Mechanistic modeling projections of antibody persistence after homologous booster regimens of COVID-19 vaccine Ad26.COV2.S in humans.

Mechanistic model-based simulations can be deployed to project the persistence of humoral immune response following vaccination. We used this approach to project the antibody persistence through 24 months from the data pooled across five clinical trials in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-seronegative participants following vaccination with Ad26.COV2.

Efficacy and safety of emapalumab in macrophage activation syndrome.

Objectives: Macrophage activation syndrome (MAS) is a severe, life-threatening complication of systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still's disease (AOSD). The objective of this study was to confirm the adequacy of an emapalumab dosing regimen in relation to interferon-γ (IFNγ) activity by assessing efficacy and safety. The efficacy outcome was MAS remission by week 8, based on clinical and laboratory criteria.

Emapalumab in primary haemophagocytic lymphohistiocytosis and the pathogenic role of interferon gamma: A pharmacometric model-based approach.

Aim: Primary haemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening, hyperinflammatory syndrome generally occurring in early childhood. The monoclonal antibody emapalumab binds and neutralises interferon γ (IFNγ). This study aimed to determine an emapalumab dosing regimen when traditional dose-finding approaches are not applicable, using pharmacokinetic-pharmacodynamic analyses to further clarify HLH pathogenesis and confirm IFNγ neutralisation as the relevant therapeutic target in pHLH.

Clustering and Kernel Density Estimation for Assessment of Measurable Residual Disease by Flow Cytometry.

Standardization, data mining techniques, and comparison to normality are changing the landscape of multiparameter flow cytometry in clinical hematology. On the basis of these principles, a strategy was developed for measurable residual disease (MRD) assessment. Herein, suspicious cell clusters are first identified at diagnosis using a clustering algorithm.