- Email: [email protected]
Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole
ARTICLE IN PRESS International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
Contents lists available at ScienceDirect
International Journal of Antimicrobial Agents j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / i j a n t i m i c a g
Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole
1
Q2
2 3 4
Q1 Fang Qi a, Liqin Zhu b,*, Na Li a, Tingyue Ge a, Gaoqi Xu a, Shasha Liao a a
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
b
Basic Medical College, Tianjin Medical University, 22# Qixiangtai Road, Heping District, Tianjin 300070, China Department of Pharmacy, Tianjin First Central Hospital, 24#Fukang Road, Nankai District, Tianjin 300192, China
A R T I C L E
I N F O
Article history: Received 1 August 2016 Accepted 25 November 2016 Keywords: Voriconazole Proton pump inhibitors PPI PBPK model Drug–drug interactions
A B S T R A C T
This study aimed to determine the influence of proton pump inhibitors (PPIs) on the pharmacokinetics of voriconazole and to characterise potential drug–drug interactions (DDIs) between voriconazole and various PPIs (omeprazole, esomeprazole, lansoprazole and rabeprazole). Using adjusted physicochemical data and the pharmacokinetic (PK) parameters of voriconazole and PPIs, physiologically based pharmacokinetic (PBPK) models were built and were verified in healthy subjects using GastroPlusTM to predict the plasma concentration–time profiles of voriconazole and PPIs. These models were then used to assess potential DDIs for voriconazole when administered with PPIs. The results indicated the PBPK model-simulated plasma concentration–time profiles of both voriconazole and PPIs were consistent with the observed profiles. In addition, the DDI simulations suggested that the PK values of voriconazole increased to various degrees when combined with several PPIs. The area under the plasma concentration– time curve for the time of the simulation (AUC0–t) of voriconazole was increased by 39%, 18%, 12% and 1% when co-administered with omeprazole, esomeprazole, lansoprazole and rabeprazole, respectively. Omeprazole was the most potent CYP2C19 inhibitor tested, whereas rabeprazole had no influence on voriconazole (omeprazole > esomeprazole > lansoprazole > rabeprazole). However, in consideration of the therapeutic concentration range, dosage adjustment of voriconazole is unnecessary regardless of which PPI was co-administered. © 2017 Published by Elsevier B.V.
38 1. Introduction
39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
A physiologically based pharmacokinetic (PBPK) model is a mathematical model that integrates anatomical and physiological parameters of the body, physicochemical properties of a drug, and formulation properties of drug products to predict in vivo absorption, distribution, metabolism and excretion of compounds in the clinic [1]. The PBPK model has been developed as a tool to predict clinical pharmacokinetic (PK) profiles and to evaluate drug–drug interactions (DDIs). Cytochrome P450s (CYPs) are the main drugmetabolising enzymes expressed in the human liver and are involved in the metabolism of many drugs. Inhibition of CYPs can result in pronounced changes in drug pharmacokinetics and may lead to DDIs [2]. Voriconazole, a novel triazole antifungal agent, is known to exhibit highly variable non-linear pharmacokinetics and is metabolised in the liver, primarily through CYP2C19 and, to a lesser
55 56 57 58 59
Q3
* Corresponding author. Department of Pharmacy, Tianjin First Central Hospital, 24#Fukang Road, Nankai District, Tianjin 300192, China. E-mail address: [email protected] (L. Zhu).
