Mechanistic Modelling of Gastric pH-Dependent Drug-Drug Interactions

Co-administration of acid reducing agents (ARAs) with drugs exhibiting pH-dependent solubility can lead to drug-drug interactions mediated by gastric pH changes (pH-dependent DDIs). Physiologically based pharmacokinetic (PBPK) modelling is recognized by the U.S. FDA as an alternative approach to clinical trials for the evaluation of pH-dependent DDIs1. To date, such DDIs have predominantly been assessed using a static approach where pH of the stomach compartment is adjusted to match the worst-case scenario.

We developed a mechanistic modelling approach encompassing a dynamic gastric acid secretion model. In our approach, specific PBPK-pharmacodynamic (PD) gastric pH effect models for proton pump inhibitors and H2-receptor antagonists were developed and verified. The new ‘Gastric pH Interaction’ functionality (Simcyp Simulator, Version 23) enables dynamic modelling of gastric pH changes upon ARA, food, and fluid administration.  Three performance verification cases of our mechanistic model (ranitidine-midazolam, ranitidine-ketoconazole, omeprazole-ibrutinib DDIs2-4) demonstrate the utility of PBPK modelling in separating the gastric pH-mediated and drug metabolizing enzyme and transporter (DMET)-mediated components of a DDI, as well as improved qualitative and quantitative DDI predictions. Dynamic modelling of gastric pH changes may be particularly useful in assessing mitigation strategies such as dose staggering/alternative formulations.

Figure 1. Comparison of mechanistic vs. worst case approach of modelling the impact of multiple dose 40 mg omeprazole on gastric pH. Observed data (circles) are from Prichard et al. (1985)5. Mechanistic modelling of the impact of ARA, food, and fluid administration on gastric pH was achieved using the ‘Gastric pH Interaction’ functionality coupled with the ‘Semi-mechanistic H+ Model for Gastric pH’ (Simcyp Simulator, Version 23) while worst case modelling was achieved by setting the pH of the gastric compartment to 66. The mechanistic approach achieved realistic prediction of gastric pH and is expected to improve quantitative DDI prediction and be particularly applicable to the assessment of mitigation strategies.

References:

  1. U.S. Food & Drug Administration. (2023). Evaluation of Gastric pH-Dependent Drug Interactions With Acid-Reducing Agents: Study Design, Data Analysis, and Clinical Implications Guidance for Industry [pdf]. Retrieved from https://www.fda.gov/
  2. Elwood, R J et al. “Ranitidine influences the uptake of oral midazolam.” 1. British journal of clinical pharmacology vol. 15,6 (1983): 743-5.
  3. Piscitelli, S C et al. “Effects of ranitidine and sucralfate on ketoconazole bioavailability.” 1. Antimicrobial agents and chemotherapy vol. 35,9 (1991): 1765-71.
  4. de Jong, Jan et al. “The pH-altering agent omeprazole affects rate but not the extent of ibrutinib exposure.” 1. Cancer chemotherapy and pharmacology vol. 82,2 (2018): 299-308.
  5. Prichard, P J et al. “Omeprazole: a study of its inhibition of gastric pH and oral pharmacokinetics after morning or evening dosage.” Gastroenterologyvol. 88,1 Pt 1 (1985): 64-9.
  6. Dong, Zhongqi et al. “Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs.” 1. CPT: pharmacometrics & systems pharmacology vol. 9,8 (2020): 456-465.

YanY

  • Certara UK
Yan Yeap, PhD is a Senior Research Scientist within the oral absorption group of Certara UK Limited (Simcyp Division). Prior to joining Certara UK she had close to a decade of bench-side biopharmaceutics research experience focusing on oral drug absorption upon co-administration with dietary lipids. She has a BPharm (Hons) and PhD in Pharmaceutics, both from Monash University, Australia. Her postdoctoral training was in food effect mechanistic modeling at Northeastern University, Boston, USA. Since joining Certara UK in 2020 she has actively contributed to the expansion of the Simcyp Simulator including compound files development, mechanistic oral absorption modelling, and more recently, the implementation of a new pH-dependent drug-drug interactions functionality.