Transporters as Drug Targets E-Book
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- Herausgeber: Wiley-VCH Verlag GmbH & Co. KGaA
- Kategorie: Wissenschaft und neue Technologien
- Serie: Methods and Principles in Medicinal Chemistry
- Sprache: Englisch
As opposed to other books on the topic, this volume is unique in also covering emerging transporter targets.
Following a general introduction to the importance of targeting transporter proteins with drugs, the book systematically presents individual transporter classes and explains their pharmacology and physiology. The text covers all transporter families with known or suspected importance as drug targets, including neurotransmitter transporters, ABC transporters, glucose transporters and organic ion transporters. The final part discusses recent advances in structural studies of transport proteins, assay methods for transport activity, and the systems biology of transporters and their regulation.
With its focus on drug development issues, this authoritative overview is required reading for researchers in industry and academia targeting transport proteins for the treatment of disease.
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Veröffentlichungsjahr: 2016
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Contents
Cover
Methods and Principles in Medicinal Chemistry
Title Page
Copyright
Preface
References
A Personal Foreword
Chapter 1: Insights into Transporter Classifications: an Outline of Transporters as Drug Targets
1.1 Introduction
1.2 Available Transporter Classifications
1.3 Function versus Sequence Similarity
1.4 Merged Top-Level Transporter Classification
1.5 Choice and Design of the New ChEMBL Classification
1.6 Transporter as Drug Targets
1.7 Drug Targets in the SLC Classification
1.8 Conclusions
Acknowledgment
References
Chapter 2: New Trends in Antidepressant Drug Research
2.1 Introduction
2.2 Reuptake Blockers
2.3 Multimodal Drugs
2.4 Conclusions
List of Abbreviations
References
Chapter 3: The Molecular Basis of the Interaction Between Drugs and Neurotransmitter Transporters
3.1 Introduction
3.2 Crystal Structures of SLC6 Transporters
3.3 The Binding Site Proper
3.4 The Transport Cycle
3.5 Conclusions and Perspectives
Acknowledgments
References
Chapter 4: γ-Aminobutyric Acid and Glycine Neurotransmitter Transporters
4.1 Introduction
4.2 GABA Transporters
4.3 Glycine Transporters
4.4 Conclusions and Future Perspectives
References
Chapter 5: ABC Transporters: From Targets to Antitargets and Back
5.1 Introduction
5.2 ABC Transporter as Drug Targets
5.3 ABC Transporter: from Targets to Antitargets
5.4 Pharmacochaperones and Beyond
5.5 Conclusions and Outlook
Acknowledgment
References
Chapter 6: ABC Transporters Involved in Cholestasis
6.1 Introduction
6.2 Canalicular ABC Transporters
6.3 Basolateral ABC Transporters
6.4 Nuclear Receptors as Drug Targets
6.5 Ursodeoxycholic Acid Treatment in Cholestatic Liver Disease
6.6 Conclusions
References
Chapter 7: Recent Advances in Structural Modeling of ABC Transporters
7.1 Introduction
7.2 ABC Transporter Modeling Attempts Since 2001
7.3 Retraction of Five Transporter Structures
7.4 First Mammalian ABC Transporter Structure
7.5 Conclusions and Perspectives
Acknowledgment
References
Chapter 8: PET Imaging of ABC Transporters at the Blood–Brain Barrier
8.1 The Blood–Brain Barrier
8.2 The Brain as a Pharmacological Sanctuary
8.3 Implication of ABC Transporters in Neurological Disorders
8.4 Positron Emission Tomography
8.5 PET Imaging of ABC Transporters
8.6 Challenges in Designing PET Tracers for ABC Transporters
8.7 Potential Applications of PET Tracers for ABC Transporters
8.8 Overview of Available PET Tracers for Cerebral ABC Transporters
8.9 Summary
Abbreviations
References
Chapter 9: The Systems Biology of Transporters – Targeting the Regulatory System for Transporters (FXR/RXR)
9.1 Introduction
9.2 Discovery and Pharmacological Characterization of FXR
9.3 Regulation of the Hepatobiliary Transport System by FXR
9.4 Genetic and Structural Properties of FXR
9.5 FXR Ligands
9.6 Conclusions and Perspectives
References
Chapter 10: ANO1 as a Novel Drug Target
10.1 Introduction
10.2 ANO1: a Calcium Activated Chloride Channel
10.3 Pharmacological Targeting of ANO1
10.4 ANO1 as a Therapeutic Target
10.5 Concluding Remarks
References
Chapter 11: Ligand Discovery for the Nutrient Transporters ASCT2 and LAT-1 from Homology Modeling and Virtual Screening
11.1 Solute Carriers in Cancer Metabolism
11.2 In Silico Methods for Structure-based Drug Design
11.3 Emerging Cancer Metabolism Targets
11.4 Conclusions and Future Outlook
Acknowledgment
References
Chapter 12: Organic Anion Transporting Polypeptides as Drug Targets
12.1 Introduction
12.2 OATPs and Genetic Diseases
12.3 OATPs and Cancer
12.4 OATPs as Diagnostic Markers
12.5 OATPs and Selective Delivery of Drugs
12.6 Potential Protective Role of OATPs
12.7 OATPs and Drug–Drug Interactions
12.8 Conclusions and Outlook
Acknowledgments
Abbreviation List
References
Index
End User License Agreement
List of Tables
Table 1.1
Table 1.2
Table 1.3
Table 1.4
Table 1.5
Table 1.6
Table 2.1
Table 2.2
Table 2.3
Table 3.1
Table 4.1
Table 4.2
Table 4.3
Table 5.1
Table 11.1
Table 12.1
Table 12.2
Table 12.3
List of Illustrations
Figure 1.1
Figure 1.2
Figure 1.3
Figure 1.4
Scheme 2.1
Scheme 2.2
Scheme 2.3
Scheme 2.4
Scheme 2.5
Scheme 2.6
Scheme 2.7
Scheme 2.8
Figure 2.1
Figure 3.1
Figure 3.2
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 5.1
Figure 5.2
Figure 5.3
Figure 6.1
Figure 6.2
Figure 6.3
Figure 7.1
Figure 7.2
Figure 7.3
Figure 7.4
Figure 8.1
Figure 8.2
Figure 9.1
Figure 9.2
Figure 9.3
Figure 9.4
Figure 9.5
Figure 9.6
Figure 9.7
Figure 9.8
Figure 9.9
Figure 9.10
Figure 9.11
Figure 9.12
Figure 9.13
Figure 9.14
Figure 9.15
Figure 10.1
Figure 10.2
Figure 11.1
Figure 11.2
Figure 12.1
Figure 12.2
Figure 12.3
Figure 12.4
Figure 12.5
Figure 12.6
Figure 12.7
Figure 12.8
Guide
Cover
Table of Contents
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Methods and Principles in Medicinal Chemistry
Edited by R. Mannhold, G. Folkers, H. BuschmannEditorial BoardJ. Holenz, H. Kubinyi, H. Timmerman, H. van de Waterbeemd, John Bondo Hansen
