Diabetic nephropathy (DN), a severe complication of type 2 diabetes mellitus (T2DM), is marked by heightened endoplasmic reticulum stress (ERS) and oxidative stress (OS) due to protein misfolding and free radical generation. We investigated the sodium-glucose co-transporter-2 inhibitor (SGLT2i), canagliflozin (Cana), in alleviating ERS and OS in DN patients and THP-1 cells under hyperglycemic condition. A total of 120 subjects were divided into four groups, with 30 subjects in each group: healthy controls, T2DM individuals, DN patients receiving standard treatment, and those treated with Cana. The control group had no history of diabetes, cardiovascular or renal diseases, or other comorbidities. Cana was administered at doses of either 100 or 300 mg per day based on the estimated glomerular filtration rate (eGFR) value of DN individuals, with a mean follow-up of 6 months. Additionally, THP-1 monocytes were exposed to HGM (33.3 mM glucose with a cytokine cocktail of TNF-α and IFN-γ at 50 ng/mL each) to evaluate the relative levels of ERS, OS markers, and nuclear factor erythroid 2-related factor 2 (Nrf2), the transcription factor regulating cellular redox, which is downregulated in diabetes. Our results revealed that ERS markers GRP78 and PERK, as well as OS markers TXNIP and p22phox, were elevated in both DN patients and HGM-treated THP-1 monocytes and were reduced by Cana intervention. Furthermore, Cana regulated the phosphorylation of Nrf2, Akt, and EIF2α in HGM-treated monocytes. In conclusion, our findings highlight the role of Cana in activating Nrf2, thereby attenuating ERS and OS to mitigate DN progression.
Dear Editor,
The article “Performance of ChatGPT on Factual Knowledge Questions Regarding Clinical Pharmacy” is the topic of present discussion in this letter.1 In this work, the researchers evaluated ChatGPT's ability to respond to factual knowledge inquiries regarding clinical pharmacy using a language model trained on medical literature. ChatGPT was asked 264 questions in all, and its answers were assessed for accuracy, consistency, substantiation quality, and repeatability. According to the findings, ChatGPT answered 79% of the questions correctly, outperforming pharmacists' accuracy rate of 66%. The agreement between ChatGPT's answers and the right answers was 95%. The fact that ChatGPT's performance was assessed using only 264 questions is one of the study's weaknesses. This might not adequately convey the limitations and strengths of the approach for a wider range of clinical pharmacy subjects. Furthermore, the study only included factual knowledge questions, which might not accurately capture the subtleties and complexities that are frequently present in clinical practice. Additionally, there might have been biases in the questions chosen or the standards of evaluation that the researchers employed. The lack of variety in the questions that are sent to ChatGPT and the possibility of irregularities in the independent pharmacists' evaluation of the substantiation's quality are two specific methodological shortcomings. Furthermore, when applying clinical pharmacy knowledge to real-world circumstances, ChatGPT's interpretative or reasoning abilities were not examined in this study. These elements are necessary for a thorough assessment of ChatGPT's usefulness in clinical settings. Extending the dataset of questions to include a greater variety of clinical pharmacy issues, including more intricate and nuanced scenarios, may be one of the research's future approaches. Furthermore, more research into ChatGPT's capacity to offer justifications and explanations for its conclusions might improve the tool's suitability for helping pharmacists make decisions. Studies with a longitudinal design could investigate ChatGPT's long-term effectiveness and evaluate how it affects clinical outcomes in pharmacy practice. Continuous upgrades and enhancements to ChatGPT might increase its functionality and solidify its position as a trustworthy resource for pharmacists as the technology advances.
Hinpetch Daungsupawong: 50% ideas; writing; analyzing; approval. Viroj Wiwanitkit: 50 % ideas; supervision; approval.
The authors declare no conflicts of interest.
