Computational models for predicting the interaction with ABC transporters.

DDTEC-416; No of Pages 9

Drug Discovery Today: Technologies

Vol. xxx, No. xx 2014

Editors-in-Chief Kelvin Lam – Simplex Pharma Advisors, Inc., Arlington, MA, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY

TODAY

TECHNOLOGIES

Transporter assays

Computational models for predicting the interaction with ABC transporters Marta Pinto*, Daniela Digles, Gerhard F. Ecker University of Vienna, Department of Pharmaceutical Chemistry, Althanstraße 14, Vienna A-1090, Austria

There is strong evidence that ATP-binding cassette

Section editor: Gerhard F. Ecker – University of Vienna, Vienna, Austria

(ABC) transporters play a critical role in the pharmacokinetic and pharmacodynamic properties of many drugs and xenobiotics. Due to their pharmacological role, several computational approaches have been developed to understand and predict the interaction between ABC transporters and their ligands. Here, we provide an overview of the current state of the art of the ligand-based models that, derived from the transport and inhibitory activities of a set of ligands, have been published for ABC transporters.

Introduction ATP-binding cassette (ABC) transporters are membrane proteins that use the energy provided by ATP hydrolysis to translocate a wide variety of molecules, ranging from ions to macromolecules, across biological membranes [1]. On the basis of similarities in gene organization and sequence, the 48 different ABC transporters identified in the human genome and which act exclusively as exporters have been have grouped into seven different gene families (ABCA–ABCG) [2]. The functional entity of these transporters is formed by a minimum of four domains: two nucleotide-binding domains (NBDs), also named ATP binding cassette domains and responsible for the binding and hydrolysis of ATP, and two or more transmembrane domains (TMDs) that, usually formed by six membrane spanning helices, provide the pathway for substrate transport. This core is commonly formed by a single polypeptide chain, with each NBD being the *Corresponding author.: M. Pinto ([email protected]) 1740-6749/$ ß 2014 Published by Elsevier Ltd.

C-terminal to each TMD. Examples of this architecture include P-glycoprotein (P-gp, MDR1, ABCB1), the Bile Salt Export Pump (BSEP, ABCB11) and the Multidrug Resistance Associated Protein MRP2 (cMOAT, ABCC2) proteins (Fig. 1a, b). There are, however, some exceptions to this scheme: the half-transporters typically represented by the Breast Cancer Resistance Protein BCRP (ABCP, MXR, ABCG2), which are formed by one TMD bound covalently to a NBD and function as homo- or heterodimers (Fig. 1c) [3]. ABC transporters are highly expressed in important pharmacological barriers, such as the brush border membrane of intestinal cells, the biliary canalicular membrane of hepatocytes or the epithelium that contributes to the blood–brain barrier. The presence of these transporters in absorption sites and excretion areas is of particular importance because they limit the absorption, distribution, tissue targeting and elimination of drugs and xenobiotics and therefore, determine their pharmacokinetic and pharmacodynamic properties [4– 6]. Several ABC transporters have been reported to interact with known drugs, which can act as substrates as well as inhibitors of these transporters, or even induce their expression. Among these proteins are P-gp, MDR3, BSEP, MRP1, MRP2, MRP3, MRP6, and BCRP [7,8]. In this scenario, computational models able to predict the interaction of new drug candidates with ABC transporters can be advantageous in the first stages of the drug development process. Despite the importance of ABC transporters, most of them are – from a structural point of view – poorly characterized due to the difficulty to express and crystallize membrane

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Please cite this article in press as: Pinto M, et al. Computational models for predicting the interaction with ABC transporters, Drug Discov Today: Technol (2014), http://

DDTEC-416; No of Pages 9 Drug Discovery Today: Technologies | Transporter assays


(a)

N-terminal N-terminal

C-terminal

(b)

C-terminal

(c)

C-terminal N-terminal Drug Discovery Today: Technologies

Figure 1. Structure of the ABC transporters. (a) MDR1 and BSEP are full length transporters that contain two identical halves. Each one of them has six spanning helices and a NBD region. (b) MRP1 and MRP2 contain an additional region at the N-terminus formed by five spanning helices. (c) BCRP is a halftransporter consisting of one NBD followed by a six membrane-spanning domain.