Previous Volumes of this Series:
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Medicinal Chemistry Approaches to Personalized Medicine
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Edited by Gerhard F. Ecker,Rasmus P. Clausen, and Harald H. Sitte
Transporters as Drug Targets
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Preface
Drug transport across biological membranes fundamentally influences both the biological activity as well as the ADMET (absorption, distribution, metabolism, excretion, toxicity) properties of all small molecules. Therefore, transport proteins represent an eminent class of drug targets and ADMET-associated genes. Besides passive diffusion, transmembrane transport proteins play a pivotal role in the translocation of compounds, both across cellular membranes and physiological barriers. Passive and active transport across membranes is a pivotal process in all living species [1]. It allows a continuous communication of neighboring cells with simultaneous separation of compartments: channels and transmembrane transporters facilitate the active translocation of materials across membranes. Being embedded in the lipid bilayer, those specialized proteins turn the cell membrane into a selective “filter” [2].
About 800 human membrane transport proteins (including channels and transporters) are currently well characterized and for about 10% (approx. 2000) of all human genes a relation to transport is estimated. The gene families encode proteins transporting substrates that range from ions to sugars, amino acids, biogenic amines, lipids, and both hydrophilic and lipophilic xenobiotics. Membrane transport proteins are of interest as potential drug targets, for drug delivery, and as a cause of side effects and drug–drug interactions [2].
Drug transporters are multispecific transmembrane proteins that facilitate the membrane passage of most drugs. Drug transporters have a distinct expression pattern in the human body lining pharmacological barrier tissues, most importantly the small intestinal epithelium, the endothelial cells in the blood–brain barrier, the epithelium of the proximal tubule cells in the kidney, and hepatocytes in the liver [3]. Membrane transporters play a central role in the pathology of many diseases and have been acknowledged as one of the major protein classes to be targeted in future drug development. Thus, they are considered important as potential drug targets or antitargets for drug delivery and for drug–drug interactions.
With the increasing knowledge on their importance, regulatory bodies also started to request studies on drug–transporter interaction for selected transporter. However, the process of drug transport is quite complex, which renders the whole issue quite challenging [1].
The present volume focuses on transporters as drug targets themselves; it perfectly completes a former volume in this series on “Transporters as drug carriers,” with the focus on drug delivery and disposition [4]. The book will not provide a comprehensive overview of the wide field of drug transporters and their impact on the current drug discovery and development field, but will focus on some of the more relevant and well-established transporter drug targets. The first chapter provides insights into transporter classifications to get an overview about a topic. The classification scheme shows instances grouped together that share common properties according to the creator of the classification, and as in the case of hierarchical classifications they allow conclusions on the relation of different classes.
As a relevant example of well-established transporter drug targets a chapter of new trends in antidepressant drug research is presented, followed by a chapter discussing the molecular basis of the interaction between drugs and neurotransmitter transporters. Another chapter is focussing on γ-aminobutyric acid and glycine neurotransmitter transporters. There is strong evidence that ATP-binding cassette (ABC) transporters play a critical role in the pharmacokinetic and pharmacodynamic properties of many drugs and xenobiotics. 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. The importance and relevance of ABC transporters is covered by four book chapters. “ABC-Transporters – From targets to antitargets and back” is providing a general overview of the ABC transporter system and is followed by a chapter showing the role of the ABC transporter exemplarily in the therapeutic indication of cholestasis. The structure-based transporter research is described in the chapter “Recent Advances in Structural Modeling of ABC Transporters.” The growing importance of imaging methods in drug development is provided in the chapter “PET imaging of ABC transporters at the blood–brain barrier.”
In addition, some of the new promising transporters along with the structure-based information are presented as well covering “The Systems Biology of Transporters – Targeting the Regulatory System for Transporters (FXR, RXR),” “ANO1 as a novel drug target,” “Ligand discovery for the nutrient transporters ASCT2 and LAT-1 from homology modeling and virtual screening” as well as the emerging role of “Organic Anion Transporting Polypeptides as Drug Targets.”
We are grateful to Gerhard F. Ecker, Rasmus P. Clausen, and Harald H. Sitte for organizing this important volume and to work with such excellent authors. Last, but not least we thank Frank Weinreich and Waltraud Wüst from Wiley-VCH for their valuable contributions to this project and to the entire book series.
DüsseldorfZürichAachenOctober 2016
Raimund MannholdGerd FolkersHelmut Buschmann
References
1
Ecker, G.F. (2014) Transmembrane drug transporter – taxonomy, assays, and their role in drug discovery.
Drug. Discov. Today Technol.
,
12
, e35–e36.
2
Viereck, M., Gaulton, A., Digles, D., and Ecker, G.F. (2014) Transporter taxonomy – a comparison of different transport protein classification schemes.
Drug. Discov. Today Technol.
,
12
, e37–e46.
3
Márton, J. and Krajcsi, P. (2014)
In vitro
methods in drug transporter interaction assessment.
Drug. Discov. Today Technol.
,
12
, e105–e112.
