Population pharmacokinetic and pharmacodynamic modeling of transformed binary effect data of triflusal in healthy Korean male volunteers: a randomized, open-label, multiple dose, crossover study

BMC Clinical Pharmacology

Seunghoon Han⁷  Kwang-Il Kwon²  Dong Heon Yang⁶  Young-Ran Yoon⁴  Hae Won Lee⁵  Mi-sun Lim¹  Mi-Ri Gwon⁴  Jong Gwang Park⁵  Sook Jin Seong⁴  Joomi Lee³  Sung Min Park⁴ 

[1] College of Pharmacy, Yeungnam University, Daegu, South Korea;College of Pharmacy, Chungnam National University, Daejeon, South Korea;Department of Biomedical Science, Kyungpook National University Graduate School, Daegu, South Korea;BK21 Plus KNU Bio-Medical Convergence Program for Creative Talent, Kyungpook National University Graduate School, 680 Gukchaebosang-ro, Jung-gu, Daegu 700-842, South Korea;Clinical Trial Center, Kyungpook National University Hospital, Daegu, South Korea;Department of Internal Medicine, Division of Cardiology, Kyungpook National University School of Medicine, Daegu, South Korea;PIPET (Pharmacometrics Institute for Practical Education and Training), Seoul, South Korea

关键词: Population pharmacokinetics and pharmacodynamics;    NONMEM;    Binary probability model;    Platelet aggregation;    Transformed binary data;    HTB;   

Others  :  1121317
DOI  :  10.1186/2050-6511-15-75

 received in 2014-05-21, accepted in 2014-12-12,  发布年份 2014

【 摘 要 】

Background

Triflusal is a drug that inhibits platelet aggregation. In this study we investigated the dose-exposure-response relationship of a triflusal formulation by population pharmacokinetic (PK) and pharmacodynamic (PD) modeling of its main active metabolite, hydroxy-4-(trifluoromethyl) benzoic acid (HTB).

Methods

This study was a randomized, open-label, multiple-dose, two-period, two-treatment, comparative crossover design. All volunteers received a single oral loading dose of 900 mg of triflusal on Day 1, followed by a dose of 600 mg/day from Day 2 to 9. Using data from 34 healthy volunteers, 476 HTB plasma concentration data points and 340 platelet aggregation data points were used to construct PK and PD models respectively using NONMEM (version 6.2). As the PD endpoint was qualitative, we implemented binary analysis of ‘inhibition’ and ‘non-inhibition’ rather than using the actual value of the test. The final PK-PD model was evaluated using a visual predictive check (VPC) and bootstrap.

Results

The time-concentration profile of HTB over the entire dosing period was described by a one-compartment model with a first-order formation rate constant for HTB. Weight was selected as a covariate for clearance and volume of triflusal, respectively. The structure and the population estimates for triflusal PK were as follows: oral clearance (CL/F) = 0.2 · (weight/71.65)^0.845 L/h, oral volume of distribution (V/F) = 8.3 · (weight/71.65) L, and k_f = 0.341 h^-1. A sigmoid relationship between triflusal concentration and the probability of significant inhibition with shape factor was chosen as the final PD model. No time delay between concentration and response was identified. The final structure between predicted concentration View MathML"> and the probability of inhibition of platelet aggregation (IPA) relationship was as follows: Probability of View MathML">. Thus, we concluded this relationship is more like quantal concentration-response relationship. The current dosing regimen was considered to be efficacious based on the EC₅₀ estimate of 84.9 μg/mL obtained in this study.

Conclusions

A PK and binary probability PD model of triflusal was successfully developed for Korean healthy volunteers. The model may be used to further prediction inhibition of platelet aggregation by triflusal.

