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Review article
Published online: 2024-10-22
Decoding the Genetic Blueprint: Advancing Personalized Medicine in Type 2 Diabetes through Pharmacogenomics
Abstract
Objective: The escalation of type 2 diabetes (T2D) as a global health crisis necessitates a shift towards personalized
medicine to optimize treatment efficacy and minimize adverse drug reactions (ADRs). This review article underscores the significant role of pharmacogenomics in refining T2D management. We explore the influence of genetic variations on the pharmacokinetics and pharmacodynamics of commonly used antidiabetic drugs, including metformin, sulfonylureas, thiazolidinediones, DPP-4 inhibitors, and SGLT2 inhibitors. Materials and methods: A systematic review of existing
literature was carried out, concentrating on studies exploring personalized medicine in T2D through pharmacogenomics.
The literature search encompassed databases such as Medline, Scopus, Web of Science (WOS), and PubMed. Key insights regarding the role of pharmacogenomics in managing T2D were compiled and analyzed. Results and conclusions: The review highlights how genetic polymorphisms in drug transporters, metabolizing enzymes, and drug targets correlate with variations in drug response and tolerance. We advocate for preemptive genotyping and integration of genetic data into clinical decision-making, which could revolutionize patient care in T2D. The future of diabetes treatment lies in harnessing pharmacogenomic insights to tailor therapeutic regimens, thereby transitioning from a one-size-fits-all approach to a more nuanced, individualized treatment strategy. With advancements in genomic technologies and a reduction in genotyping costs, the implementation of genetic testing in routine clinical practice is becoming increasingly viable, signaling a new era in the personalized management of T2D.
Keywords: type 2 diabetespharmacogenomicspersonalized medicineantidiabetic drugsgenotyping
References
- Roden DM, Van Driest SL. Pharmacogenetics and Pharmacogenomics. In: Brunton LL, Knollmann BC. eds. Goodman & Gilman's: The Pharmacological Basis of Therapeutics, 14th Edition. McGraw-Hill Education; 2023. https://accessmedicine.mhmedical.com/content.aspx?bookid=3191§ionid=268215725 (12.04.2024).
- Flannick J, Thorleifsson G, Beer NL, et al. Go-T2D Consortium, T2D-GENES Consortium. Loss-of-function mutations in SLC30A8 protect against type 2 diabetes. Nat Genet. 2014; 46(4): 357–363.
- Marques SC, Ikediobi ON. The clinical application of UGT1A1 pharmacogenetic testing: gene-environment interactions. Hum Genomics. 2010; 4(4): 238–249.
- Pulley JM, Shirey-Rice JK, Lavieri RR, et al. Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics. Assay Drug Dev Technol. 2017; 15(3): 113–119.
- Rollinson V, Turner R, Pirmohamed M. Pharmacogenomics for Primary Care: An Overview. Genes (Basel). 2020; 11(11).
- Pearson ER. Diabetes: Is There a Future for Pharmacogenomics Guided Treatment? Clin Pharmacol Ther. 2019; 106(2): 329–337.
- Rena G, Hardie DG, Pearson ER. The mechanisms of action of metformin. Diabetologia. 2017; 60(9): 1577–1585.
- Umamaheswaran G, Praveen RG, Damodaran SE, et al. Influence of SLC22A1 rs622342 genetic polymorphism on metformin response in South Indian type 2 diabetes mellitus patients. Clin Exp Med. 2015; 15(4): 511–517.
- Xiao Di, Guo Yu, Li Xi, et al. The Impacts of SLC22A1 rs594709 and SLC47A1 rs2289669 Polymorphisms on Metformin Therapeutic Efficacy in Chinese Type 2 Diabetes Patients. Int J Endocrinol. 2016; 2016: 4350712.
- Zhou Y, Ye W, Wang Y, et al. Genetic variants of OCT1 influence glycemic response to metformin in Han Chinese patients with type-2 diabetes mellitus in Shanghai. Int J Clin Exp Pathol. 2015; 8(8): 9533–9542.
- Dujic T, Causevic A, Bego T, et al. Organic cation transporter 1 variants and gastrointestinal side effects of metformin in patients with Type 2 diabetes. Diabet Med. 2016; 33(4): 511–514.
