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Front Pharmacol. 2019 Apr 25;10:448. doi: 10.3389/fphar.2019.00448. eCollection 2019.

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background.

Author information

1
Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States.
2
Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.
3
Discovery Sciences, IMed Biotech Unit, AstraZeneca R&D, Cambridge, United Kingdom.
4
Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul, Turkey.
5
Donnelly Centre, University of Toronto, Toronto, ON, Canada.
6
Department of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON, Canada.
7
Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Canadian Institute for Advanced Research, Toronto, ON, Canada.
8
Axcella Health, Cambridge, MA, United States.

Abstract

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays. Here, we used a single large-scale dataset of 376 drugs with relative efficacy on an engineered yeast strain with all ABC-transporter genes deleted (ABC-16), to explore the relationship between a drug's chemical structure and ABC-transporter substrate-likeness. We represented a drug's chemical structure by an array of substructure keys and explored several machine learning methods to predict the drug's efficacy in an ABC-16 yeast strain. Gradient-Boosted Random Forest models outperformed all other methods with an AUC of 0.723. We prospectively validated the model using new experimental data and found significant agreement with predictions. Our analysis expands the previously reported chemical substructures associated with ABC-transporter substrates and provides an alternative means to investigate ABC-transporter substrate-likeness.

KEYWORDS:

ABC transport protein; cheminformatics; drug structure; machine learning; membrane transport

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