Trial registration

Clinical Research Information Service (CRIS), KCT0001299 (Registered December 5, 2014)

【 授权许可】

   
2014 Park et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Matias-Guiu J, Ferro JM, Alvarez-Sabin J, Torres F, Jiménez MD, Lago A, Melo T: Comparison of triflusal and aspirin for prevention of vascular events in patients after cerebral infarction: the TACIP Study: a randomized, double-blind, multicenter trial. Stroke 2003, 34:840-848.
• [2]McNeely W, Goa KL: Triflusal. Drugs 1998, 7:823-833.
• [3]Valle M, Barbanoj MJ, Donner A, Izquierdo I, Herranz U, Klein N, Eichler HG, Müller M, Brunner M: Access of HTB, main metabolite of triflusal, to cerebrospinal fluid in healthy volunteers. Eur J Clin Pharmacol 2005, 61:103-111.
• [4]Lee HW, Lim MS, Seong SJ, Lee J, Park J, Seo JJ, Yun HY, Baek IH, Kwon KI, Yoon YR: A phase I study to characterize the multiple-dose pharmacokinetics, pharmacodynamics and safety of new enteric-coated triflusal formulations in healthy male volunteers. Expert Opin Drug Metab Toxicol 2011, 7:1471-1479.
• [5]Anninos H, Andrikopoulos G, Pastromas S, Sakellariou D, Theodorakis G, Vardas P: Triflusal: an old drug in modern antiplatelet therapy. Review of its action, use, safety and effectiveness. Hellenic J Cardiol 2009, 50:199-207.
• [6]Rao GH, Reddy KR, White JG: Effect of acetaminophen and salicylate on aspirin-induced inhibition of human platelet cyclo-oxygenase. Prostaglandins Leukot Med 1982, 9:109-115.
• [7]Murdoch D, Plosker GL: Triflusal: a review of its use in cerebral infarction and myocardial infarction, and as thromboprophylaxis in atrial fibrillation. Drugs 2006, 66:671-692.
• [8]Gonzalez-Correa JA, De La Cruz JP: Triflusal: an antiplatelet drug with a neuroprotective effect? Cardiovasc Drug Rev 2006, 24:11-24.
• [9]Cho HY, Jeong TJ, Lee YB: Simultaneous determination of triflusal and its major active metabolite, 2-hydroxy-4-trifluoromethyl benzoic acid, in rat and human plasma by high-performance liquid chromatography. J Chromatogr B Analyt Technol Biomed Life Sci 2003, 798:257-264.
• [10]Ramis J, Mis R, Forn J, Torrent J, Gorina E, Jane F: Pharmacokinetics of triflusal and its main metabolite HTB in healthy subjects following a single oral dose. Eur J Drug Metab Pharmacokinet 1991, 16:269-273.
• [11]Dong HP, Wu HM, Chen SJ, Chen CY: The effect of butanolides from Cinnamomum tenuifolium on platelet aggregation. Molecules 2013, 18:11836-11841.
• [12]Harrison P, Mackie I, Mumford A, Briggs C, Liesner R, Winter M, Machin S: Guidelines for the laboratory investigation of heritable disorders of platelet function. Br J Haematol 2011, 155:30-44.
• [13]Zhou L, Schmaier AH: Platelet aggregation testing in platelet-rich plasma: description of procedures with the aim to develop standards in the field. Am J Clin Pathol 2005, 123:172-183.
• [14]Beal S, Sheiner L: NONMEM User’s Guide Part I. San Francisco: University of California at San Francisco; 1992.
• [15]Chrono-log corporation: Instruction manual for the chrono-log model 490 optical aggregometers. Havertown, PA, USA; 2005:31.
• [16]Agresti A: Logistic Regression. In Categorical Data Analysis. 2nd edition. New York: Wiley; 2002:166.
• [17]Casella G, Berger RL: Regression Models. In Statistical Inference. 2nd edition. California: Wadsworth; 2001:592.
• [18]Gabrielsson J, Weiner D: Pharmacodynamic Concepts. In Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. 4th edition. Stockholm: Swedish Pharmaceutical Press; 2007:265.
• [19]Jonsson EN, Karlsson MO: Xpose–an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM. Comput Methods Programs Biomed 1999, 58:51-64.
• [20]Chow SC, Wang H: On sample size calculation in bioequivalence trials. J Pharmacokinet Pharmacodyn 2001, 28:155-169.
• [21]Yun HY, Kang W, Lee BY, Park S, Yoon YR, Yeul Ma J, Kwon KI: Semi-Mechanistic Modelling and Simulation of Inhibition of Platelet Aggregation by Antiplatelet Agents. Basic Clin Pharmacol Toxicol 2014. in press