2019
DOI: 10.1093/nar/gkz779
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VARIDT 1.0: variability of drug transporter database

Abstract: The absorption, distribution and excretion of drugs are largely determined by their transporters (DTs), the variability of which has thus attracted considerable attention. There are three aspects of variability: epigenetic regulation and genetic polymorphism, species/tissue/disease-specific DT abundances, and exogenous factors modulating DT activity. The variability data of each aspect are essential for clinical study, and a collective consideration among multiple aspects becomes crucial in precision medicine.… Show more

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Cited by 125 publications
(44 citation statements)
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“…Out of the seven transporters chosen by the International Transporter Consortium and the FDA for evaluation during drug development, three (OAT1, OAT3, and OCT2) are members of the SLC22 family [49]. 17 SLC22 members are also identified as drug transporters by the VARIDT database [50]. In addition to their pharmacological importance, many of these transporters transport metabolites that play a role in the response to endogenous stressors such as oxidative stress induced by reactive oxygen species.…”
Section: Discussionmentioning
confidence: 99%
“…Out of the seven transporters chosen by the International Transporter Consortium and the FDA for evaluation during drug development, three (OAT1, OAT3, and OCT2) are members of the SLC22 family [49]. 17 SLC22 members are also identified as drug transporters by the VARIDT database [50]. In addition to their pharmacological importance, many of these transporters transport metabolites that play a role in the response to endogenous stressors such as oxidative stress induced by reactive oxygen species.…”
Section: Discussionmentioning
confidence: 99%
“…Nowadays there is an increasing number of databases on human and animal protein expression differences (for a review see [12]) which, on the one hand, makes it easier for researchers to locate and cite existing data; but, on the other hand, might stimulate animal research to be conducted independently of in vitro and in silico data to populate such databases.…”
Section: Plos Onementioning
confidence: 99%
“…As an alternative to costly and labor-intensive laboratory experiments, robust, swift, and inexpensive computational methods for RNA chemical modification prediction have emerged recently, owing to the increasing amount of data generated in this post-genomics era (Libbrecht and Noble, 2015). A large number of m6A (Chen et al, 2015(Chen et al, , 2018a(Chen et al, ,b, 2019aZhou et al, 2016;Zhao et al, 2019;Zou et al, 2019) and m5C (Feng et al, 2016;Qiu et al, 2017;Li et al, 2018;Sabooh et al, 2018;Zhang et al, 2018;Yin et al, 2019) site predictors based on traditional machine learning and emerging deep learning algorithms have been proposed. However, few computational tools have been developed to predict pseudouridine sites.…”
Section: Introductionmentioning
confidence: 99%