Nuclear Receptor‐Mediated Regulation of Drug Transporters

Burk, Oliver

doi:10.1002/9780470409107.ch4

Cited by 1 publication

(1 citation statement)

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“…Efflux transporters and metabolic enzymes seem to act in a coordinated or synergic manner, with the biotransformation products being often substrates for these drug carriers [1]. Furthermore, metabolizing enzymes and efflux transporters are also upregulated in a coordinated manner by common nuclear receptors that, upon environmental chemical triggering agents, induce the expression of host defense systems towards potentially toxic chemical agents [1, 6, 7]. …”

Section: Introductionmentioning

confidence: 99%

Development of Conformation Independent Computational Models for the Early Recognition of Breast Cancer Resistance Protein Substrates

Gantner

Ianni

Ruiz

et al. 2013

BioMed Research International

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ABC efflux transporters are polyspecific members of the ABC superfamily that, acting as drug and metabolite carriers, provide a biochemical barrier against drug penetration and contribute to detoxification. Their overexpression is linked to multidrug resistance issues in a diversity of diseases. Breast cancer resistance protein (BCRP) is the most expressed ABC efflux transporter throughout the intestine and the blood-brain barrier, limiting oral absorption and brain bioavailability of its substrates. Early recognition of BCRP substrates is thus essential to optimize oral drug absorption, design of novel therapeutics for central nervous system conditions, and overcome BCRP-mediated cross-resistance issues. We present the development of an ensemble of ligand-based machine learning algorithms for the early recognition of BCRP substrates, from a database of 262 substrates and nonsubstrates compiled from the literature. Such dataset was rationally partitioned into training and test sets by application of a 2-step clustering procedure. The models were developed through application of linear discriminant analysis to random subsamples of Dragon molecular descriptors. Simple data fusion and statistical comparison of partial areas under the curve of ROC curves were applied to obtain the best 2-model combination, which presented 82% and 74.5% of overall accuracy in the training and test set, respectively.

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