4
Ecker, F. and Chiba, P. (2010)
Transporters as drug carriers: structure, function, substrates
, vol.
44
, Methods and Principles in Medicinal Chemistry, Series Editors: R. Mannhold, H. Kubinyi, and G. Folkers, Wiley-VCH.
A Personal Foreword
The generation of shielded compartments by phospholipid bilayers is of fundamental importance for the separation of internal and external milieus and, thus, for preserving the cell integrity. Owing to its hydrophobic nature, the lipid barrier imposes constraints on the movement of solutes, but it does not provide a completely impermeable barrier. Accordingly, cells have evolved mechanisms to selectively accumulate individual compounds or to preclude entry of xenobiotic or toxic compounds. This is, in general, achieved by a great variety of transmembrane transporters (TMTs). A genomic survey shows that there are more than 400 gene families, which encode proteins transporting substrates that range from ions to sugars, amino acids, biogenic amines, lipids, and both hydrophilic and lipophilic xenobiotics (including anticancer drugs, antimicrobial agents, and drugs of abuse).
It is obviously of intrinsic interest to understand how one or several molecules can be translocated through the hydrophobic core of the lipid bilayer in a way that prevents a short circuit. In addition, it is self-evident that TMTs are vital to afford the selective excretion and retention of solutes in a multicellular organism. It has long been known that TMTs are very important targets of therapeutically relevant drugs. In fact, in the early 1960s, Axelrod, Whitby, and Hertting made the seminal discovery that antidepressant drugs block the transport of monoamines. TMTs for norepinephine, serotonin, and dopamine are still exploited as the most important drug targets for the treatment of depression, narcolepsy, and ADHD. TMTs are not only important as therapeutic targets but are also relevant for understanding the pathophysiology of diseases, the individual variability in susceptibility both to drugs and to environmental input. Subtle differences may, for instance, predispose to diseases (e.g., depression or psychosis) or may confer resistance to pharmacotherapy. Important examples for these nontarget proteins are the product of the MDR1 gene (multidrug resistance protein 1/ABCB1 or P-glycoprotein, P-gp) and other members of the ABC transporter family, including BCRP/ABCG2, MRP1/ABCC1, and MRP2/ABCC2. Bile salts are also transported by an ABC transporter (ABCB11). Thus, it is of clinical relevance to explore the role of canalicular ABC transporters. They modulate the milieu in the downstream bile ducts and thus affect the function of cholangiocytes. They also play a prominent role in bile duct injury.
Previously, “transporters as drug carriers” focused on drug delivery and disposition. In this book, we focus on the transporters as drug targets themselves. Many transporters are well-known drug targets like the monoamine transporters, where compounds have been clinically approved for the treatment of depression disorders, but new transporter targets are also coming up. For the latter transporter group, development of tool compounds is critical to complement information gained by biological means like knockout studies in the validation of these transporters as drug targets. The book is by no means comprehensive, but it will focus on some of the most important well-established transporter drug targets and present some of the new promising transporters along with the wealth of information that has been gained in recent years on the molecular structure from X-ray crystallographic studies. In combination with molecular pharmacological and/or structure–activity relationship studies, this has provided detailed insights into uptake mechanisms, how the compounds interact with transporters and modulate their action.
We would like to thank all authors for their excellent contributions and also for their patience during the editing process. We would also like to express our sincere appreciation to Frank Weinreich, Waltraud Wüst, and the helpful hands at Wiley-VCH for their excellent support in the production of this book. Finally, we also thank Raimund Mannhold, Hugo Kubinyi, and Gerd Folkers for their enthusiasm and continuous efforts to provide the medicinal chemistry community with this outstanding Methods and Principles series of books.
Enjoy reading!July 2016:
Gerhard F. Ecker, ViennaRasmus P. Clausen, CopenhagenHarald H. Sitte, Vienna
1Insights into Transporter Classifications: an Outline of Transporters as Drug Targets
Michael Viereck,1 Anna Gaulton,2 and Daniela Digles1
1University of Vienna, Department of Pharmaceutical Chemistry, Althanstrasse 14, 1090 Vienna, Austria
2EMBL – European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
1.1 Introduction
Classifications are a useful tool to get an overview of a topic. They show instances grouped together that share common properties according to the creator of the classification, and as in the case of hierarchical classifications, they allow to draw conclusions on the relation of different classes. In the case of the identification of drug targets (including transporters as the main drug target), several publications show classifications as a helpful tool.
Imming et al. [1] categorized drugs according to their targets, to get an estimate on the number of known drug targets, including channels and transporters. Drugs on the market were connected to a target only if it was described as the main target in the literature. For transport proteins (including uniporters, symporters, and antiporters), this identified six different types of transporter groups that are relevant as drug targets. These are the cation-chloride cotransporter (CCC) family (SLC 12), Na+/H+ antiporters (SLC 9), proton pumps, Na+/K+ ATPases, the eukaryotic (putative) sterol transporter (EST) family, and the neurotransmitter/Na+ symporter (NSS) family (SLC 6). These families mainly belong to either ATPases or solute carriers (SLC). Whether the EST family is treated as transport protein or not depends on the classification used.
Rask-Andersen et al. [2] used a manually curated and extended version of the DrugBank [3] data from 2009 to analyze drug targets. They identified 435 therapeutic effect-mediating targets, where the third largest group (67) is of transporter proteins (including 35 ion channels). These transport proteins are mainly targeted by antihypertensive drugs, diuretics, anaesthetics, and antiarrhythmic drugs. In a more recent study [4], they analyzed the Drugs in the Clinical Trials Database by CenterWatch to investigate the targets of new drug candidates. Transporter proteins in this data set were classified using the transporter classification (TC) system established by Saier et al. [5].
This chapter will first give an overview on selected existing transporter classifications and then describe our process of creating a combined classification scheme for the ChEMBL [6] database. Finally, the investigation of the counts of drugs and diseases for one example protein superfamily is provided, to show the usefulness of classifications in characterizing related proteins and to give a first overview on the topic of this book, focusing on transporters as drug targets.
