QSP modeling of loncastuximab tesirine with T-cell-dependent bispecific antibodies guides dose-regimen strategy.

Antibody-drug conjugates (ADCs) and T-cell-dependent bispecific antibodies (TDBs) show single-agent efficacy in relapsed/refractory (R/R) lymphomas. While coadministering therapeutics with orthogonal mechanisms of action may safely enhance efficacy, testing every potential combination regimen is infeasible in the clinic. An integrated quantitative systems pharmacology model of a CD19-targeted ADC and CD3/CD20-targeted TDBs was developed to predict combination regimen efficacy in R/R diffuse large B-cell lymphoma (DLBCL).

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.

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

The 21 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.

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).

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).

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.