Selected cutaneous adverse events in patients treated with ICI monotherapy and combination therapy: a retrospective pharmacovigilance study and meta-analysis.

Introduction: Cutaneous adverse events are commonly reported immune-related adverse events (irAEs), some of which are serious or even life-threatening, and it is essential to study these specific cutaneous AEs to understand their characteristics and risk. Methods: We performed a meta-analysis of published clinical trials for immune checkpoint inhibitors (ICIs) to evaluate the incidence of cutaneous adverse events, using data from PubMed, Embase, and the Cochrane Library databases. Results: A total of 232 trials with 45,472 patients were involved. Results showed that anti-PD-1 and targeted therapy combinations were associated with higher risk for most of the selected cutaneous adverse events. In addition, a retrospective pharmacovigilance study was conducted using the Food and Drug Administration (FDA) Adverse Events System database. Reporting odds ratio (ROR) and Bayesian information components (IC) were used to perform the disproportionality analysis. Cases were extracted from January 2011 to September 2020. We identified 381 (20.24%) maculopapular rash, 213 (11.32%) vitiligo, 215 (11.42%) Stevens-Johnson syndrome (SJS), and 165 (8.77%) toxic epidermal necrolysis (TEN) cases. For vitiligo, anti-PD-1/L1 combined with anti-CTLA-4 therapy showed the strongest signal (ROR: 55.89; 95% CI: 42.34-73.78; IC025: 4.73). Palmar-plantar erythrodysesthesia (PPE) was reported with the most significant association with combined anti-PD-1/L1 and VEGF (R)-TKIs (ROR: 18.67; 95% CI: 14.77-23.60; IC025: 3.67). For SJS/TEN, antiPD-1 inhibitors showed the strongest signal (ROR: 3.07; 95% CI: 2.68-3.52; IC025: 1.39). The median onset time of vitiligo and SJS/TEN was 83 and 24 days, respectively. Conclusion: Overall, in selected cutaneous AEs, each of them showed specific characteristics. It is necessary to realize their differences and take appropriate interventions in patients with different regimens.

Remdesivir for COVID-19 and acute kidney injury: disproportionality analysis of data from the U.S. Food and Drug Administration Adverse Event Reporting System.

BACKGROUND: Evidence about remdesivir-associated acute kidney injury (AKI) among patients with novel coronavirus disease 2019 (COVID-19) was controversial. AIM: To investigate the signal of disproportionate reporting of remdesivir-related AKI in COVID-19 patients over time with data from US Food and Drug Administration Adverse Event Reporting System. METHOD: Adverse events in COVID-19 patients reported between April 2020 and September 2022 were included. Reporting odds ratios (RORs) of AKI and renal disorders (a more sensitive definition for AKI) were estimated to compare remdesivir with other medications prescribed in comparable situations of COVID-19. RESULTS: During the entire study period, significant signals were identified for remdesivir-related AKI (ROR 2.00, 95% CI: 1.83-2.18) and renal disorder (ROR 2.35, 95% CI: 2.17-2.54) when compared to all comparable drugs. However, in the third quarter of 2022 (the most recent quarter) signals disappeared as the ROR of AKI was 1.50 (95% CI 0.91-2.45) and ROR of renal disorder was 1.69 (95% CI 1.06-2.70). Number of signals in sensitivity analyses and the proportion of AKI in remdesivir-associated events decreased over time. CONCLUSION: In COVID-19 patients, we observed diminishing signals of remdesivir-associated AKI over time and no significant signal in the most recent quarter, suggesting remdesivir might not be nephrotoxic.

Multi-task learning models for predicting active compounds.

The computational drug discovery methods can find potential drug-target interactions more efficiently and have been widely studied over past few decades. Such methods explore the relationship between the structural properties of compounds and their biological activity with the assumption that similar compounds tend to share similar biological targets and vice versa. However, traditional Quantitative Structure - Activity Relationship (QSAR) methods often do not have desired accuracy due to insufficient data of compound activity. In this paper, we focus on building Multi-Task Learning (MTL)-based QSAR models by considering multiple similar biological targets together and make shared information transfer across from one task to another, thereby improving not only the learning efficiency, but also the prediction accuracy. This paper selects 6 assay groups with similar biological targets from PubChem and builds their QSAR models with MTL simultaneously. According to the experiment results, our MTL-based QSAR models have better performance over traditional prominent machine learning algorithms and the improvements are even more obvious when other baseline models have low accuracy. The superiority of our models is also proved by Student's t-test with level of significance 5%. Moreover, this paper also explores three different assumptions on the underlying pattern in the dataset and finds that the joint feature MTL models further improve the performance of the QSAR models and are more suitable for building QSAR models for multiple similar biological targets.

Comparison of GLP-1 analogues versus sitagliptin in the management of type 2 diabetes: systematic review and meta-analysis of head-to-head studies.

BACKGROUND: Incretin-based therapies which include glucagon-like peptide-1 (GLP-1) receptor agonists and dipeptidyl peptidase-4 (DPP-4) inhibitors are recommended by several practice guidelines as second-line agents for add-on therapy to metformin in patients with type 2 diabetes (T2DM) who do not achieve glycemic control with metformin plus lifestyle interventions alone. The purpose of this study is to perform a systematic review with meta-analysis of existing head to head studies to compare the efficacy and safety of GLP-1 analogues with DPP-4 inhibitors. METHODS: We performed a systematic review and meta-analysis of head-to-head studies to compare GLP-1 analogues with DPP-4 inhibitors in the management of type 2 diabetes. A random effects model was selected to perform the meta-analyses, results were expressed as weighted mean differences for continuous outcomes and relative risks for dichotomous outcomes, both with 95% confidence intervals, and with I2 values and P values as markers of heterogeneity. RESULTS: Four head-to-head randomized controlled studies with 1755 patients were included. Compared to sitagliptin, GLP-1 analogues are more effective in reducing HbA1C (weight mean difference -0.41%, 95% CI -0.51 to -0.31) and body weight (weight mean difference -1.55 kg, 95% CI -1.98 to -1.12). Conversely, GLP-1 analogues are associated with a higher incidence of gastrointestinal adverse events compared to sitagliptin: nausea (relative risk 3.14, 95% CI 2.15 to 4.59), vomiting (relative risk 2.60, 95% CI 1.48 to 4.56), diarrhea (relative risk 1.82, 95% CI 1.24 to 2.69), and constipation (relative risk 2.50, 95% CI 1.33 to 4.70). CONCLUSIONS: The result of this meta-analysis demonstrates that compared to sitagliptin, GLP-1 analogues are more effective for glycemic control and weight loss, but have similar efficacy in reducing blood pressure and lipid parameters, however, GLP-1 analogues are associated with a higher incidence of gastrointestinal adverse events and a similar incidence of hypoglycemia compared to sitagliptin.