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CNS & Neurological Disorders - Drug Targets

Editor-in-Chief

ISSN (Print): 1871-5273
ISSN (Online): 1996-3181

Research Article

The Development of a PBPK Model for Atomoxetine Using Levels in Plasma, Saliva and Brain Extracellular Fluid in Patients with Normal and Deteriorated Kidney Function

Author(s): Mo'tasem M. Alsmadi*, Laith N. AL Eitan, Nasir M. Idkaidek and Karem H. Alzoubi

Volume 21, Issue 8, 2022

Published on: 21 June, 2021

Page: [704 - 716] Pages: 13

DOI: 10.2174/1871527320666210621102437

Price: $65

Abstract

Background: Atomoxetine is a treatment for attention-deficit hyperactivity disorder. It inhibits Norepinephrine Transporters (NET) in the brain. Renal impairment can reduce hepatic CYP2D6 activity and atomoxetine elimination which may increase its body exposure. Atomoxetine can be secreted in saliva.

Objective: The objective of this work was to test the hypothesis that atomoxetine saliva levels (sATX) can be used to predict ATX brain Extracellular Fluid (bECF) levels and their pharmacological effects in healthy subjects and those with End-Stage Renal Disease (ESRD).

Methods: The pharmacokinetics of atomoxetine after intravenous administration to rats with chemically induced acute and chronic renal impairments were investigated. A physiologically-based pharmacokinetic (PBPK) model was built and verified in rats using previously published measured atomoxetine levels in plasma and brain tissue. The rat PBPK model was then scaled to humans and verified using published measured atomoxetine levels in plasma, saliva, and bECF.

Results: The rat PBPK model predicted the observed reduced atomoxetine clearance due to renal impairment in rats. The PBPK model predicted atomoxetine exposure in human plasma, sATX and bECF. Additionally, it predicted that ATX bECF levels needed to inhibit NET are achieved at 80 mg dose. In ESRD patients, the developed PBPK model predicted that the previously reported 65% increase in plasma exposure in these patients can be associated with a 63% increase in bECF. The PBPK simulations showed that there is a significant correlation between sATX and bECF in human.

Conclusion: Saliva levels can be used to predict atomoxetine pharmacological response.

Keywords: Atomoxetine, brain extracellular fluid, CYP2D6, physiologically-based pharmacokinetic modeling, renal impairment, salivary excretion classification system.