extent, through CYP3A4 and CYP2C9 [3]. Voriconazole interacts with an exhaustive list of medications that can significantly impact plasma concentrations. Proton pump inhibitors (PPIs) are considered a key advancement in the treatment of acid-related gastrointestinal disorders and five PPIs are currently available on the market (omeprazole, esomeprazole, lansoprazole, pantoprazole and rabeprazole). PPIs are of particular interest because they are widely used medications that undergo CYP450-dependent metabolism by CYP2C19, CYP3A4 and CYP2C9, which makes these drugs competitive inhibitors of voriconazole [4,5]. There are currently no studies examining DDIs associated with the concurrent use of voriconazole and PPIs. Thus, it is unclear how a suitable PPI to administer with voriconazole in the clinic is chosen. A previous study reported that omeprazole increased the plasma concentration of voriconazole [6]. However, there are a limited number of studies reporting how other PPIs alter voriconazole PK parameters. Furthermore, PPIs may vary in the extent to which they increase the plasma concentration of voriconazole because individual PPIs differ in their inhibition of CYP450 enzymes. The objective of this study was to use a PBPK model to predict PK profiles and to assess the DDI potential of voriconazole when co-administered with multiple doses of PPIs.
http://dx.doi.org/10.1016/j.ijantimicag.2016.11.025 0924-8579/© 2017 Published by Elsevier B.V.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
2
82 83 84 85 86 87 88 89 90 91
2. Materials and methods
146
n
i Vmax i =1 K + C t ,u
CL int,u = ∑
All PBPK models and simulations of drug interactions were performed using GastroPlusTM v.9.0 (Simulations Plus Inc., Lancaster, CA). Each model was constructed and refined to match the observed maximum plasma concentration (Cmax) and area under the plasma concentration–time curve (AUC) values for the object drug. The Population Estimates for Age-Related (PEAR) human physiology model was used to assume that the typical study subject was a 30-year-old American healthy male weighing 75 kg.
92 2.1. Structure and validation of the voriconazole model 93 94 The voriconazole PBPK model was composed of 14 tissue com95 partments, including heart, lung, brain, adipose, muscle, skin, spleen, 96 reproductive organs, gastrointestinal tract, liver, kidney, yellow 97 marrow, red marrow and the rest of the body. Each compartment 98 was defined by an associated tissue blood flow rate, volume and 99 tissue-to-plasma partition coefficient (Kp) that were predicted using 100 established tissue composition-based models [7–9]. Intestinal ab101 sorption of voriconazole was simulated using the default Advanced 102 Compartmental Absorption and Transit (ACAT) model in fasted states. 103 The model was developed using physicochemical and PK proper104 ties. The following basic physicochemical parameters were used to 105 construct the model: the acid dissociation constant (pKa); solubil106 ity at one or more pH values; effective permeability (Peff); dose; 107 formulation; molecular weight; blood/plasma concentration ratio 108 (Rbp); fraction unbound in plasma (fup); and partition coefficient (logP 109 or logD) at one or more pH values. These parameters were initial110 ly required to run simulations with GastroPlusTM (depicted in Table 1). 111 The key parameters were obtained from published literature and 112 113 Q4 DrugBank (https://www.drugbank.ca/). Several parameters were acquired using the ADMET PredictorTM (Simulations Plus Inc.), which 114 is an inbuilt module within GastroPlusTM. Adjusted plasma fup values 115 were used in the models. 116 The recombinant enzyme and kinetic inputs [maximum reac117 tion velocity (Vmax) and Michaelis–Menten constant (Km)] data were 118 obtained from the published literature [4,19]. The apparent Km and 119 Vmax values of CYP2C19, CYP3A4 and CYP2C9 for voriconazole were 120 as follows: 9.3 ± 3.6 μM and 40 ± 13.9 pmol/min/pmol for CYP2C19; 121 834.7 ± 182.2 μM and 32.2 ± 28.4 pmol/min/pmol for CYP3A4; and 122 20 μM and 0.056 pmol/min/pmol for CYP2C9. Moreover, CYP2C19, 123 CYP3A4 and CYP2C9 contribute 85.0%, 14.5% and 0.5%, respective124 ly, of the total metabolism of voriconazole. The drug absorption, 125 distribution, metabolism and excretion processes were described 126 by a set of differential equations available in the GastroPlusTM soft127 ware. In this model, the intrinsic unbound metabolic clearance (CLint,u) 128 by CYP enzymes and the systemic plasma clearance (CLpb) were cal129 culated using Eq. 1 and Eq. 2. 130
CL pb
(1)
i m
147 148
⎛ ⎞ ⎜ ⎟ CL int,u = R bp × Q ⎜ ⎟ ⎜ CL int,u + Q R bp ⎟ ⎜⎝ fup ⎟⎠
(2) 149
i where K m is the Michaelis–Menten constant of voriconazole for the i is the maximum rate of meith metabolising CYP isozyme, Vmax tabolism for the ith CYP isozyme, Ct,u is the unbound tissue drug concentration, Rbp is the blood/plasma concentration ratio, Q is tissue blood flow, and fup is the fraction unbound in plasma. After the base PBPK model was constructed, a simulation was performed with an initial dose of 400 mg. The predicted plasma concentration–time curves were validated using data from an experimental human study of volunteers who received a single dose of 400 mg voriconazole. The overall accuracy of the predicted PK parameters was assessed from the fold-error (difference between predicted and observed in vivo values), and the prediction was considered successful if the fold-error was <2 [20].
