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Updated: Apr 9, 2026

An In Vitro Batch-culture Model to Estimate the Effects of Interventional Regimens on Human Fecal Microbiota
Published on: July 31, 2019
Translational Modeling of Gut Microbiome-Mediated Drug Metabolism: A Case Example of Sulfasalazine
Lisa Cheng1,2, Jingwei Cai1, Alexis Auster1
1Drug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.
Abstract:
The gut microbiome can contribute to drug metabolism and significantly influence pharmacokinetic (PK) behavior. Sulfasalazine is well-known to be metabolized by gut bacterial azoreductases into sulfapyridine and mesalamine. Despite in vitro and in vivo evidence of the gut microbiome's role in drug metabolism, quantitative predictions of its impact on drug PK are lacking. To address this gap, we used sulfasalazine and its metabolites as a case example to build a translational modeling framework to predict the extent of gut microbiome-mediated drug metabolism and subsequent PK of the metabolites. First, sulfasalazine conversion kinetics was measured in vitro using pooled human fecal homogenate incubation. In vitro Vmax was 650.5 and 200.9 pmol/min/mg feces, and Km was 3648 and 1605 μM for sulfapyridine and mesalamine formation, respectively. Based on colon and feces bacterial counts from nine healthy humans, a ratio of 0.47 was used to scale in vitro fecal Vmax to the colon level. Second, physiologically-based pharmacokinetic (PBPK) models for sulfasalazine, sulfapyridine, and mesalamine were built in Simcyp and verified to predict their oral PK when dosed directly. Lastly, sulfapyridine or mesalamine PK after dosing sulfasalazine was predicted by linking the parent and metabolite PBPK models with colon luminal metabolism kinetics. The observed sulfapyridine and mesalamine PK after dosing sulfasalazine were predicted with weighted average fold-errors of 1.21, 1.22, and 1.05 for Cmax, Tmax, and AUC, respectively. Overall, this in vitro to in vivo translation and modeling framework provides valuable insights for quantitatively predicting the in vivo impact of gut microbiome-mediated drug metabolism.
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