Graphical Abstract
[1]
Sharma A, Couture J. A review of the pathophysiology, etiology, and treatment of attention-deficit hyperactivity disorder (ADHD). Ann Pharmacother 2014; 48(2): 209-25.
[2]
Yu G, Li G-F, Markowitz JS. Atomoxetine: a review of its pharmacokinetics and pharmacogenomics relative to drug disposition. J Child Adolesc Psychopharmacol 2016; 26(4): 314-26.
[3]
Kielbasa W, Stratford RE. Exploratory translational modeling approach in drug development to predict human brain pharmacokinetics and pharmacologically relevant clinical doses. Drug Metab Dispos 2012; 40(5): 877-83.
[4]
Witcher JW, Long A, Smith B, et al. Atomoxetine pharmacokinetics in children and adolescents with attention deficit hyperactivity disorder. J Child Adolesc Psychopharmacol 2003; 13(1): 53-63.
[5]
Sauer J-M, Ponsler GD, Mattiuz EL, et al. Disposition and metabolic fate of atomoxetine hydrochloride: the role of CYP2D6 in human disposition and metabolism. Drug Metab Dispos 2003; 31(1): 98-107.
[6]
Sauer J-M, Ring BJ, Witcher JW. Clinical pharmacokinetics of atomoxetine. Clin Pharmacokinet 2005; 44(6): 571-90.
[8]
Idkaidek N, Arafat T. Saliva versus plasma pharmacokinetics: theory and application of a salivary excretion classification system. Mol Pharm 2012; 9(8): 2358-63.
[9]
Papaseit E, Marchei E, Farré M, Garcia-Algar O, Pacifici R, Pichini S. Concentrations of atomoxetine and its metabolites in plasma and oral fluid from paediatric patients with attention deficit/hyperactivity disorder. Drug Test Anal 2013; 5(6): 446-52.
[10]
Alsmadi MM, Alfarah MQ, Albderat J, et al. The development of a population physiologically based pharmacokinetic model for mycophenolic mofetil and mycophenolic acid in humans using data from plasma, saliva, and kidney tissue. Biopharm Drug Dispos 2019; 40(9): 325-40.
[11]
Rowland Yeo K, Aarabi M, Jamei M, Rostami-Hodjegan A. Modeling and predicting drug pharmacokinetics in patients with renal impairment. Expert Rev Clin Pharmacol 2011; 4(2): 261-74.
[12]
Nestorov I. Whole-body physiologically based pharmacokinetic models. Expert Opin Drug Metab Toxicol 2007; 3(2): 235-49.
[13]
Huang W, Nakano M, Sager J, Ragueneau-Majlessi I, Isoherranen N. Physiologically based pharmacokinetic model of the cyp2d6 probe atomoxetine: extrapolation to special populations and drug- drug interactions. Drug Metab Dispos 2017; 45(11): 1156-65.
[14]
Okabe H, Hasunuma M, Hashimoto Y. The hepatic and intestinal metabolic activities of P450 in rats with surgery-and drug-induced renal dysfunction. Pharm Res 2003; 20(10): 1591-4.
[15]
Al Za’abi M, Al Busaidi M, Yasin J, Schupp N, Nemmar A, Ali BH. Development of a new model for the induction of chronic kidney disease via intraperitoneal adenine administration, and the effect of treatment with gum acacia thereon. Am J Transl Res 2015; 7(1): 28-38.
[16]
Zhu H-J, Wang J-S, Donovan JL, DeVane CL, Gibson BB, Markowitz JS. Sensitive quantification of atomoxetine in human plasma by HPLC with fluorescence detection using 4-(4, 5-diphenyl-1H-imidazole-2-yl) benzoyl chloride derivatization. J Chromatogr B Analyt Technol Biomed Life Sci 2007; 846(1): 351-4.
[19]
Zhang Y, Huo M, Zhou J, Xie S. PKSolver: An add-in program for pharmacokinetic and pharmacodynamic data analysis in Microsoft Excel. Comput Methods Programs Biomed 2010; 99(3): 306-14.
[20]
Mechanistic Modeling of Pharmacokinetics and Dynamics. Available from: http://docs.open-systems-pharmacology.org/
[21]
Hughes JH, Upton RN, Reuter SE, Rozewski DM, Phelps MA, Foster DJ. Development of a physiologically based pharmacokinetic model for intravenous lenalidomide in mice. Cancer Chemother Pharmacol 2019; 84(5): 1073-87.
[22]
Wong YC, Centanni M, de Lange EC. Physiologically based modeling approach to predict dopamine d2 receptor occupancy of antipsychotics in brain: translation from rat to human. J Clin Pharmacol 2019; 59(5): 731-47.
[23]
Kielbasa W, Kalvass JC, Stratford RE. Microdialysis evaluation of atomoxetine brain penetration and central nervous system pharmacokinetics in rats. Drug Metab Dispos 2008; 37(1): 137-42.
[24]
Willmann S, Lippert J, Schmitt W. From physicochemistry to absorption and distribution: predictive mechanistic modelling and computational tools. Expert Opin Drug Metab Toxicol 2005; 1(1): 159-68.
[25]
Bagnis C, Beaufils H, Jacquiaud C, et al. Erythropoietin enhances recovery after cisplatin-induced acute renal failure in the rat. Nephrol Dial Transplant 2001; 16(5): 932-8.
