Effects of medicines used to treat gastrointestinal diseases on the pharmacokinetics of coadministered drugs:A PEARRL Review

Background: Drugs used to treat gastrointestinal diseases (GI drugs) are widely used either as prescription or over23 the-counter (OTC) medications and belong to both the ten most prescribed and ten most sold OTC medications worldwide. Current clinical practice shows that in many cases, these drugs are administered concomitantly with other drug products. Due to their metabolic properties and mechanisms of action, the drugs used to treat gastrointestinal diseases can change the pharmacokinetics of some co27 administered drugs. In certain cases, these interactions can lead to failure of treatment or to the occurrence of serious adverse events. The mechanism of interaction depends highly on drug properties and differs among therapeutic categories. Understanding these interactions is essential to providing recommendations for optimal drug therapy. Objective: To discuss the most frequent interactions between GI and other drugs, including identification of the mechanisms behind these interactions, where possible. Conclusion: Interactions with GI drugs are numerous and can be highly significant clinically. Whilst alterations in bioavailability due to changes in solubility, dissolution rate and metabolic interactions can be (for the most part) easily identified, interactions that are mediated through other mechanisms, such as permeability or microbiota, are less well understood. Future work should focus on characterizing these aspects

Model-based analysis of biopharmaceutic experiments to improve mechanistic oral absorption modeling : an integrated in vitro in vivo extrapolation perspective using Ketoconazole as a model drug

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