Literature DB >> 23719679

The role of pharmacogenetics in drug disposition and response of oral glucose-lowering drugs.

N van Leeuwen1, J J Swen, H-J Guchelaar, L M 't Hart.   

Abstract

The primary goal of type 2 diabetes mellitus (T2DM) disease management is improvement of quality of life and prevention of complications. One way to achieve these goals is improving glycemic control by using different types of oral glucose-lowering medications. Currently seven different pharmacological oral glucose-lowering drug classes are available, each with its own mechanism of action and characteristics regarding absorption, distribution, metabolism, and elimination. Unfortunately, the response to the different types of glucose-lowering medication is highly variable between individuals resulting in unnecessary treatment failure. Genetic factors are thought to contribute to the variability in response and may present an opportunity to improve treatment outcome. In recent years, many efforts were taken to identify genetic variants that influence the pharmacokinetics, pharmacodynamics, and ultimately the therapeutic response of the different oral glucose-lowering drugs. Indeed several genetic variants are associated with the response to oral glucose-lowering drugs. This review comprises current knowledge on genetic variants affecting both pharmacokinetics and pharmacodynamics of oral glucose-lowering drugs. Included variants are located in genes coding for drug transporters, i.e., the organic anion-transporting family and the organic cation transporter family; genes involved in metabolism, i.e., cytochrome P450 superfamily; genes coding for drug receptors; T2DM-associated genes; and genes identified by genome-wide association studies (GWASs). Furthermore, this review provides insight into current status and future directions for personalized medicine in T2DM.

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Year:  2013        PMID: 23719679     DOI: 10.1007/s40262-013-0076-3

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  153 in total

1.  Association between CYP2C9 slow metabolizer genotypes and severe hypoglycaemia on medication with sulphonylurea hypoglycaemic agents.

Authors:  A Holstein; A Plaschke; M Ptak; E-H Egberts; J El-Din; J Brockmöller; J Kirchheiner
Journal:  Br J Clin Pharmacol       Date:  2005-07       Impact factor: 4.335

2.  Effect of SLCO1B1 genetic polymorphism on the pharmacokinetics of nateglinide.

Authors:  Wei Zhang; Yi-Jing He; Chun-Ting Han; Zhao-Qian Liu; Qing Li; Lan Fan; Zhi-Rong Tan; Wei-Xia Zhang; Bang-Ning Yu; Dan Wang; Dong-Li Hu; Hong-Hao Zhou
Journal:  Br J Clin Pharmacol       Date:  2006-06-23       Impact factor: 4.335

3.  Type 2 diabetes-associated missense polymorphisms KCNJ11 E23K and ABCC8 A1369S influence progression to diabetes and response to interventions in the Diabetes Prevention Program.

Authors:  Jose C Florez; Kathleen A Jablonski; Steven E Kahn; Paul W Franks; Dana Dabelea; Richard F Hamman; William C Knowler; David M Nathan; David Altshuler
Journal:  Diabetes       Date:  2007-02       Impact factor: 9.461

4.  The effects of genetic polymorphisms in the organic cation transporters OCT1, OCT2, and OCT3 on the renal clearance of metformin.

Authors:  M V Tzvetkov; S V Vormfelde; D Balen; I Meineke; T Schmidt; D Sehrt; I Sabolić; H Koepsell; J Brockmöller
Journal:  Clin Pharmacol Ther       Date:  2009-06-17       Impact factor: 6.875

5.  Data-driven prediction of drug effects and interactions.

Authors:  Nicholas P Tatonetti; Patrick P Ye; Roxana Daneshjou; Russ B Altman
Journal:  Sci Transl Med       Date:  2012-03-14       Impact factor: 17.956

6.  Standards of medical care in diabetes--2013.

Authors: 
Journal:  Diabetes Care       Date:  2013-01       Impact factor: 19.112

7.  Association studies of variants in promoter and coding regions of beta-cell ATP-sensitive K-channel genes SUR1 and Kir6.2 with Type 2 diabetes mellitus (UKPDS 53).

Authors:  A L Gloyn; Y Hashim; S J Ashcroft; R Ashfield; S Wiltshire; R C Turner
Journal:  Diabet Med       Date:  2001-03       Impact factor: 4.359

8.  The E23K variant of KCNJ11 encoding the pancreatic beta-cell adenosine 5'-triphosphate-sensitive potassium channel subunit Kir6.2 is associated with an increased risk of secondary failure to sulfonylurea in patients with type 2 diabetes.