1.2 Available Transporter Classifications
As a consequence of the significance of the transport process for every living organism, already numerous classification schemes for membrane transport proteins of several organisms exist. Quite a few focus only on specific families, for instance, the SLC superfamily or the ABC transporter superfamily. Table 1.1 shows a selection of membrane transport protein databases with a focus on human proteins. To keep the table concise, databases focusing on different organisms (e.g., the plant membrane protein database Aramemnon [7], the yeast transport protein database YTPdb [8], or ABCdb [9], a database about bacterial ABC systems) are not included, even though bacterial or protozoal channels and transporters can also be promising drug targets [10,11].
Table 1.1 Selection of transporter collections with a focus on human membrane transport proteins.
Database
Description
URL
Included proteins
Limited to organisms
Bioparadigms SLC Series [12]
Online resource of the 52 human solute carrier families
slc.bioparadigms.org/
Solute carriers
Homo sapiens
ChEMBL [6]
Large-scale bioactivity database for drug discovery
https://www.ebi.ac.uk/chembldb/target/browser
Proteins with bioactivity data
—
Human ABC transporters [13]
Basic information about human ABC transporters
www.nutrigene.4t.com/humanabc.htm
ABC transporters
Homo sapiens
IUPHAR/BPS Guide to PHARMACOLOGY [14]
Overview of human drug targets with their pharmacology
www.guidetopharmacology.org
Drug targets
Homo sapiens
, and
Mus musculus
,
Rattus norvegicus
TCDB [5]
Provides a classification scheme for all membrane transport proteins in all living organism
www.tcdb.org/
Channels, transporters, auxiliary transport proteins
—
TransportDB [15]
Genomic comparison of membrane transport proteins, prediction of their function, and classification according to TCDB
www.membranetransport.org/index.html
Channels, transporters, auxiliary transport proteins
Complete genome-sequenced organisms (365 total)
Transporter substrate database (TSdb) [16]
Provides a transporter substrate repository with mappings to KEGG pathways
http://tsdb.cbi.pku.edu.cn/home.cgi
Transporters
—
UCSF-FDA TransPortal [17]
Provides information on transporters important in the drug discovery process
http://bts.ucsf.edu/fdatransportal/
Transporters
Homo sapiens
We were interested in classification schemes that not only try to cover the full variety of human membrane transport proteins but also provide their own complete classification. Therefore, we analyzed the functional and phylogenetic classification scheme of the Transporter Classification Database (TCDB), the more pharmacology-driven IUPHAR/BPS classification, and the mainly functional-driven classification of channels and transporters in the bioactivity database ChEMBL-16 [18]. These were recently reviewed in [19], which provides a more detailed discussion for the interested reader. In addition, we included the SLC series [12], which is a well-known nomenclature system.
1.2.1 TCDB
The transporter classification system developed in the laboratory of Saier is to some extent comparable to the Enzyme Commission (EC) classification. But while the EC system concentrates on the function of enzymes, the transporters in the TC system are classified according to function and phylogeny [20]. Both schemes are recommended by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology [21,22] (URL: http://www.chem.qmul.ac.uk/iubmb/mtp/). The TC is also included in several other databases such as UniProt [23] (URL: www.UniProt.org/) or the Protein Data Bank (PDB) [24] (URL: http://www.rcsb.org/pdb/). TCDB is an exhaustive classification including over 750 transporter families, and over 10 000 protein sequences are included in the Transporter Classification Database [5] (URL: www.tcdb.org/). The database stores sequences and information regarding all classified transport proteins. Transporters can be found in the database either by browsing the classification or by directly searching for the protein of interest (e.g., by UniProt accession number). For unclassified proteins, similar sequences can be found using a BLAST search.
The classification scheme contains five levels as exemplified for 2.A.22.6.3 in Table 1.2. The first level number indicates the class of membrane transport protein. This can be, for example, a channel or primary active transporter. Interestingly, also accessory factors involved in transport are included. Classes 6 and 7 are currently empty and serve as placeholders for yet undiscovered types of transport and class 9 contains not yet fully characterized transporters. Next, a letter indicates the transporter subclass (e.g., energy source for primary active transport), followed by a number for the transporter family or superfamily. The assignment of a transporter to a specific family follows strict statistical criteria of homology, requiring comparison over a region of at least 60 residues and a probability of 10−19 or less than this degree of sequence similarity occurred by coincidence [25]. The fourth level indicates the transporter subfamily and the last level classifies a transport system according to its substrate or range of substrates. To summarize, the first two levels describe the function of the transporter, the next two classify according to phylogenetic similarity, and the last one defines the substrate or indicates the belonging to a transport system.
Table 1.2 Transport system 2.A.22.6.3 as an example of the TCDB classification.
TCDB level
TC number of the level
TCDB name of the exemplary level
Transporter class (level 1, functional):
2
Electrochemical potential-driven transporters
Transporter subclass (level 2, functional):
2.A
Porters (uniporters, symporters, and antiporters)
Transporter family/superfamily (level 3, phylogenetic):
2.A.22
The neurotransmitter:sodium symporter family
Transporter subfamily (level 4, phylogenetic):
2.A.22.6
No explicit level name
TCDB level 5 (examples for the transport system having the same substrate):
2.A.22.6.3
No explicit level name, given examples:
Sodium-dependent neutral amino acid transporter B(0)AT1 (human)
Sodium-dependent neutral amino acid transporter B(0)AT1 (mouse)
Transmembrane protein 27 aka TMEM27
1.2.2 IUPHAR/BPS
The Guide to PHARMACOLOGY database and web page (IUPHAR/BPS Guide to PHARMACOLOGY; URL: www.guidetopharmacology.org/) is created by cooperation between the British Pharmacological Society (BPS) and the International Union of Basic and Clinical Pharmacology (IUPHAR). Originally, they provided on their web page access to their two independent databases, the BPS Guide to Receptors and Channels [26] and the IUPHAR database [27]. Since 2014, the IUPHAR database is included in the BPS database and the web page was renamed from BPS Guide to Receptors and Channels to IUPHAR/BPS Guide to PHARMACOLOGY [28].
