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Virtual Screening for the Discovery of Microbiome β-Glucuronidase Inhibitors to Alleviate Cancer Drug Toxicity.
- Source :
-
Journal of chemical information and modeling [J Chem Inf Model] 2022 Apr 11; Vol. 62 (7), pp. 1783-1793. Date of Electronic Publication: 2022 Mar 31. - Publication Year :
- 2022
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Abstract
- Despite the potency of most first-line anti-cancer drugs, nonadherence to these drug regimens remains high and is attributable to the prevalence of "off-target" drug effects that result in serious adverse events (SAEs) like hair loss, nausea, vomiting, and diarrhea. Some anti-cancer drugs are converted by liver uridine 5'-diphospho-glucuronosyltransferases through homeostatic host metabolism to form drug-glucuronide conjugates. These sugar-conjugated metabolites are generally inactive and can be safely excreted via the biliary system into the gastrointestinal tract. However, β-glucuronidase (βGUS) enzymes expressed by commensal gut bacteria can remove the glucuronic acid moiety, producing the reactivated drug and triggering dose-limiting side effects. Small-molecule βGUS inhibitors may reduce this drug-induced gut toxicity, allowing patients to complete their full course of treatment. Herein, we report the discovery of novel chemical series of βGUS inhibitors by structure-based virtual high-throughput screening (vHTS). We developed homology models for βGUS and applied them to large-scale vHTS against nearly 400,000 compounds within the chemical libraries of the National Center for Advancing Translational Sciences at the National Institutes of Health. From the vHTS results, we cherry-picked 291 compounds via a multifactor prioritization procedure, providing 69 diverse compounds that exhibited positive inhibitory activity in a follow-up βGUS biochemical assay in vitro . Our findings correspond to a hit rate of 24% and could inform the successful downstream development of a therapeutic adjunct that targets the human microbiome to prevent SAEs associated with first-line, standard-of-care anti-cancer drugs.
Details
- Language :
- English
- ISSN :
- 1549-960X
- Volume :
- 62
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Journal of chemical information and modeling
- Publication Type :
- Academic Journal
- Accession number :
- 35357819
- Full Text :
- https://doi.org/10.1021/acs.jcim.1c01414