2.2. Structure and validation of the proton pump inhibitor models The PBPK models constructed for PPIs focused on voriconazole, and the relevant physicochemical parameters are listed in Table 1. When no information was available for a specific input parameter, default settings proposed by the program were used. The enzymatic clearance values were calculated in human liver microsomes using the enzyme kinetic parameters estimated from experiments performed with cDNA-expressed enzymes. The values were adjusted to fit plasma concentration–time profiles and are summarised in Table 2. The contributions of metabolism by CYP2C19, CYP3A4 and CYP2C9 are listed in Table 2. After the models were constructed, the experimental observed data were loaded to verify the accuracy of the basic models, and the fold-errors of the predicted PK parameters were required to be <2. 2.3. Quantitative prediction of drug–drug interactions The DDIs between voriconazole and PPIs in virtual healthy volunteers were simulated using GastroPlusTM. The in vitro inhibition studies of the PPIs (omeprazole, esomeprazole, lansoprazole and rabeprazole) on human P450 enzymes were previously described and compared based on Li et al [5]. The concentration that causes 50% inhibition of the marker reaction (IC50) and the unbound in vitro inhibition constant (Ki) were obtained from the published literature and are listed in Table 2. The intensity of inhibition for PPIs can be judged by [I]/Ki values, where [I] is the simulated Cmax (using
131 132 133
Table 1 Summary of proton pump inhibitors’ input physicochemical parameters in simulations [10–18].
134
Parameter
Voriconazole
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
135 136 137 138 139 140 141 142
Molecular weight (g/mol) Dosage form Base pKa Solubility (mg/mL) Peff (cm/s × 10–4)b Rbp fup (%) logP
349.32 Tablet 1.76 0.2 4.5 1 42 1.8
345.42 Tablet 4.06 0.359a 12 0.59 5 2.23b
345.42 Capsule 4.06 0.353a 1.87 0.59 5 2.23
369.37 Capsule 3.83 0.044 7.15 0.74 3 2.11
359.45 Capsule 4.53 0.336a 1.49 0.75 4 2.3
143 144 145
pKa, acid dissociation constant; Peff, effective permeability; Rbp, blood/plasma concentration ratio; fup, fraction unbound in plasma; logP, partition coefficient. a From DrugBank (https://www.drugbank.ca/). b Estimated by ADMET PredictorTM.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
191 192
Table 2 Summary of proton pump inhibitors’ input enzyme kinetic parameters in simulations [5,21–24].
193
CYP450 enzyme/kinetic parametera
194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
2C19 Vmax Km Ki Fraction of metabolism (%) 3A4 Vmax Km Ki Fraction of metabolism (%) 2C9 Vmax Km Ki Fraction of metabolism (%)
209 210
3
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
162 1.75 2.40 ± 0.05 91.1
990 6.01 7.90 ± 0.5 71.5
405 15.1 0.74 ± 0.09 81.6
110 5.1 ± 0.2 18.8 ± 1.3 33.3
170 22 41.9 ± 5.9 7.4
740 262 46.6 ± 6.8 28.3
83.1 65.5 IC50 > 200 16.6
264 12 50.7 ± 6.4 33.7
82 57 16.4 ± 3.0 1.5
770 107 81.5 ± 72.7 0.2
11.2 82.3 20.8 ± 3.3 1.8
33 1.9 51.0 ± 9.4 26.4
Vmax, maximum reaction velocity; Km, Michaelis–Menten constant; Ki, unbound in vitro inhibition constant; IC50, concentration causing 50% inhibition of the marker reaction. a Km and Ki are expressed as μM; Vmax is expressed as pmol/min/pmol of P450.
211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
a typical dose of each PPI). A PBPK model incorporating competitive inhibition was used to simulate the inhibitory effects of the different strength inhibitors on the plasma concentration–time profiles of the substrate voriconazole. Dynamic simulations of voriconazole plasma concentration–time profiles taken with and without PPIs were run with the DDI Module within GastroPlusTM. The dose, dose interval and duration of administration of the substrate and inhibitors were set based on the US Food and Drug Administration (FDA) drug instructions and the regimens for clinical routine medications. All virtual subjects received oral voriconazole using a 400 mg twice-daily loading dose (Day 1), which was followed by a 200 mg twice-daily maintenance dose regimen (Days 2–10). Once-daily oral omeprazole (40 mg), esomeprazole (40 mg), lansoprazole (30 mg) or rabeprazole (20 mg) was coadministered with the morning dose of voriconazole on Days 1–10. 3. Results
Q5 3.1. Physiologically based pharmacokinetic model and verification A full-body mechanistic PBPK model for voriconazole was constructed based on the physiological and enzyme kinetic parameters using GastroPlusTM software. The parameters were optimised to obtain acceptable correlations between the in silico simulations and in vivo data. The Kp values of voriconazole were predicted using established tissue composition-based models and the results are shown in Table 3. The in vivo data were loaded to verify the predictive accuracy. The simulations and verification of the plasma concentration–
240 241 242 243
time curves for intravenous (i.v.) (2 h) voriconazole and oral voriconazole at a dose of 400 mg are shown in Fig. 1a,b. The predicted and observed PK parameters with prediction accuracy are summarised in Table 4. The simulated i.v. and oral plasma concentration–time profiles of voriconazole obtained from PBPK modelling corresponded well with the observed profiles. In addition, the predicted PK parameters were reasonably consistent (<2fold error) with the observed values. The physicochemical, physiological and enzyme kinetic parameters for each PPI were entered into the PBPK model to produce plasma concentration–time curves (Fig. 1c–f). The predicted plasma concentration data were validated using data obtained from the literature [4,25–27]. The predicted and observed PK parameters and fold-error values are summarised in Table 4. Fig. 1 shows that the simulated profiles for PPIs are qualitatively similar to the observed data. All of the fold-errors of the predicted and observed PK parameters were <2, which indicated that the models were successful and accurately simulated the DDIs for voriconazole and PPIs.
Table 3 Tissue-to-plasma partition coefficients (Kp values) of voriconazole calculated using established tissue composition-based models.
