Abstract
With the rapid progress in pharmaceutical experiments and clinical investigations, extensive knowledge of drug transporters (DTs) has accumulated, which is valuable data for the understanding of drug metabolism and disposition. However, such data are largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis. A variety of databases have, therefore, been constructed to provide DT-related data, and they were reviewed in this study. First, several knowledge bases providing data regarding clinically important drugs and their corresponding transporters were discussed, which constituted the most important resources of DT-centered data. Second, some databases describing the general transporters and their functional families were reviewed. Third, various databases offering transporter information as part of their entire data collection were described. Finally, customized database functions that are available to facilitate DT-related research were discussed. This review provided an overview of the whole collection of DT-related databases, which might facilitate research on precision medicine and rational drug use.
SIGNIFICANCE STATEMENT A collection of well established databases related to drug transporters were comprehensively reviewed, which were organized according to their importance in drug absorption, distribution, metabolism, and excretion research. These databases could collectively contribute to the research on rational drug use.
Introduction
Drug efficacy and safety are largely determined by multiple processes (absorption, distribution, metabolism, and excretion) that regulate pharmacokinetics (Terada et al., 2015). A variety of endogenous molecules (mostly proteins) are determinants of these processes (Giacomini et al., 2010). Typical examples of these molecules include drug-metabolizing enzymes that transform parent drugs to metabolites of very different physicochemical and pharmacological properties (Yu and Zhong, 2016; Yu et al., 2017; Hitchings and Kelly, 2019), and drug transporters (DTs) that mediate the uptake of endo/exogenous substances into cells as well as their efflux (Rodieux et al., 2016; To et al., 2017; Shu et al., 2019). Among these molecules, DTs are capable of 1) determining the pharmacokinetic profile of drugs by regulating their absorption, distribution, and excretion or indirectly modifying their metabolism (DeGorter et al., 2012; Yang et al., 2019), 2) affecting drug pharmacodynamics by delivering them to proper target sites, controlling differential drug concentrations among tissues or altering their interactions with other molecules (Hu et al., 2015), 3) inducing drug toxicity through DT’s vulnerability to drug-drug interaction (DDI) or leading to drug resistance by reducing its concentration in targeted cells (Zhang and Hagenbuch, 2019), and 4) facilitating target discovery and rational use of the drug by revealing the mechanism of DDI, identifying the potential therapeutic target or improving the treatment of specific disease (Garibsingh and Schlessinger, 2019). Due to the essential role of DTs in drug efficacy and safety, it is necessary to acquire as much DT-centered knowledge as possible (Li et al., 2017; Zhu et al., 2019; Tang et al., 2020; Wang et al., 2021; Zhou et al., 2021).
With the advancement of experimental analysis (Li et al., 2018a) and clinical investigation (Stopfer et al., 2016), an extensive amount of DT knowledge has accumulated and mainly involves five types of DT-centered information: 1) expression, distribution, and function (Lin et al., 2015), 2) epigenetic modification (Hirota et al., 2017), 3) structural conformation and variation (Zheng et al., 2018), 4) exogenous regulation (Li et al., 2018a), and 5) genetic polymorphism (Peng et al., 2016). Particularly, the data regarding DT’s expression, distribution and function demonstrate its disease-differential expression (Evers et al., 2018), organism-dependent abundance (Durmus et al., 2015), tissue-specific distribution (Nixon et al., 2016), transporting functional family (Shen et al., 2017), and so on; the data of epigenetic modification on DT describe the DNA/histone methylation and acetylation (Liu et al., 2016), noncoding RNA regulation (Yu et al., 2019), and so on; the data of DT’s conformation and structural variations involve species-specific evolution (Dias and Sa-Correia, 2014), the structures of the entire transporter (Penmatsa et al., 2013) and functional conserved/substrate-binding domain (Xue et al., 2016), and so on; the data of DT’s exogenous regulation discuss the clinical drug-drug interactions (Kosa et al., 2018), regulatory substrate, inhibitor and inducer (Muller et al., 2018), and so on; the data describing DT’s genetic polymorphisms provide the cytogenetic locations (Lewis and Girisha, 2020), disease indication induced by hereditary factor (Karimian et al., 2020), genetic variant and frequency (Veldic et al., 2019), and so on. The above knowledge is valuable for understanding the drug ADME process (Nigam, 2015; Ye et al., 2019), which is thus essential for current research on drug metabolism (Li et al., 2020b; Wang et al., 2020a; He et al., 2021) and disposition (Bai et al., 2016; Kawahara et al., 2020).