proteins. Nevertheless, some crystal structures of ABC transporters have been determined, including some bacterial homologs (e.g. Sav1866 (PDB ID: 2HYD) [9] and MsbA (PDB ID: 3B60) [10] the crystal structure of murine (PDB ID: 3G5U) [11] and C. elegans MDR1 (PDB ID: 4F4C) [12] and, the human ABC-me (ABCB10 gene) (PDB ID: 4AYT) [13]. Although these structures allow the creation of homology models useful for structurebased studies, the low identity between members of different ABC subfamilies and the crystallographic templates together with the presence of several binding sites in these proteins, render the structure-based methods difficult to apply. In this scenario, ligand-based techniques provide a promising strategy to describe and predict the interaction of new compounds with ABC transporters. These methods, which do not require prior knowledge of the 3D structure of the protein, correlate the activity of a series of validated substrates or inhibitors with their molecular descriptors or their common chemical features to create predictive models. In this domain, pharmacophore model and quantitative structure activity relationships (QSARs) are the most used methodologies. e2

Here, we provide an overview and update of the ligandbased methods developed in the past few years to predict the interaction of substrates or inhibitors with ABC transporters. To the best of our knowledge, we included all ABC transporters for which models have been reported.

ABC transporter classification models P-glycoprotein (ABCB1) Due to the presence of P-glycoprotein in many pharmacological barriers such as the luminal membrane of the intestine, the apical membrane of kidney and liver cells, or the endothelia of the brain [14,15], and its association with drug resistance, a large number of ligand-based models have been developed for identifying potential substrates and inhibitors. Most of these models have been previously reviewed, for example in [16]. For this reason, we will only discuss the most recent ones that have not been included previously (Table 1). It seems that in the recent years, there is a progressive tendency to move from linear to non-linear methods such as Random Forest or Support Vector Machine, reflecting the

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DDTEC-416; No of Pages 9 Vol. xxx, No. xx 2014

Drug Discovery Today: Technologies | Transporter assays

Table 1. Ligand-based models developed for P-glycoprotein. Model

Inhibition

Dataset

TR = 772

Methodology Descriptors

Method

VolSurf

PLS-DA

Important features

Performance

Ref.

Size, hydrophobic surface area, flexibility and LogPn-oct

LV1: Q2 = 0.40; R2 = 0.40

[17]

TS = 85 EV = 418

LV2: LV3: LV4: LV5: FLAP Models combination (VolSurf-PLS-D + FLAP-LDA)

TR = 1268

LDA

Q2 = 0.44; Q2 = 0.46; Q2 = 0.47; Q2 = 0.48;

R2 = 0.44 R2 = 0.48 R2 = 0.51 R2 = 0.52

1 HBA + 2 HF/Ar TR: Accur. = 0.88; Spec. = 0.91; Sens. = 0.84

Fingerprints

RF

Inhibitors are hydrophobic. Cationic. Contain basic nitrogen atoms. Tertiary amines and at least. two hydrogen bond acceptors

1081 2D/3D

SVM

Molecular volume. Hydrophobicity. and aromaticity

62 2D + 166 MACCS + 307 fingerprints

RF

Size. Hydrophobicity. Aromatic rings. carboxylic groups and carbothioic S esters

TS: Accur. = 0.85; Spec. = 0.87; Sens. = 0.82 EV: Accur. = 0.86; Spec. = 0.80; Sens. = 0.90 TS: Accur. = 0.75; Spec. = 0.63; Sens. = 0.84; MCC = 0.48

[17] [17]

[18]

TS = 667 TR = 857 TS = 418

TR = 1201

TR: Accur. = 0.89; Spec. = 0.85; Sens. = 0.89; AUC = 0.94; TS: Accur. = 0.85; Spec. = 0.63; Sens. = 0.97; MCC = 0.93 TS: Accur. = 0.82; Spec. = 0.69; Sens. = 0.91; G-mean = 0.79; EV: Accur. = 0.73; Spec. = 0.57; Sens. = 0.99; G-mean = 0.75

[19]

[20]

TS = 407 EV = 346 SVM

Transport

TR = 177

3250 2D/3D

SVM

Molecular weight, electronegativity and polarizability

TS = 20

4 2D

RP

Molecular weight, number of hydrogen bond donors, number of hydrogen bond acceptors and topological PSA

TS = 52

TR = 198

TS = 63

[20]

TR: Accur. = 0.80; Spec. = 0.81; Sens. = 0.79

[21]

TS: Accur. = 0.75; Spec. = 0.75; Sens. = 0.75 EV: Accur. = 0.76; Spec. = 0.77; Sens. = 0.74