244
Tissue/organ
Kp value
245 246 247 248 249 250 251 252 253 254 255 256 257
Lung Adipose Muscle Liver Spleen Heart Brain Kidney Skin Reproductive organs Red marrow Yellow marrow Rest of body
0.58 2.55 1.31 1.98 1.35 1.48 3.00 1.34 1.64 1.35 3.19 2.55 1.36
258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276
3.2. Drug–drug interaction simulation with omeprazole The [I]/Ki values, presented in Table 5, reveal a potential for PPIs to inhibit the metabolism of voriconazole. A dynamic DDI simulation was performed to predict the effect of omeprazole on voriconazole using a single dose of 40 mg of omeprazole in the PBPK model for 24 h. Omeprazole is a competitive inhibitor of CYP2C19. The plasma concentration–time curves of voriconazole at baseline and following DDI are shown in Fig. 2. The model-predicted ratios of voriconazole Cmax and AUC0–t (AUC for the time of the simulation) with omeprazole co-administration were 1.09 and 1.11 (Table 6). Referring to clinical experience and the study of Wood et al [6], the increases were considered unlikely to be of clinical significance. As expected, when continuing to simulate the DDI with omeprazole in regular multiple doses over 10 days, the predicted PK values of voriconazole were higher when co-administered with omeprazole. Furthermore, the ratios were significantly higher than the ratios observed in the single-dose regimen. The ratios of Cmax and AUC0–t values were ca. 1.14 and 1.39, respectively (Table 6). These results indicate that there is a significant influence on voriconazole. However, the Cmax of voriconazole was <5.5 mg/mL (Fig. 3). This finding indicated that the dosage remained within the therapeutic concentration range of voriconazole (1.0–5.5 mg/L) [28]. The increases were considered unlikely to be of clinical relevance and did not warrant dosage adjustments.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
4
303 304
Table 4 Observed and simulated pharmacokinetic parameters of voriconazole and proton pump inhibitors (PPIs).
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325
Voriconazole 400 mg i.v.
Voriconazole 400 mg oral
Omeprazole 40 mg oral
Esomeprazole 40 mg oral
Lansoprazole 30 mg oral
Rabeprazole 20 mg oral
Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error
Cmax (μg/mL)
Tmax (h)
AUC0–inf (μg · h/mL)
AUC0–t (μg · h/mL)
4.08 3.95 1.03 2.59 2.49 1.04 0.93 0.87 1.06 0.79 0.82 1.03 0.60 0.61 1.01 0.25 0.24 1.02
2.15 2.00 1.07 1.60 1.92 1.20 0.53 0.48 1.10 1.50 1.36 1.10 3.033 2.96 1.02 1.10 0.88 1.24
28.70 30.71 1.07 22.27 23.92 1.07 1.49 1.77 1.19 2.48 3.04 1.23 2.42 2.76 1.14 0.62 0.91 1.47
25.09 27.12 1.08 20.18 21.07 1.04 1.29 1.77 1.37 2.36 3.04 1.29 2.42 2.76 1.14 0.60 0.91 1.66
Cmax, maximum plasma concentration; Tmax, time to Cmax; AUC0–inf, area under the plasma concentration–time curve from 0 h to infinity; AUC0–t, area under the plasma concentration–time curve for the time of the simulation; i.v., intravenous.
326 327 328 329 330 331 332 333
3.3. Drug–drug interaction simulation with other multiple doses of proton pump inhibitors The voriconazole PBPK model was used to predict DDIs caused by esomeprazole, lansoprazole and rabeprazole. A dynamic simulation was conducted for the co-administration of multiple doses of esomeprazole (capsule, 40 mg once daily) and a regular dose of
voriconazole. The results are shown in Table 6 and Fig. 3. The simulation revealed that the Cmax and AUC0–t values were increased within a narrow range and the ratios were 1.08 and 1.18, respectively. Co-administration of multiple doses of lansoprazole (enteric coating capsule, 30 mg once daily) was also simulated and the predicted Cmax and AUC0–t ratios for voriconazole were 1.06 and 1.19, respectively (Table 6). The influence of lansoprazole was smaller than esomeprazole, which indicates there is no significant clinical drug interaction. For rabeprazole, the result showed that there was no appreciable change in voriconazole plasma exposure.
340 341 342 343 344 345 346 347 348 349 350
Table 5 Estimated mean [I]/Ki values for each enzyme by proton pump inhibitors. CYP450 enzyme 2C19 3A4 2C9
351 352
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
353 354 355
0.36 0.02 0.05
0.10 0.02 0.01
0.82 <0.01 0.03
0.01 <0.01 <0.01
356 357 358
[I]/Ki value
334
359
335 336 337 338 339
Fig. 1. Observed (symbols) and physiologically based pharmacokinetic (PBPK) modelsimulated (lines) plasma concentration–time profile of voriconazole and proton pump inhibitors: (a) 400 mg voriconazole intravenous; (b) 400 mg voriconazole oral; (c) 40 mg omeprazole oral; (d) 40 mg esomeprazole oral; (e) 30 mg lansoprazole oral; and (f) 20 mg rabeprazole oral.