However, such valuable knowledge is largely dispersed in the literature, which hampers its utility and significantly limits its possibility for comprehensive analysis (Li et al., 2018b; Yang et al., 2020b). Therefore, a variety of databases have been constructed to offer DT-related data (Wang et al., 2020c; Yin et al., 2020; Saier et al., 2021). Some of them offer explicit information on drugs together with their corresponding transporters, and give special emphasis on DT variability (Yin et al., 2020); some others describe general transporters together with their (phylogenetic) classifications, and specifically highlight the ones of human origin (Elbourne et al., 2017); the remaining databases aim to provide general data on various transporters as a part of their data collection (UniProt, 2021). These databases guarantee the accessibility to DT-related knowledge, which is anticipated to be the key data resource for current ADME studies.
Therefore, a comprehensive review of these popular databases was conducted. First, the databases providing the data of clinically important drugs together with their corresponding transporters were discussed, which constituted the most important resources for DT-centered data. Second, several databases describing the general protein transporters and their functional families were reviewed, which were crucial for any study requiring transporter (especially DT) classification. Finally, the databases offering general transporter information as a part of their data collection were described, which could be adopted as essential complements to other available databases. The overview of these various types of databases were shown in Table 1.
Summary of the data completeness of DT and its corresponding drugs, data availability, developer, updating frequency of the databases included in this review
The “*” in the first column indicated that the data provided in the corresponding databases were primarily based on computational calculation or simulation, whereas the databases without “*” represented that their data were manually collected from scientific literatures that were based on experimental validations.
Databases Providing Drugs and Their Corresponding Transporters
As estimated, approximately 10% (∼2,000) of all proteins in the human genome are functionally associated with the transporting of endo or exogenous molecules (Hediger et al., 2013). However, regarding the transporting of clinically important drugs, the total number of involved DTs is still under debate (Yin et al., 2020). The valuable data of DTs together with their transporting drugs have been described in a variety of databases (Fig. 1), and the characteristic groups of data covered by different databases were comprehensively reviewed and discussed in Table 2.
Ten representative databases providing the information of drugs together with the corresponding DTs. Five types of DT-related data were shown in the circles with various colors, including 3D structure, endogenous substrates, expression/distribution/function, polymorphism, and epigenetic-related data.
The availability of DT-related data types and groups among those ten databases (shown in Fig. 1) that provided the information of drugs and their corresponding transporters (DTs)
‘√’ indicated that the data type is available, whereas ‘–’ denotes that the data type is not available.
Pairing Data between Drug Transporter and Pharmaceutical Agent.
Several databases are providing the pairing data between DTs and pharmaceutical agents. As the most widely used data resources of pharmaceutical agents, Therapeutic Target Database [https://idrblab.org/ttd/, (Wang et al., 2020d)], DrugBank [https://go.drugbank.com/, (Wishart et al., 2018)], ChEMBL [https://www.ebi.ac.uk/chembl/, (Mendez et al., 2019)], IUPHAR/BPS guide to pharmacology [https://www.guidetopharmacology.org/, (Armstrong et al., 2020)], KEGG DRUG [https://www.kegg.jp/kegg/drug/, (Kanehisa et al., 2019)], and Metabolism and Transport Database [http://www-metrabase.ch.cam.ac.uk/, (Mak et al., 2015)] offered millions of molecules that were paired with the ADME-associated proteins. As shown in Fig. 1, all these databases focused on the data of exogenous substances and their regulation (colored in green), especially the data of regulatory substances and the inhibitors/inducers of a studied DT (as shown in Table 2). Moreover, all the databases described the DTs of the approved/clinical trial drugs, and the number of DTs covered by these six databases varied greatly (from ∼10 to over 100, as shown in Table 1). Among these databases, the Therapeutic Target Database and Metrabase were the only two describing the tissue-specific distribution of DTs, and such data could facilitate the critical analysis of distribution-induced adverse drug reactions (Yang et al., 2016).
