Algorithmic Identification of Treatment-Emergent Adverse Events From Clinical Notes Using Large Language Models: A Pilot Study in Inflammatory Bowel Disease.

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT) have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event (AE) detection.

Trends in Influenza Vaccination Rates among a Medicaid Population from 2016 to 2021.

Seasonal influenza is a leading cause of death in the U.S., causing significant morbidity, mortality, and economic burden.

Algorithmic identification of treatment-emergent adverse events from clinical notes using large language models: a pilot study in inflammatory bowel disease.

Background And Aims: Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLM) like BERT have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event detection.

Novel computational methods on electronic health record yields new estimates of transfusion-associated circulatory overload in populations enriched with high-risk patients.

Background: Transfusion-associated circulatory overload (TACO) is a severe adverse reaction (AR) contributing to the leading cause of mortality associated with transfusions. As strategies to mitigate TACO have been increasingly adopted, an update of prevalence rates and risk factors associated with TACO using the growing sources of electronic health record (EHR) data can help understand transfusion safety.

Study Design And Methods: This retrospective study aimed to provide a timely and reproducible assessment of prevalence rates and risk factors associated with TACO.

Detection of allergic transfusion-related adverse events from electronic medical records.

Background: Transfusion-related adverse events can be unrecognized and unreported. As part of the US Food and Drug Administration's Center for Biologics Evaluation and Research Biologics Effectiveness and Safety initiative, we explored whether machine learning methods, such as natural language processing (NLP), can identify and report transfusion allergic reactions (ARs) from electronic health records (EHRs).

Study Design And Methods: In a 4-year period, all 146 reported transfusion ARs were pulled from a database of 86,764 transfusions in an academic health system, along with a random sample of 605 transfusions without reported ARs.

Analyzing real world data of blood transfusion adverse events: Opportunities and challenges.

Background: Blood transfusions are a vital component of modern healthcare, yet adverse reactions to blood product transfusions can cause morbidity, and rarely result in mortality. Therefore, accurate reporting of transfusion related adverse events (TRAEs) is paramount to improved transfusion practice. This study aims to investigate real-world data (RWD) on TRAEs by evaluating differences between ICD 9/10-based electronic health records (EHR) and blood bank-specific reporting.

Development and Validation of a Treatment Benefit Index to Identify Hospitalized Patients With COVID-19 Who May Benefit From Convalescent Plasma.

Importance: Identifying which patients with COVID-19 are likely to benefit from COVID-19 convalescent plasma (CCP) treatment may have a large public health impact.

Objective: To develop an index for predicting the expected relative treatment benefit from CCP compared with treatment without CCP for patients hospitalized for COVID-19 using patients' baseline characteristics.

Design, Setting, And Participants: This prognostic study used data from the COMPILE study, ie, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) evaluating CCP vs control in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization.

Opportunities and challenges for applying model-informed drug development approaches to gene therapies.

As part of the US Food and Drug Administration (FDA)'s Prescription Drug User Fee Act (PDUFA) VI commitments, the Center for Biologics Evaluation and Research (CBER) and Center for Drug Evaluation and Research (CDER) are conducting a model-informed drug development (MIDD) pilot program. Sponsor(s) who apply and are selected will be granted meetings that aim to facilitate the application of MIDD approaches throughout the product development lifecycle and the regulatory process. Due to their complex mechanisms of action and limited clinical experience, cell and gene therapies have the potential to benefit from the application of MIDD methods, which may facilitate their safety and efficacy evaluations.

A Quantitative Evaluation of COVID-19 Epidemiological Models.

Quantifying how accurate epidemiological models of COVID-19 forecast the number of future cases and deaths can help frame how to incorporate mathematical models to inform public health decisions. Here we analyze and score the predictive ability of publicly available COVID-19 epidemiological models on the COVID-19 Forecast Hub. Our score uses the posted forecast cumulative distributions to compute the log-likelihood for held-out COVID-19 positive cases and deaths.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed.

Comparing clotting factors attributes across different methods of preference elicitation in haemophilia patients.

Introduction: Emerging, systematic approaches for capturing patient input, such as preference elicitation, can provide valuable information for the benefit-risk assessment of medical products for treating bleeding disorders, such as haemophilia.

Aim: This study aims to identify existing and develop new methods to capture, rank and summarize preference scores for clotting factor therapies.

Methods: Haemophilia patient preference data were compiled from studies identified through literature review and publicly available US FDA patient-focused drug development meeting documents.

A solid-phase PEGylation strategy for protein therapeutics using a potent FGF21 analog.

Fibroblast growth factor 21 (FGF21) is an endocrine-acting hormone that has the potential to treat metabolic diseases, such as type 2 diabetes and obesity. Development of FGF21 into a therapeutic has been hindered due to its low intrinsic bio-stability, propensity towards aggregation and its susceptibility to in vivo proteolytic degradation. In order to address these shortcomings, we've developed recombinant human FGF21 variants by strategically introducing cysteine residues via site-directed mutagenesis, and have also developed a solid-phase nickel affinity PEGylation strategy, whereby engineered, surface-exposed cysteine residues of immobilized proteins were used as a platform to efficiently and site-selectively conjugate with PEG-maleimide.

Molecular mechanisms of fibroblast growth factor signaling in physiology and pathology.

Fibroblast growth factors (FGFs) signal in a paracrine or endocrine fashion to mediate a myriad of biological activities, ranging from issuing developmental cues, maintaining tissue homeostasis, and regulating metabolic processes. FGFs carry out their diverse functions by binding and dimerizing FGF receptors (FGFRs) in a heparan sulfate (HS) cofactor- or Klotho coreceptor-assisted manner. The accumulated wealth of structural and biophysical data in the past decade has transformed our understanding of the mechanism of FGF signaling in human health and development, and has provided novel concepts in receptor tyrosine kinase (RTK) signaling.

Grb2, a double-edged sword of receptor tyrosine kinase signaling.

Receptor tyrosine kinases (RTKs) exhibit basal tyrosine phosphorylation and activity in the absence of ligand stimulation, which has been attributed to the "leaky" nature of tyrosine kinase autoinhibition and stochastic collisions of receptors in the membrane bilayer. This basal phosphorylation does not produce a signal of sufficient amplitude and intensity to manifest in a biological response and hence is considered to be a passive, futile process that does not have any biological function. This paradigm has now been challenged by a study showing that the basal phosphorylation of RTKs is a physiologically relevant process that is actively inhibited by the intracellular adaptor protein growth factor receptor-bound 2 (Grb2) and serves to "prime" receptors for a rapid response to ligand stimulation.