Authors:  Giorgio Sesti; Emanuela Laratta; Marina Cardellini; Francesco Andreozzi; Silvia Del Guerra; Concetta Irace; Agostino Gnasso; Maria Grupillo; Renato Lauro; Marta Letizia Hribal; Francesco Perticone; Piero Marchetti
Journal:  J Clin Endocrinol Metab       Date:  2006-04-04       Impact factor: 5.958

9.  A pilot study suggests that the G/G genotype of resistin single nucleotide polymorphism at -420 may be an independent predictor of a reduction in fasting plasma glucose and insulin resistance by pioglitazone in type 2 diabetes.

Authors:  Hideichi Makino; Ikki Shimizu; Satoshi Murao; Shiori Kondo; Yasuharu Tabara; Masao Fujiyama; Yasuhisa Fujii; Yasuharu Takada; Kazuaki Nakai; Kenichi Izumi; Jun Ohashi; Ryoichi Kawamura; Junko Yamauchi; Yasunori Takata; Wataru Nishida; Mitsuru Hashiramoto; Hiroshi Onuma; Haruhiko Osawa
Journal:  Endocr J       Date:  2009-09-09       Impact factor: 2.349

10.  No significant effect of SLCO1B1 polymorphism on the pharmacokinetics of rosiglitazone and pioglitazone.

Authors:  Annikka Kalliokoski; Mikko Neuvonen; Pertti J Neuvonen; Mikko Niemi
Journal:  Br J Clin Pharmacol       Date:  2007-07-17       Impact factor: 4.335

View more
  9 in total

1.  Genetic variants of OCT1 influence glycemic response to metformin in Han Chinese patients with type-2 diabetes mellitus in Shanghai.

Authors:  Yong Zhou; Weiwei Ye; Yi Wang; Zhikui Jiang; Xiangying Meng; Qian Xiao; Qian Zhao; Jian Yan
Journal:  Int J Clin Exp Pathol       Date:  2015-08-01

2.  Role of human organic cation transporter-1 (OCT-1/SLC22A1) in modulating the response to metformin in patients with type 2 diabetes.

Authors:  Fizalah Kawoosa; Zafar A Shah; Shariq R Masoodi; Asif Amin; Roohi Rasool; Khalid M Fazili; Abid Hamid Dar; Asif Lone; Samir Ul Bashir
Journal:  BMC Endocr Disord       Date:  2022-05-26       Impact factor: 3.263

3.  Pharmacogenetic-guided glimepiride therapy in type-2 diabetes mellitus: a cost-effectiveness study.

Authors:  Cécile Fokoun; Hassan Serrier; Hugo Rabier; Sylvain Goutelle; Michel Tod; Laurent Bourguignon
Journal:  Pharmacogenomics J       Date:  2021-03-17       Impact factor: 3.550

Review 4.  Genetic polymorphisms of organic cation transporter 1 (OCT1) and responses to metformin therapy in individuals with type 2 diabetes: A systematic review.

Authors:  Edith Pascale Mofo Mato; Magellan Guewo-Fokeng; M Faadiel Essop; Peter Mark Oroma Owira
Journal:  Medicine (Baltimore)       Date:  2018-07       Impact factor: 1.889

Review 5.  Diabetes: Is There a Future for Pharmacogenomics Guided Treatment?

Authors:  Ewan R Pearson
Journal:  Clin Pharmacol Ther       Date:  2019-08       Impact factor: 6.875

6.  No significant association of type 2 diabetes-related genetic risk scores with glycated haemoglobin levels after initiating metformin or sulphonylurea derivatives.

Authors:  Doti P Martono; Hiddo J L Heerspink; Eelko Hak; Petra Denig; Bob Wilffert
Journal:  Diabetes Obes Metab       Date:  2019-06-24       Impact factor: 6.577

7.  Analysis of OCT1, OCT2 and OCT3 gene polymorphisms among Type 2 diabetes mellitus subjects in Indian ethnicity, Malaysia.

Authors:  Sabah Ghasan Abood Al-Ashoor; Vasudevan Ramachandran; Liyana Najwa Inche Mat; Nur Afiqah Mohamad; Mohd Hazmi Mohamed; Wan Aliaa Wan Sulaiman
Journal:  Saudi J Biol Sci       Date:  2021-09-14       Impact factor: 4.219

Review 8.  Metformin as a geroprotector: experimental and clinical evidence.

Authors:  Veronika Piskovatska; Nadiya Stefanyshyn; Kenneth B Storey; Alexander M Vaiserman; Oleh Lushchak
Journal:  Biogerontology       Date:  2018-09-25       Impact factor: 4.277

Review 9.  Medical Internet of Things and Big Data in Healthcare.

Authors:  Dimiter V Dimitrov
Journal:  Healthc Inform Res       Date:  2016-07-31
  9 in total

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