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Use of Physiologically Based Pharmacokinetic Modeling for Predicting Drug–Food Interactions: Recommendations for Improving Predictive Performance of Low Confidence Food Effect Models

  • Research Article
  • Theme: Use of PBPK Modeling to Inform Clinical Decisions: Current Status of Prediction of Drug-Food Interactions
  • Published:
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Abstract

Food can alter drug absorption and impact safety and efficacy. Besides conducting clinical studies, in vitro approaches such as biorelevant solubility and dissolution testing and in vivo dog studies are typical approaches to estimate a drug’s food effect. The use of physiologically based pharmacokinetic models has gained importance and is nowadays a standard tool for food effect predictions at preclinical and clinical stages in the pharmaceutical industry. This manuscript is part of a broader publication from the IQ Consortium’s food effect physiologically based pharmacokinetic model (PBPK) modeling working group and complements previous publications by focusing on cases where the food effect was predicted with low confidence. Pazopanib-HCl, trospium-Cl, and ziprasidone-HCl served as model compounds to provide insights into why several food effect predictions failed in the first instance. Furthermore, the manuscript depicts approaches whereby PBPK-based food effect predictions may be improved. These improvements should focus on the PBPK model functionality, especially better reflecting fasted- and fed-state gastric solubility, gastric re-acidification, and complex mechanisms related to gastric emptying of drugs. For improvement of in vitro methodologies, the focus should be on the development of more predictive solubility, supersaturation, and precipitation assays. With regards to the general PBPK modeling methodology, modelers should account for the full solubility profile when modeling ionizable compounds, including common ion effects, and apply a straightforward strategy to account for drug precipitation.

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Acknowledgements

The authors would like to thank Julian Semeraro, Sabrina Wacker, and Melanie Schneider (Merck KGaA) as well as Amy Dai and Varsha Dhamankar (Vertex) for conducting the in vitro experiments for the model compounds used in this study.

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Authors and Affiliations

  1. Pharmaceutical Technologies, Chemical and Pharmaceutical Development, Merck KGaA, Frankfurter Str. 250, 64293, Darmstadt, Germany

    Christian Wagner

  2. Pharmaceutical Sciences, Merck & Co., Inc., Kenilworth, New Jersey, USA

    Filippos Kesisoglou

  3. New Modalities and Parenteral Development, Pharmaceutical Technology & Development, Operations, AstraZeneca, Macclesfield, UK

    Xavier J. H. Pepin

  4. Pharmaceutical Sciences, Roche Pharmaceutical Research and Early Development, Roche Innovation Center, Basel, Switzerland

    Neil Parrott

  5. DMPK and Translational Modeling, AbbVie Inc., North Chicago, Illinois, USA

    Arian Emami Riedmaier

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Wagner, C., Kesisoglou, F., Pepin, X.J.H. et al. Use of Physiologically Based Pharmacokinetic Modeling for Predicting Drug–Food Interactions: Recommendations for Improving Predictive Performance of Low Confidence Food Effect Models. AAPS J 23, 85 (2021). https://doi.org/10.1208/s12248-021-00601-0

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