EV = 32

TR = 209

TS: Accur. = 0.80; Spec. = 0.62; Sens. = 0.93; G-mean = 0.76; EV: Accur. = 0.75; Spec. = 0.62; Sens. = 0.97; G-mean = 0.77

TR: Accur. = 0.79; Spec. = 0.83; Sens. = 0.76; MCC = 0.58

[22]

TS: Accur. = 0.69; Spec. = 0.73; Sens. = 0.65; MCC = 0.39 2 2D + log P + PSA

RP

Molecular weight, number of hydrogen bond donors, log P and PSA

TR: Accur. = 0.79; Spec. = 0.74; Sens. = 0.84; MCC = 0.58 TS: Accur. = 0.79; Spec. = 0.79; Sens. = 0.79; MCC = 0.59


[22]

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Table 1 (Continued ) Model

Dataset

TR = 150


Method

VolSurf+

NB

Important features

Performance

Size, shape, and hydrophobic features

TR: Accur. = 0.81; MCC = 0.62; AUC = 0.86 TS: Accur. = 0.81; MCC = 0.30; AUC = 0.90 TR: Accur. = 0.81; MCC = 0.62; AUC = 0.86 TS: Accur. = 0.86; MCC = 0.72; AUC = 0.85 TR: Accur. = 0.83; MCC = 0.65; AUC = 0.90 TS: Accur. = 0.81; MCC = 0.63; AUC = 0.89 TR: Accur. = 0.81; MCC = 0.62; AUC = 0.87 TS: Accur. = 0.84; MCC = 0.66; AUC = 0.88 TS: Accur. = 0.70; Spec. = 0.68; Sens. = 0.72; MCC = 0.41

TS = 37

TR = 282

VolSurf+

NB


VolSurf+

GA-kNN


VolSurf+

RF


Checkmol fingerprints

RF

Presence of hydroxyl groups and tertiary aliphatic amines

Ref.

[23] [23]

[23]

[23]

[18]

TS = 202 Dataset: TR = training set; TS = test set; EV = external validation set. Descriptors: FLAP = Fingerprints for Ligands and Proteins; PSA = Polar Surface Area. Methods: PLS-DA = Partial Least Square Discriminant Analysis; LDA = Linear Discriminant Analysis; RF = Random Forest; SVM = Support Vector Machine; RP = Recursive Partitioning; NB = Naı¨ve Bayes; GAkNN = Genetic Algorithm-kappa Nearest Neighbor. Pharmacophoric features: HBA = Hydrogen Bond Acceptor; HF = Hydrophobic; Ar = Aromatic. Performance: Accur. = accuracy; Spec. = specificity; Sens. = sensitivity; AUC = Area Under the Curve; MCC = Matthews correlation coefficient.

Table 2. Ligand-based models developed for BSEP. Model

Dataset

Inhibition

Methodology

Important features

Descriptors

Method

37

Chemical fragmentation codes

MLR

TR = 437

C log P + MW

RP

TS = 187

196 2D/3D

SVM

TR = 163

10 2D

OPLS-DA

TS = 86

A ring-linking group containing one carbon atom. An ester or thioester directly attached to a heterocyclic carbon and a carbocyclic system containing at least one aromatic ring contribute positively to BSEP inhibition. In contrast. hydroxyl groups attached to an aliphatic carbon have a negative contribution. Compounds showing high lipophilicity and molecular weight 296.2 or alternatively intermediate lipophilicity and molecular weight 360.4, are more likely to be BSEP inhibitors

BSEP inhibition is positively correlated with lipophilicity. Hydrophobicity and the number of halogen atoms in the molecule. Negative correlation was seen for descriptors of positive charge. Hydrophilicity and hydrogen bond acceptors

Performance

Ref.

[29]

TS: Accur. = 0.83; Spec. = 0.83; Sens. = 0.84; MCC = 0.67

[30]

TR: Accur. = 0.87; Spec. = 0.87; Sens. = 0.87 MCC = 0.73 TS: Accur. = 0.87; Spec. = 0.84; Sens. = 0.90; MCC = 0.74 TR: Accur. = 0.91; Spec. = 0.94; Sens. = 0.80 MCC = 0.75

[30]

[31]

TS: Accur. = 0.89; Spec. = 0.94; Sens. = 0.76; MCC = 0.73

Dataset: TR = training set; TS = test set. Methods: MLR = Multiple Linear Regression; RP = Recursive Partitioning; SVM = Support Vector Machine; OPLS-DA = Orthogonal Partial Least Square Discriminant Analysis. Performance: Accur. = accuracy; Spec. = specificity; Sens. = sensitivity; AUC = Area Under the Curve; MCC = Matthews correlation coefficient.

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Table 3. Ligand-based models developed for MRP1. Model

Inhibition

Dataset

Methodology

29

Descriptors

Method

8 2D

SMR

5 TR = 12

Pharmacophore Pharmacophore

TS = 20 Unspecified

Pharmacophore

107 107

CoMFA CoMSIA

Important features

Performance

Ref.

The number of methoxylated moieties. the number of hydroxyl groups and the dihedral angle between the B- and C-rings 3 Ar + 3 HBA 1 HF + 1 Ar + 2 HBA + 1+

R2 = 0.77; P < 0.001

[35]

At least one HBA of limited flexibility + HF in a flexible side chain attached to a planar hydrophobic ring and far from the HBA group + NH3+ Electrostatic field Electrostatic field

R2 = 0.80; Q2 = 0.68; R = 0.88

[36] [37]

[38]

Q2 = 0.71 Q2 = 0.73

[38] [38]

Dataset: TR = training set; TS = test set. Methods: SMR = Stepwise Multiple Regression; CoMFA = Comparative Molecular Field Analysis; CoMSIA = Comparative Molecular Similarity Index Analysis. Performance: R = Pearson-R value; P = partial correlation coefficient.

ability of P-glycoprotein to interact with a wide diversity of compounds. However, these methods non-linear methods have also shown to properly work on small congeneric series.

BSEP (ABCB11) BSEP, formerly called sister of P-glycoprotein (SPGP), is exclusively expressed in the liver [24] where it plays a critical role in the excretion of bile acids and xenobiotic compounds into

the bile [25,26]. Mutations in the BSEP gene are associated with cholestatic diseases such as the progressive and benign recurrent forms of inherited familial intrahepatic cholestasis (PFIC and BRIC, respectively) [27]. Moreover, inhibition of BSEP by drugs and other xenobiotics is also related to cholestasis [28]. Consequently, different models have been developed to predict compounds with potential risk of druginduced cholestasis (Table 2).

Table 4. Ligand-based models developed for MRP2. Model

Inhibition

Dataset


Method

29 26

7 2D/3D Torsion angle between biphenyl rings

SMR B3LYP/6-31G*

191

669 2D/3D

OPLS-DA

TR = 257

186

SVM

Important features

Performance

Ref.

Number of pyrogallol and/or catechol moieties Compounds displaying the smallest torsion angles (>458) were neither substrates nor inhibitors. In contrast, those having torsion angles between 78 and 878 exhibited inhibitory activity. whereas compounds with torsion angles between 54 and 658 behaved as substrates as well as mild-inhibitors Lipophilicity, aromaticity and size. Additionally, it was observed that one third of the inhibitors carry a positive charge. whereas substrates and stimulators are neutral or negatively charged Polarizability. hydrogen bond features. Aromaticity. lipophilicity. TPSA. Number of rings and rotatable bonds

R = 0.41; P = 0.028

[35] [42]

TS = 61 TR = 9

Transport

TR = 964

TS = 240

16 2D

Pharmacophore

2 HBA + 1 HF

CostSensitive + RF

Hydrophobicity. Polarizability. The fractional negative charge and hydrogen bond donor properties

[43]

TR: Accur. = 0.83; Spec. = 0.82; Sens. = 0.83 TS: Accur. = 0.77; Spec. = 0.76; Sens. = 0.79 Whole database: fit value > 2.1: inhibitors (sens. = 0.78); Fit value < 2.1: non-inhibitors (spec. = 0.70); Overall accuracy = 0.74 TR: Accur. = 0.69; Spec. = 0.71; Sens. = 0.68 MCC = 0.23 TS: Accur. = 0.76; Spec. = 0.74; Sens. = 0.78 MCC = 0.30

[44]

[44]

[45]

Dataset: TR = training set; TS = test set. Methods: SMR = Stepwise Multiple Regression; OPLS-DA = Orthogonal Partial Least Square-Discriminant Analysis; SVM = Support Vector Machine; RF = Random Forest. Performance: Accur. = accuracy; Spec. = specificity; Sens. = sensitivity; AUC = Area Under the Curve; MCC = Matthews correlation coefficient. www.drugdiscoverytoday.com

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The relevance of this transporter has received special attention in the last few years, which is reflected by growing of both available data and models.

