Modeling the differential effect of \emph{CYP2C9} gene alleles on losartan metabolism

D. S. Babaev ; E. O. Kutumova ; F. A. Kolpakov

Matematičeskaâ biologiâ i bioinformatika, Tome 19 (2024) no. 2, pp. 533-564

Résumé

Arterial hypertension is a multifactorial disease characterized by an increase in systolic and/or diastolic blood pressure. This pathology affects about a billion people worldwide. Hypertension is also a common risk factor for the development of different cardiovascular diseases, which are the leading cause of death in the population, claiming about 17 million lives annually. Various antihypertensive drugs are used to control hypertension, for example, losartan, a selective angiotensin II AT1-receptor antagonist. Losartan is primarily metabolized in the liver by CYP2C9, a member of the cytochrome P450 superfamily, to the pharmacologically active metabolite carboxylosartan (E-3174). The CYP2C9 gene is highly polymorphic: numerous single nucleotide polymorphisms that alter the function of the enzyme have been described in the literature. The most widespread CYP2C9 alleles are CYP2C9*1 (wild-type), CYP2C9*2 and CYP2C9*3. Here, we performed mathematical modeling of the metabolism of orally administered losartan to E-3174, taking into account combinations of the most common CYP2C9 alleles. It was shown that, depending on the CYP2C9 genotype, the pharmacokinetics of losartan and E-3174 differ significantly, which is confirmed by previously published experimental data. To develop and analyze the model, we used the BioUML software. The results of the modeling can potentially be used to personalize drug therapy for arterial hypertension.

Bibliographie

[1] D. R. Nelson, “Cytochrome P450 nomenclature, 2004”, Cytochrome P450 Protocols, Methods in Molecular Biology, 320, eds. Phillips I. R., E. A. Shephard, Humana Press, Totowa, NJ, 2006, 1–10 | DOI | DOI

[2] D. W. Nebert, D. W. Russell, “Clinical importance of the cytochromes P450”, The Lancet, 360 (2002), 1155–1162 | DOI | DOI

[3] H. Takahashi, H. Echizen, “Pharmacogenetics of warfarin elimination and its clinical implications”, Clin. Pharmacokinet, 40 (2001), 587–603 | DOI | DOI

[4] U. M. Zanger, M. Turpeinen, K. Klein, M. Schwab, “Functional pharmacogenetics/genomics of human cytochromes P450 involved in drug biotransformation”, Anal. Bioanal. Chem, 392 (2008), 1093–1108 | DOI | DOI

[5] U. M. Zanger, M. Schwab, “Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation”, Pharmacol. Ther, 138 (2013), 103–141 | DOI | DOI

[6] J. O. Miners, D. J. Birkett, “Cytochrome P4502C9: an enzyme of major importance in human drug metabolism”, Br. J. Clin. Pharmacol, 45 (1998), 525–538 | DOI | DOI

[7] I. C. Gray, C. Nobile, R. Muresu, S. Ford, N. K. Spurr, “A 2,4-megabase physical map spanning the CYP2C gene cluster on chromosome 10q24”, Genomics, 28 (1995), 328–332 | DOI | DOI

[8] C. R. Lee, J. A. Goldstein, J. A. Pieper, “Cytochrome P450 2C9 polymorphisms: a comprehensive review of the in-vitro and human data”, Pharmacogenetics, 12 (2002), 251–263 | DOI | DOI

[9] C. Ged, D. R. Umbenhauer, T. M. Bellew, R. W. Bork, P. K. Srivastava, N. Shinriki, R. S. Lloyd, F. P. Guengerich, “Characterization of cDNAs, mRNAs, and proteins related to human liver microsomal cytochrome P-450 (S)-mephenytoin 4'-hydroxylase”, Biochemistry, 27 (1988), 6929–6940 | DOI | DOI

[10] M. J. Stubbins, L. W. Harries, G. Smith, M. H. Tarbit, C. R. Wolf, “Genetic analysis of the human cytochrome P450 CYP2C9 locus”, Pharmacogenetics, 6 (1996), 429–439 | DOI | DOI

[11] T. H. Sullivan-Klose, B. I. Ghanayem, D. A. Bell, Z. Y. Zhang, L. S. Kaminsky, G. M. Shenfield, J. O. Miners, D. J. Birkett, J. A. Goldstein, “The role of the CYP2C9-Leu359 allelic variant in the tolbutamide polymorphism”, Pharmacogenetics, 6 (1996), 341–349 | DOI | DOI

[12] U. I. Schwarz, “Clinical relevance of genetic polymorphisms in the human CYP2C9 gene”, Eur. J. Clin. Invest., 33, Suppl. 2 (2003), 23–30 | DOI | DOI

[13] D. Si, Y. Guo, Y. Zhang, L. Yang, H. Zhou, D. Zhong, “Identification of a novel variant CYP2C9 allele in Chinese”, Pharmacogenetics, 14 (2004), 465–469 | DOI | DOI

[14] J. B. McCrea, A. Cribb, T. Rushmore, B. Osborne, L. Gillen, M. W. Lo, S. Waldman, T. Bjornsson, S. Spielberg, M. R. Goldberg, “Phenotypic and genotypic investigations of a healthy volunteer deficient in the conversion of losartan to its active metabolite E-3174”, Clin. Pharmacol. Ther, 65 (1999), 348–352 | DOI | DOI

[15] R. A. Stearns, P. K. Chakravarty, R. Chen, S. H. Chiu, “Biotransformation of losartan to its active carboxylic acid metabolite in human liver microsomes. Role of cytochrome P4502C and subfamily members”, Drug Metab. Dispos, 23 (1995), 207–215, 3 pp.

[16] K. M. Kaukonen, K. T. Olkkola, P. J. Neuvonen, “Fluconazole but not itraconazole decreases the metabolism of losartan to E-3174”, Eur. J. Clin. Pharmacol, 53 (1998), 445–449 | DOI | DOI

[17] K. M. Williamson, J. H. Patterson, R. H. McQueen, K. F.J. Adams, J. A. Pieper, “Effects of erythromycin or rifampin on losartan pharmacokinetics in healthy volunteers”, Clin. Pharmacol. Ther, 63 (1998), 316–323 | DOI | DOI

[18] M. R. Goldberg, M. W. Lo, T. E. Bradstreet, M. A. Ritter, P. Hoglund, “Effects of cimetidine on pharmacokinetics and pharmacodynamics of losartan, an AT1-selective non-peptide angiotensin II receptor antagonist”, Eur. J. Clin. Pharmacol, 49 (1995), 115–119 | DOI | DOI

[19] M. Kobayashi, M. Takagi, K. Fukumoto, R. Kato, K. Tanaka, K. Ueno, “The effect of bucolome, a CYP2C9 inhibitor, on the pharmacokinetics of losartan”, Drug Metab. Pharmacokinet, 23 (2008), 115–119 | DOI | DOI

[20] A. Wennerholm, A. Nordmark, M. Pihlsgard, M. Mahindi, L. Bertilsson, Gustafsson L. L. Amodiaquine, “its desethylated metabolite, or both, inhibit the metabolism of debrisoquine (CYP2D6) and losartan (CYP2C9) in vivo”, Eur. J. Clin. Pharmacol, 62 (2006), 539–546 | DOI | DOI

[21] P. B. Timmermans, P. C. Wong, A. T. Chiu, W. F. Herblin, P. Benfield, D. J. Carini, R. J. Lee, R. R. Wexler, J. A. Saye, R. D. Smith, “Angiotensin II receptors and angiotensin II receptor antagonists”, Pharmacol. Rev, 45 (1993), 205–251

[22] U. Yasar, G. Tybring, M. Hidestrand, M. Oscarson, M. Ingelman-Sundberg, M. L. Dahl, E. Eliasson, “Role of CYP2C9 polymorphism in losartan oxidation”, Drug Metab. Dispos, 29 (2001), 1051–1056

[23] C. H. Yun, H. S. Lee, H. Lee, J. K. Rho, H. G. Jeong, F. P. Guengerich, “Oxidation of the angiotensin II receptor antagonist losartan (DuP 753) in human liver microsomes. Role of cytochrome P4503A(4) in formation of the active metabolite EXP3174”, Drug Metab. Dispos, 23 (1995), 285–289

[24] M. W. Lo, M. R. Goldberg, J. B. McCrea, H. Lu, C. I. Furtek, T. D. Bjornsson, “Pharmacokinetics of losartan, an angiotensin II receptor antagonist, and its active metabolite EXP3174 in humans”, Clin. Pharmacol. Ther, 58 (1995), 641–649 | DOI | DOI

[25] Yasar U., Forslund-Bergengren C., Tybring G., Dorado P., Llerena A., Sjoqvist F., Eliasson E., Dahl M.L., “Pharmacokinetics of losartan and its metabolite E-3174 in relation to the CYP2C9 genotype”, Clin. Pharmacol. Ther, 71 (2002), 89–98 | DOI | DOI

[26] E. Karatza, V. Karalis, “Modelling gastric emptying: A pharmacokinetic model simultaneously describing distribution of losartan and its active metabolite EXP-3174”, Basic Clin. Pharmacol. Toxicol, 126 (2020), 193–202 | DOI | DOI

[27] F. Kolpakov, I. Akberdin, T. Kashapov, l. Kiselev, S. Kolmykov, Y. Kondrakhin, E. Kutumova, N. Mandrik, S. Pintus, A. Ryabova et al, “BioUML: an integrated environment for systems biology and collaborative analysis of biomedical data”, Nucleic Acids Res., 47 (2019), W225-W233 | DOI | DOI

[28] F. Kolpakov, I. Akberdin, I. Kiselev, S. Kolmykov, Y. Kondrakhin, M. Kulyashov, E. Kutumova, S. Pintus, A. Ryabova, R. Sharipov et al, “BioUML-towards a universal research platform”, Nucleic Acids Res., 50 (2022), W124-W131 | DOI | DOI

[29] I. N. Kiselev, B. V. Semisalov, E. A. Biberdorf, R. N. Sharipov, A. M. Blokhin, F. A. Kolpakov, “Modulnoe modelirovanie serdechno-sosudistoi sistemy cheloveka”, Matematicheskaya biologiya i bioinformatika, 7:2 (2012), 703–736 | DOI | DOI

[30] E. O. Kutumova, “Reduktsiya modeli CD95-indutsiruemogo apoptoza”, Matematicheskaya biologiya i bioinformatika, 7:1 (2012), 139–151 | DOI | DOI

[31] E. O. Kutumova, A. Yu. Zinovev, R. N. Sharipov, F. A. Kolpakov, “Primenenie metodov reduktsii dlya postroeniya kompleksnoi modeli apoptoticheskikh putei”, Matematicheskaya biologiya i bioinformatika, 7:2 (2012), 572–588 | DOI | DOI

[32] I. N. Kiselev, E. A. Biberdorf, V. I. Baranov, T. G. Komlyagina, V. N. Melnikov, I. Yu. Suvorova, S. G. Krivoschekov, F. A. Kolpakov, “Personalizatsiya parametrov i validatsiya modeli serdechno-sosudistoi sistemy cheloveka”, Matematicheskaya biologiya i bioinformatika, 10:2 (2015), 526–547 | DOI | DOI

[33] I. N. Kiselev, E. O. Kutumova, A. F. Kolpakova, G. I. Lifshits, F. A. Kolpakov, “Matematicheskoe modelirovanie deistviya antigipertenzivnykh preparatov”, Matematicheskaya biologiya i bioinformatika, 14:1 (2019), 233–256 | DOI | DOI

