Exploring Cell Tropism as a Possible Contributor to Influenza Infection Severity

Several mechanisms have been proposed to account for the marked increase in severity of human infections with avian compared to human influenza strains, including increased cytokine expression, poor immune response, and differences in target cell receptor affinity. Here, the potential effect of target cell tropism on disease severity is studied using a mathematical model for in-host influenza viral infection in a cell population consisting of two different cell types. The two cell types differ only in their susceptibility to infection and rate of virus production. We show the existence of a parameter regime which is characterized by high viral loads sustained long after the onset of infection. This finding suggests that differences in cell tropism between influenza strains could be sufficient to cause significant differences in viral titer profiles, similar to those observed in infections with certain strains of influenza A virus. The two target cell mathematical model offers good agreement with experimental data from severe influenza infections, as does the usual, single target cell model albeit with biologically unrealistic parameters. Both models predict that while neuraminidase inhibitors and adamantanes are only effective when administered early to treat an uncomplicated seasonal infection, they can be effective against more severe influenza infections even when administered late

In silico exploration of amyloid‐related imaging abnormalities in the gantenerumab open‐label extension trials using a semi‐mechanistic model

Abstract Introduction Amyloid‐related imaging abnormalities with edema/effusion (ARIA‐E) are commonly observed with anti‐amyloid therapies in Alzheimer's disease. We developed a semi‐mechanistic, in silico model to understand the time course of ARIA‐E and its dose dependency. Methods Dynamic and statistical analyses of data from 112 individuals that experienced ARIA‐E in the open‐label extension of SCarlet RoAD (a study of gantenerumab in participants with prodromal Alzheimer's disease) and Marguerite RoAD (as study of Gantenerumab in participants with mild Alzheimer's disease) studies were used for model building. Gantenerumab pharmacokinetics, local amyloid removal, disturbance and repair of the vascular wall, and ARIA‐E magnitude were represented in the novel vascular wall disturbance (VWD) model of ARIA‐E. Results The modeled individual‐level profiles provided a good representation of the observed pharmacokinetics and time course of ARIA‐E magnitude. ARIA‐E dynamics were shown to depend on the interplay between drug‐mediated amyloid removal and intrinsic vascular repair processes. Discussion Upon further refinement and validation, the VWD model could inform strategies for dosing and ARIA monitoring in individuals with an ARIA‐E history

Model Description Language (MDL): A Standard for Modeling and Simulation

Recent work on Model Informed Drug Discovery and Development (MID3) has noted the need for clarity in model description used in quantitative disciplines such as pharmacology and statistics.1-3 Currently, models are encoded in a variety of computer languages and are shared through publications that rarely include original code and generally lack reproducibility. The DDMoRe Model Description Language (MDL) has been developed primarily as a language standard to facilitate sharing knowledge and understanding of models