Computer simulations for bioequivalence trials: Selection of analyte in BCS class II and IV drugs with first-pass metabolism, two metabolic pathways and intestinal efflux transporter

Alternative Pharmacokinetic Metrics in Single-Dose Studies to Ensure Bioequivalence of Prolonged-Release Products at Steady State-A Case Study

(1) Background: this article investigates which PK metrics in a single-dose study (concentration at the end of posology interval, C_τ, partial areas under the curve, pAUCs, or half-value duration, HVD) are more sensitive and less variable for predicting the failure of a prolonged-release product at steady-state that was the bioequivalent for C_max, AUC_0-t and AUC_0-inf, in the single-dose study; (2) Methods: a cross-over study was performed in 36 subjects receiving desvenlafaxine 100 mg prolonged-release tablets. Conventional (C_max, AUC_0-t and AUC_0-inf) and additional (C_τ, pAUCs and HVD) PK metrics were considered after single-dose conditions. Predicted PK metrics at steady state (AUC_0-τ, C_max,ss, and C_τ,ss) were derived using a population PK model approach; (3) Results: the existing differences in the shape of the concentration-time curves precluded to show equivalence for C_τ,ss in the simulated study at steady state. This failure to show equivalence at steady state was predicted by C_τ, pAUCs and HVD in the single-dose study. C_τ was the most sensitive metric for detecting the different shape, with a lower intra-subject variability than HVD; (4) Conclusions: conventional PK metrics for single-dose studies (C_max, AUC_0-t and AUC_0-inf) are not enough to guarantee bioequivalence at steady state for prolonged-release products.

A multilevel object-oriented modelling methodology for physiologically-based pharmacokinetics (PBPK): Evaluation with a semi-mechanistic pharmacokinetic model

BACKGROUND AND OBJECTIVE

The aims of this study are (i) to assess the predictive reliability of the physiologically based software PhysPK versus the well-known population approach software NONMEM for the cited semi-mechanistic PK model, (ii) to determine whether these modelling approaches are interchangeable and (iii) to compare acausal with causal modelling approaches in the framework of semi-mechanistic PK models.

METHODS

A semi-mechanistic model was proposed, which assumed oral administration of a solid dosage form with a peripheral compartment and two active metabolites. The model incorporates intestinal transit, dissolution limited by solubility, variable efflux transporter expression along the gut and linear and non-linear metabolism in the gut and liver. Four different approximations to the theoretical model were developed in order to validate both the new software and modelling methodology.

RESULTS

Plasmatic concentrations correlation plots as well as relative errors in AUC_0-48 and C_max predictions revealed the accuracy of PhysPK in the prediction of these exposition parameters. Physiological and acausal object oriented version systematically under-estimated AUC_0-48 and C_max of the parent drug, whereas metabolites were over-estimated when taking the semi-mechanistic and extraction-based metabolism version as the reference.

CONCLUSIONS

PhysPK has been properly validated, where differences are related to numerical precision of integrators and solvers. A systematic bias for the parent drug and active metabolites was predicted when a semi-mechanistic approach including extraction-based metabolism was compared to the physiologic and acausal approach, showing that interchangeability might be possible when intrinsic-clearance metabolism is implemented in the semi-mechanistic approach. The acausal and object-oriented methodology allows for defining the semi-mechanistic model through its local mechanisms and relationships among entities, without the need to build the final set of Ordinary Differential Equations.

In Vitro Dissolution and in Silico Modeling Shortcuts in Bioequivalence Testing

Purpose: To review in vitro testing and simulation platforms that are in current use to predict in vivo performances of generic products as well as other situations to provide evidence for biowaiver and support drug formulations development. Methods: Pubmed and Google Scholar databases were used to review published literature over the past 10 years. The terms used were “simulation AND bioequivalence” and “modeling AND bioequivalence” in the title field of databases, followed by screening, and then reviewing. Results: A total of 22 research papers were reviewed. Computer simulation using software such as GastroPlus™, PK-Sim^® and SimCyp^® find applications in drug modeling. Considering the wide use of optimization for in silico predictions to fit observed data, a careful review of publications is required to validate the reliability of these platforms. For immediate release (IR) drug products belonging to the Biopharmaceutics Classification System (BCS) classes I and III, difference factor (ƒ₁) and similarity factor (ƒ₂) are calculated from the in vitro dissolution data of drug formulations to support biowaiver; however, this method can be more discriminatory and may not be useful for all dissolution profiles. Conclusions: Computer simulation platforms need to improve their mechanistic physiologically based pharmacokinetic (PBPK) modeling, and if prospectively validated within a small percentage of error from the observed clinical data, they can be valuable tools in bioequivalence (BE) testing and formulation development.