MRP1 (ABCC1) MRP1 is expressed in many tissues is expressed in tissues throughout the body, including the intestine, lung, testis, kidney, brain, cardiac and skeletal muscle, and placenta [32]. In contrast to members of the ABCB subfamily, it contains an additional domain with five putative transmembrane segments fused to the core domain by means of a long internal loop [33]. MRP1 confers resistance to a variety of natural anticancer drugs and transports conjugated organic anions

(e.g. leukotriene C4) and xenobiotics and their conjugates with glutathione (GSH), glucuronide or sulfate [34]. The models reported for MRP1 are briefly summarized in Table 3. As in the case of BSEP, all models reported in the literature are related to inhibition, possibly due to the lack of useful data for modelling the MRP1 transport properties.

MRP2 (ABCC2) MRP2 is involved in the biliary excretion of important anionic drugs as well as intracellular formed GSH-, sulfate- or glucuronide-conjugates [39]. In contrast to BSEP, it widely distributed, being expressed in the liver, kidney and in the intestinal entrocytes [39]. Mutations of the MRP2 transporter are related

Table 5. Ligand-based models developed for BCRP. Model

Dataset

Inhibition


Method

TR = 34 TS = 7

MIFs + VolSurf

LSER

31

Steric. hydrophobic and electrostatic fields

CoMFA

31

CoMSIA

31

Steric. hydrophobic and electrostatic fields Structural properties

Free-Wilson

13

Structural properties

Free-Wilson

TR = 124 TS = 79

Fingerprints

Bayes

TR = 30 TS = 79 Transport

TR = 164

Pharma-cophore 867 2D

MLR-LDA

3250 2D/3D

SVM

Important features

Performance

Ref.

Polarizability. Hydrophobic volumes and size. The hydrogen bond donor capacity is unfavorable. whereas the hydrogen bond acceptor capacity is negligible Hydrogen bonding. Inclusion of steric and electrostatic fields only led to small decrease of Q2 Electrostatic and hydrogen bond acceptor fields Hydroxy group in position 5. Double bond between positions 2 and 3 and methoxy group in position 3 Electron withdrawing at position R3 is important for activity. At positions R1 and R2 all substituents led a decrease of activity

TR: R2 = 0.77; Q2 = 0.70 TS: R2 = 0.67

[49]

n = 23: Q2 = 0.62

[50]

n = 23: Q2 = 0.62

[50]

n = 23: p = 0.053; R2 = 0.90; s = 0.19; F = 25.6

[50]

R2 = 0.81; s = 0.16; F = 45.9

[51]

TS: Accur. = 0.90; Spec. = 0.71; Sens. = 0.95; MCC = 0.69 TS: Accur. = 0.66; Spec. = 0.71; Sens. = 0.65; MCC = 0.29

[52]

TR: Accur. = 0.90; Spec. = 0.85; Sens. = 0.80; AUC = 0.90 TS: Accur. = 0.75; Spec. = 0.76; Sens. = 0.70; AUC = 0.80 TR: Accur. = 0.76; Spec. = 0.60; Sens. = 0.86; MCC = 0.48

[53]

3 HF + 1 HBA

TS = 98 TR = 167

TS = 56 EV = 40

3-Dimensional structure of a substrate is likely the determining factor for BCRP/ substrate interactions

[52]

[54]

TS: Accur. = 0.75; Spec. = 0.52; Sens. = 0.89; MCC = 0.45 EV: Accur. = 0.73; Spec. = 0.67; Sens. = 0.76; MCC = 0.42

Dataset: TR = training set; TS = test set; EV = external validation set. Descriptors: MIF = Molecular Interaction Field. Methods: LSER = Linear Solvation-Energy Relationship; CoMFA = Comparative Molecular Field Analysis; CoMSIA = Comparative Molecular Similarity Index Analysis; MLR-LDA = Multiple Linear Regression-Linear Discriminant Analysis. Pharmacophoric features: HBA = Hydrogen Bond Acceptor; HF = Hydrophobic; HBA = Hydrogen Bond Acceptor. Performance: R = Pearson-R value; Accur. = accuracy; Spec. = specificity; Sens. = sensitivity; AUC = Area Under the Curve; MCC = Matthews correlation coefficient.

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with the Dubin-Johnson syndrome [40]. Structure–activity relationships of MRP2-interacting compounds have been already reviewed some years ago [41]. For this reason, we will only include those not reviewed until now (Table 4). As for MRP1, recent publications cover mainly inhibition data for smaller series of compounds. To overcome the limitation produced by the lack of data sets related to MRP2 transport and to identify the main properties responsible of it, our group built a data set of substrates/non substrates by correlating the mRNA level of this transporter with the cytotoxicity of different compounds [45].