PharmGKB [https://www.pharmgkb.org/, (Barbarino et al., 2018)] is a worldwide resource for pharmacogenomics knowledge that provides the alteration data of drug pharmacokinetics and pharmacodynamics that originate from genetic polymorphism. It focuses on the alterations in drug response and the effects on their clinical phenotypes and contains ∼1,000 drugs related to the genetic variations on ∼100 DTs. As shown in Fig. 1, in addition to the exogenous regulation data, PharmGKB offered additional DT data on expression/distribution/function and genetic polymorphism. Compared with the databases above, PharmGKB offered many diverse groups of DT data (Table 2) by describing functional family, cytogenetic location, disease/phenotype induced by the hereditary factor together with the genetic variant and frequency.
UCSF-FDA TransPortal [https://transportal.compbio.ucsf.edu/, (Morrissey et al., 2012)] and Transformer [http://bioinformatics.charite.de/transformer, (Hoffmann et al., 2014)] were two popular databases providing important drug transporters together with the exogenous substance and regulation, which contain 31 and 60 DTs for transporting approved or in clinical trial drugs, respectively. As demonstrated in Fig. 1 and Table 2, UCSF-FDA TransPortal described the tissue-specific distribution information of DTs, whereas Transformer offered distinctive data on species-specific structural evolution and the three-dimensional crystal structure of the entire transporter. Moreover, as shown in Table 2, these two databases are distinguished in covering the data of clinical DDI and are therefore applied to predict potential adverse drug reactions based on their DDI data (Cesar-Razquin et al., 2018; Carrascal-Laso et al., 2020). It is important to emphasize that the latest update of UCSF-FDA TransPortal was in 2012.
As a recently constructed pharmaceutical database, VARIDT [https://idrblab.org/varidt/, (Yin et al., 2020)] offered the most comprehensive set of DTs that were confirmed by the transporting drugs (either approved or clinical trial). As shown in Fig. 1, this database contained the most diverse types of DT-related data (with the only exception of structure-based data). As described in Table 2, the data for all data groups under four different types were collected and provided. Moreover, the total number of DTs covered in this database was the largest compared with those knowledge bases in Fig. 1. In particular, a comprehensive literature review of all drugs approved by the FDA and ∼1,100 clinical trial drugs were first conducted. Then, a total of ∼180 DTs were confirmed to transport approved drugs, and ∼150 DTs were to transport clinical trial ones, which were substantially different from the relatively small numbers of DTs shown in available databases (Table 2). Due to the huge amount of accumulated DT data, the VARIDT is expected to provide strong support to the optimization of clinical treatment.
Diverse Data Illustrating Various Aspects of DT Variability.
The variability data of DTs are essential for the determination of the interindividual variations in drug response and side effects (Yee et al., 2018; Nie et al., 2020). Besides the variabilities in exogenous regulation and genetic polymorphism explicitly discussed in the 2.1 section, two additional aspects of variability (varied protein abundances and diverse epigenetic regulation) should be considered for DTs because of their importance in bridging the preclinical investigations with clinical trials (Durmus et al., 2015) and leading to multidrug resistance in complex disease (Zhou et al., 2020), respectively. Therefore, current databases available for providing these two additional variability data were explicitly described in this review as follows.
The protein abundance of DTs plays an important role in several aspects of drug research, such as clinical toxicity analysis, clinical pharmacokinetics research, and adverse reaction evaluation (Lin et al., 2015; Safar et al., 2019). There are three kinds of variability of DT abundances: 1) organism-specific expressions (Durmus et al., 2015), 2) tissue-differential distributions (Nixon et al., 2016), and 3) disease-dependent abundances (Evers et al., 2018). As provided in Table 2, the tissue-differential distribution data have been provided by multiple databases, such as TTD, PharmGKB, UCSF-FDA TransPortal, Metrabase, and VARIDT, which further demonstrate the critical roles of such variability in drug disposition (Kawahara et al., 2020). For the remaining two kinds of variability data, VARIDT is the only knowledge base of such information, and the differential expression patterns are provided for 108 diseases and 3 model organisms.