The IUPHAR/BPS Guide to PHARMACOLOGY is an expert-driven collection of pharmacological targets and the substances that act on them. It contains several different sections, including G protein-coupled receptors (GPCRs), ion channels, nuclear hormone receptors (NHRs), kinases, catalytic receptors, enzymes, other protein targets, and transporters.
The transporters are divided into the ATP-binding cassette family, F-type and V-type ATPases, P-Type ATPases, major facilitator superfamily (MFS) of transporters, and the SLC superfamily of solute carriers. The nomenclature follows mainly the HGNC gene families. In addition to the standard SLC nomenclature (SLC family 1–52), some of the SLC families are further divided according to commonality of the substrate.
The channels are divided into voltage-gated ion channels, ligand-gated ion channels, and other ion channels.
1.2.3 ChEMBL-16 and ChEMBL-18
The ChEMBL database [6] (URL: https://www.ebi.ac.uk/chembl/) provides large-scale bioactivity data, linking small molecules to the protein targets through which they exert their effects. In order to facilitate browsing and analysis of these data, it was necessary to provide a protein family classification system within the database.
The emphasis of the ChEMBL-16 classification was on a functional rather than a sequence-based classification. Since the ChEMBL-16 transporter hierarchy was heavily focused on protein function, inclusion of new proteins was a largely manual process, relying on the availability of significant knowledge around these proteins. However, the exact transport mechanism (e.g., antiporter, uniporter, Na-symporter, or H-symporter) for a number of transporters, such as the OATPs, is unknown or not completely understood [29]. This makes it difficult to include them in this classification scheme.
Therefore, ChEMBL decided, starting with ChEMBL-18 [30], to move to a more phylogenetic classification that is easier to maintain. The classification is derived mainly from IUPHAR/BPS and TCDB. The idea and schema of this combined classification is described below and depicted in Table 1.5.
1.2.4 SLC Series
In the 1990s, Matthias A. Hediger developed the nomenclature of solute carrier families in collaboration with Phyllis McAlpine [31]. The HUGO Gene Nomenclature Committee (HGNC, available from www.genenames.org) included this nomenclature for gene names of classical membrane transport proteins. Although originally introduced for human genes, the term is sometimes used for nonhuman species as well.
A collection of this 52 family-containing series is available from www.bioparadigms.org. Although the number of included transporters is limited to a specific type of membrane transport proteins, available reviews for each family by experts [12], and manually curated information on transport type, substrates, and expression, make this collection a valuable resource.
Members within an individual SLC family share more than 20% sequence similarity with each other, but the homology between the 52 families is often quite low or nonexistent [32]. The SLC members are treated in different ways in other classifications. IUPHAR/BPS summarizes the SLC families in a superfamily. Due to the fact that SLC families have only a vague definition in common (membrane transport proteins that are not driven by ATP) and their sometimes missing sequence similarity, TCDB has no single class that contains all SLC families. SLC families that are not found in class 2 of TCDB (electrochemical potential-driven transporters) are given in Table 1.3.
Table 1.3 SLC members counterparts in TCDB that do not belong to TCDB class 2: electrochemical potential-driven transporters.
SLC family
TC class
TC family
SLC42 Rh ammonium transporter family
Channels/pores
1.A.11 the ammonia transporter channel (Amt) family
SLC41 MgtE-like magnesium transporter family
Channels/pores
1.A.26 the Mg2+ transporter-E (MgtE) family
SLC14 urea transporter (UT) family
Channels/pores
1.A.28 the urea transporter (UT) family
SLC31 copper transporter family
Channels/pores
1.A.56 the copper transporter (Ctr) family
SLC27 fatty acid transporter family
Group translocators
4.C.1 the proposed fatty acid transporter (FAT) family
SLC3 Heavy subunits of the heteromeric amino acid transporters
Accessory factors involved in transport
8.A.9 the rBAT transport accessory protein (rBAT) family
SLC52 riboflavin transporter family RFVT/SLC52
Incompletely characterized transport systems
9.A.53 the eukaryotic riboflavin transporter (E-RFT) family
SLC50 sugar efflux transporters
Incompletely characterized transport systems
9.A.58 the sweet; PQ-loop; saliva; MtN3 (sweet) family
The SLC proteins belong to several different Pfam [33] clans, thus sharing specific sequence motifs. The largest ones are the major facilitator superfamily and the amino acid-polyamine-organocation (APC) superfamily, which contain members of 14 and 9 SLC families, respectively [32]. The affiliation of the SLC families to a superfamily is depicted in Figure 1.2.
1.3 Function versus Sequence Similarity
Regarding their function, channels and transporters are clearly distinguishable, but protein classifications in our days classify besides functional criteria often according to phylogenetic relationships. Therefore, ABC transporter proteins that act as channels (CFTR (ABCC7), SUR1 (ABCC8), and SUR2 (ABCC9)) or as translation factors (ABCE1, ABCF1, ABCF2, and ABCF3) are assigned as transporters in TCDB due to their sequence similarity to the functional ABC transporters. Also, the majority of proteins in the solute carrier superfamily act as secondary active transporter and, therefore, the 52 SLC families are included as transporters in IUPHAR/BPS. However, for more than a few solute carrier members, the transport mechanism is currently unrevealed and for some it is known that they function as channels. Table 1.4 shows the contradictory classification of some membrane transport proteins in TCDB, IUPHAR/BPS, and ChEMBL-16.
Table 1.4 Contradictory classification in TCDB, IUPHAR/BPS, and ChEMBL-16.
