Multisite Assembly of Gateway Induced Clones (MAGIC): a flexible cloning toolbox with diverse applications in vertebrate model systems.

Here we present the Multisite Assembly of Gateway Induced Clones (MAGIC) system, which harnesses site-specific recombination-based cloning via Gateway technology for rapid, modular assembly of between 1 and 3 "Entry" vector components, all into a fourth, standard high copy "Destination" plasmid backbone. The MAGIC toolkit spans a range of in vitro and in vivo uses, from directing tunable gene expression, to driving simultaneous expression of microRNAs and fluorescent reporters, to enabling site-specific recombinase-dependent gene expression. All MAGIC system components are directly compatible with existing multisite gateway Tol2 systems currently used in zebrafish, as well as existing eukaryotic cell culture expression Destination plasmids, and available mammalian lentiviral and adenoviral Destination vectors, allowing rapid cross-species experimentation.

Model-based meta-analysis using latent variable modeling to set benchmarks for new treatments of systemic lupus erythematosus.

Several investigational agents are under evaluation in systemic lupus erythematosus (SLE) clinical trials but quantitative frameworks to enable comparison of their efficacy to reference benchmark treatments are lacking. To benchmark SLE treatment effects and identify clinically important covariates, we developed a model-based meta-analysis (MBMA) within a latent variable model framework for efficacy end points and SLE composite end point scores (BILAG-based Composite Lupus Assessment and Systemic Lupus Erythematosus Responder Index) using aggregate-level data on approved and investigational therapeutics. SLE trials were searched using PubMed and www.

Bayesian estimation in NONMEM.

Bayesian estimation is a powerful but underutilized tool for answering drug development questions. In this tutorial, the principles of Bayesian model development, assessment, and prior selection will be outlined. An example pharmacokinetic (PK) model will be used to demonstrate the implementation of Bayesian modeling using the nonlinear mixed-effects modeling software NONMEM.

Population model-based analysis of the memory B-cell response following belimumab therapy in the treatment of systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by B-cell hyperactivity and breach of tolerance. Autoreactive memory B cells, which have a decreased activation threshold and the ability to survive in absence of antigen, are believed to contribute to chronicity in autoimmune diseases like SLE. Belimumab, the first approved biological treatment of active SLE and lupus nephritis, reduces B cells dependent on B-lymphocyte stimulator protein (BLyS) for survival, whereas memory B cells are spared; several studies reported circulating memory B-cell concentrations increase following BLyS neutralization.

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl.

Physiologically-based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well-suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters.