This article has Open Peer Review reports available.
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
© Park et al.; licensee BioMed Central. 2014
Received: 21 May 2014
Accepted: 12 December 2014
Published: 23 December 2014
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).
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.
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 and the probability of inhibition of platelet aggregation (IPA) relationship was as follows: Probability of . Thus, we concluded this relationship is more like quantal concentration-response relationship. The current dosing regimen was considered to be efficacious based on the EC 50 estimate of 84.9 μg/mL obtained in this study.
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.
Clinical Research Information Service (CRIS), KCT0001299 (Registered December 5, 2014)
Triflusal (2-acetoxy-4-(trifluoromethyl) benzoic acid), which is chemically related to salicylate  but is not a derivative of acetylsalicylic acid, is an antiplatelet drug [2, 3] that selectively inhibits arachidonic acid (AA) metabolism in platelets by irreversibly inhibiting cyclooxygenase-1 (COX-1) and reduces thromboxane B2 (TXB2) production [4, 5]. 2-Hydroxy-4-(trifluoromethyl) benzoic acid (HTB), the main active metabolite of triflusal, is formed by deacetylation upon passage through the liver . Triflusal is less effective in inhibiting COX-1 and reducing TXB2 than aspirin, but more effective than HTB . HTB increases the effects of triflusal on COX-1 inhibition compared with salicylic acid, the main metabolite of aspirin, which competes with the prodrug for the active site on the cyclooxygenase enzyme [5, 6].
Several previous reports have described the pharmacokinetic (PK) characteristics of triflusal. After oral administration, triflusal is absorbed rapidly within the small intestine, showing an absolute bioavailability of 83–100% . The plasma half-life (T 1/2) is 0.5 ± 0.1 h for triflusal and 34.4 ± 0.1 h for HTB . More than 60% of the parent drug is eliminated by the kidney  and the values for renal clearance for triflusal and HTB were found to be 0.8 ± 0.2 L/h and 0.18 ± 0.04 L/h, respectively [5, 8]. HTB reaches steady-state levels after 8–10 days of treatment .
The aim of this study was to quantitatively determine the dose-exposure-response relationship of triflusal in healthy Korean male subjects using a population analysis. Additionally, we suggest a method of binary analysis that might be applied to platelet aggregation data to identify pharmacodynamic (PD) markers.
Inclusion and exclusion criteria
Volunteers aged 20–55 years and within 20% of their ideal body weight [IBW (kg) = height (cm) - 100) × 0.9] were enrolled in the study. Those who were not suitable on the basis of physical examinations and routine laboratory tests (blood hematology, biochemistry, prothrombin time, bleeding time, urinalysis) were excluded.
Study design and ethical considerations
The analysis was performed using data from a randomized, open-label, multiple-dose, two-period, two-treatment, comparative crossover study involving 38 healthy adult males. The clinical trial was conducted at Kyungpook National University Hospital Clinical Trial Center (KNUH CTC) to determine the bioequivalence and non-inferiority of two different triflusal formulations, as reported previously .
The study was conducted in accordance with the guidelines of Good Clinical Practice and the Declaration of Helsinki and its amendments at the KNUH CTC, and was approved by the institutional review board at KNUH. Written informed consent was obtained from all volunteers before their participation.
Study drugs and dosage regimen
Triflusal capsule (Disgren capsule, 300 mg), as the reference formulation, and triflusal EC capsule (Disgren enteric-coated capsule, 300 mg), as the test formulation were manufactured and provided by Myung-In Pharm. Co., Ltd (Seoul, Republic of Korea). The subjects received the test or reference formulation in multiple doses, followed by a 13-day washout period and subsequent administration of the alternative formulation. During each period, each subject received a single 900 mg oral loading dose on Day 1, followed by a 600 mg/day maintenance dose (given as two 300 mg capsules once daily) from Day 2 to 9.
Blood sampling procedures
Blood (7 mL) for PK analysis was collected into tubes containing sodium heparin, and blood (3 mL) for PD analysis was collected into tubes containing 0.109 mmol/L sodium citrate. An aliquot (1 mL) of each plasma sample was placed in a microcentrifuge tube containing 0.1 mL 0.1 M HCl to prevent degradation of triflusal in the plasma. The remaining samples were chilled promptly on crushed ice to maintain the distribution ratio between triflusal and HTB .
