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oai:riubu.ubu.es:10259/119202026-07-17T00:05:20Zcom_10259_11209com_10259_5086com_10259_2604col_10259_11210

Identification of Transporter Polymorphisms Influencing Metformin Pharmacokinetics in Healthy Volunteers Saiz-Rodríguez, Miriam Ochoa, Dolores Zubiaur, Pablo Navares-Gómez, Marcos Román, Manuel Camargo-Mamani, Paola Luquero-Bueno, Sergio Villapalos-García, Gonzalo Alcaraz, Raquel Mejía-Abril, Gina Santos-Mazo, Estefanía Abad-Santos, Francisco Metformin Pharmacogenetics Transporters SLC ABC Sex Ethnicity Farmacogenómica Diabetes tipo 2 Pharmacogenomics Non-insulin-dependent diabetes For patients with type 2 diabetes, metformin is the most often recommended drug. However, there are substantial individual differences in the pharmacological response to metformin. To investigate the effect of transporter polymorphisms on metformin pharmacokinetics in an environment free of confounding variables, we conducted our study on healthy participants. This is the first investigation to consider demographic characteristics alongside all transporters involved in metformin distribution. Pharmacokinetic parameters of metformin were found to be affected by age, sex, ethnicity, and several polymorphisms. Age and SLC22A4 and SLC47A2 polymorphisms affected the area under the concentration-time curve (AUC). However, after adjusting for dose-to-weight ratio (dW), sex, age, and ethnicity, along with SLC22A3 and SLC22A4, influenced AUC. The maximum concentration was affected by age and SLC22A1, but after adjusting for dW, it was affected by sex, age, ethnicity, ABCG2, and SLC22A4. The time to reach the maximum concentration was influenced by sex, like half-life, which was also affected by SLC22A3. The volume of distribution and clearance was affected by sex, age, ethnicity and SLC22A3. Alternatively, the pharmacokinetics of metformin was unaffected by polymorphisms in ABCB1, SLC2A2, SLC22A2, or SLC47A1. Therefore, our study demonstrates that a multifactorial approach to all patient characteristics is necessary for better individualization. The project was financed by the Regional Health Management of Castilla y León (GRS 2432/A/21) and partially by Fundación Burgos por la Investigación de la Salud (FBIS). M.S.R. research is supported by Instituto de Salud Carlos III (ISCIII), Spanish Ministry of Science and Innovation, through the Sara Borrell Program (CD21/00022). G.V.G. is cofinanced by Instituto de Salud Carlos III (ISCIII) and the European Social Fund (PFIS predoctoral grant, number FI20/00090). M.N.G. is financed by the ICI20/00131 grant, Acción Estratégica en Salud 2017–2020, ISCIII. P.Z. is financed by Universidad Autónoma de Madrid, Margarita Salas contract, grants for the requalification of the Spanish university system. The MassArray genotyping service was carried out at the Spanish genotyping center–Centro Español de Genotipado CEGEN-PRB3-ISCIII, which is supported by grant PT17/0019 of the PE I+D+i 2013–2016, funded by ISCIII and European Regional Development Fund ERDF. 2026-07-16T11:57:12Z 2026-07-16T11:57:12Z 2023-03 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2075-4426 https://hdl.handle.net/10259/11920 10.3390/jpm13030489 eng Journal of Personalized Medicine. 2023, V.13, n. 3, art. 489 https://doi.org/10.3390/jpm13030489 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess MDPI https://riubu.ubu.es/bitstream/10259/11920/4/Saiz-JPM_2023.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by/4.0/ RIUBU. Repositorio Institucional de la Universidad de Burgos https://hdl.handle.net/10259/11920