Protein name
Gene name
TCDB
IUPHAR/BPS
ChEMBL-16
Ammonium transporter Rh type C
RHCG C15orf6 CDRC2 PDRC2 RHGK
Channels/pores
Transporters
—
Ammonium transporter Rh type B
RHBG
Channels/pores
Transporters
—
Ammonium transporter Rh type A
RHAG RH50
Channels/pores
Transporters
—
Solute carrier family 41 member 1
SLC41A1
Channels/pores
Transporters
—
Solute carrier family 41 member 2
SLC41A2
Channels/pores
Transporters
—
Solute carrier family 41 member 3
SLC41A3
Channels/pores
Transporters
—
Urea transporter 1
SLC14A1 HUT11 JK RACH1 UT1 UTE
Channels/pores
Transporters
—
Urea transporter 2
SLC14A2 HUT2 UT2
Channels/pores
Transporters
—
High-affinity copper uptake protein 1
SLC31A1 COPT1 CTR1
Channels/pores
Transporters
—
Probable low-affinity copper uptake protein 2
SLC31A2 COPT2 CTR2
Channels/pores
Transporters
—
ATP-binding cassette subfamily E member 1
ABCE1 RLI RNASEL1 RNASELI RNS4I OK/SW-cl.40
Primary active transporters
—
—
ATP-binding cassette subfamily F member 1
ABCF1 ABC50
Primary active transporters
—
—
ATP-binding cassette subfamily F member 3
ABCF3
Primary active transporters
—
—
ATP-binding cassette subfamily F member 2
ABCF2 HUSSY-18
Primary active transporters
—
—
Cystic fibrosis transmembrane conductance regulator
CFTR ABCC7
Primary active transporters
Ion channels
Transporter
ATP-binding cassette subfamily C member 9
ABCC9 SUR2
Primary active transporters
Transporters
Ion channel
ATP-binding cassette subfamily C member 8
ABCC8 HRINS SUR SUR1
Primary active transporters
Transporters
Ion channel
Figure 1.1 tries to give an overall impression of human proteins involved in transmembrane transport. Furthermore, basic differences between the more phylogenetic-driven classification TCDB and the more pharmacological-driven classification IUPHAR/BPS can be read out.
Figure 1.1 Overview of human membrane transport proteins.
Figure 1.1 contains some simplification. Not for every IUPHAR/BPS level is an exact counterpart available in TCDB and vice versa. For instance, there is no SLC superfamily in TCDB. Equivalents of SLC families can be found in TCDB class 1, 2, 4, and 8, but they are classified mainly into class 2 (electrochemical potential-driven transporters). Table 1.3 lists the SLC counterparts in TCDB that are not labeled as electrochemical potential-driven transporters. Furthermore, the equivalent of an SLC family in TCDB can contain proteins related to this SLC family but not belonging to this SLC family. For instance, the Rh ammonium transporter family (SLC 42) comprises three Rh glycoproteins, namely, RhAG, RhBG, and RhCG. In red blood cells, the ammonium transport is mediated by a complex of RhAG, RhCE, and RhD [34]. The latter two are not included in SLC 42, but due to their function and sequence similarity all five proteins share the same family (1.A.11 the ammonia channel transporter (Amt) family) and subfamily (1.A.11.4) in TCDB.
1.4 Merged Top-Level Transporter Classification
For a combined overview on human transporters, Figure 1.2 shows a coarse classification into four major groups (solute carriers, ATPases, ABC proteins, and other transporters). Figure 1.2 tries to reflect both human transporter classifications in TCDB and IUPHAR/BPS.
Figure 1.2 Simplified overview of human transport protein families. Abbreviations: MFS, major facilitator superfamily; APC, amino acid/polyamine/organocation superfamily; CPA, cation:proton antiporter superfamily.
The reported names of the protein groups and the number of proteins in Figure 1.2 provide only a rough guide that can vary considerably between the actual classifications. For instance, in a pure functional transporter classification, you may find only 41 human ABC proteins and 381 solute carriers. In a pharmacology-driven classification like IUPHAR/BPS, only the target of levetiracetam (the synaptic vesicle glycoprotein 2A, SV2A) from the group of other transporters is included. Nevertheless, the splitting into four major groups is inspired by the IUPHAR/BPS Guide to PHARMACOLOGY classification of transporters. The assignment to a Pfam clan or family (e.g., the MFS) for the 52 SLC families is color-coded to show their phylogenetic heterogeneity. The MFS group in the group of other transporters is also color-coded, to show the connection to proteins that are not assigned to an SLC family (e.g., SV2A). The subgroups of the last group in Figure 1.2 (other transporters) are derived from TCDB.
1.5 Choice and Design of the New ChEMBL Classification
Within the framework of the Open PHACTS project [35,36], we were interested to find a classification suitable for channels and transporters. For this, integrating the classification into the existing ChEMBL classification was chosen to facilitate maintainability. By querying different databases (UniProt: reviewed+human+keyword:transport (April 3, 2013); TCDB: all human proteins (May 30, 2013); HGNC: known channel and transporter gene families; GeneOntology: Homo sapiens+GO:0022857 transmembrane transport activity (June 15, 2013)), we compiled a list with 1144 human membrane transport proteins and additionally included 300 nonhuman transporters and channels of ChEMBL-16.
The comparison of the classifications for this list of proteins is shown in Figure 1.3. Each of the data sources contains proteins that are unique to this database. Even TCDB, which uses a comprehensive classification of all transport proteins, does not include all identified transporters. An explanation for this is that for each family, only some examples are provided but not an exhaustive list. On the other hand, TCDB covers even other drug targets such as proteins involved in endocytosis with the term “membrane transport protein.” For instance, targets of the medically used botulinum neurotoxin A, for example, SNAP-25 (TC-ID 1.F.1: the synaptosomal vesicle fusion pore (SVF-Pore) family) [37], and a target of the cholesterol-lowering drug ezetimibe, the Niemann-Pick C-1-like protein (TC-ID 2.A.6: the eukaryotic (putative) sterol transporter family) [38] are included in TCDB. SNAP-25 is not contained in IUPHAR/BPS and Niemann-Pick C-1-like protein is classified into other protein targets and patched family. Nevertheless, IUPHAR/BPS includes the highest number of transport proteins from this list, reflecting its focus on pharmacologically relevant proteins.
Figure 1.3 Overlap of classified membrane transport proteins in IUPHAR/BPS, TCDB, and ChEMBL-16.