The lack of data on drug–drug interactions in pediatrics represents a relevant problem in making appropriate therapeutic decisions. Our study aimed to investigate the incidence and risk factors for potential drug–drug interactions (pDDIs) in pediatric pneumonology units, including cystic fibrosis patients. We performed a 6-month prospective observational study during which clinical pharmacists, using the Lexicomp Drug Interactions checker, screened medical records to identify pDDIs. Spearman's rank coefficient, logistic regression, and the Mann–Whitney U test were used to identify correlations, analyze risk factors for pDDIs, and compare cystic fibrosis patients with the rest, respectively. Recommendations were provided for the D and X pDDIs categories. Within the 218 patients, 428 pDDIs were identified, out of which 237 were classified as clinically significant. Almost 60% of patients were exposed to at least one relevant interaction. The number of pDDIs correlated with the number of; drugs (rs = 0.53, P < .001), hospitalization length (rs = 0.20, P < .01), and off-label medicines (rs = 0.25, P < .001). According to the multivariate analysis, at least 6 administered medications (OR = 4.15; 95% CI = 2.21-7.78), 4 days of hospitalization (OR = 6.41; 95% CI = 2.29-17.97), and off-label therapy (OR = 3.37; 95% CI = 1.69-6.70) were the risk factor for pDDIs. Despite significant differences in the number of medications taken, comorbidities, and off-label drugs, cystic fibrosis patients were not more exposed to pDDI. Given the lack of data on pDDIs in the pediatric population, the need for close cooperation between clinicians and clinical pharmacists to improve the safety and efficacy of pharmacotherapy is highlighted.
Ivermectin has been used since the 1980s as an anthelmintic and antiectoparasite agent worldwide. Currently, the only available oral formulation is tablets designed for adult patients. A patient-friendly orodispersible tablet formulation designed for pediatric use (CHILD-IVITAB) has been developed and is entering early phase clinical trials. To inform the pediatric program of CHILD-IVITAB, 16 healthy adults were enrolled in a phase I, single-center, open-label, randomized, 2-period, crossover, single-dose trial which aimed to compare palatability, tolerability, and bioavailability and pharmacokinetics of CHILD-IVITAB and their variability against the marketed ivermectin tablets (STROMECTOL) at a single dose of 12 mg in a fasting state. Palatability with CHILD-IVITAB was considerably enhanced as compared to STROMECTOL. Both ivermectin formulations were well tolerated and safe. Relative bioavailability of CHILD-IVITAB compared to STROMECTOL was estimated as the ratios of geometric means for Cmax, AUC 0-∞, and AUC0-last, which were 1.52 [90% CI: 1.13-2.04], 1.27 [0.99-1.62], and 1.29 [1.00-1.66], respectively. Maximum drug concentrations occurred earlier with the CHILD-IVITAB formulation, with a median Tmax at 3.0 h [range 2.0-4.0 h] versus 4.0 h [range 2.0-5.0 h] with STROMECTOL (P = .004). With CHILD-IVITAB, variability in exposure was cut in half (coefficient of variation: 37% vs 70%) compared to STROMECTOL. Consistent with a more controlled absorption process, CHILD-IVITAB was associated with reduced variability in drug exposure as compared to STROMECTOL. Together with a favorable palatability and tolerability profile, these findings motivate for further clinical studies to evaluate benefits of such a patient-friendly ODT formulation in pediatric patients with a parasitic disease, including infants and young children <15 kg.
Previous studies evaluating the risk of spontaneous abortions following exposure to macrolides reported controversial results. The goal of the current study was to examine the risk for spontaneous abortions following exposure to macrolides during pregnancy.
We conducted a population-based retrospective cohort study by linking three computerized databases: Clalit Health Services drug dispensation database, Soroka Medical Center (SMC) birth database, and SMC hospitalizations database. Multivariate time-varying Cox regressions were performed and adjusted for suspected confounders and known risk factors for spontaneous abortions. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated. A secondary analysis was performed to assess the association between exposure to macrolides in terms of the defined daily dose dispensed and spontaneous abortions.
The study cohort included 65,457 pregnancies that ended at Soroka Medical Center between 2004 and 2009, of which 6508 (9.9%) resulted in a spontaneous abortion. A total of 825 (1.26%) pregnancies were exposed to macrolides during the exposure period. Exposure to macrolides was not associated with spontaneous abortions as a group (adjusted HR 1.00 95% CI 0.77-1.31) or as specific medications. There was no evidence of a dose-response relationship between exposure to macrolides and spontaneous abortions.
In conclusion, this population-based retrospective cohort study did not detect an increased risk for spontaneous abortion following exposure to macrolides during the first trimester of pregnancy.