BCRP (ABCG2) BCRP is an ABC transporter expressed in the placenta, liver, intestine, blood–brain barrier and testes [46]. BCRP substrates include numerous anticancer agents, statins, antibiotics, environmental toxins and endogenous substrates such as conjugated steroid hormones, folates and uric acid [47]. A review focused on QSAR and molecular modelling of BCRPspecific inhibitors has been already published in [48]. For this reason, we will mention only the models recently published (Table 5). As in the case of many other transporters, most of the models are built using congeneric series of compounds, which not only reduce the applicability of these models, but also fail in providing general information about the transport or inhibition properties of the transporter.

Conclusions and outlook Due to the role of ABC transporters in drug safety and therapeutic efficacy, different computational approaches have been applied to model the interaction between these transporters and their ligands. These approaches can be classified into structure-based and ligand-based methods. However, because of the limited number of transporter templates, ligand-based approaches have been extensively used. In this domain, several computational techniques have been used to predict the interactions of new compounds with clinically relevant ABC transporters, ranging from pharmacophores to more sophisticated machine learning algorithms. However, although pharmacophore modelling provides an attractive strategy due to its simplicity and easy interpretation, the number of pharmacophoric models published for ABC transporters is lower in comparison with QSAR models. One of the main reasons for this is that substrates and inhibitors of ABC transporters are structurally very diverse, making the identification of common structural features difficult. A tendency observed in the last years is the progressive move from linear to non-linear methods, which have been demonstrated to perform well on both small and larger compounds data sets. Although QSAR models have shown a good performance, they have some limitations. First, the lack of specific


substrates and inhibitors for ABC transporters. Second, the large differences in the reported biological activities across different cell expression systems, assay types and research laboratories. For example, many compounds, such as cyclosporine for P-gp and Imatinib for BCRP, have been reported as substrates and inhibitors of these transporters, making the assignation of the categorical nature to these compounds difficult. Third, several models have been developed on congeneric series, which limits their applicability (usually referred to as applicability domain) to structurally related compounds and the acquisition of useful information for drug development. The applicability domain of these models is not always clearly defined in the literature. In some cases, as in the case of the congeneric series, it is implicitly defined by the chemicals used to build the model. However, in many other cases, it has not been provided or there is no evidence if the concept of applicability domain has been applied. However, in spite of all these limitations, ligand-based methods provide an inexpensive and fast way to assess the interaction of new compounds with ABC transporters and to aid in identification of drug candidates with better pharmacokinetic and pharmacodynamic properties.

Acknowledgements We gratefully acknowledge financial support provided by the Austrian Science Fund under the framework of the special research programme SFB35 ‘Transmembrane Transporters in Health and Disease (F3502)’. The research leading to these results has also received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements n8 115002 (eTOX), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.

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Thermodynamics of ABC transporters.

Radiopharmaceuticals for assessing ABC transporters at the blood-brain barrier.

Structural diversity of ABC transporters.

Structure and mechanism of ABC transporters.

Importance of ABC transporters in different tissues.

MicroRNAs and their relevance to ABC transporters.

Biochemical interaction of anti-HCV telaprevir with the ABC transporters P-glycoprotein and breast cancer resistance protein.

Peroxisomal ABC transporters: functions and mechanism.

ABC transporters: from microorganisms to man.

Alzheimer's and ABC transporters--new opportunities for diagnostics and treatment.

ABC transporters in fish species: a review.

Regulation of ABC transporters at the blood-brain barrier.

The role of ABCG-type ABC transporters in phytohormone transport.

Computational Studies of Glutamate Transporters.

Mercury toxicokinetics of the healthy human term placenta involve amino acid transporters and ABC transporters.

Computational approaches for predicting biomedical research collaborations.

Class C ABC transporters and Saccharomyces cerevisiae vacuole fusion.

ABC transporters as differentiation markers in glioblastoma cells.

Detergent-free purification of ABC (ATP-binding-cassette) transporters.

Current advances on ABC drug transporters in fish.

Interactions of retinoids with the ABC transporters P-glycoprotein and Breast Cancer Resistance Protein.

Diversity in ABC transporters: type I, II and III importers.

Schistosome ABC multidrug transporters: From pharmacology to physiology.

Idealized computational models for auditory receptive fields.