Epigenetic regulation of DT genes has emerged as an important mechanism of individualized drug responses (Peng and Zhong, 2015; Hirota et al., 2017). Few epigenetic regulation data of DTs (Table 2) are provided by currently available knowledge bases, and the VARIDT is currently the only resource describing such variability. Particularly, it provided epigenetic regulation data on 1) epigenetic types (DNA methylation, non-coding RNA regulation, histone acetylation/methylation, etc.), 2) prevalence of occurrence, 3) locations, 4) description of the epigenetic phenomenon, 5) experimental methods, and 6) materials adopted for validating each phenomenon. In total, over 20,000 DNA methylations, ∼100 histone modifications, and over 7,000 noncoding RNA regulations were collected and comprehensively provided in the latest VARIDT.
Databases Describing Transporters and Transporter Families
Membrane transporters play critical roles in discovering new drugs and elucidating disease mechanisms (Nigam, 2018), which can be divided into channels, carriers, electron flow carriers, group translocators, and pumps for determining the molecular composition and energy state of cells (Cook et al., 2014; Saier et al., 2021). The identified drug uptake/efflux transporters may constitute only a small fraction of all these general transporters, but these general transporters are of great importance for elucidating drug metabolism (Wang et al., 2020b) and disposition (Kawahara et al., 2020). Thus, the databases available for describing general transporters and transporter families are valuable treasures for current DT research.
These databases include TCDB (Saier et al., 2021), METscout (Geffers et al., 2013), CFMD (Sosnay et al., 2011), iMusta4SLC (Higuchi et al., 2018), ABCMdb (Tordai et al., 2017), ALD info (Kemp et al., 2001), ABCA4 DataBase (Trezza et al., 2017), TransportDB (Elbourne et al., 2017), and SLC Tables (Hediger et al., 2013), which contain the data on classifying general transporters or describing a specific class of proteins. Such data provided the resources for the expansion of DT-related knowledge, which was described in many databases (as illustrated in Fig. 2), and a variety of distinct data groups covered by these popular databases were also comprehensively reviewed and discussed in Table 3.
Nine representative databases describing the information of transporters and transporter families. Four types of transporter-related data were shown in the circles with various colors, including endogenous substrates, expression/distribution/function, structure, and polymorphism-related data.
The availability of transporter-related data types and groups among those nine databases (shown in Fig. 2) that described the information of transporters and transporter families
‘√’ indicated that the data type is available, whereas ‘–’ denotes that the data type is not available.
Systematic Classification of the General Protein Transporters.
TCDB [https://www.tcdb.org/, (Saier et al., 2021)] is a well established database for classifying general protein transporters. It contains transporter information of diverse species and describes the general transporter in terms of structure, function, mechanism, evolution, disease/medicine, and related endogenous compounds (Fig. 2 and Table 3). Collectively, this database covered over 20,000 transporters classified into thousands of nonredundant function families, and it enables the assessments of the family members, the discovery of their evolutionary relationships, and inference of substrate and molecular functions. It has been adopted recently for optimizing experimental designs (Hong et al., 2020; Wang et al., 2020b; Yang et al., 2020a; Graf et al., 2021; Peng et al., 2021).
TransportDB [http://www.membranetransport.org/transportDB2/, (Elbourne et al., 2017)] is a functional annotation database containing information on a wide range of transporters derived from diverse species. Particularly, it annotates membrane transporters from ∼2,000 genomes and results in ∼800,000 transporters grouped into ∼160 families. As shown in Fig. 2, this database contained the data of expression, distribution, function, and endogenous substrates for general transporter, which gives functional family, transporter phylogenetic classification, and structure of endogenous compounds (Table 3). TransportDB was suggested to be adopted for characterizing transporters of specific functions (Frioux et al., 2020; Bernstein et al., 2021).
Knowledge Bases Describing a Specific Transporter Family.