- Phani NM, Vohra M, Kakar A, et al. Implication of critical pharmacokinetic gene variants on therapeutic response to metformin in Type 2 diabetes. Pharmacogenomics. 2018; 19(11): 905–911.
- Nikolac N, Simundic AM, Katalinic D, et al. Metabolic control in type 2 diabetes is associated with sulfonylurea receptor-1 (SUR-1) but not with KCNJ11 polymorphisms. Arch Med Res. 2009; 40(5): 387–392.
- Klen J, Dolžan V, Janež A. CYP2C9, KCNJ11 and ABCC8 polymorphisms and the response to sulphonylurea treatment in type 2 diabetes patients. Eur J Clin Pharmacol. 2014; 70(4): 421–428.
- Li Q, Chen M, Zhang R, et al. KCNJ11 E23K variant is associated with the therapeutic effect of sulpho- nylureas in Chinese type 2 diabetic patients. Clin Exp Pharmacol Physiol. 2014; 41(10): 748–754.
- Li Q, Tang TT, Jiang F, et al. Polymorphisms of the KCNQ1 gene are associated with the therapeutic responses of sulfonylureas in Chinese patients with type 2 diabetes. Acta Pharmacol Sin. 2017; 38(1): 80–89.
- Javorsky M, Klimcakova L, Schroner Z, et al. KCNJ11 gene E23K variant and therapeutic response to sulfonylureas. Eur J Intern Med. 2012; 23(3): 245–249.
- Zhou K, Donnelly L, Burch L, et al. Loss-of-function CYP2C9 variants improve therapeutic response to sulfonylureas in type 2 diabetes: a Go-DARTS study. Clin Pharmacol Ther. 2010; 87(1): 52–56.
- Niemi M, Backman JT, Kajosaari LI, et al. Polymorphic organic anion transporting polypeptide 1B1 is a major determinant of repaglinide pharmacokinetics. Clin Pharmacol Ther. 2005; 77(6): 468–478.
- Pei Qi, Liu JY, Yin JY, et al. Repaglinide-irbesartan drug interaction: effects of SLCO1B1 polymorphism on repaglinide pharmacokinetics and pharmacodynamics in Chinese population. Eur J Clin Pharmacol. 2018; 74(8): 1021–1028.
- Kalliokoski A, Backman J, Neuvonen PJ, et al. Effects of the SLCO1B1*1B haplotype on the pharmacokinetics and pharmacodynamics of repaglinide and nateglinide. Pharmacogenet Genomics. 2008; 18(11): 937–942.
- Dai XP, Huang Q, Yin JY, et al. Polymorphisms of the KCNQ1 gene are associated with the therapeutic responses of sulfonylureas in Chinese patients with type 2 diabetes. Clin Exp Pharmacol Physiol. 2012; 39(5): 462–468.
- Daily EB, Aquilante CL. Cytochrome P450 2C8 pharmacogenetics: a review of clinical studies. Pharmacogenomics. 2009; 10(9): 1489–1510.
- Martis S, Peter I, Hulot JS, et al. Multi-ethnic distribution of clinically relevant CYP2C genotypes and haplotypes. Pharmacogenomics J. 2013; 13(4): 369–377.
- Stage TB, Christensen MMH, Feddersen S, et al. The role of genetic variants in CYP2C8, LPIN1, PPARGC1A and PPARγ on the trough steady-state plasma concentrations of rosiglitazone and on glycosylated haemoglobin A1c in type 2 diabetes. Pharmacogenet Genomics. 2013; 23(4): 219–227.
- Dawed AY, Donnelly L, Tavendale R, et al. CYP2C8 and SLCO1B1 Variants and Therapeutic Response to Thiazolidinediones in Patients With Type 2 Diabetes. Diabetes Care. 2016; 39(11): 1902–1908.
- Zhang KH, Huang Q, Dai XP, et al. Effects of the peroxisome proliferator activated receptor-γ coactivator-1α (PGC-1α) Thr394Thr and Gly482Ser polymorphisms on rosiglitazone response in Chinese patients with type 2 diabetes mellitus. J Clin Pharmacol. 2010; 50(9): 1022–1030.
