Understanding how CD19 expression levels impact the response to loncastuximab tesirine: a plain language summary.

What Is This Summary About?: In this article, we summarize results from a clinical study called LOTIS-2, in which researchers looked at patients with a type of blood cancer called diffuse large B-cell lymphoma, or DLBCL for short. Patients received the drug loncastuximab tesirine, or Lonca for short, which targets a marker on the surface of tumor cells called CD19.Patient information from the LOTIS-2 study, other studies of Lonca, and information from scientific publications was used to develop a quantitative systems pharmacology (QSP) model, which can predict how Lonca works in the body.

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters.

In relapsed or refractory diffuse large B-cell lymphoma, CD19 expression by immunohistochemistry alone is not a predictor of response to loncastuximab tesirine.

CD19-targeting treatments have shown promise in relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL). Loncastuximab tesirine (loncastuximab tesirine-lpyl [Lonca]) is a CD19-targeting antibody-drug conjugate indicated for R/R DLBCL after at least two systemic treatments. CD19 expression was evaluated in patients receiving Lonca in the LOTIS-2 clinical trial with available tissue samples obtained after last systemic therapy/before Lonca treatment.

A mathematical model of flagellar gene regulation and construction in Salmonella enterica.

Millions of people worldwide develop foodborne illnesses caused by Salmonella enterica (S. enterica) every year. The pathogenesis of S.

Quantification of the Impact of Partition Coefficient Prediction Methods on Physiologically Based Pharmacokinetic Model Output Using a Standardized Tissue Composition.

Tissue:plasma partition coefficients are key parameters in physiologically based pharmacokinetic (PBPK) models, yet the coefficients are challenging to measure in vivo. Several mechanistic-based equations have been developed to predict partition coefficients using tissue composition information and the compound's physicochemical properties, but it is not clear which, if any, of the methods is most appropriate under given circumstances. Complicating the evaluation, each prediction method was developed, and is typically employed, using a different set of tissue composition information, thereby making a controlled comparison impossible.

A Mathematical Model for Inheritance of DNA Methylation Patterns in Somatic Cells.

DNA methylation is an essential epigenetic mechanism used by cells to regulate gene expression. Interestingly, DNA replication, a function necessary for cell division, disrupts the methylation pattern. Since perturbed methylation patterns are associated with aberrant gene expression and many diseases, including cancer, restoration of the correct pattern following DNA replication is crucial.