HTB plasma concentration measurements
Platelet aggregation assessment
The tubes containing sodium citrate were centrifuged (1,000 rpm, 10 min) and 500 μL of platelet-rich plasma (PRP) was collected as the supernatant. The PRP was transferred to microcentrifuge tubes, which were capped to prevent changes in pH upon exposure to air, and the samples were kept on ice. To obtain 500 μL of platelet-poor plasma (PPP), the remaining sample in the original tubes was centrifuged again (3,000 rpm, 10 min) . Platelet aggregation in PRP was assessed using a Chronolog optical aggregometer (Model 490-4D; Chrono-log, Havertown, PA, USA). Cuvettes were lined up along the two rows of the aggregometer, filled with 250 μL of PRP or PPP, and warmed for 5 min at 37°C . To measure the degree of platelet aggregation, 0.5 μL AA was added to the cuvettes as an agonist, and light transmission aggregometry was observed for 5 min. The aggregometer was calibrated with a cuvette containing PRP, equaling 0% light transmission, as the baseline and with a second cuvette containing PPP, equaling 100% light transmission, as the reference . Based on analyses of the blank plasma samples, which were validated in three individuals, the intra-day and inter-day precisions were calculated as (SD/mean) × 100 (%) and ranged from 2.1 to 6.8% and from 2.1 to 7.6%, respectively .
In the current study, PK and PD data from 34 healthy volunteers who received the reference drug were used for population PK-PD analysis. In total, 476 HTB plasma concentration data points and 340 platelet aggregation (%) data points were included. The latent variables used as candidate covariates were age, height, and weight, and levels of albumin, creatinine, serum aspartate transaminase (AST), and alanine transaminase (ALT). Creatinine clearance (CLCR), calculated using the Cockcroft-Gault equation, was also included.
Population PK-PD model development
where C ij is the j th observed value in the i th individual, and ϵ pro,ij and ϵ add,ij are the residual intra-individual variability with a mean of zero and variances of and , respectively. A similar approach was made to the PD observations. The standard errors, which give information on the precision of the parameter estimates, were explored using NONMEM’s “$COVARIANCE” step.
Models were evaluated using diagnostic scatter plots, goodness-of-fit plots, and the log likelihood ratio test (LRT). The results for LRT were considered statistically significant if the objective function value (OFV) decreased by 3.84 (p = 0.05); this was used as a cut-off criterion for model improvement.
where dR/dt is the rate of change of the measured response (R) over time, k in is the turnover input rate for production of the response, I max is the fraction representing the maximal capacity by which the drug can inhibit platelet aggregation (0 ≤ I max ≤ 1), C is the HTB concentration, IC 50 is the median inhibition concentration, and k out is the turnover output rate of the response.
where the s0 is an intercept, and s1(X1), · · ·, sp(Xp) are smooth functions that are estimated in a nonparametric fashion. In the equation, candidate covariates with the greatest decrease in the Akaike information criterion were selected as final covariates in the PK model when there was a reduction of at least 3.84 compared with the previous model, and the relationship between the random variable, η, for each parameter and the candidate covariates was improved. Otherwise, they were dropped from the model.
Graphical analyses using goodness-of-fit plots – including observed-versus-population-predicted, observed-versus-individual-predicted, and residual diagnostics – were also conducted to diagnose the degree of model development.
Bootstrap was recruited to check the robustness of the PK-PD model and parameter estimates. The median value and 95% confidence interval for each parameter were obtained from 1,000 bootstrap dataset. For the PK model, a visual predictive check (VPC) was implemented to further evaluate the accuracy and predictive performance. A total of 1,000 replicate simulation was performed and the 50th (estimated population median), 5th, and 95th percentiles of the created concentrations plotted against time were compared with the overlaid observed concentrations. In addition, predicted concentration-response relationship is plotted with the predicted probability of each concentration data observed at the time of PD sampling.
Sample size determination
We considered the sample size for having an 80% power at a significance level of 0.05 for bioequivalence trials. It was calculated under the condition of the equivalence limit of 22.3%, the true mean difference of 10%, and the standard deviation of 24.2%. The calculation was performed by a formula suggested from Chow et al. . Volunteers were randomly assigned to one of two sequences with simple randomization using SAS software (ver. 9.2; SAS Institute, Inc., Cary, NC, USA).
The demographic data were presented using descriptive statistical analysis performed with the standard SPSS package (version 12.0 for Windows, SPSS, College Station, TX, USA). The mean, standard deviation, and range were obtained for each variable.
Demographic information of the healthy volunteers
Number of subjects (n = 34)
mean ± SD (Min – Max)
24.1 ± 1.7 (21 – 28)
176.1 ± 4.9 (167.1 – 184.2)
70.8 ± 9.0 (53.3 – 89.7)
Population PK modeling
where θ1 and θ2 were the estimated typical values of CL and V with a median weight (71.65) and θ4 was the estimated influential factor for weight. Weight produced 30.782 (equivalent to p < 0.001, 1 degree of freedom) decrease in the minimized OFV as well as the change in the random BSV for CL/F from 18.5% to 14.9% and for V/F from 15.6% to 9.5% (as % CV). Because only one demographic variable (weight) was selected as a meaningful covariate, the process of backward covariate exclusion was omitted.
Final estimates of population PK and PD parameters
Estimates from final model
Median (95% CI)
CL/F = θ 1 * (weight/71.65) θ4
TV of CL/F (L/h) for subject whose weight = 71.65 kg
0.1998 (0.1995 – 0.2002)
Exponent of weight proportional to CL/F
0.840 (0.830 – 0.850)
V d /F = θ 2 * (weight/71.65)
TV of V d (L) for subject whose weight = 71.65 kg
8.281 (8.267 – 8.295)
k f = θ 3
TV of k f (h-1)
0.345 (0.341 – 0.348)
TV of EC 50 (μg/mL)
85.19 (84.98 – 85.40)
TV of γ
20.70 (20.32 – 21.08)
ω 1 2
BSV for CL/F
14.4 (14.3 – 14.5)
ω 2 2
BSV for V d
8.6 (8.4 – 8.8)
ω 3 2
BSV for k f
73.5 (72.8 – 74.1)
ω 4 2
BSV for EC 50
21.4 (21.2 – 21.5)
ω 5 2
BSV for γ
Residual error (σ2)b
0.0977 (0.0973 – 0.0981)
Population PD modeling
Observed and predicted IPA over time
Number of subjects
Number of observed IPA (%)
Number of predicted IPA (%)
The PK and PD characteristics of triflusal were investigated using a non-linear mixed effects analysis. The objective of this study was to develop a population PK and PD model of the triflusal formulation, an established uncoated capsule, in healthy Korean male subjects. Plasma concentrations of the active metabolite, HTB, and the degree of platelet aggregation inhibition were measured to demonstrate the PK and PD properties. Reasonable estimates for parameters of the PK model were obtained, reflecting the potential for accurately predicting the information sought. The plots of the observed concentrations versus population-predicted or individual-predicted concentrations showed that the PK model was well structured, and the plot of time versus conditional weighted residuals suggested that the errors had homogeneous variances.
Our final HTB PK structure includes first-order formation and 1-compartment disposition. This is plausible judging from the immediate increase in HTB concentration on the day of full-PK study after dosing time and the rate of concentration increase which is the fastest at immediate post-dose and decreases thereafter (Figure 2). This finding is consistent to the model used by Valle et al. . On the contrary, Yun et al.  suggested a 2-compartment model for HTB disposition. But we considered this model would behavior like a 1-compartment model because the distributional rate constant between compartments were much (approximately 60 times) larger than the elimination rate constant and the equilibrium between compartments will be obtained within 1 h after dose. Judging from the time of maximum plasma concentration attainment (postdose 4–5 h), this distributional characteristic might be superimposed by the absorption as well as the elimination.
Covariate model building was established by the relationship between the subjects’ body features and parameters in the PK model. Weight accounted for approximately 19 % and 39 % of between-subject variability for CL/F and V/F, respectively, judging from the decrease of ω2 for corresponding parameters. Particularly, V/F was directly proportional to weight (exponent = 1) and the fact suggested that weight may be used as a scaling factor for triflusal dose. However, definite conclusions could not be made for following reason; 1) this study did not consider the PK of triflusal; thus, weight might be influential to the disposition of triflusal including metabolic fraction to HTB (which was only explained with F in this study), 2) the clinical significance of this finding needs further investigations together with its pharmacodynamics variability, therapeutic index and clinical effectiveness. In addition, even though HTB is mainly (>60%) excreted in urine, CLCR was not selected as a meaningful covariate for HTB CL/F. We consider that the effect of weight superimposed that of CLCR. It was expected that studies involving triflusal and HTB PK in patients with various renal function will be essential to elucidate the exact influence of renal function.
Based on the value of EC 50 (=84.9 μg/mL) estimated in this study, the current dosing regimen was considered to be efficacious judging from the C ss,min value (=103.5 μg/mL) and the concentration-response relationship which showed quantal characteristics (γ = 19). The saturated value of E max as 1 seemed reasonable, because there was no significant model improvement when the parameter was allowed to be estimated.
