Oral drug absorption predictions using “in silico” models (SIMCYP®): Statins
Abstract
Using Simcyp® software, simulation studies were done in order to evaluate the capacity of this software to predict clinical evidence related to absorption profile of two statins: simvastatin and rosuvastatin. Specifically, we evaluate the influence of intestinal transit time, transporter abundance and enzymes responsible of metabolism in gastrointestinal tract. First of all we looked for bibliographic information about the absorption profile of these drugs. Then we carried out simulations in different population groups, mainly in normal populations, populations with accelerated intestinal transit and poor metabolizers. For rosuvastatin its efflux transporter (ABCG2) was also blocked and its permeability altered. The results are consistent with observations drawn from clinical experience. They show that the transit rate significantly affects the absorbed fraction of simvastatin, but not the rate of rosuvastatin; however the transporter abundance and permeability value are relevant in the absorption of rosuvastatin. CYP3A4 is shown as the most important isoenzyme responsible of the biotransformation of simvastatin, confirming that CYP3A4 does not participate in rosuvastatin intestinal metabolism.
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Kato. Intestinal first-pass metabolism of CYP3A4 substrates. Drug Metabolism and Pharmacokinetics. 2008; 23(2):87-94. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/18445987
Lennernäs H. Intestinal permeability and its relevance for absorption and elimination. Xenobiotica. 2007; 37(10-11):1015-1051. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/17968735
Maliepaard M, et al. Subcellular localization and distribution of the breast cancer resistance protein transporter in normal human tissues. Cancer Res. 2001; 61(8): p. 3458-64. Disponible en: http://cancerres.aacrjournals.org/content/61/8/3458.long
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SimCyp Simulator (v. 16) [software]. 2011-2016. Princeton: Certara USA, Inc. Obtenido de: https://support.certara.com
Wang I, Hopper I. Celiac disease and drug absorption: Implications fo cardiovascular therapeutics. Cardiovascular Therapeutics. 2014; 32 253–256. Sede web: http://onlinelibrary.wiley.com/doi/10.1111/1755-5922.12094/epdf
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006 Jan 1; 34(Database issue):D668-72. 16381955
Fujikawa M, Ano R, Nakao K, Shimizu R, Akamatsu M. Relationships between structure and high-throughput screening permeability of diverse drugs with artificial membranes: application to prediction of Caco-2 cell permeability. Bioorganic & Medicinal Chemistry. 2005; 13(15):4721-4732. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/15936203
Giacomini KM, Huang SM, Tweedie DJ, Benet LT, Brouwer K, Chu X et al. Membrane transporters in drug development. Nature Reviews Drug Discovery. 2010; 9: 215-236. Disponible en: http://www.nature.com/nrd/journal/v9/n3/fig_tab/nrd3028_F1.html
Horio M, Chin KV, Currier SJ, Goldenberg S, Williams C, Pastan I et al. Transepithelial transport of drugs by the multidrug transporter in cultured Madin-Darby Canine Kidney cell epithelia. Journal of Biological Chemistry. 1989; 264(25):14880-14484. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/2570070
Hwang KK, Martin NE, Jiang L, Zhu C. Permeation prediction of M100240 using the parallel artificial membrane permeability assay. Journal of Pharmacy & Pharmaceutical Sciences. 2003; 6(3):315-320. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/14738711
Irvine JD, Takahashi L, Lockhart K, Cheong J, Tolan JW et al. MDCK (Madin Darby Canine Kidney) Cells: A tool for membrane permeability screening. Journal of Pharmaceutical Sciences. 1999; 88(1):28-33. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/9874698
Jamei M, Turner D, Yang J, Neuhoff S, Polak S, Rostami-Hodjegan et al. Population-Based Mechanistic Prediction of Oral Drug Absorption. The AAPS Journal. 2009; 11 (2):225-237. Disponible en: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2691459/
Kansy M, Senner F, Gubernator K. Physicochemical High Throughput Screening: Parallel Artificial membrane Permeation Assay in the Description of Passive Permeation Process. Journal of Medicinal Chemistry. 1998; 41(7):1007-1010. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/9544199
Kato. Intestinal first-pass metabolism of CYP3A4 substrates. Drug Metabolism and Pharmacokinetics. 2008; 23(2):87-94. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/18445987
Lennernäs H. Intestinal permeability and its relevance for absorption and elimination. Xenobiotica. 2007; 37(10-11):1015-1051. Disponible en: http://www.ncbi.nlm.nih.gov/pubmed/17968735
Maliepaard M, et al. Subcellular localization and distribution of the breast cancer resistance protein transporter in normal human tissues. Cancer Res. 2001; 61(8): p. 3458-64. Disponible en: http://cancerres.aacrjournals.org/content/61/8/3458.long
Ranaldi G, Islam K, Sambuy Y. Epithelial Cells in Culture as a Model for the Intestinal Transport of Antimicrobial Agents. Antimicrobial Agents & Chemotherapy. 1992; 36(7):1374-1381. Disponible en: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC191589/
SimCyp Simulator (v. 16) [software]. 2011-2016. Princeton: Certara USA, Inc. Obtenido de: https://support.certara.com
Wang I, Hopper I. Celiac disease and drug absorption: Implications fo cardiovascular therapeutics. Cardiovascular Therapeutics. 2014; 32 253–256. Sede web: http://onlinelibrary.wiley.com/doi/10.1111/1755-5922.12094/epdf
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006 Jan 1; 34(Database issue):D668-72. 16381955
López, A., Santos, M. D., & García, M. J. (2017). Oral drug absorption predictions using “in silico” models (SIMCYP®): Statins. FarmaJournal, 2(2), 69–79. Retrieved from https://revistas.usal.es/cinco/index.php/2445-1355/article/view/16015
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