[34] I. N. Kiselev, I. R. Akberdin, A. Yu. Vertyshev, D. V. Popov, F. A. Kolpakov, “Modulnaya graficheskaya model energeticheskogo metabolizma v kletkakh skeletnoi myshtsy”, Matematicheskaya biologiya i bioinformatika, 14:2 (2019), 373–392 | DOI | DOI

[35] I. R. Akberdin, A. Yu. Vertyshev, S. S. Pintus, D. V. Popov, F. A. Kolpakov, “Matematicheskaya model, svyazyvayuschaya Ca2+-zavisimyi signalnyi put s regulyatsiei ekspressii genov v kletkakh skeletnoi myshtsy cheloveka”, Matematicheskaya biologiya i bioinformatika, 15:1 (2020), 20–39 | DOI | DOI

[36] A. D. Borodulina, E. O. Kutumova, G. I. Lifshits, F. A. Kolpakov, “Matematicheskoe modelirovanie antigipertenzivnoi terapii azilsartana medoksomilom na primere klinicheskikh dannykh realnogo patsienta”, Matematicheskaya biologiya i bioinformatika, 18:1 (2023), 228–250 | DOI | DOI

[37] P. Yu. Kondrakhin, F. A. Kolpakov, “Mnogourovnevaya matematicheskaya model epilepticheskikh pripadkov”, Matematicheskaya biologiya i bioinformatika, 18:2 (2023), 479–516 | DOI | DOI

[38] S. M. Keating, D. Waltemath, M. Konig, F. Zhang, A. Drager, C. Chaouiya, F. T. Bergmann, A. Finney, C. S. Gillespie, T. Helikar et al, “SBML Level 3: an extensible format for the exchange and reuse of biological models”, Mol. Syst. Biol., 16 (2020), e9110 | DOI | DOI

[39] R. S. Malik-Sheriff, M. Glont, T. V.N. Nguyen, K. Tiwari, M. G. Roberts, A. Xavier, M. T. Vu, J. Men, M. Maire, S. Kananathan et al, “BioModels-15 years of sharing computational models in life science”, Nucleic Acids Res., 48 (2020), D407-D415 | DOI | DOI

[40] T. Runarsson, X. Yao, “Stochastic ranking for constrained evolutionary optimization”, IEEE Transactions on Evolutionary Computation, 4 (2000), 284–294 | DOI | DOI

[41] M. Bjorkman, K. Holmstrom, “Global optimization using the DIRECT algorithm in Matlab”, Adv. Model. Optim., 1 (2004)

[42] S. M. Goldfeld, R. E. Quandt, H. F. Trotter, “Maximization by Quadratic Hill-Climbing”, Econometrica, 34 (1966), 541–551 | DOI | Zbl | DOI | Zbl

[43] M. R. Sierra, C. A.C. Coello, “Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance”, International Conference on Evolutionary Multi-Criterion Optimization, 2005

[44] A. J. Nebro, J. J. Durillo, F. Luna, B. Dorronsoro, E. Alba, “MOCell: A cellular genetic algorithm for multiobjective optimization”, Int. J. Intell. Syst, 24 (2009) | Zbl | Zbl

[45] L. Ingber, “Adaptive simulated annealing (ASA): Lessons learned”, Control Cybern, 25 (1996), 33–54 | DOI | Zbl | DOI | Zbl

[46] A. Raue, C. Kreutz, T. Maiwald, J. Bachmann, M. Schilling, U. Klingmuller, J. Timmer, “Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood”, Bioinformatics, 25 (2009), 1923–1929 | DOI | DOI

[47] A. Raue, V. Becker, U. Klingmuller, J. Timmer, “Identifiability and observability analysis for experimental design in nonlinear dynamical models”, Chaos Woodbury, 20:2010, 045105 | DOI | Zbl | DOI | Zbl

[48] H. A. Rabitz, M. A. Kramer, D. K. Dacol, “Sensitivity Analysis in Chemical Kinetics”, Annu. Rev. Phys. Chem, 34 (1983), 419–461

[49] P. L. Toutain, A. Bousquet-Melou, “Plasma terminal half-life”, J. Vet. Pharmacol. Ther, 27 (2004), 427–439 | DOI | DOI

