Papilledema in Children With Cystic Fibrosis Receiving Elexacaftor/Tezacaftor/Ivacaftor: A Multicenter Case Series.

Following the approval of elexacaftor/tezacaftor/ivacaftor (ETI), there have been post-marketing reports and published cases of papilledema and intracranial hypertension in children with cystic fibrosis (CF) taking ETI. In those reports, the patients often presented with marked symptoms and concomitant hypervitaminosis A. In this multicenter case series, we report eight cases of papilledema in children with CF taking ETI that were diagnosed via routine eye exam, the majority of whom presented with minimal to no symptoms and all had normal serum Vitamin A levels.

Predictors of Selective Serotonin Reuptake Inhibitor Treatment Failure in Persons With Cystic Fibrosis.

Introduction: Depression and anxiety are common in persons with cystic fibrosis (PwCF). Genetic polymorphisms in CYP2C19 and CYP2D6 are well-established predictors of selective serotonin reuptake inhibitors (SSRIs) treatment failure yet have not been studied specifically in PwCF. The purpose of this study was to determine the rate of SSRI failure in PwCF and to identify factors that predict treatment failure.

Inhaled Medications for Maintenance Therapy in Pediatric Noncystic Fibrosis Bronchiectasis.

Objective: This review focuses on evaluating literature for the use of inhaled mucolytics (hypertonic saline, mannitol, and dornase alfa), inhaled antibiotics (tobramycin, aztreonam, colistin, and amikacin), and inhaled corticosteroids in pediatric noncystic fibrosis bronchiectasis.

Data Sources: A literature search via PubMed was conducted using the search terms "non-cystic fibrosis bronchiectasis," "primary ciliary dyskinesia," and "bronchiectasis" in combination with each inhaled agent of interest.

Study Selection And Data Extraction: Studies were included if they were specific to patients with a clinical diagnosis of noncystic fibrosis bronchiectasis published from 1998 to July 2024.

Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity.

AI-driven Discovery of Morphomolecular Signatures in Toxicology.

Early identification of drug toxicity is essential yet challenging in drug development. At the preclinical stage, toxicity is assessed with histopathological examination of tissue sections from animal models to detect morphological lesions. To complement this analysis, toxicogenomics is increasingly employed to understand the mechanism of action of the compound and ultimately identify lesion-specific safety biomarkers for which assays can be designed.