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

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.

[1]  Mazhar Adli,et al.  The CRISPR tool kit for genome editing and beyond , 2018, Nature Communications.

[2]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[3]  W. Guggino,et al.  New insights into cystic fibrosis: molecular switches that regulate CFTR , 2006 .

[4]  Gang Fu,et al.  PubChem Substance and Compound databases , 2015, Nucleic Acids Res..

[5]  Chung-Pu Wu,et al.  The pharmacological impact of ATP-binding cassette drug transporters on vemurafenib-based therapy , 2014, Acta pharmaceutica Sinica. B.

[6]  Andreas Bender,et al.  P-glycoprotein Substrate Models Using Support Vector Machines Based on a Comprehensive Data set , 2011, J. Chem. Inf. Model..

[7]  Youyong Li,et al.  ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning Approaches. , 2017, Molecular pharmaceutics.

[8]  Michael I. Jordan,et al.  Chemogenomic profiling: identifying the functional interactions of small molecules in yeast. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[9]  Andreas Bender,et al.  Simultaneous Prediction of four ATP‐binding Cassette Transporters’ Substrates Using Multi‐label QSAR , 2016, Molecular informatics.

[10]  Lan V. Zhang,et al.  Knocking out multi-gene redundancies via cycles of sexual assortment and fluorescence selection , 2010, Nature Methods.

[11]  Le Cong,et al.  Multiplex Genome Engineering Using CRISPR/Cas Systems , 2013, Science.

[12]  A Rzhetsky,et al.  The human ATP-binding cassette (ABC) transporter superfamily. , 2001, Journal of lipid research.

[13]  A. M. George,et al.  The ABC transporter structure and mechanism: perspectives on recent research , 2004, Cellular and Molecular Life Sciences CMLS.

[14]  Gerhard F. Ecker,et al.  Prediction of drug–ABC-transporter interaction — Recent advances and future challenges☆ , 2015, Advanced drug delivery reviews.

[15]  Tingjun Hou,et al.  ADME evaluation in drug discovery , 2002, Journal of molecular modeling.

[16]  Robert P. St.Onge,et al.  The Chemical Genomic Portrait of Yeast: Uncovering a Phenotype for All Genes , 2008, Science.

[17]  N. Krogan,et al.  Phenotypic Landscape of a Bacterial Cell , 2011, Cell.

[18]  K. Locher.  Mechanistic diversity in ATP-binding cassette (ABC) transporters , 2016, Nature Structural &Molecular Biology.

[19]  T. Stockner,et al.  Comparison of mechanistic transport cycle models of ABC exporters , 2017, Biochimica et biophysica acta. Biomembranes.

[20]  Erik D. Demaine,et al.  K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data , 2002, WABI.

[21]  R L Juliano,et al.  A surface glycoprotein modulating drug permeability in Chinese hamster ovary cell mutants. , 1976, Biochimica et biophysica acta.

[22]  Zsolt Bikádi,et al.  Predicting substrates of the human breast cancer resistance protein using a support vector machine method , 2013, BMC Bioinformatics.

[23]  Dan Li,et al.  ADMET evaluation in drug discovery. 13. Development of in silico prediction models for P-glycoprotein substrates. , 2014, Molecular pharmaceutics.

[24]  Tudor I. Oprea,et al.  High-Throughput Flow Cytometry Screening of Multidrug Efflux Systems. , 2018, Methods in molecular biology.

[25]  Charles X. Ling,et al.  AUC: A Better Measure than Accuracy in Comparing Learning Algorithms , 2003, Canadian Conference on AI.

[26]  James G. Nourse,et al.  Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..

[27]  Max Kuhn,et al.  Building Predictive Models in R Using the caret Package , 2008 .

[28]  Michael Costanzo,et al.  Systematic exploration of synergistic drug pairs , 2011, Molecular systems biology.

[29]  Shufeng Zhou,et al.  Structure, function and regulation of P-glycoprotein and its clinical relevance in drug disposition , 2008, Xenobiotica; the fate of foreign compounds in biological systems.

[30]  Youyong Li,et al.  ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity. , 2017, Molecular pharmaceutics.

[31]  Murodzhon Akhmedov,et al.  Large-scale identification and analysis of suppressive drug interactions. , 2014, Chemistry & biology.

[32]  P. Borst,et al.  Mammalian ABC transporters in health and disease. , 2002, Annual review of biochemistry.

[33]  Yuichi Sugiyama,et al.  Comparative Studies on in Vitro Methods for Evaluating in Vivo Function of MDR1 P-Glycoprotein , 2001, Pharmaceutical Research.

[34]  Yo Suzuki,et al.  The green monster process for the generation of yeast strains carrying multiple gene deletions. , 2012, Journal of visualized experiments : JoVE.

[35]  Alois Knoll,et al.  Gradient boosting machines, a tutorial , 2013, Front. Neurorobot..

[36]  Lyn H. Jones,et al.  Applications of chemogenomic library screening in drug discovery , 2017, Nature Reviews Drug Discovery.