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Abstract Polypharmacy in older adults increases the risk of adverse drug reactions (ADRs), but studying this relationship is complex. In real-world data, the high number of medications, coupled with rare drug combinations, results in high-dimensional datasets that are difficult to analyze using conventional statistical methods. This study applies horseshoe and lasso regression for analyzing rare events in polypharmacy contexts, focusing on severe ADRs such as falls and bleedings. These regression models are executed on a multi-center dataset compiling 7175 cases from the ADRED project to detect potential ADR-associated drugs among 100 most common drugs in emergency department admissions. Positive predictors are classified by using 50% and 90% credibility intervals. This study demonstrates that regression models with horseshoe or lasso priors are effective for analyzing ADRs, providing a comprehensive consideration of multiple factors in large, sparse datasets and improving signal detection in polypharmacy, addressing a significant challenge in pharmacovigilance. Both priors yielded consistent and clinically meaningful results. The horseshoe regression resulted in fewer potential positive predictors overall, which could make it suitable as a diagnostic tool. While these regressions generate valuable information, there are still challenges in setting appropriate thresholds for determining and interpreting the positive results.

Background: The analysis of substrates of polymorphic cytochrome P450 (CYP) enzymes is important information to enable drug–drug interactions (DDIs) analysis and the relevance of pharmacogenetics in this context in large datasets. Our aim was to compare different approaches to assess the substrate properties of drugs for certain polymorphic CYP2 enzymes. Methods: A standardized manual method and an automatic method were developed and compared to assess the substrate properties for the metabolism of drugs by CYP2D6, 2C9, and 2C19. The automatic method used a matching approach to three freely available resources. We applied the manual and automatic methods to a large real-world dataset deriving from a prospective multicenter study collecting adverse drug reactions in emergency departments in Germany (ADRED). Results: In total, 23,878 medication entries relating to 895 different drugs were analyzed in the real-world dataset. The manual method was able to assess 12.2% (n = 109) of drugs, and the automatic method between 12.1% (n = 109) and 88.9% (n = 796), depending on the resource used. The CYP substrate classifications demonstrated moderate to almost perfect agreements for CYP2D6 and CYP2C19 (Cohen’s Kappa (κ) 0.48–0.90) and fair to moderate agreements for CYP2C9 (κ 0.20–0.48). Conclusion: A closer look at different classifications between methods revealed that both methods are prone to error in different ways. While the automated method excels in time efficiency, completeness, and actuality, the manual method might be better able to identify CYP2 substrates with clinical relevance.

In light of an increasing number of vaccinated and convalescent individuals, there is a major need for the development of robust methods for the quantification of neutralizing antibodies; although, a defined correlate of protection is still missing. Sera from hospitalized COVID-19 patients suffering or not suffering from acute respiratory distress syndrome (ARDS) were comparatively analyzed by plaque reduction neutralization test (PRNT) and pseudotype-based neutralization assays to quantify their neutralizing capacity. The two neutralization assays showed comparable data. In case of the non-ARDS sera, there was a distinct correlation between the data from the neutralization assays on the one hand, and enzyme-linked immune sorbent assay (ELISA), as well as biophysical analyses, on the other hand. As such, surface plasmon resonance (SPR)-based assays for quantification of binding antibodies or analysis of the stability of the antigen–antibody interaction and inhibition of syncytium formation, determined by cell fusion assays, were performed. In the case of ARDS sera, which are characterized by a significantly higher fraction of RBD-binding IgA antibodies, there is a clear correlation between the neutralization assays and the ELISA data. In contrast to this, a less clear correlation between the biophysical analyses on the one hand and ELISAs and neutralization assays on the other hand was observed, which might be explained by the heterogeneity of the antibodies. To conclude, for less complex immune sera—as in cases of non-ARDS sera—combinations of titer quantification by ELISA with inhibition of syncytium formation, SPR-based analysis of antibody binding, determination of the stability of the antigen–antibody complex, and competition of the RBD-ACE2 binding represent alternatives to the classic PRNT for analysis of the neutralizing potential of SARS-CoV-2-specific sera, without the requirement for a BSL3 facility.

Abstract Reports linking proton pump inhibitors (PPIs) with cognition and dementia show conflicting results, with limited evidence on underlying biological mechanisms. However, existing studies did not investigate brain microstructure, which could provide valuable insights into early structural changes indicative of cognitive decline. Analyses were based on cross-sectional baseline data from the Rhineland Study (n = 7,465; mean age 55.3 ± 13.7 years, range 30–95 years, 56.5% women). Using multivariate linear regression, we investigated associations between PPI use and cognition and brain macro- and microstructural measures (fractional anisotropy (FA) and mean diffusivity (MD) as measures of white matter integrity). Analyses were stratified by short-term (