVID-19 outcomes. Conclusion This study revealed an association between antidepressants and COVID-19 prognosis. SSRIs were significantly associated with a lower risk of severe outcomes, whereas TCAs were related to the poor prognosis of COVID-19. Received: 28 October 2021 Received: 27 April 2022 Accepted: 28 April 2022 Article published online: 02 June 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

Adrenal insufficiency is a rare, yet life-threatening immune-related adverse event of immune checkpoint inhibitors (ICIs). This study aimed to establish a risk scoring system for adrenal insufficiency in patients receiving anti-programmed cell death 1 (PD-1) or anti-programmed cell death-ligand 1 (PD-L1) agents. Moreover, several machine learning methods were utilized to predict such complications. This study included 209 ICI-treated patients from July 2015 to February 2021, excluding those with prior adrenal insufficiency, previous steroid therapy, or incomplete data to ensure data integrity. Patients were continuously followed up at Gyeongsang National University Hospital, with morning blood samples taken for basal cortisol level measurements, facilitating a comprehensive analysis of their adrenal insufficiency risk. Using a chi-squared test and logistic regression model, we derived the odds ratio and adjusted odds ratio (AOR) through univariate and multivariable analyses. This study utilized machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and logistic regression to predict adrenal insufficiency in patients treated with ICIs. The performance of each algorithm was evaluated using metrics like accuracy, sensitivity, specificity, precision, and the area under the receiver operating characteristic curve (AUROC), ensuring rigorous assessment and reproducibility. A risk scoring system was developed from the multivariable and machine learning analyses. In a multivariable analysis, proton pump inhibitors (PPIs) (AOR 4.5), and α-blockers (AOR 6.0) were significant risk factors for adrenal insufficiency after adjusting for confounders. Among the machine learning models, logistic regression and elastic net showed good predictions, with AUROC values of 0.75 (0.61–0.90) and 0.76 (0.64–0.89), respectively. Based on multivariable and machine learning analyses, females (1 point), age ≥ 65 (1 point), PPIs (1 point), α-blockers (2 points), and antipsychotics (3 points) were integrated into the risk scoring system. From the logistic regression curve, patients with 0, 1, 2, 4, 5, and 6 points showed approximately 1.1%, 2.8%, 7.3%, 17.6%, 36.8%, 61.3%, and 81.2% risk for adrenal insufficiency, respectively. The application of our scoring system could prove beneficial in patient assessment and clinical decision-making while administering PD-1/PD-L1 inhibitors.