[50] N. Le Novere, M. Hucka, H. Mi, S. Moodie, F. Schreiber, A. Sorokin, E. Demir, K. Wegner, M. I. Aladjem, S. M. Wimalaratne et al, “The Systems Biology Graphical Notation”, Nat. Biotechnol, 27 (2009), 735–741 | DOI | DOI

[51] E. Deloose, P. Janssen, I. Depoortere, J. Tack, “The migrating motor complex: control mechanisms and its role in health and disease”, Nat. Rev. Gastroenterol. Hepatol, 9 (2012), 271–285 | DOI | DOI

[52] J. Feher, Intestinal and Colonic Chemoreception and Motility, 2017, 796–809 | DOI | DOI

[53] A. F. Dominiczak, D. C. Negrin, J. S. Clark, M. J. Brosnan, M. W. McBride, M. Y. Alexander, “Genes and hypertension: from gene mapping in experimental models to vascular gene transfer strategies”, Hypertens. Dallas Tex., 35 (1979), 164–172 | DOI | DOI

[54] C. Menni, M. Mangino, F. Zhang, G. Clement, H. Snieder, S. Padmanabhan, T. D. Spector, “Heritability analyses show visit-to-visit blood pressure variability reflects different pathological phenotypes in younger and older adults: evidence from UK twins”, J. Hypertens, 31 (2013), 2356–2361 | DOI | DOI

[55] H. Chavda, C. Patel, I. S. Anand, “Biopharmaceutics Classification System”, Syst. Rev. Pharm, 1 (2010), 62–69 | DOI | DOI

[56] M. Sugihara, S. Takeuchi, M. Sugita, K. Higaki, M. Kataoka, S. Yamashita, “Analysis of Intra- and Intersubject Variability in Oral Drug Absorption in Human Bioequivalence Studies of 113 Generic Products”, Mol. Pharm, 12 (2015), 4405–4413 | DOI | DOI

[57] Y. Tsume, G. L. Amidon, “The biowaiver extension for BCS class III drugs: the effect of dissolution rate on the bioequivalence of BCS class III immediate-release drugs predicted by computer simulation”, Mol. Pharm., 7 (2010), 1235–1243 | DOI | DOI

[58] K. Higaki, S. Y. Choe, R. Lobenberg, L. S. Welage, G. L. Amidon, “Mechanistic understanding of time-dependent oral absorption based on gastric motor activity in humans”, Eur. J. Pharm. Biopharm, 70 (2008), 313–325 | DOI | DOI

[59] P. Langguth, K. M. Lee, H. Spahn-Langguth, G. L. Amidon, “Variable gastric emptying and discontinuities in drug absorption profiles: dependence of rates and extent of cimetidine absorption on motility phase and pH”, Biopharm. Drug Dispos, 15 (1994), 719–746 | DOI | DOI

[60] R. L. Oberle, G. L. Amidon, “The influence of variable gastric emptying and intestinal transit rates on the plasma level curve of cimetidine; an explanation for the double peak phenomenon”, J. Pharmacokinet. Biopharm, 15 (1987), 529–544 | DOI | DOI

[61] N. Takamatsu, L. S. Welage, Y. Hayashi, R. Yamamoto, J. L. Barnett, V. P. Shah, L. J. Lesko, C. Ramachandran, G. L. Amidon, “Variability in cimetidine absorption and plasma double peaks following oral administration in the fasted state in humans: correlation with antral gastric motility”, Eur. J. Pharm. Biopharm, 53 (2002), 37–47 | DOI | DOI

[62] H. B. Shin, E. H. Jung, P. Kang, C. W. Lim, K. Y. Oh, C. K. Cho, Y. J. Lee, C. I. Choi, C. G. Jang, S. Y. Lee et al, “ABCB1 c2677G>T/c.3435C>T diplotype increases the early-phase oral absorption of losartan”, Arch. Pharm. Res., 43 (2020), 1187–1196 | DOI | DOI