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.

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.

A Quantitative Modeling and Simulation Framework to Support Candidate and Dose Selection of Anti-SARS-CoV-2 Monoclonal Antibodies to Advance Bamlanivimab Into a First-in-Human Clinical Trial.

Neutralizing monoclonal antibodies (mAb), novel therapeutics for the treatment of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), have been urgently researched from the start of the pandemic. The selection of the optimal mAb candidate and therapeutic dose were expedited using open-access in silico models. The maximally effective therapeutic mAb dose was determined through two approaches; both expanded on innovative, open-science initiatives.

Impact of Obesity on Brexpiprazole Pharmacokinetics: Proposal for Improved Initiation of Treatment.

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia or as adjunctive treatment for major depressive disorder. As obesity (body mass index ≥35 kg/m ) has the potential to affect drug pharmacokinetics and is a common comorbidity of both schizophrenia and major depressive disorder, it is important to understand changes in brexpiprazole disposition in this population. This study uses a whole-body physiologically based pharmacokinetic model to compare the pharmacokinetics of brexpiprazole in obese and normal-weight (body mass index 18-25 kg/m ) individuals known to be cytochrome P450 2D6 extensive metabolizers (EMs) and poor metabolizers (PMs).

Brexpiprazole Pharmacokinetics in CYP2D6 Poor Metabolizers: Using Physiologically Based Pharmacokinetic Modeling to Optimize Time to Effective Concentrations.

Brexpiprazole is an oral antipsychotic agent indicated for use in patients with schizophrenia, or as adjunctive treatment for major depressive disorder. As cytochrome P450 (CYP) 2D6 contributes significantly to brexpiprazole metabolism, there is a label-recommended 50% reduction in dose among patients with the CYP2D6 poor metabolizer phenotype. This study uses a whole-body physiologically based pharmacokinetic (PBPK) model to compare the pharmacokinetics of brexpiprazole in patients known to be extensive metabolizers (EMs) and poor metabolizers (PMs).

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.

Low-dose empagliflozin as adjunct-to-insulin therapy in type 1 diabetes: A valid modelling and simulation analysis to confirm efficacy.

Aim: To confirm the observed reduction in HbA1c for the 2.5 mg dose in EASE-3 by modelling and simulation analyses.

Materials And Methods: Independent of data from EASE-3 that tested 2.

Quantitative Systems Pharmacology and Physiologically-Based Pharmacokinetic Modeling With mrgsolve: A Hands-On Tutorial.

mrgsolve is an open-source R package available on the Comprehensive R Archive Network. It combines R and C++ coding for simulation from hierarchical, ordinary differential equation-based models. Its efficient simulation engine and integration into a parallelizable, R-based workflow makes mrgsolve a convenient tool both for simple and complex models and thus is ideal for physiologically-based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) model.

Single-molecule localization microscopy as nonlinear inverse problem.

We present a statistical framework to model the spatial distribution of molecules based on a single-molecule localization microscopy (SMLM) dataset. The latter consists of a collection of spatial coordinates and their associated uncertainties. We describe iterative parameter-estimation algorithms based on this framework, as well as a sampling algorithm to numerically evaluate the complete posterior distribution.

Optimal Drift Correction for Superresolution Localization Microscopy with Bayesian Inference.

Single-molecule-localization-based superresolution microscopy requires accurate sample drift correction to achieve good results. Common approaches for drift compensation include using fiducial markers and direct drift estimation by image correlation. The former increases the experimental complexity and the latter estimates drift at a reduced temporal resolution.