To generate a classification that can include all proteins from the list, we first predicted, where possible, the classification of the proteins that were unclassified in IUPHAR/BPS (500) or TCDB (604). For proteins where this was not possible in IUPHAR/BPS, new groups were created or added from TCDB.
Finally, IUPHAR/BPS was used as the basis for ion channels, including some subclasses of TCDB. For the transporters, a combination of IUPHAR/BPS and TCDB was used, following TCDB for the first and second level, and afterward using an IUPHAR/BPS-based classification (including the concept of an SLC superfamily). This introduced some contradictions, which were accepted as the SLC classification is well known, thus increasing the usability. In addition, a top-level group of auxiliary transport proteins was introduced according to TCDB class 8.
Table 1.5 shows human transport protein containing classes and subclasses of TCDB and the equivalent groups in IUPHAR/BPS and ChEMBL-19. Text in italics indicates IUPHAR/BPS as source for the ChEMBL-19 classification.
Table 1.5 Human membrane transport protein classification of TCDB, IUPHAR/BPS, and ChEMBL-19 in contrast.
TCDB
IUPHAR/BPS
ChEMBL-19
Channels/pores
Alpha-type channels
Beta-barrel porins
Pore-forming toxins
Vesicle fusion pores
Paracellular channels
Membrane-bounded channels
Ion channels
Ligand-gated ion channels
Voltage-gated ion channels
Other ion channels
Aquaporins
Chloride channels
Connexins and pannexins
Sodium-leak channel, nonselective
Ion channels
Ligand-gated ion channels
Voltage-gated ion channels
Other ion channels
Aquaporins
Chloride channels
Connexins and pannexins
Sodium-leak channel, nonselective
Vesicle fusion pores
Annexins
…
Electrochemical transporter
Porters (uniporters, symporters, antiporters)
Primary active transporters
P-P-bond-hydrolysis-driven transporters
Oxidoreduction-driven transporters
Group translocators
Acyl CoA ligase-coupled transporters
Polysaccharide synthase/exporters
Transporters
SLC-superfamily of solute carriers
Major facilitator superfamily (MFS) of transporters
ATP-binding cassette transporter family
P-Type ATPases
F-type and V-type ATPases
Transporters
Electrochemical transporter
SLC superfamily of solute carriers
Vesicular neurotransmitter transporter family
Primary active transporter
ATP-binding cassette
P-Type ATPases
F-type and V-type ATPases
Endoplasmic reticular retrotranslocon family
Oxidoreduction-driven transporters
Group translocator
Transmembrane 1-electron transfer carriers
Transmembrane electron carriers
Transmembrane 1-electron transfer carriers
Accessory factors involved in transport
Auxiliary transport proteins
—
Auxiliary transport proteins
Incompletely characterized transport systems
—
—
1.6 Transporter as Drug Targets
To get the first insight into the topic of transporter as drug targets, Table 1.6 shows examples of approved drugs and the targeted transport protein group. The drugs in Table 1.6 are all derived from DrugBank. For some, the exact pharmacological mechanism is largely unknown, for example, artemisinin and derivates. Furthermore, the transporter may be one but not the main target for the indication. For instance, the diuretic effect of amiloride is mainly assigned to the inhibition of epithelial Na+-channels. Table 1.6 also includes some nonclassical transporters (printed in italics). These were included because these may be found in phylogenetic transporter classifications like TCDB and we share with Ashcroft et al. the view of a blurred boundary between channels and transporters [39]. For instance, the cystic fibrosis transmembrane conductance regulator (CFTR) protein acts as a chloride channel but is often classified as ABC transporter due to phylogenetic reasons. Also, the target of ezetimibe, which is neither a functional channel nor a transporter, is included as it was one of the examples given by Imming et al. [1].
Table 1.6 Approved drugs and targeted transport proteins.
Major group
Classification in IUPHAR/BPS (TC family and TC-ID of the targeted protein)
Example for approved drug
Drug group (ATC code)
Solute carrier
SLC2 facilitative GLUT transporter family (solute:sodium symporter (SSS) family; 2.A.21.3.16)
Dapaglifocin
Antidiabetic drug (A10BX09)
SLC6 sodium- and chloride-dependent neurotransmitter transporter family (neurotransmitter:sodium symporter family; 2.A.22.1.1)
Fluoxetin
Antidepressant (N06AB03)
SLC6 sodium- and chloride-dependent neurotransmitter transporter family (neurotransmitter:sodium symporter family; 2.A.22.3.2)
Tiagabine
Antiepileptics (N03AG06)
SLC9 Na+/H+ exchanger family (the monovalent cation:proton antiporter-1 (CPA1) family; 2.A.36.1.13)
Amiloride
Diuretics, potassium-sparing (C03DB01)
SLC12 electroneutral cation-coupled Cl cotransporter family (cation-chloride cotransporter family; 2.A.30.1.2)
Furosemide
Diuretics, high-ceiling (C03CA01)
SLC18 vesicular amine transporter family (the drug:H+ antiporter-1 (12 spanner) (DHA1) family; 2.A.1.2.29)
Reserpine
Antihypertensives (C02AA02)
Tetrabenazine
Hyperkinetic movement disorder (N07XX06)
SLC22 organic cation/anion/zwitterion transporter family (organic cation transporter (OCT) family; 2.A.1.19.10|2.A.1.19.31|2.A.1.19.34)
Probenecid
Uricosuric drug (M04AB01)
SLC25 mitochondrial carrier (MC) family (MC family; 2.A.29.1.2|2.A.29.1.1|2.A.29.1.10)
Clodronate
Osteoporosis, bone metastases (M05BA02)
SLC52 riboflavin transporter family RFVT/SLC52 (E-RFT family; 9.A.53.1.3)
Gamma hydroxybutyric acid
Anesthetics (N01AX11)
ATPases
P-type ATPase (P-type ATPase superfamily; 3.A.3.1.1)
Digitoxin
Cardiac glycosides (c1AA04)
P-type ATPase (P-type ATPase superfamily; 3.A.3.1.2)
Omeprazol
Proton pump inhibitors (A02Bc1)
P-type ATPase (P-type ATPase superfamily; 3.A.3.2.?)
