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Mechanistic modeling of in vitro data generated from metabolic enzyme systems (viz., liver microsomes, hepatocytes, rCYP enzymes, etc.) facilitates in vitro–in vivo extrapolation (IVIV_E) of metabolic clearance which plays a key role in the successful prediction of clearance in vivo within physiologically-based pharmacokinetic (PBPK) modeling. A similar concept can be applied to solubility and dissolution experiments whereby mechanistic modeling can be used to estimate intrinsic parameters required for mechanistic oral absorption simulation in vivo. However, this approach has not widely been applied within an integrated workflow. We present a stepwise modeling approach where relevant biopharmaceutics parameters for ketoconazole (KTZ) are determined and/or confirmed from the modeling of in vitro experiments before being directly used within a PBPK model. Modeling was applied to various in vitro experiments, namely: (a) aqueous solubility profiles to determine intrinsic solubility, salt limiting solubility factors and to verify pKa; (b) biorelevant solubility measurements to estimate bile-micelle partition coefficients; (c) fasted state simulated gastric fluid (FaSSGF) dissolution for formulation disintegration profiling; and (d) transfer experiments to estimate supersaturation and precipitation parameters. These parameters were then used within a PBPK model to predict the dissolved and total (i.e., including the precipitated fraction) concentrations of KTZ in the duodenum of a virtual population and compared against observed clinical data. The developed model well characterized the intraluminal dissolution, supersaturation, and precipitation behavior of KTZ. The mean simulated AUC0–t of the total and dissolved concentrations of KTZ were comparable to (within 2-fold of) the corresponding observed profile. Moreover, the developed PBPK model of KTZ successfully described the impact of supersaturation and precipitation on the systemic plasma concentration profiles of KTZ for 200, 300, and 400 mg doses. These results demonstrate that IVIV_E applied to biopharmaceutical experiments can be used to understand and build confidence in the quality of the input parameters and mechanistic models used for mechanistic oral absorption simulations in vivo, thereby improving the prediction performance of PBPK models. Moreover, this approach can inform the selection and design of in vitro experiments, potentially eliminating redundant experiments and thus helping to reduce the cost and time of drug product development.
Introduction: When developing bio-enabling formulations, innovative tools are required to understand and predict in vivo performance and may facilitate approval by regulatory authorities. EMEND® is an example of such a formulation, in which the active pharmaceutical ingredient, aprepitant, is nano-sized. The aims of this study were 1) to characterize the 80 mg and 125 mg EMEND® capsules in vitro using biorelevant tools, 2) to develop and parameterize a physiologically based pharmacokinetic (PBPK) model to simulate and better understand the in vivo performance of EMEND® capsules and 3) to assess which parameters primarily influence the in vivo performance of this formulation across the therapeutic dose range.
Methods: Solubility, dissolution and transfer experiments were performed in various biorelevant media simulating the fasted and fed state environment in the gastrointestinal tract. An in silico PBPK model for healthy volunteers was developed in the Simcyp Simulator, informed by the in vitro results and data available from the literature.
Results: In vitro experiments indicated a large effect of native surfactants on the solubility of aprepitant. Coupling the in vitro results with the PBPK model led to an appropriate simulation of aprepitant plasma concentrations after administration of 80 mg and 125 mg EMEND® capsules in both the fasted and fed states. Parameter Sensitivity Analysis (PSA) was conducted to investigate the effect of several parameters on the in vivo performance of EMEND®. While nano-sizing aprepitant improves its in vivo performance, intestinal solubility remains a barrier to its bioavailability and thus aprepitant should be classified as DCS IIb.
Conclusions: The present study underlines the importance of combining in vitro and in silico biopharmaceutical tools to understand and predict the absorption of this poorly soluble compound from an enabling formulation. The approach can be applied to other poorly soluble compounds to support rational formulation design and to facilitate regulatory assessment of the bio-performance of enabling formulations.