First-time adverse drug reactions, survival analysis, and the share of adverse drug reactions in treatment discontinuation in real-world rheumatoid arthritis patients: a comparison of first-time treatment with adalimumab and etanercept.

BACKGROUND: This study aims to compare nature and frequency of adverse drug reactions (ADRs), time to first ADR, drug survival, and the share of ADRs in treatment discontinuation of first-time treatment with adalimumab (ADA) and etanercept (ETN) in real-world RA patients. RESEARCH DESIGN AND METHODS: Retrospective, single-center cohort study including naïve patients treated between January 2003-April 2020. Time to first ADR and drug survival of first-time treatment were studied using Kaplan-Meier and Cox-regression models up to 10 years, with 2- and 5-year post-hoc sensitivity analysis. Nature and frequencies of first-time ADRs and causes of treatment discontinuation were assessed. RESULTS: In total, 416 patients (ADA: 255, ETN: 161, 4865 patient years) were included, of which 92 (22.1%) experienced ADR(s) (ADA: 59, 23.1%; ETN: 33, 20.4%). Adjusted for age, gender and concomitant conventional DMARD use, ADA was more likely to be discontinued than ETN up to 2-, 5- and 10-year follow-up (adjusted HRs 1.63; 1.62; 1.59 (all p<0.001)). ADRs were the second reason of treatment discontinuation (ADA 20.7%, ETN 21.4%). CONCLUSIONS: Despite seemingly different nature and frequencies, ADRs are the second reason of treatment discontinuation for both bDMARDs. Furthermore, 2-, 5-, and 10-year drug survival is longer for ETN compared to ADA.

Analysis and visualization of the course and burden over time of adverse drug reactions (ADRs) attributed to TNFα-inhibitors in patients with inflammatory rheumatic diseases (IRDs).

BACKGROUND: We aimed to investigate course and burden over time of ADRs attributed to TNFα-inhibitors in IRD-patients, and whether Sankey diagrams and polar plots can visualize this. RESEARCH DESIGN AND METHODS: Data on ADRs experienced during the Dutch Biologic Monitor (January 2017 till December 2022) were used in this study. We selected IRD-patients using a TNFα-inhibitor, reporting skin reactions/infections/injection site reactions and completing ≥3 questionnaires (i.e. the initial report and ≥2 follow-ups). Course was scored as worsening/improving/remaining stable/resolving and as (non-)recurrent. Patients scored burden from 1 (no burden) to 5 (very high burden). Sankey diagrams and polar plots visualized this. RESULTS: 202 patients were included, reporting 353 ADRs. Most skin reactions were stable (25.0%). Most infections resolved (50.8%). Injection site reactions were mostly recurrent (72.3%). Skin reactions and infections tended to decrease in burden . Infections had highest burden at start, which mostly decreased over time. Injection site reactions had a low and stable burden. CONCLUSIONS: Skin reactions attributed to TNFα-inhibitors by IRD-patients are stable with a slightly decreasing burden over time. Infections have highest burden at start but resolved mostly. Injection site reactions have a low and stable burden. Sankey diagrams and polar plots are suitable to visualize this.

Description of Frequencies of Reported Adverse Events Following Immunization Among Four Different COVID-19 Vaccine Brands.

INTRODUCTION: The rapid rollout of coronavirus disease 2019 (COVID-19) vaccines for a large proportion of the population necessitates a strong emphasis on safety. Complementary to the existing spontaneous reporting system, The Netherlands Pharmacovigilance Centre Lareb conducted patient-reported cohort event monitoring (CEM). OBJECTIVE: The primary aim was to investigate differences in the frequencies of any and commonly reported, 'well-known', systemic adverse events following immunization (AEFIs) with four COVID-19 vaccines (Pfizer's Comirnaty®, Moderna's Spikevax®, AstraZeneca's Vaxzevria® and the Janssen vaccine). As a secondary aim, we analyzed the frequencies of well-known systemic adverse events after the first and, if applicable, second COVID-19 vaccinations, taking into account age, sex and prior COVID-19 infection. METHODS: Patient-reported outcomes (PROs) in the Netherlands starting in February 2021 were analyzed using a prospective cohort design. RESULTS: Data of 27,554 participants who received one vaccination and 20,682 participants who received complete immunization were analyzed. The percentage of patients reporting any AEFI was high and ranged from approximately 53% for the Pfizer vaccine to approximately 94% for the Moderna vaccine. The frequency of serious AEFIs was low, with the highest frequency found for the AstraZeneca vaccine (0.228%). AEFIs were most often experienced by participants receiving the first dose of the AstraZeneca and Janssen vaccines and the second dose of the Moderna vaccine; the Pfizer vaccine was associated with the lowest rate of AEFIs. Participants with a COVID-19 history before vaccination experienced commonly reported systemic AEFIs more frequently after the first vaccination than after the second vaccination. Women and young people experienced more AEFIs than men and older people, respectively. CONCLUSIONS: The analysis of a large cohort provides important information about the rates of AEFIs across age groups, among brands of vaccines and between those with and without prior COVID-19 infection. Participants reported a high number of AEFIs in general, but the frequency of serious AEFIs was low.

Predicting protein condensate formation using machine learning.

Membraneless organelles are liquid condensates, which form through liquid-liquid phase separation. Recent advances show that phase separation is essential for cellular homeostasis by regulating basic cellular processes, including transcription and signal transduction. The reported number of proteins with the capacity to mediate protein phase separation (PPS) is continuously growing. While computational tools for predicting PPS have been developed, obtaining a proteome-wide overview of PPS probabilities has remained challenging. Here, we present a phase separation analysis and prediction (PSAP) machine-learning classifier that, based solely on the amino acid content of a training set of known PPS proteins, can determine the phase separation likelihood for each protein in a given proteome. Through comparison with PPS databases, existing predictors, and experimental evidence, we demonstrate the validity and advantages of the PSAP classifier. We anticipate that the PSAP predictor provides a useful tool for future research aimed at identifying phase separating proteins in health and disease.

Aminoacyl-tRNA synthetase deficiencies in search of common themes.

PURPOSE: Pathogenic variations in genes encoding aminoacyl-tRNA synthetases (ARSs) are increasingly associated with human disease. Clinical features of autosomal recessive ARS deficiencies appear very diverse and without apparent logic. We searched for common clinical patterns to improve disease recognition, insight into pathophysiology, and clinical care. METHODS: Symptoms were analyzed in all patients with recessive ARS deficiencies reported in literature, supplemented with unreported patients evaluated in our hospital. RESULTS: In literature, we identified 107 patients with AARS, DARS, GARS, HARS, IARS, KARS, LARS, MARS, RARS, SARS, VARS, YARS, and QARS deficiencies. Common symptoms (defined as present in ≥4/13 ARS deficiencies) included abnormalities of the central nervous system and/or senses (13/13), failure to thrive, gastrointestinal symptoms, dysmaturity, liver disease, and facial dysmorphisms. Deep phenotyping of 5 additional patients with unreported compound heterozygous pathogenic variations in IARS, LARS, KARS, and QARS extended the common phenotype with lung disease, hypoalbuminemia, anemia, and renal tubulopathy. CONCLUSION: We propose a common clinical phenotype for recessive ARS deficiencies, resulting from insufficient aminoacylation activity to meet translational demand in specific organs or periods of life. Assuming residual ARS activity, adequate protein/amino acid supply seems essential instead of the traditional replacement of protein by glucose in patients with metabolic diseases.