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Usefulness of Optimized Human Fecal Material in Simulating the Bacterial Degradation of Sulindac and Sulfinpyrazone in the Lower Intestine.

Christina KostantiniSumit AroraErik SöderlindJens CeulemansChristos ReppasMaria Vertzoni
Published in: Molecular pharmaceutics (2022)
The first aim of this study was to evaluate the usefulness of optimized human fecal material in simulating sulforeductase activity in the lower intestine by assessing bacterial degradation of sulindac and sulfinpyrazone, two sulforeductase substrates. The second aim was to evaluate the usefulness of drug degradation half-life generated in simulated colonic bacteria (SCoB) in informing PBPK models. Degradation experiments of sulfinpyrazone and of sulindac in SCoB were performed under anaerobic conditions using recently described methods. For sulfinpyrazone, the abundance of clinical data allowed for construction of a physiologically based pharmacokinetic (PBPK) model and evaluation of luminal degradation clearance determined from SCoB data. For sulindac, the availability of sulindac sulfide and sulindac sulfone standards allowed for evaluating the formation of the main metabolite, sulindac sulfide, during the experiments in SCoB. Both model compounds degraded substantially in SCoB. The PBPK model was able to adequately capture exposure of sulfinpyrazone and its sulfide metabolite in healthy subjects, in ileostomy and/or colectomy subjects, and in healthy subjects pretreated with metoclopramide by implementing degradation half-lives in SCoB to calculate intrinsic colon clearance. Degradation rates of sulindac and formation rates of sulindac sulfide in SCoB were almost identical, in line with in vivo data suggesting the sulindac sulfide is the primary metabolite in the lower intestine. Experiments in SCoB were useful in simulating sulforeductase related bacterial degradation activity in the lower intestine. Degradation half-life calculated from experiments in SCoB is proven useful for informing a predictive PBPK model for sulfinpyrazone.
Keyphrases
  • endothelial cells
  • electronic health record
  • big data
  • emergency department
  • risk assessment
  • machine learning
  • heavy metals
  • data analysis
  • adverse drug