Priyanka Yalamanchili

Objective: The objective of this study is to describe recent trends in disease‐modifying antirheumatic drug (DMARD) use for children with juvenile idiopathic arthritis (JIA) in the United States.Methods: We used commercial claims data (2000–2022) to perform a serial cross‐sectional utilization study of children aged 1 to 18 that were diagnosed with JIA. Initiations of conventional synthetic DMARDs (csDMARDs), biologic DMARDs (bDMARDs), or targeted synthetic DMARDs (tsDMARDs) were identified after a ≥12‐month baseline and expressed as a percentage of all new DMARD initiations per year, by category, class, and individual agent. Trends were evaluated using linear regression. We also examined the first bDMARDs and tsDMARDs initiated after csDMARD monotherapy.Results: We identified 20,258 new DMARD use episodes among 13,696 individuals (median age 14 years, 67.5% female). csDMARDs, although most used overall, declined from 89.5% of new use episodes to 43.2% (2001–2022, P < 0.001 for trend). In contrast, bDMARD use increased (10.5–50.0%, P < 0.001). For tumor necrosis factor inhibitors (TNFi), etanercept peaked at 28.3% in 2006 and declined to 4.2% in 2022 (P = 0.002). Meanwhile, adalimumab use doubled (7.0–14.0%, 2007–2008) after JIA approval, increasing further following a less painful formulation release (20.5% in 2022, P < 0.001). However, overall TNFi use has declined with increasing use of other bDMARDs and tsDMARDs, particularly ustekinumab, secukinumab, and tofacitinib. By 2022, adalimumab was the most common b/tsDMARD initiated first after csDMARDs (77.8%).Conclusion: Among commercially insured children with JIA in the United States, new b/tsDMARD use is rising and new csDMARD use is declining. For b/tsDMARDs, adalimumab is most used and is the predominant b/tsDMARD initiated first after csDMARDs. Patterns in DMARD use for JIA have evolved relative to multiple factors, including regulatory approvals and tolerability.

Breast cancer treatments have evolved rapidly, and clinically meaningful biomarkers have been used to guide therapy. These biomarkers hold utility within the drug development process to increase th...

Background: The use of biomarkers varies from disease etiognosis and diagnosis to signal detection, risk prediction, and management. Biomarker use has expanded in recent years, however, there are limited reviews on the use of biomarkers in pharmacovigilance and specifically in the monitoring and management of adverse drug reactions (ADRs). Objective: The objective of this manuscript is to identify the multiple uses of biomarkers in pharmacovigilance irrespective of the therapeutic area. Design: This is a systematic review of the literature. Data Sources and Methods: Embase and MEDLINE database searches were conducted for literature published between 2010-March 19, 2021. Scientific articles that described the potential use of biomarkers in pharmacovigilance in sufficient detail were reviewed. Papers that did not fulfill the United States Food and Drug Administration (US FDA) definition of a biomarker were excluded, which is based on the International Conference on Harmonisation (ICH)−E16 guidance. Results: Twenty-seven articles were identified for evaluation. Most articles involved predictive biomarkers (41%), followed by safety biomarkers (38%), pharmacodynamic/response biomarkers (14%), and diagnostic biomarkers (7%). Some articles described biomarkers that applied to multiple categories. Conclusion: Various categories of biomarkers including safety, predictive, pharmacodynamic/response, and diagnostic biomarkers are being investigated for potential use in pharmacovigilance. The most frequent potential uses of biomarkers in pharmacovigilance in the literature were the prediction of the severity of an ADR, mortality, response, safety, and toxicity. The safety biomarkers identified were used to evaluate patient safety during dose escalation, identify patients who may benefit from further biomarker testing during treatment, and monitor ADRs.

Introduction Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development. Objective The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review. Methods Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as ‘pharmacovigilance,’ ‘patient safety,’ ‘artificial intelligence,’ and ‘machine learning’ in the title or Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded. Results Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug–drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source. Conclusion Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.