Prediction of Negative Food‐Effect on Drug Bioavailability by Mechanistic Biopharmaceutics Classification System (mBCS)‐guided Physiologically‐based Pharmacokinetic modeling
Authors: Sharma Sheena , Prasad Bhagwat 
Published: April 2020

Food can influence drug bioavailability by altering gut physiology and microenvironment. Conventional approaches such as dissolution testing and biopharmaceutics classification system (BCS) often fail to predict food effect on drug bioavailability. To address this, we proposed a mechanistic BCS (mBCS) (Figure
1) based on analysing literature data. The mBCS first classifies drugs based on if their absorption is solubility‐limited (Class I) or permeability‐limited (Class II), which are sub‐grouped based on if the food effect is predictable (IA and IIA) or not predictable (IB, IC, IIB and IIC) using the dissolution testing (Figure
1A). Finally, mBCS allows identifying the mechanisms that lead to unpredictable food‐effect on drug bioavailability. Particularly, out of 288 drugs where the food‐effect data were reported, 105 drugs exhibited negative food effect. Further evaluation suggested that the primary reason for the unpredictable negative food effect is the interplay of intestinal efflux transport and delayed gastric emptying (Figure
1B). For example, amongst all drugs that showed negative food effect, majority (58%) were substrates of GI efflux transporters (e.g., maraviroc, afatinib, furosemide, ixazomib, asciminib, and omadacycline). We thus hypothesized that since gastric emptying time in fed state is higher (>2 h) as compared to the fasting state (15 min), drug co‐administration with food results in decreased concentration gradient across intestinal wall (lumen to blood), which eventually causes desaturation of GI efflux transporters. To test this hypothesis, we developed an mBCS‐guided physiologically based pharmacokinetic (PBPK) model in MATLAB (MathWorks, Natick, MA) by incorporating the regional protein abundance and saturation kinetics (Km and Vmax) of selected drugs (maraviroc, afatinib, and furosemide). The results from maraviroc PBPK modeling are presented below. The model was successful in predicting non‐linear PK within 2‐fold at varied dose ranges (e.g., 0.5, 0.61, 0.88, 1.17, 1.07 and 1.02 folds for 30, 100, 300, 600, 900 and 1200 mg, respectively for maraviroc) in the fasted state. The model successfully captured ~37% reduction in AUC in fed state at lower clinical doses of 100 and 300 mg of maraviroc. Similarly, consistent with the observed clinical data
1, the predicted magnitude of negative food effect was significantly lower (~22 and 18% reduction in AUC) at higher doses of 900 and 1200 mg, respectively. Parameter sensitivity analysis showed that gastric emptying time and kinetic parameters of P‐gp are important mechanisms that regulate non‐linear PK and food effect for maraviroc. Therefore, the proposed mBCS‐guided PBPK modeling approach can be used for prospective prediction of negative food effect, which can also be used to justify clinical trial waiver for the food effect studies.Support or Funding InformationDepartment of Pharmaceutical Sciences, Washington State University, SpokaneMechanistic biopharmaceutics classification system (mBCS) framework (A) and proposed mechanism of interplay between GI efflux and gastric emptying in fasting and fed states for mBCS class‐IIC drugs (B).Figure 1