Lumefantrine, artemether (artemisinin derivates)
Antimalarials (P01BF01)
ABC protein
ATP-binding cassette subfamily C member 7 (
cystic fibrosis transmembrane conductance exporter
(
CFTR
) family; 3.A.1.202.1)
Ivacaftor
Cystic fibrosis (R07AX02)
ATP-binding cassette subfamily C member 8 (the drug conjugate transporter (DCT) family; 3.A.1.208.4)
Repaglinide
Antidiabetic drug (A10BX02)
Other transport proteins
Major facilitator superfamily of transporters, non-SLC (vesicular neurotransmitter transporter (VNT) family; 2.A.1.22.?)
Levetiracetam
Antiepileptics (N03AX14)
Other protein targets
(
eukaryotic (putative) sterol transporter
(
EST
) family; 2.A.6.6.6)
Ezetimibe
Lipid-modifying agent (C10AX09)
Note
: The transport proteins are not necessarily the target responsible for the reported drug indication.
Regarding prospective targets, various transporters are and were considered promising drug targets in chemotherapy, but so far the candidates have failed in clinical trials. At present, Winter et al. describe SLC35F2 as a prospective target in cancer therapy and Rask-Andersen et al. mention members of SLC2, SLC5, SLC7, and SLC9 as promising targets in cancer therapy and members of SLC10 as potential targets against constipation and hypercholesterolemia [40,41].
1.7 Drug Targets in the SLC Classification
Several solute carriers are reported targets of approved drugs [41]. Here, we use the SLC classification as a framework to give an overview on the diseases a transporter is connected to, known drug molecules that directly target the transporter, and a count of bioactivity data available in ChEMBL to give an estimate on the degree of interest in the target.
The data shown in the figures was collected from different sources. Molecules targeting transporters were retrieved from the DrugBank xml [42], using a modified KNIME [43] workflow from the available example workflows [44]. Disease information was downloaded from DisGeNET [45] using the curated gene–disease associations [46]. Protein/gene mappings were generated from UniProt [23]. Bioactivity data counts were retrieved from ChEMBL-19, using single proteins only. Cladograms were generated with FigTree [47] using SLC sequences retrieved from Uniprot. Multiple sequence alignments for the members of one Pfam clan (e.g., the major facilitator superfamily) were generated with Clustal Omega [48] using the default parameters on the EBI Web server [49]. Counts for each target were added manually.
Figure 1.4 shows the counts for SLC members belonging to the amino acid-polyamine-organocation superfamily. Seven of the families show members with reported drugs, with three of them being previously reported by Rask-Andersen et al. [41] to be targets of approved drugs or under investigation (SLC5, SLC7, and SLC12). Closer investigation of the drugs for the remaining families shows that these are mostly vitamins or amino acids. Investigating the number of associated diseases for families without known drugs finds SLC4 and SLC26 as interesting families. Indeed, these are mentioned as potential new targets by Rask-Andersen et al., however, as target for antineoplastic agents, which is not one of the associated diseases. Figure 1.4 thus shows their association with several other diseases as well.
Figure 1.4 Cladogram of the 9 SLC families belonging to the APC superfamily and the number of connected diseases, drugs, and bioactivity values.
1.8 Conclusions
In this chapter, we gave an overview of available transporter classification schemes. A new version of the ChEMBL classification was introduced. For this, we wanted to have a less complex, browsable classification and, therefore, merged TCDB with the IUPHAR/BPS classification. The advantage compared to the pure IUPHAR/BPS transporter classification is that you still easily find the main transporter groups (ABC transporter, SLC members, and ATPases) and, if new bioactivity data for less common human or nonhuman transporters/transporter families are reported, these transporters can be easily integrated in conformity with TCDB, which is more complicated with a classification following IUPHAR/BPS only. For ChEMBL, we wanted to use the well-known SLC families to have a less complex transport protein classification than TCDB but keep the possibility to extend the scheme with the corresponding TCDB classes if it becomes significant.
Classifications allow (semi)automatic clustering of information. We used the SLC families to give an overview of interacting drugs and associated diseases of members of the APC clan. One disadvantage of an automated approach, however, is that false positive connections can be drawn. For example, the only human member of SLC32, the vesicular inhibitory amino acid transporter (VIAAT), seems to have a target drug according to Figure 1.3. On closer inspection, this is glycine, which is one of the natural substrates of this transporter. A more detailed investigation will, therefore, be necessary to draw valid conclusions from these investigations.
Acknowledgment
The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115191, 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. We also acknowledge financial support provided by the Austrian Science Fund, grant F3502.
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2New Trends in Antidepressant Drug Research
Benny Bang-Andersen,1 Klaus P. Bøgesø,1 Jan Kehler,1 and Connie Sánchez2
1H. Lundbeck A/S, Global Research, 9 Ottiliavej, 2500 Valby, Denmark
2Lundbeck USA, Brintellix Science Team, 215 College Road, Paramus, NJ 07652, USA
2.1 Introduction
2.1.1 Major Depressive Disorder and Antidepressant Drugs
Major depressive disorder (MDD) is a severe personal as well as a societal burden. The approximate lifetime prevalence of MDD is 17% and onset is typically in childhood or adolescence [1]. According to the World Health Organization's (WHO's) Global Burden of Disease Project, MDD will become the second leading cause of disability worldwide in 2020 [2,3]. Treatment of MDD most frequently consists of pharmacotherapy or psychotherapy, or a combination of both. However, nonpharmacological remedies, such as electroconvulsive therapy (ECT), transcranial magnetic stimulation, deep brain stimulation, or vagus nerve stimulation are also being used [4–6]. Without doubt, pharmacotherapy is today and will also in future remain the preferred therapeutic approach to treat depression, and research to improve the therapeutic benefits of antidepressants remains a high priority and challenge in drug discovery.
