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 21^st 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 (CL_M ) 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.