SLC Tables [http://slc.bioparadigms.org/, (Hediger et al., 2013)] is a database with specific data on ∼400 absolute carriers (SLCs) classified into 52 functional families. This database is popular for describing detailed data on the functional family, endogenous substances, and tissue-differential distribution (Fig. 2 and Table 3). Because of its specific scope of describing the SLC transporter family, it has been frequently used to facilitate SLC-related studies (Girardi et al., 2020). As another SLC-centered database, the METscout [http://metscout.mpg.de/, (Geffers et al., 2013)] provides the metabolic pathway and gene expression landscape, which describe where in an organism each metabolic reaction takes place and where the SLCs transport the metabolites. It contains ∼350 SLCs and covers almost all components within the mouse metabolic network. As illustrated in Fig. 2, it provides the functional family, organism-specific abundance, tissue-differential distribution of transporters, together with the scRNA sequencing atlas (described in Table 3).
Other databases for a specific transporter family include: ABCMdb [http://abcm2.hegelab.org/, (Trezza et al., 2017)] and iMusta4SLC [http://cib.cf.ocha.ac.jp/slc/, (Higuchi et al., 2018)]. Both knowledge bases focus on providing the genetic mutation information for a particular family of transporters (shown in Fig. 2). ABCMdb has 45 ABC transcripts classified into 5 families, and iMusta4SLC provides 573 SLC transcripts classified into 52 families. Both databases can predict potential disease liability based on the mutations in a general transporter and can help to develop extensive disease knowledge networks for improving disease management (Verkman et al., 2013; Tracewska et al., 2019; Chien et al., 2021).
Knowledge Bases Describing a Specific Transporter.
A number of additional knowledge bases were constructed to describe genetic polymorphisms for specific transporter, which included CFMD [http://www.genet.sickkids.on.ca/cftr/, (Sosnay et al., 2011)], ALD Info [https://adrenoleukodystrophy.info/, (Kemp et al., 2001)], and ABCA4 DataBase [http://www.sbl.unisi.it/abca4/abcr_mainlist.php, (Trezza et al., 2017)]. As indicated by their name, these knowledge bases contain information on only a single transporter, such as CFTR1, ABCD1, and ABCA4. Particularly, they provided comprehensive disease data that were associated with the sequential/structural polymorphism of a transporter. As illustrated in Fig. 2 and Table 3, CFMD contains the data of cytogenetic locations and genetic variants and frequency, ALD Infor provides the data of genetically induced disease indications, and ABCA4 DataBase includes the data of genetically correlated phenotypes.
Databases Providing Transporters as Part of Their Data Collection
With the advancement of next-generation sequencing techniques (Lane et al., 2016; Yin et al., 2021), many protein-coding genes have been successfully characterized. In addition to the databases discussed above, some knowledge bases describing the whole protein atlas were also available. Within these databases, the transporters were described as a part of their entire data collection. Specifically, these databases included: dbSNP, EBI Expression Atlas, EncoMPASS, FINDbase, Human Protein Atlas, OMIM, PDB, PPTdb, membrane proteins embedded in lipid bilayers database, and UniProt. These databases focus on macroscopic and comprehensive descriptions of all proteins, and the information on all transporters is therefore provided in a generally described manner.
