Abstract
Purpose
To study the inhibition patterns of the three major human ABC transporters P-gp (ABCB1), BCRP (ABCG2) and MRP2 (ABCC2), using a dataset of 122 structurally diverse drugs.
Methods
Inhibition was investigated in cellular and vesicular systems over-expressing single transporters. Computational models discriminating either single or general inhibitors from non-inhibitors were developed using multivariate statistics.
Results
Specific (n = 23) and overlapping (n = 19) inhibitors of the three ABC transporters were identified. GF120918 and Ko143 were verified to specifically inhibit P-gp/BCRP and BCRP in defined concentration intervals, whereas the MRP inhibitor MK571 was revealed to inhibit all three transporters within one log unit of concentration. Virtual docking experiments showed that MK571 binds to the ATP catalytic site, which could contribute to its multi-specific inhibition profile. A computational model predicting general ABC inhibition correctly classified 80% of both ABC transporter inhibitors and non-inhibitors in an external test set.
Conclusions
The inhibitor specificities of P-gp, BCRP and MRP2 were shown to be highly overlapping. General ABC inhibitors were more lipophilic and aromatic than specific inhibitors and non-inhibitors. The identified specific inhibitors can be used to delineate transport processes in complex experimental systems, whereas the multi-specific inhibitors are useful in primary ABC transporter screening in drug discovery settings.
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Notes
A recently published structure of mouse P-gp will facilitate more accurate modeling of the binding of both P-gp substrates and inhibitors (Aller et al., Science 2009;323:1718-22. doi:10.1126/science.1168750).
The approximate concentration range was calculated assuming a sigmoidal concentration dependency of the inhibitory effect: \(\% transport = {100} \mathord{\left/ {\vphantom {{100} {1 + 10^{{{\left( {I - IC50} \right)}\gamma }} }}} \right. \kern-\nulldelimiterspace} {1 + 10^{{{\left( {I - IC50} \right)}\gamma }} }\), where I is the inhibitor concentration, IC50 is the inhibitor concentration resulting in a 50% reduction of the transport rate, and γ is the slope of the curve. Assuming a slope of 1, as generally observed for concentration-dependent transport inhibition, a compound resulting in a 10% reduction of the transport rate at a concentration C will inhibit the transport by 50% at a concentration 9 × C. If the sigmoidal relationship has a steeper slope of, say, 3, the compound will give 50% inhibition at a concentration 2.1 × C.
Abbreviations
- ABC:
-
ATP-binding cassette transporter
- BCRP:
-
Breast cancer resistance protein (ABCG2)
- BSEP:
-
Bile salt efflux pump (ABCB11)
- logD 7.4 :
-
Octanol–water partition coefficient at pH 7.4
- HBSS:
-
Hank’s balanced salt solution
- MRP2:
-
Multidrug resistance associated protein 2 (ABCC2)
- PBS:
-
Phosphate-buffered saline
- P-gp:
-
P-glycoprotein (ABCB1)
- PLS-DA:
-
Partial least squares projection to latent structures discriminant analysis
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This work was supported by the Swedish Research Council (Grant 9478), the Knut and Alice Wallenberg Foundation, the Swedish Fund for Research without Animal Experiments, and the Swedish Animal Welfare Agency.
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Fig. S1
A. The position of the dataset in the chemical space of registered oral drugs. The positions of the compounds in the drug space are determined by the first three ChemGPS principal components (t1, t2, and t3), which are summarized from a large number of molecular descriptors and describe mainly the size, polarity, and flexibility of the molecules, respectively. The large circles denote the ABC inhibitors, with open circles representing inhibitors that were specific for one transporter within the studied concentration range, and closed circles representing compounds with overlapping inhibitor specificity. The small black circles denote noninhibitors. B. The positions of a reference set of 500 registered drugs from the Physician’s Desk Reference (2007) (PDF 64.3 KB).
Fig. S2
Comparison of prediction results from models of general ABC transporter inhibition. The model in A was developed to discriminate inhibitors of any of the three ABC transporters from non-inhibitors. The classification accuracy was comparable to the accuracy of the individual models (See Fig. 5). In B, the predictions from the individual models were combined: compounds were classified as ABC transporter inhibitors if they were predicted as inhibitors by one or more of the individual models. This approach resulted in correct classification of as many as 91% of the ABC transporter inhibitors in the test set, but this was counterbalanced by a low accuracy for the non-inhibitors (PDF 110 KB).
Fig. S3
Physicochemical properties of the dataset subgroups. A. Total structure connectivity index. B. Number of aromatic bonds. The boxes show the inter-quartile distances and the median values, and the whiskers show the span between the lowest and the highest value. Inhibitors are in general slightly larger (lower values of the total structure connectivity index) than the non-inhibitors. This difference is more pronounced for the overlapping inhibitors. Although the median number of aromatic bonds varies slightly among the groups of inhibitors, the distributions are similar. The specific P-gp inhibitors in this dataset contain fewer aromatic bonds than the rest of the inhibitors. The symbols to the left describe the compounds included in each subset, with P-gp as the top left circle, BCRP as the top right circle, and MRP2 as the circle at the bottom. Anova with Tukey’s post test was used to test the significance of the inter-group differences, with one, two and three stars denoting p < 0.05, p < 0.01, and p < 0.001, respectively. n.s.: not significant (p > 0.05) (PDF 68.6 KB).
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Matsson, P., Pedersen, J.M., Norinder, U. et al. Identification of Novel Specific and General Inhibitors of the Three Major Human ATP-Binding Cassette Transporters P-gp, BCRP and MRP2 Among Registered Drugs. Pharm Res 26, 1816–1831 (2009). https://doi.org/10.1007/s11095-009-9896-0
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DOI: https://doi.org/10.1007/s11095-009-9896-0