Abstract
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.
Highlights
Applications of physiologically based pharmacokinetic (PBPK)models are diverse, and they have been widely used in industry, academia, and regulatory agencies.[1−4] This interest is markedly reflected in the increased number of research publications on this topic[5] and their applications in regulatory submissions and recently approved drug labels,[6] regulatory guidance,[7,8] and concept papers.[9]
Metabolic rates can be determined from various enzyme systems and extrapolated to in vivo using in vitro−in vivo extrapolation (IVIV_E) techniques, which play an important role in the successful prediction of in vivo clearance.[23−26] Similar concepts can be applied to solubility and dissolution experiments in the form of mechanistic analysis of such assays to confirm and/or estimate intrinsic parameters required for mechanistic oral absorption simulations in vivo
The aqueous phase solubility parameters having been established, the bile-micelle partition coefficients were assessed using biorelevant solubility measured in fasted state simulated gastric fluid (FaSSGF)-V2, FaSSIF, and FeSSIF media
Summary
Applications of physiologically based pharmacokinetic (PBPK)models are diverse, and they have been widely used in industry, academia, and regulatory agencies.[1−4] This interest is markedly reflected in the increased number of research publications on this topic[5] and their applications in regulatory submissions and recently approved drug labels,[6] regulatory guidance,[7,8] and concept papers.[9]. Metabolic rates can be determined from various enzyme systems (viz., liver microsomes, hepatocytes, recombinantly expressed enzyme systems, etc.) and extrapolated to in vivo using in vitro−in vivo extrapolation (IVIV_E) techniques, which play an important role in the successful prediction of in vivo clearance.[23−26] Similar concepts can be applied to solubility and dissolution experiments in the form of mechanistic analysis of such assays to confirm and/or estimate intrinsic parameters required for mechanistic oral absorption simulations in vivo This approach has not widely been applied within an integrated workflow
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