Bottom-Up Physiologically Based Oral Absorption Modeling of Free Weak Base Drugs

Abstract

In this study, we systematically evaluated “bottom-up” physiologically based oral absorption modeling, focusing on free weak base drugs. The gastrointestinal unified theoretical framework (the GUT framework) was employed as a simple and transparent model. The oral absorption of poorly soluble free weak base drugs is affected by gastric pH. Alternation of bulk and solid surface pH by dissolving drug substances was considered in the model. Simple physicochemical properties such as pKa, the intrinsic solubility, and the bile micelle partition coefficient were used as input parameters. The fraction of a dose absorbed (Fa) in vivo was obtained by reanalyzing the pharmacokinetic data in the literature (15 drugs, a total of 85 Fa data). The AUC ratio with/without a gastric acid-reducing agent (AUCr) was collected from the literature (22 data). When gastric dissolution was neglected, Fa was underestimated (absolute average fold error (AAFE) = 1.85, average fold error (AFE) = 0.64). By considering gastric dissolution, predictability was improved (AAFE = 1.40, AFE = 1.04). AUCr was also appropriately predicted (AAFE = 1.54, AFE = 1.04). The Fa values of several drugs were slightly overestimated (less than 1.7-fold), probably due to neglecting particle growth in the small intestine. This modeling strategy will be of great importance for drug discovery and development.

Lost in modelling and simulation?

“…The above methods showed similar F a values when applied to the same drug in most cases [50,79,82,83], confirming the validity of these methods. More than 600 clinical F a data has been compiled from the literature and has been used to evaluate OA PBPK models [5,50,73,79,83,86,89,90]. For low F a drugs the inter-subject variability is usually very high, and a clinical study may be an unrepresentative sample of the population.…”

“…However, there is no exact method to measure in vivo F a . Therefore, one or a few approximations have been used to estimate F a from in vivo PK data [50,70,79,[82][83][84]. The mass-balance data would be the most reliable data to estimate F a .…”

“…For the use in the early drug discovery stages, simple models can show sufficient prediction performance [20,64,82,132,133]. A simple OA PBPK model has shown good prediction performance for the fraction of a dose absorbed (F a ) [5,50,79,83,134] and plasma concentration (C p ) -time profiles [135][136][137][138][139][140][141][142][143]. For relative bioavailability (F rel ) prediction, a simple OA-PBPK model is also available for the food effect (via bile micelles) and gastric acid effect predictions [51,79,100].…”

An In Vivo Predictive Dissolution Methodology (iPD Methodology) with a BCS Class IIb Drug Can Predict the In Vivo Bioequivalence Results: Etoricoxib Products

“…[ 6 ]), dissolution was not complete in the stomach and (undissolved) drug particles were transferred to the duodenal chambers where, a maximum concentration around 0.35 mg/mL was observed, which is far from its thermodynamic solubility. This incomplete dissolution in the stomach can be explained as the dissolution rate becomes relatively slow because the acidic content is neutralized by the dissolving free bases at the solid surface [ 22 ], i.e., the surface pH of drug particles becomes higher than the bulk pH, dictating a lower solubility value at the solid surface and thus slowing down dissolution rate. The difference among the three oral tablets becomes evident already in gastric chamber.…”

A Novel Approach to Justify Dissolution Differences in an Extended Release Drug Product using Physiologically Based Biopharmaceutics Modeling and Simulation

“…For model validation, ±10% prediction error (PE) is considered suitable when data are available from controlled, cross-over clinical studies. However, when including independent clinical dataset for model verification, the acceptance criteria should be relaxed 49 .…”