Fig. 2. Simulated mean plasma concentrations of single-dose voriconazole dosed alone or with concomitant omeprazole.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
360 361
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
362 363
Table 6 Model-predicted pharmacokinetic parameters and ratios for voriconazole given alone and with proton pump inhibitors.
364
Compound
365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383
Omeprazole Single-baseline DDI with omeprazole Ratio with omeprazole (10 days)-baseline DDI with omeprazole Ratio with omeprazole Esomeprazole (10 days)-baseline DDI with esomeprazole Ratio with esomeprazole Lansoprazole (10 days)-baseline DDI with lansoprazole Ratio with lansoprazole Rabeprazole (10 days)-baseline DDI with rabeprazole Ratio with rabeprazole
384 385
5
Fa (%)
F (%)
Cmax (μg/mL)
Tmax (h)
AUC0–inf (ng · h/mL)
AUC0–t (ng · h/mL)
99.97 99.97 1.00 99.95 99.95 1.00
77.28 82.47 1.07 79.97 84.74 1.06
2.49 2.72 1.09 3.42 3.88 1.14
1.92 2.00 1.04 14.00 14.00 1.00
23900 26600 1.11 368000 502000 1.36
21100 23400 1.11 363000 505000 1.39
99.95 99.95 1.00
79.97 84.32 1.05
3.42 3.60 1.08
14.00 14.00 1.00
368000 416000 1.18
363000 406000 1.18
99.95 99.95 1.00
79.97 81.00 1.01
3.42 3.62 1.06
14.00 14.04 1.00
368000 412000 1.12
363000 406000 1.19
99.95 99.95 1.00
79.97 80.62 1.01
3.42 3.43 1.00
14.00 14.00 1.00
368000 37100 1.01
363000 367000 1.01
Fa, absorption fraction; F, bioavailability; Cmax, maximum plasma concentration; Tmax, time to Cmax; AUC0–inf, area under the plasma concentration–time curve from 0 h to infinity; AUC0–t, area under the plasma concentration–time curve for the time of the simulation; DDI, drug–drug interaction.
386 387 388 389 390 391 392 393 394 395
4. Discussion This is the first study to compare the effects of different PPIs on the pharmacokinetics of voriconazole using a PBPK model. The results suggested that omeprazole was the most potent CYP2C19 inhibitor, and the PK values of voriconazole increased to various degrees when administered concurrently with PPIs. However, the increases are considered unlikely to be of clinical relevance. In addition, an in vitro study has indicated that lansoprazole was the most
potent inhibitor, which contrasts with the findings of the current study (omeprazole > esomeprazole > lansoprazole > rabeprazole). The interactions between voriconazole and PPIs can be interpreted as similar mechanisms of action, and there is less influence on absorption. Consequently, bioavailability did not change significantly. Omeprazole is almost completely metabolised in the liver into its pharmacologically inactive metabolites. As mentioned above, omeprazole has a high affinity for CYP2C19. Clinically available omeprazole is a racemic mixture of esomeprazole and R-omeprazole
396
397 398
Fig. 3. Simulated mean plasma concentrations of multidose voriconazole dosed alone or with (a) concomitant omeprazole (40 mg once daily), (b) esomeprazole (40 mg once daily), (c) lansoprazole (30 mg once daily) or (d) rabeprazole (20 mg once daily).
