Evidence map›Paper›PMID 41948819›Full record

ArticleCPT: pharmacometrics & systems pharmacology2026

Translational Modeling of Gut Microbiome-Mediated Drug Metabolism: A Case Example of Sulfasalazine.

Lisa Cheng, Jingwei Cai, Alexis Auster, Sungjoon Cho, Mary Keir, Horace Rhee, Bennett Kapili, Dari Shalon, Miaoran Ning

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In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Lisa ChengDrug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.ORCID https://orcid.org/0009-0009-3376-7164
Jingwei CaiDrug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.
Alexis AusterDrug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.
Sungjoon ChoDrug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.
Mary KeirHuman Pathobiology and OMNI Reverse Translation, Genentech, Inc., South San Francisco, California, USA.
Horace RheeEarly Clinical Development, OMNI, Genentech, Inc., South San Francisco, California, USA.
Bennett KapiliEnvivo Bio, Inc., San Francisco, California, USA.
Dari ShalonEnvivo Bio, Inc., San Francisco, California, USA.
Miaoran NingDrug Metabolism and Pharmacokinetics, Genentech, Inc., South San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 V

Indexed as

Anti-Inflammatory Agents, Non-SteroidalGastrointestinal MicrobiomeModels, BiologicalSulfasalazineColonFecesHumansMesalamineSulfapyridineAnti-Inflammatory Agents, Non-SteroidalMesalamineSulfapyridineSulfasalazineabsorptiongut‐microbiomein vitro to in vivo extrapolationPBPKSimcypsulfasalazine

Identifiers

PMID41948819
PMCPMC13274740

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.