Proof of Concept in Assignment of Within-Subject Variability During Virtual Bioequivalence Studies: Propagation of Intra-Subject Variation in Gastrointestinal Physiology Using Physiologically Based Pharmacokinetic Modeling

Abstract

While the concept of ‘Virtual Bioequivalence’ (VBE) using a combination of modelling, in vitro tests and integration of pre-existing data on systems and drugs is growing from its infancy, building confidence on VBE outcomes requires demonstration of its ability not only in predicting formulation-dependent systemic exposure but also the expected degree of population variability. The concept of variation influencing the outcome of BE, despite being hidden with the cross-over nature of common BE studies, becomes evident when dealing with the acceptance criteria that consider the 90% confidence interval (CI) around the relative bioavailability. Hence, clinical studies comparing a reference product against itself may fail due to within-subject variations associated with the two occasions that the individual receives the same formulation. In this proof-of-concept study, we offer strategies to capture the most realistic predictions of CI around the pharmacokinetic parameters by propagating physiological variations through physiologically based pharmacokinetic modelling. The exercise indicates feasibility of the approach based on comparisons made between the simulated and observed WSV of pharmacokinetic parameters tested for a clinical bioequivalence case study. However, it also indicates that capturing WSV of a large array of physiological parameters using backward translation modelling from repeated BE studies of reference products would require a diverse set of drugs and formulations. The current case study of delayed-release formulation of posaconazole was able to declare certain combinations of WSV of physiological parameters as ‘not plausible’. The eliminated sets of WSV values would be applicable to PBPK models of other drugs and formulations.

Delineating gene–environment effects using virtual twins of patients treated with clozapine

“…Participants carrying CYP1A2 alleles with unknown function (e.g., *1L or *1V ) were excluded. The concomitant inhibitors and inducers with potential to influence clozapine exposure were fluoxetine, citalopram, sertraline, clobazam, esomeprazole, pantoprazole, oral contraceptives, valproate, and cigarette smoking 1 . Ethical approval for the clinical study with clozapine was provided by the Melbourne Health Human Research Ethics Committee (MHREC ID 2012.069 and 2012.066) and complied with the Declaration of Helsinki and its subsequent revisions 21…”

“…An individual demographic, genetic, or environmental covariate may not influence pharmacokinetics (PKs) significantly when studied in isolation. But when modeled collectively using interaction terms, the combined effect of covariates may be significant 1,2 . Modeling approaches that quantify these interactions between variables across a target population may be used to optimize starting and continued dosing, an important consideration for model‐informed precision dosing (MIPD) 3 .…”

Concept and application of the probability of pharmacological success (PoPS) as a decision tool in drug development: a position paper

“…It is not necessary to assign BSV to all parameters; judgement should be used to ensure reasonable overall variability. For example, when using a highly mechanistic model with a large number of parameters, assigning experimentally observed BSVs to most or all parameters would inevitably inflate the biological variability [ 13 ]. It is also appropriate to include covariance between clearance and volume parameters (both are positively related to body size), and potentially between baseline and maximum PD effects E0 and Emax (depending on the nature of the measurement and model parameterisation).…”

Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series

“…Traditionally, WSV within a BE context has been accounted for with the addition of variability to PK parameters (C max , AUC), based on prior clinical studies, with the assumption that the WSV established for the reference formulation is applicable to the test formulation(s), an assumption which may be incorrect [ 43 ]. An alternative approach to post hoc addition of WSV to PK endpoints is to use PBPK modelling, whereby both BSV and WSV are applied to relevant physiological parameters (gastric emptying, pH, bile salt conc, etc.)…”

“…While there is some information in the literature on BSV, this is limited and a major gap in the effective application of VBE tools is information on WSV. Bego et al (2021) [ 43 ] provide a rigorous discussion of the issues related to VBE and suggest an interesting model-based approach to estimating WSV of physiological values from multiple clinical datasets.…”