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Physiologically Based Biopharmaceutics Model (PBBM) of Minimally Absorbed Locally Acting Drugs in the Gastrointestinal Tract-Case Study: Tenapanor.

Konstantinos StamatopoulosNena MistryNikoletta FotakiDavid B TurnerBrandon Swift
Published in: Pharmaceutics (2023)
A physiologically based biopharmaceutics model (PBBM) was developed to predict stool and urine sodium content in response to tenapanor administration in healthy subjects. Tenapanor is a minimally absorbed small molecule that inhibits the sodium/hydrogen isoform 3 exchanger (NHE3). It is used to treat irritable bowel syndrome with constipation (IBS-C). Its mode of action in the gastrointestinal tract reduces the uptake of sodium, resulting in an increase in water secretion in the intestinal lumen and accelerating intestinal transit time. The strategy employed was to perform drug-drug interaction (DDI) modelling between sodium and tenapanor, with sodium as the "victim" administered as part of daily food intake and tenapanor as the "perpetrator" altering sodium absorption. Food effect was modelled, including meal-induced NHE3 activity using sodium as an inducer by normalising the induction kinetics of butyrate to sodium equivalents. The presented model successfully predicted both urine and stool sodium content in response to tenapanor dosed in healthy subjects (within 1.25-fold error) and provided insight into the clinical observations of tenapanor dosing time relative to meal ingestion. The PBBM model was applied retrospectively to assess the impact of different forms of tenapanor (free base vs. HCl salt) on its pharmacodynamic (PD) effect. The developed modelling strategy can be effectively adopted to increase confidence in using PBBM models for the prediction of the in vivo behaviour of minimally absorbed, locally acting drugs in the gastrointestinal tract, when other approaches (e.g., biomarkers or PD data) are not available.
Keyphrases
  • irritable bowel syndrome
  • small molecule
  • emergency department
  • physical activity
  • endothelial cells
  • electronic health record
  • big data
  • risk assessment
  • adverse drug