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. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl.

Landscape analysis for a neonatal disease progression model of bronchopulmonary dysplasia: Leveraging clinical trial experience and real-world data.

The 21st Century Cures Act requires FDA to expand its use of real-world evidence (RWE) to support approval of previously approved drugs for new disease indications and post-marketing study requirements. To address this need in neonates, the FDA and the Critical Path Institute (C-Path) established the International Neonatal Consortium (INC) to advance regulatory science and expedite neonatal drug development. FDA recently provided funding for INC to generate RWE to support regulatory decision making in neonatal drug development. One study is focused on developing a validated definition of bronchopulmonary dysplasia (BPD) in neonates. BPD is difficult to diagnose with diverse disease trajectories and few viable treatment options. Despite intense research efforts, limited understanding of the underlying disease pathobiology and disease projection continues in the context of a computable phenotype. It will be important to determine if: 1) a large, multisource aggregation of real-world data (RWD) will allow identification of validated risk factors and surrogate endpoints for BPD, and 2) the inclusion of these simulations will identify risk factors and surrogate endpoints for studies to prevent or treat BPD and its related long-term complications. The overall goal is to develop qualified, fit-for-purpose disease progression models which facilitate credible trial simulations while quantitatively capturing mechanistic relationships relevant for disease progression and the development of future treatments. The extent to which neonatal RWD can inform these models is unknown and its appropriateness cannot be guaranteed. A component of this approach is the critical evaluation of the various RWD sources for context-of use (COU)-driven models. The present manuscript defines a landscape of the data including targeted literature searches and solicitation of neonatal RWD sources from international stakeholders; analysis plans to develop a family of models of BPD in neonates, leveraging previous clinical trial experience and real-world patient data is also described.

Dosing regimen optimisation for oseltamivir in immunocompromised paediatric patients with influenza: Extrapolation of efficacy.

AIMS: To optimise the dosing regimen of oseltamivir for immunocompromised (IC) paediatric patients (<18 years) with influenza, we used an extrapolation approach alongside clinical data. METHODS: Efficacy was extrapolated from adult IC patients to paediatric IC patients by leveraging existing efficacy, safety, pharmacokinetic (PK)/pharmacodynamic (PD), and disease-progression models of oseltamivir and oseltamivir carboxylate (OC). Data of IC paediatric patients from two studies (NV25719 and NV20234) were included in the population PK (n = 30), PK/PD analysis (n = 22) and disease modelling approach (n = 36). Simulations were performed to identify the optimal dosing regimen. RESULTS: Clearance of oseltamivir (CL) and OC (CLM ) were similar in IC and otherwise-healthy (OwH) patients <10 years, but decreased by 44.4% (95% CI: 26.8-62.0) and 49.1% (95% CI: 34.5-63.8), respectively, in IC patients aged 10-17 years versus OwH patients. There were no notable exposure-response relationships for any of the virologic PD analyses. Thus, no additional benefit was seen with oseltamivir carboxylate exposures higher than achieved with the conventional dose (75 mg twice daily, age- and weight-adjusted for children <13 years). The disease model illustrated that doses above the conventional oseltamivir dose had limited impact on viral kinetics in IC paediatric patients and a prolonged treatment duration of 10 days was favoured to limit potential viral rebound. CONCLUSION: An oseltamivir dosage recommendation (conventional dose, twice daily for 10 days) was established in IC paediatric patients with influenza, based on extrapolation of efficacy from IC adults, leveraging population PK, PK/PD, and disease modelling, whilst taking resistance and safety data into account.

Positive Charge of "Sticky" Peptides and Proteins Impedes Release From Negatively Charged PLGA Matrices.

The influence of electrostatic interactions and/or acylation on release of charged ("sticky") agents from biodegradable polymer matrices was systematically characterized. We hypothesized that release of peptides with positive charge would be hindered from negatively charged poly(lactic-co-glycolic acid) (PLGA) microparticles. Thus, we investigated release of peptides with different degrees of positive charge from several PLGA microparticle formulations, with different molecular weights and/or end groups (acid- or ester-terminated). Indeed, release studies revealed distinct inverse correlations between the amount of positive charge on peptides and their release rates from each PLGA microparticle formulation. Furthermore, we examined the case of peptides with net charge that changes from negative to positive within the pH range observed in degrading microparticles. These charge changing peptides displayed counterintuitive release kinetics, initially releasing faster from slower degrading (less acidic) microparticles, and releasing slower from the faster degrading (more acidic) microparticles. Importantly, trends between agent charge and release rates for model peptides also translated to larger, therapeutically relevant proteins and oligonucleotides. The results of these studies may improve future design of controlled release systems for numerous therapeutic biomolecules exhibiting positive charge, ultimately reducing time-consuming and costly trial and error iterations of such formulations.

A systems approach to modeling drug release from polymer microspheres to accelerate in vitro to in vivo translation.

Mathematical models of controlled release that span the in vitro to in vivo transition are needed to speed the development and translation of clinically-relevant controlled release drug delivery systems. Fully mechanistic approaches are often challenged due to the use of highly-parameterized mathematically complex structures to capture the release mechanism. The simultaneous scarcity of in vivo data to inform these models and parameters leads to a situation where overfitting to capture observed phenomena is common. A data-driven approach to model development for controlled drug release from polymeric microspheres is taken herein, where physiological mechanisms impacting controlled release are incorporated to capture observed changes between in vitro release profiles and in vivo device dynamics. The model is generalizable, using non-specific binding to capture drug-polymer interactions via charge and molecular structure, and it has the ability to describe both inhibited (slowed) and accelerated release resulting from electrostatic or steric interactions. Reactive oxygen species (ROS)-induced degradation of biodegradable polymers was incorporated via a reaction-diffusion formalism, and this suggests that ROS may be the primary effector of the oft-observed accelerated in vivo release of polymeric drug delivery systems. Model performance is assessed through comparisons between model predictions and controlled release of several drugs from various-sized microparticles in vitro and in vivo.