[26]
Tikoo K, Kumar P, Gupta J. Rosiglitazone synergizes anticancer activity of cisplatin and reduces its nephrotoxicity in 7, 12-dimethyl benz {a} anthracene (DMBA) induced breast cancer rats. BMC Cancer 2009; 9(1): 107-18.
[27]
Yokozawa T, Zheng PD, Oura H, Koizumi F. Animal model of adenine-induced chronic renal failure in rats. Nephron 1986; 44(3): 230-4.
[28]
Ali B, Al Za’abi M, Ramkumar A, Yasin J, Nemmar A. Anemia in adenine-induced chronic renal failure and the influence of treatment with gum acacia thereon. Physiol Res 2014; 63(3): 351-8.
[29]
Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (5th ed),. 5th ed. 2005.
[30]
Kielbasa W, Pan A, Pereira A. A pharmacokinetic/pharmacodynamic investigation: assessment of edivoxetine and atomoxetine on systemic and central 3, 4-dihydroxyphenylglycol, a biochemical marker for norepinephrine transporter inhibition. Eur Neuropsychopharmacol 2015; 25(3): 377-85.
[31]
Ring BJ, Gillespie JS, Eckstein JA, Wrighton SA. Identification of the human cytochromes P450 responsible for atomoxetine metabolism. Drug Metab Dispos 2002; 30(3): 319-23.
[32]
Berezhkovskiy LM. Volume of distribution at steady state for a linear pharmacokinetic system with peripheral elimination. J Pharm Sci 2004; 93(6): 1628-40.
[33]
Chalon SA, Desager JP, DeSante KA, et al. Effect of hepatic impairment on the pharmacokinetics of atomoxetine and its metabolites. Clin Pharmacol Ther 2003; 73(3): 178-91.
[34]
Upreti VV, Wahlstrom JL. Meta-analysis of hepatic cytochrome P450 ontogeny to underwrite the prediction of pediatric pharmacokinetics using physiologically based pharmacokinetic modeling. J Clin Pharmacol 2016; 56(3): 266-83.
[35]
Tanaka G. Anatomical and physiological characteristics for asian reference man-male and female of different age: tanaka model. NIRS 1996; 32(3-4): 5-265.
[36]
Willmann S, Höhn K, Edginton A, et al. Development of a physiology-based whole-body population model for assessing the influence of individual variability on the pharmacokinetics of drugs. J Pharmacokinet Pharmacodyn 2007; 34(3): 401-31.
[37]
Couto N, Al-Majdoub ZM, Achour B, Wright PC, Rostami-Hodjegan A, Barber J. Quantification of proteins involved in drug metabolism and disposition in the human liver using label-free global proteomics. Mol Pharm 2019; 16(2): 632-47.
[38]
Maharaj AR, Wu H, Hornik CP, Cohen-Wolkowiez M. Pitfalls of using numerical predictive checks for population physiologically-based pharmacokinetic model evaluation. J Pharmacokinet Pharmacodyn 2019; 46(3): 263-72.
[39]
Choi C, Jang C, Bae J, Lee S. Validation of an analytical LC-MS/MS method in human plasma for the pharmacokinetic study of atomoxetine. J Anal Chem 2013; 68(11): 986-91.
[40]
Martignoni M, Groothuis GM, de Kanter R. Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin Drug Metab Toxicol 2006; 2(6): 875-94.
[41]
Leblond FA, Giroux L, Villeneuve J-P, Pichette V. Decreased in vivo metabolism of drugs in chronic renal failure. Drug Metab Dispos 2000; 28(11): 1317-20.
[42]
Yousef M, Saad A, El-Shennawy L. Protective effect of grape seed proanthocyanidin extract against oxidative stress induced by cisplatin in rats. Food Chem Toxicol 2009; 47(6): 1176-83.
[43]
Kaysen GA. Biological basis of hypoalbuminemia in ESRD. J Am Soc Nephrol 1998; 9(12): 2368-76.
[44]
Tsamandouras N, Rostami-Hodjegan A, Aarons L. Combining the ‘bottom up’and ’top down’approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data. Br J Clin Pharmacol 2015; 79(1): 48-55.
[45]
Okino MS, Mavrovouniotis ML. Simplification of mathematical models of chemical reaction systems. Chem Rev 1998; 98(2): 391-408.
[46]
Nestorov IA, Aarons LJ, Arundel PA, Rowland M. Lumping of whole-body physiologically based pharmacokinetic models. J Pharmacokinet Biopharm 1998; 26(1): 21-46.
[47]
Sale M, Sherer EA. A genetic algorithm based global search strategy for population pharmacokinetic/pharmacodynamic model selection. Br J Clin Pharmacol 2015; 79(1): 28-39.
[48]
Pan S, Duffull SB. Automated proper lumping for simplification of linear physiologically based pharmacokinetic systems. J Pharmacokinet Pharmacodyn 2019; 46(4): 361-70.
[49]
Bayer Technology Services GmbH. Open Systems Pharmacology Suite. https://docs.open-systems-pharmacology.org/working-with-pk-sim/pk-sim-documentation

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