B Marquez

CF Higgins

G Szakács

Gerhard F Ecker

Marta Pinto

English

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A computational model for predicting the transport of compounds by ABCC2

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POSTER PRESENTATION Open Access
A computational model for predicting the
transport of compounds by ABCC2
Marta Pinto*, Gerhard F Ecker
From 7th German Conference on Chemoinformatics: 25 CIC-Workshop
Goslar, Germany. 6-8 November 2011
The ATP-binding cassette (ABC) proteins represent a
large family of transmembrane proteins that use the
energy of ATP hydrolysis to transport a wide variety of
physiological substrates across biological membranes [1].
Of them, particular attention has been focused in the
last years on the role of the ABCC2 transporter in drug
clearance and disposition.
The ABCC2 transporter is a transmembrane protein
expressed in the apical cell membrane of hepatocytes
and epithelial cells of small intestine and kidney, where
it is involved in the elimination of many endogenous
and exogenous substrates from the cell, including com-
pounds clinically relevant [2]. Alteration in the disposi-
tion and elimination of these compounds can modify
their pharmacokinetic and pharmacological profiles,
leading to reduced efficacy or increased toxicity.
In this scenario, the aim of the present work was the
development of an in silico model based on the Gottes-
man database [3] able to predict if certain compounds
of interest are ABCC2 substrates or not. To this end,
several machine learning methods were explored using
the WEKA data mining software [4]. Molecules were
represented by 2D and 3D descriptors calculated with
the MOE software [5]. Feature selection was used to
improve the efficiency of the data mining algorithms
and identify the contribution of different features. Mis-
classification cost was used in order to deal with data
set imbalance. According to our results, naive Bayesian
updatable (NBU) had the highest performance, with an
overall prediction accuracy of 72.1% and a Matthew’s
correlation coefficient of 0.44. Furthermore, sensitivity
and specificity values were significantly improved with
values of 72.7% and 71.6%, respectively.
Published: 1 May 2012
References
1. Higgins CF: ABC transporters: from microorganisms to man. Annu Rev Cell
Biol 1992, 18:67-113.
2. Marquez B, Van Bambeke F: ABC Multidrug Transporters: Target for
Modulation of Drug Pharmacokinetics and Drug-Drug Interactions. Curr
Drug Targets 2011, 12:600-620.
3. Szakács G, Annereau JP, Lababidi S, Shankavaram U, Arciello A, Bussey KJ,
Reinhold W, Guo Y, Kruh GD, Reimers M, Weinstein JN, Gottesman MM:
Predicting drug sensitivity and resistance: Profiling ABC transporter
genes in cancer cells. Cancer Cell 2004, 6:129-137.
4. [http://www.cs.waikato.ac.nz/ml/].
5. [http://www.chemcomp.com/].
doi:10.1186/1758-2946-4-S1-P53
Cite this article as: Pinto and Ecker: A computational model for
predicting the transport of compounds by ABCC2. Journal of
Cheminformatics 2012 4(Suppl 1):P53.
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* Correspondence: marta.pinto@univie.ac.at
Department of Medicinal Chemistry, University of Vienna, Vienna, 1090,
Austria
Pinto and Ecker Journal of Cheminformatics 2012, 4(Suppl 1):P53
http://www.jcheminf.com/content/4/S1/P53
© 2012 Pinto and Ecker; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.


Pinto Marta

Ecker Gerhard F

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Bambeke F: ABC Multidrug Transporters: Target for Modulation of Drug Pharmacokinetics and Drug-Drug Interactions. Curr Drug Targets

CF: ABC transporters: from microorganisms to man. Annu Rev Cell Biol

MM: Predicting drug sensitivity and resistance: Profiling ABC transporter genes in cancer cells. Cancer Cell

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A computational model for predicting the transport of compounds by ABCC2

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