Another method, the binary probability model to construct the PD model using transformed binary data, was introduced to show characteristics of the PD data without much information loss. This model resulted in successful explanation of binary data characteristics by the estimates, even though a turnover model was not constructed because of the modified binary data characteristics. To date, this is the first report evaluating quantitative PD data transformed into qualitative binary data possessing one of two values. We recommend this new approach of using probability to estimate parameters that show predictive ability for the presence of platelet aggregation. This model also provided a specific method for potential use with binary data in the case of high intra-individual variability for PD.
This study has some limitations. First, only HTB concentrations were used in our population PK and PD model without measurement of the parent drug. Although triflusal is a prodrug which has no pharmacologic effect, if a study involving the concentration measurement of both triflusal and HTB is conducted, a better description for the HTB formation will be obtained. This approach may enable the identification of influential factors for HTB formation process including first-pass metabolism and hepatic clearance of triflusal. Second, despite the possible loss of information from the data transformation, a PD model using probability (binary data model) was developed to evaluate the extreme values in our PD data, as seen in the scatter diagram. To overcome this problem, more accurate and precise quantitative analytic method to measure the degree of platelet aggregation should be required.
Using the VPC, which is one method of model evaluation, prediction is possible when given not only different dosage- and time-concentrations but also the probability of IPA. Accordingly, optimal dosage regimens could be determined by model-fitted parameter estimates without additional clinical trials. The model may be clinically useful for other similar studies. Finally, this study was conducted in healthy volunteers, rather than in patients. Thus, it is also necessary to construct a model estimating reasonable parameters from patients in a more realistic setting.
A PK and binary probability PD model of triflusal was successfully developed for Korean healthy volunteers. The model may be used to further predict inhibition of platelet aggregation by triflusal.
This research was supported by grants from the National Project for Personalized Genomic Medicine, Ministry of Health & Welfare, Republic of Korea (A111218-PG02); the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning, Republic of Korea (NRF-2013M3A9B6046416); the Pfizer Modeling & Simulation Education Center in Korea (PMECK); and the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (A070001).
- 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. 10.1161/01.STR.0000063141.24491.50.View ArticlePubMedGoogle Scholar
- McNeely W, Goa KL: Triflusal. Drugs. 1998, 7: 823-833.View ArticleGoogle Scholar
- 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. 10.1007/s00228-004-0887-0.View ArticlePubMedGoogle Scholar
- 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. 10.1517/17425255.2011.630661.View ArticlePubMedGoogle Scholar
- 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.PubMedGoogle Scholar
- 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. 10.1016/0262-1746(82)90076-2.View ArticlePubMedGoogle Scholar
- 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. 10.2165/00003495-200666050-00009.View ArticlePubMedGoogle Scholar
- Gonzalez-Correa JA, De La Cruz JP: Triflusal: an antiplatelet drug with a neuroprotective effect?. Cardiovasc Drug Rev. 2006, 24: 11-24. 10.1111/j.1527-3466.2006.00011.x.View ArticlePubMedGoogle Scholar
- 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. 10.1016/j.jchromb.2003.09.049.View ArticlePubMedGoogle Scholar
- 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. 10.1007/BF03189971.View ArticlePubMedGoogle Scholar
- Dong HP, Wu HM, Chen SJ, Chen CY: The effect of butanolides from Cinnamomum tenuifolium on platelet aggregation. Molecules. 2013, 18: 11836-11841. 10.3390/molecules181011836.View ArticlePubMedGoogle Scholar
- 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. 10.1111/j.1365-2141.2011.08793.x.View ArticlePubMedGoogle Scholar
- 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. 10.1309/Y9EC63RW3XG1V313.View ArticlePubMedGoogle Scholar
- Beal S, Sheiner L: NONMEM User’s Guide Part I. 1992, San Francisco: University of California at San FranciscoGoogle Scholar
- Chrono-log corporation: Instruction manual for the chrono-log model 490 optical aggregometers. 2005, Havertown, PA, USA, 31-Google Scholar
- Agresti A: Logistic Regression. Categorical Data Analysis. 2002, New York: Wiley, 166-2View ArticleGoogle Scholar
- Casella G, Berger RL: Regression Models. Statistical Inference. 2001, California: Wadsworth, 592-2Google Scholar
- Gabrielsson J, Weiner D: Pharmacodynamic Concepts. Pharmacokinetic and Pharmacodynamic Data Analysis: Concepts and Applications. 2007, Stockholm: Swedish Pharmaceutical Press, 265-4Google Scholar
- 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.View ArticlePubMedGoogle Scholar
- Chow SC, Wang H: On sample size calculation in bioequivalence trials. J Pharmacokinet Pharmacodyn. 2001, 28: 155-169. 10.1023/A:1011503032353.View ArticlePubMedGoogle Scholar
- 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 pressGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/2050-6511/15/75/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.