- Li Z, Peng X, Wu Y, et al. The influence of adiponectin gene polymorphism on the pioglitazone response in the Chinese with type 2 diabetes. Diabetes Obes Metab. 2008; 10(9): 794–802.
- Mannino GC, Andreozzi F, Sesti G. Pharmacogenetics of type 2 diabetes mellitus, the route toward tailored medicine. Diabetes Metab Res Rev. 2019; 35(3): e3109.
- Fortin JP, Schroeder JC, Zhu Y, et al. Pharmacological characterization of human incretin receptor missense variants. J Pharmacol Exp Ther. 2010; 332(1): 274–280.
- Nitz I, Fisher E, Weikert C, et al. Association analyses of GIP and GIPR polymorphisms with traits of the metabolic syndrome. Mol Nutr Food Res. 2007; 51(8): 1046–1052.
- Mohammad S, Patel RT, Bruno J, et al. A naturally occurring GIP receptor variant undergoes enhanced agonist-induced desensitization, which impairs GIP control of adipose insulin sensitivity. Mol Cell Biol. 2014; 34(19): 3618–3629.
- Tokuyama Y, Matsui K, Egashira T, et al. Five missense mutations in glucagon-like peptide 1 receptor gene in Japanese population. Diabetes Res Clin Pract. 2004; 66(1): 63–69.
- Jensterle M, Pirš B, Goričar K, et al. Genetic variability in GLP-1 receptor is associated with inter-individual differences in weight lowering potential of liraglutide in obese women with PCOS: a pilot study. Eur J Clin Pharmacol. 2015; 71(7): 817–824.
- Gotthardová I, Javorský M, Klimčáková L, et al. KCNQ1 gene polymorphism is associated with glycaemic response to treatment with DPP-4 inhibitors. Diabetes Res Clin Pract. 2017; 130: 142–147.
- Boulton DW, Kasichayanula S, Keung CF, et al. Simultaneous oral therapeutic and intravenous ¹⁴C-microdoses to determine the absolute oral bioavailability of saxagliptin and dapagliflozin. Br J Clin Pharmacol. 2013; 75(3): 763–768.
- Fischer TT, Ehrlich BE. Wolfram Syndrome: a Monogenic Model to Study Diabetes Mellitus and Neurodegeneration. Curr Opin Physiol. 2020; 17: 115–123.
- Pereira MJ, Lundkvist P, Kamble PG, et al. A Randomized Controlled Trial of Dapagliflozin Plus Once-Weekly Exenatide Versus Placebo in Individuals with Obesity and Without Diabetes: Metabolic Effects and Markers Associated with Bodyweight Loss. Diabetes Ther. 2018; 9(4): 1511–1532.
- Machado CM, Leite NC, França PH, et al. PNPLA3 gene polymorphism in Brazilian patients with type 2 diabetes: A prognostic marker beyond liver disease? Nutr Metab Cardiovasc Dis. 2019; 29(9): 965–971.
- Francke S, Mamidi RN, Solanki B, et al. In vitro metabolism of canagliflozin in human liver, kidney, intestine microsomes, and recombinant uridine diphosphate glucuronosyltransferases (UGT) and the effect of genetic variability of UGT enzymes on the pharmacokinetics of canagliflozin in humans. J Clin Pharmacol. 2015; 55(9): 1061–1072.
- Heo CUk, Choi CI. Current Progress in Pharmacogenetics of Second-Line Antidiabetic Medications: Towards Precision Medicine for Type 2 Diabetes. J Clin Med. 2019; 8(3): 393.
- Zimdahl H, Haupt A, Brendel M, et al. Influence of common polymorphisms in the SLC5A2 gene on metabolic traits in subjects at increased risk of diabetes and on response to empagliflozin treatment in patients with diabetes. Pharmacogenet Genomics. 2017; 27(4): 135–142.
- Kaur P, Behera BS, Singh S, et al. "The pharmacological profile of SGLT2 inhibitors: Focus on mechanistic aspects and pharmacogenomics". Eur J Pharmacol. 2021; 904: 174169.