Drug Transporters: Advances and Opportunities

Govindarajan, Rajgopal; Sparreboom, Alex

doi:10.1002/cpt.454

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…Multi-specific “drug” transporters consist of roughly 50–100 solute carrier (SLC) and ATP-binding cassette (ABC) transporters expressed in all epithelial, as well as many non-epithelial, tissues throughout the body 1–3 . This is a subset of the more than 400 SLC and ABC transporters with mono-specificity, oligo-specificity and multi-specificity 4,5 . Together with Phase1 and Phase2 “drug” metabolizing enzymes, these genes are considered critical in the absorption, distribution, metabolism and elimination (ADME) of small molecule drugs and toxins.…”

Section: Introductionmentioning

confidence: 99%

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Rosenthal

Bush

Nigám

2019

Sci Rep

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Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network—including SLC and ABC “drug” transporters, “drug” metabolizing enzymes (DMEs), and regulatory genes—is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60–70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney “remote sensing and signaling network” consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules.

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Section: Introductionmentioning

confidence: 99%

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Rosenthal

Bush

Nigám

2019

Sci Rep

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“…Given the overwhelming evidence [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] that pharmaceutical drugs must and do exploit endogenous transporters that normally transport biological metabolites, and that normally any diffusion of such drugs through the phospholipid bilayer portions of undamaged biological membranes is negligible [1, 3, 5-7, 10, 11, 13, 21], we [2,[22][23][24] and others (e.g. [16,[25][26][27][28][29][30]) have been assessing the extent to which marketed (hence successful) xenobiotic drugs are similar in structural terms to endogenous human metabolites (that we sometimes refer to as 'endogenites').…”

Section: Introductionmentioning

confidence: 99%

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

O’Hagan¹,

Kell

2017

ADMET DMPK

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We compare several molecular fingerprint encodings for marketed, small molecule drugs, and assess how their rank order varies with the fingerprint in terms of the Tanimoto similarity to the most similar endogenous human metabolite as taken from Recon2. For the great majority of drugs, the rank order varies very greatly depending on the encoding used, and also somewhat when the Tanimoto similarity (TS) is replaced by the Tversky similarity. However, for a subset of such drugs, amounting to some 10 % of the set and a Tanimoto similarity of ~0.8 or greater, the similarity coefficient is relatively robust to the encoding used. This leads to a metric that, while arbitrary, suggests that a Tanimoto similarity of 0.75-0.8 or greater genuinely does imply a considerable structural similarity of two molecules in the drug-endogenite space. Although comparatively few (<10 % of) marketed drugs are, in this sense, robustly similar to an endogenite, there is often at least one encoding with which they are genuinely similar (e.g. TS > 0.75). This is referred to as the Take Your Pick Improved Cheminformatic Analytical Likeness or TYPICAL encoding, and on this basis some 66 % of drugs are within a TS of 0.75 to an endogenite.We next explicitly recognise that natural evolution will have selected for the ability to transport dietary substances, including plant, animal and microbial ‘secondary’ metabolites, that are of benefit to the host. These should also be explored in terms of their closeness to marketed drugs. We thus compared the TS of marketed drugs with the contents of various databases of natural products. When this is done, we find that some 80 % of marketed drugs are within a TS of 0.7 to a natural product, even using just the MACCS encoding. For patterned and TYPICAL encodings, 80 % and 98 % of drugs are within a TS of 0.8 to (an endogenite or) an exogenous natural product. This implies strongly that it is these exogeneous (dietary and medicinal) natural products that are more to be seen as the ‘natural’ substrates of drug transporters (as is recognised, for instance, for the solute carrier SLC22A4 and ergothioneine). This novel analysis casts an entirely different light on the kinds of natural molecules that are to be seen as most like marketed drugs, and hence potential transporter substrates, and further suggests that a renewed exploitation of natural products as drug scaffolds would be amply rewarded.

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“…It is becoming increasingly clear that the transmembrane transport of drugs and xenobiotics via any trans-phospholipid bilayer diffusion is probably negligible, and thus that they have to “hitchhike” on the transporters of intermediary metabolism in order to get into cells [1–19]. Consequently, we [2, 20–22] and others (e.g.…”

Section: Introductionmentioning

confidence: 99%

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

O’Hagan

Kell

2017

J Cheminform

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In previous work, we have assessed the structural similarities between marketed drugs (‘drugs’) and endogenous natural human metabolites (‘metabolites’ or ‘endogenites’), using ‘fingerprint’ methods in common use, and the Tanimoto and Tversky similarity metrics, finding that the fingerprint encoding used had a dramatic effect on the apparent similarities observed. By contrast, the maximal common substructure (MCS), when the means of determining it is fixed, is a means of determining similarities that is largely independent of the fingerprints, and also has a clear chemical meaning. We here explored the utility of the MCS and metrics derived therefrom. In many cases, a shared scaffold helps cluster drugs and endogenites, and gives insight into enzymes (in particular transporters) that they both share. Tanimoto and Tversky similarities based on the MCS tend to be smaller than those based on the MACCS fingerprint-type encoding, though the converse is also true for a significant fraction of the comparisons. While no single molecular descriptor can account for these differences, a machine learning-based analysis of the nature of the differences (MACCS_Tanimoto vs MCS_Tversky) shows that they are indeed deterministic, although the features that are used in the model to account for this vary greatly with each individual drug. The extent of its utility and interpretability vary with the drug of interest, implying that while MCS is neither ‘better’ nor ‘worse’ for every drug–endogenite comparison, it is sufficiently different to be of value. The overall conclusion is thus that the use of the MCS provides an additional and valuable strategy for understanding the structural basis for similarities between synthetic, marketed drugs and natural intermediary metabolites. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0198-y) contains supplementary material, which is available to authorized users.

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Drug Transporters: Advances and Opportunities

Cited by 9 publications

References 24 publications

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Consensus rank orderings of molecular fingerprints illustrate the most genuine similarities between marketed drugs and small endogenous human metabolites, but highlight exogenous natural products as the most important ‘natural’ drug transporter substrates

Analysis of drug–endogenous human metabolite similarities in terms of their maximum common substructures

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