As shown in Fig. 3, three types of transporter-related data were provided in these databases. For the data of genetic polymorphism, three databases were available, which included: dbSNP [https://www.ncbi.nlm.nih.gov/snp/, (Sherry et al., 2001)], FINDbase [http://www.findbase.org, (Kounelis et al., 2020)], and OMIM [https://omim.org/, (Amberger et al., 2019)]. As described in Table 4, all three databases illustrated the genetic variant and frequency information of the studied transporters, with OMIM being the only source for tissue-specific distribution, genetically correlated phenotype, and cytogenetic location. For the data of expression, distribution, and function, three popular databases were available, which included: UniProt [https://www.uniprot.org/, (UniProt, 2021)] and the Human Protein Atlas [https://www.proteinatlas.org/, (Uhlen et al., 2015)] together with EBI Expression Atlas [https://www.ebi.ac.uk/gxa/home, (Papatheodorou et al., 2020)]. Based on the descriptions in Table 4, all three databases focus on tissue-differential distribution and organism-specific abundance. UniProt provided the comprehensive information on functional families and transporter sequences, whereas EBI Expression Atlas places more emphasis on disease-varied expression. Among these three databases, the Human Protein Atlas has the widest coverage on the transporter data groups as described in Table 4. For the data of structural conformation, four reputable databases were available, which included: PDB [https://www.rcsb.org/, (Burley et al., 2021)], EncoMPASS [http://encompass.ninds.nih.gov, (Sarti et al., 2019)], membrane proteins embedded in lipid bilayers database [http://memprotmd.bioch.ox.ac.uk/, (Newport et al., 2019)], and PPTdb [http://pptdb.cgu.edu.tw, (Lee et al., 2019)]. As described in Table 4, all four databases provided structural conformation for the entire transporter (if available). As the most comprehensive database for protein crystal structures, PDB also described the substrate-binding domain of transporter with relatively high resolution. PPTdb had more data groups (Table 4) and gave the unique data on species-specific evolution and the structure of functionally conserved domain.
Ten representative databases providing transporter information as part of their data collections. These databases included the transporter information of three types which were shown by the circles of different colors, including expression/distribution/function data, polymorphism data, and 3D structure.
The availability of data types and groups among the ten databases (shown in Fig. 3) that provided the transporter-related information as part of their data collections
‘√’ indicated that the data type is available, whereas ‘–’ denotes that the data type is not available.
Customized Database Functions Facilitating DT-related Research
Besides the valuable DT-related data, a variety of customized functions facilitating DT-related research were also provided in several databases. These functions included sequence-based DT similarity screening (Elbourne et al., 2017), structure-based similarity screening for transported drug (Mendez et al., 2019), disease/tissue-specific differential expression analysis (Uhlen et al., 2015), interplay analysis among multiple DT variabilities (Yin et al., 2020), functional analysis based on signaling pathways (Kanehisa et al., 2019), DT annotation and classification (Saier et al., 2021), and so on. Based on these valuable functions together with their comprehensive DT-related information, the available databases provided much-enhanced power in the research of drug metabolism and disposition. As shown in Table 5, these functions facilitated the structure-based drug design/identification (Yu et al., 2016), discovery of target drugability based on DT sequence (Frioux et al., 2020), disease/tissue-specific differential expression analysis (Yu et al., 2020), structure similarity search by the transported drug (Sakai et al., 2021), interplay analysis among multiple DT variabilities (Wang et al., 2021), functional analysis based on the signaling pathways (Sakil et al., 2017), functional annotation and systematic classification of DTs (Peng et al., 2021), prediction of potential DDIs (Carrascal-Laso et al., 2020), drug safety assessment and toxicity discovery (Zhou et al., 2020), and identification of potential drug resistance (Hlaváč et al., 2020). Overall, these customized database functions are very diverse, which are capable of promoting DT-based research on the drug ADME process.
The customized functions of all 29 databases discussed in this study and their representative applications. These functions fall into three classes: facilitating the rational use of drugs, discovering the potential therapeutic targets, and developing the new strategy for disease treatment
Summary and Prospect.
Based on the above discussions, the available databases are useful for translating experimental results into clinical evidence, which can enable clinicians to formulate appropriate medications for a specific patient and provide qualified solutions for drug discovery. Recent studies showed that there is an increasing interest in the variability of DTs, which emphasized the importance of 1) epigenetic regulation and genetic polymorphism of DT, 2) species-, tissue-, and disease-specific DT abundances, and 3) exogenous factors modulating DT activity (Yin et al., 2020). These data have been provided by some available databases, such as PharmaGKB (Barbarino et al., 2018), UCSF-FDA TransPortal (Morrissey et al., 2012), and Transformer (Hoffmann et al., 2014), and each database focuses on one particular aspect of DT variability.