The purpose of this overview was to perform an exploratory analysis of in-house drug-drug interaction (DDI) studies conducted with investigational drugs and oral midazolam to assess the value of measuring 1-OH-midazolam (1-OHM) in such studies. The perpetrator effect of the investigational drugs on cytochrome P450 3A (CYP3A) was assessed by analyzing both midazolam and 1-OHM in plasma and evaluating their pharmacokinetic parameters. Given the almost exclusive metabolism of the parent drug by CYP3A to the main metabolite 1-OHM, an increase in midazolam and a decrease in 1-OHM exposure in the case of CYP3A inhibition caused by a perpetrator drug would be expected. The opposite would be anticipated in the case of CYP3A induction. For this analysis, the perpetrator potential of eight different investigational drugs was incorporated. Among the 10 studies included, the identified CYP3A inhibitors (n = 4) and inducers (n = 1) were classified based on the data generated with midazolam per se, with 1-OHM levels not contributing to the interpretation of the data as they did not corroborate the findings of the parent compound. Therefore, it was concluded that continued analysis of 1-OHM in plasma may be questionable as it does not add value to the interpretation of the results when performing CYP3A DDI studies with an investigational drug as a perpetrator.
Tacrolimus metabolism is heavily influenced by the CYP3A5 genotype, which varies widely among African Americans (AA). We aimed to assess the performance of a published genotype-informed tacrolimus dosing model in an independent set of adult AA kidney transplant (KTx) recipients. CYP3A5 genotypes were obtained for all AA KTx recipients (n = 232) from 2010 to 2019 who met inclusion criteria at a single transplant center in Philadelphia, Pennsylvania, USA. Medical record data were used to calculate predicted tacrolimus clearance using the published AA KTx dosing equation and two modified iterations. Observed and model-predicted trough levels were compared at 3 days, 3 months, and 6 months post-transplant. The mean prediction error at day 3 post-transplant was 3.05 ng/mL, indicating that the model tended to overpredict the tacrolimus trough. This bias improved over time to 1.36 and 0.78 ng/mL at 3 and 6 months post-transplant, respectively. Mean absolute prediction error—a marker of model precision—improved with time to 2.33 ng/mL at 6 months. Limiting genotype data in the model decreased bias and improved precision. The bias and precision of the published model improved over time and were comparable to studies in previous cohorts. The overprediction observed by the published model may represent overfitting to the initial cohort, possibly limiting generalizability.
Belzutifan (Welireg, Merck & Co., Inc., Rahway, NJ, USA) is an oral, potent hypoxia-inducible factor-2α inhibitor, recently approved in the United States for the treatment of von Hippel–Lindau (VHL) disease-associated renal cell carcinoma (RCC) and other VHL disease-associated neoplasms. Safety and efficacy were investigated in two clinical studies: a Phase 1 dose escalation/expansion study in solid tumors and RCC and a Phase 2 study in VHL-RCC. A population pharmacokinetic model was used to estimate belzutifan exposures to facilitate exposure–response (E-R) analyses for efficacy and safety endpoints. Relationships between exposure and efficacy (overall response rate, disease control rate, progression-free survival, best overall tumor size response, and other endpoints), safety outcomes (Grade ≥3 anemia, Grade ≥3 hypoxia, and time to first dose reduction/dose interruption), and pharmacodynamic biomarkers (erythropoietin [EPO] and hemoglobin [Hgb]) were evaluated using various regression techniques and time-to-event analyses. Efficacy E-R was generally flat with non-significant positive trends with exposure. The safety E-R analyses demonstrated a lack of relationship for Grade ≥3 hypoxia and a positive relationship for Grade ≥3 anemia, with incidences also significantly dependent on baseline Hgb. Exposure-dependent reductions in EPO and Hgb were observed. Based on the cumulative benefit–risk assessment in VHL disease-associated neoplasms using E-R, no a priori dose adjustment is recommended for any subpopulation. These analyses supported the benefit–risk profile of belzutifan 120 mg once daily dosing in patients with VHL-RCC for labeling and the overall development program.
Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism-based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low-dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data-driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX-type functions is presented. This “translation” was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism-based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE-based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical-statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development.
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