Recent studies revealed the urgent necessity of conducting interplay analysis among multiple aspects of DT variability (Chen et al., 2016; Genovese et al., 2017; Ye et al., 2018; Yang et al., 2020b, 2020c). Taking the multidrug resistance as an example, the impaired uptake of organic cation transporter 1 (hOCT1) was found responsible for the chemoresistance of sorafenib in treating the cholangiocarcinoma. The decrease of the hOCT1 mRNA level was identified to be correlated with the hypermethylation status of its promoter, and treatment of cholangiocarcinoma cells with decitabine (a demethylating agent) was found to be able to restore hOCT1’s expression and increase the uptake of sorafenib (Lozano et al., 2019). This example explicitly demonstrates an interplay between (1) epigenetic regulation of DT and (2) exogenous regulation modulating DT activity.
Similar to hOCT1, organic anion transporter 2 (hOAT2) is another hotspot in current research, and its aberrant expression was reported to lead to insufficient intracellular drug accumulation, which is responsible for the failure of chemotherapy in the patient with hepatocellular carcinoma. The transcriptional repression of hOAT2 is associated with histone deacetylations, and the activation of hOAT2’s transcription and enhanced uptake of the OAT2 substrate zidovudine can thus be achieved by histone deacetylases inhibitor Vorinostat (Wang et al., 2021). All in all, the joint consideration of multiple DT variabilities (in this situation, epigenetic regulation and exogenous chemicals) can help to discover potential chemo-sensitization strategies for treating cancers. Such valuable information has been provided in the latest version of VARIDT.
Finally, with the advent of the big-data era, the available pharmaceutical knowledge bases are expected to be fully connected to avoid the problem of “information isolated islands” (Fu et al., 2020). A careful review of all those discussed databases above-identified several reputable databases that were fully connected with other available knowledge bases, such as: ChEMBL, DrugBank, Therapeutic Target Database, PharmGKB, VARIDT, and so on. However, there are still some databases that are not fully linked to other information resources. To promote the publicity of these databases and truly demonstrate the capacity of big-data analysis, the global scientific community should strive together to build a comprehensive database that includes integral information on DTs and their variabilities. This comprehensive database may facilitate the discovery of the correlation between disease/adverse drug reaction and the studied DT.
Authorship Contributions
Participated in research design: Zhu, Zeng.
Performed data analysis: Yin, F. Li, Z. Li, Yu.
Wrote or contributed to the writing of the manuscript: Yin, Zhu, Zeng.
Footnotes
- Received February 10, 2021.
- Accepted August 20, 2021.
This work was funded by National Natural Science Foundation of China [81973394] and Natural Science Foundation of Zhejiang Province [LR21H300001] and supported by the Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Alibaba Cloud, and Information Technology Center of Zhejiang University.
No author has an actual or perceived conflict of interest with the contents of this article.
Abbreviations
- ABCMdb
- ABC proteins mutation database
- ADME
- absorption, distribution, metabolism, and excretion
- CFMD
- CF mutation database
- dbSNP
- NCBI database of genetic variation
- DDI
- drug-drug interaction
- DT
- drug transporter
- encoMPASS
- encyclopedia of membrane proteins analyzed by structure and symmetry; FDA, The United States Food and Drug Administration
- FINDbase
- frequency of inherited disorders database; hOAT2, human organic anion transporter 2; hOCT1, human organic cation transporter 1
- iMusta4SLC
- integrated mutational and structural analysis for solute carrier transporters database
- IUPHAR/BPS
- IUPHAR/BPS guide to pharmacology database
- metrabase
- Metabolism and Transport Database
- METscout
- metabolites enzymes and transporters database
- OMIM
- online mendelian inheritance in man database
- PDB
- RCSB protein data bank
- PharmGKB
- pharmacogenomics knowledgebase
- PPTdb
- pathogenic protist transmembranome database; SLC, solute carrier
- TCDB
- transporter classification database
- Transformer
- metabolism of xenobiotics database
- TransportDB
- genomic comparisons of membrane transporter systems
- TTD
- therapeutic target database
- UniProt
- universal protein knowledgebase; UCSF‐FDA, University of California San Francisco–Food and Drug Administration
- VARIDT
- variability of drug transporter database
- Copyright © 2021 by The American Society for Pharmacology and Experimental Therapeutics
References
In this issue
Drug Transporter–Related Databases