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
399 400 401 402 403 404 405 406 407
ARTICLE IN PRESS 6
408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473
F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
and there are subtle differences in the pharmacokinetics and metabolism of esomeprazole compared with racemic omeprazole. Previous in vitro experiments have suggested that esomeprazole has a lower systemic clearance than the omeprazole racemate [29]. In this study, administration of an equivalent dose of esomeprazole resulted in a higher AUC than the omeprazole racemate. According to Li et al [5], esomeprazole has a slightly weaker inhibitory potency on CYP2C19 than omeprazole, and esomeprazole is metabolised to a great extent by CYP3A4 (and concurrently less by CYP2C19) than the racemic form. That is to say, its elimination is less dependent on CYP2C19. Thus, esomeprazole has a reduced effect on the pharmacokinetics of voriconazole than omeprazole. A previous in vitro study showed lansoprazole was the most potent inhibitor of CYP2C19. Therefore, it is likely that lansoprazole inhibits CYP2C19 more than omeprazole (Table 5). However, lansoprazole does not cause clinically significant inhibition of CYP2C19, and several in vivo investigations have found only minor increases of CYP2C19 substrate serum levels. These changes are unlikely to cause clinically significant DDIs [30]. The results obtained from in vitro experiments may not translate to in vivo effects owing to confounding factors such as age, sex, nutritional status and other co-morbidities that may affect hepatic function. Omeprazole, having been on the market the longest, has undergone the most thorough investigation for DDIs. Omeprazole has always been known to inhibit the metabolism of diazepam in vivo, whereas lansoprazole has no interaction with the CYP2C19 substrate diazepam, which is similar to warfarin [30,31]. The lack of clinically relevant direct inhibition of CYP2C19 caused by lansoprazole is likely explained by its relatively short half-life and high plasma protein binding [17,32]. Although omeprazole also has a short half-life, inhibition of CYP2C19 by omeprazole is of clinical relevance because a significant amount of CYP2C19 activity may yet be ‘bound’ by omeprazole metabolism [33]. Moreover, the interaction between omeprazole and voriconazole is particularly complex. Ogilvie et al [32] presented that omeprazole, but not lansoprazole, was an irreversible inhibitor of CYP2C19. The results of this study showed that rabeprazole has significantly less DDIs than other PPIs (except pantoprazole). The main reason for the decrease in drug interaction may be related to its nonenzyme-catalysed degradation, which forms a thioether product. Thus, oxidative metabolism catalysed by CYP2C19 and CYP3A4 plays a minor role in its biotransformation, and rabeprazole has a low affinity for a range of CYP isoenzymes [34]. Rabeprazole was reported to be associated with no significant changes in the AUCs of theophylline, phenytoin, warfarin and diazepam in subjects whose CYP2C19 genotypes or phenotypes were unknown [29]. Limitations of this study include the lack of pantoprazole, which was due to missing data. Using GastroPlusTM software to simulate pantoprazole, enzymatic kinetic parameters are essential. However, there are limited published data available. Pantoprazole has a lower affinity for CYP2C19 and CYP3A4 than other PPIs, with Ki values of 14–69 μM for CYP2C19 and 22 μM for CYP3A4 [5], which implies that there is a slight effect on voriconazole. Although pantoprazole showed the highest CYP2C9 inhibition (with a Ki of 6 μM) of the five PPIs, it is possible that pantoprazole would have no effect on voriconazole. Voriconazole is partially metabolised by CYP2C9, and CYP2C19 is the major enzyme involved in the metabolism of voriconazole. There are several possible DDIs following pantoprazole co-administration with drugs metabolised via CYP2C9. In contrast to the other currently available PPIs, the initial metabolite of the drug undergoes phase II sulfate conjugation [35]. This conjugation reaction, a relatively non-saturable route of drug metabolism, is often considered to explain the reduced incidence of drug interactions caused by pantoprazole. CYP2C19 is a highly polymorphic pharmacogene and genetic variants in the CYP2C19 gene locus may alter CYP2C19 substrate
metabolism, which results in interindividual phenotypic variability [36–38]. The CYP2C19 genotype has been found to contribute to the pharmacokinetics and plasma concentration of voriconazole. The frequency of CYP2C19 poor metabolisers is ca. 3.52% in Europeans and 14% in Chinese [39]. Therefore, the influence of PPIs on voriconazole should be assessed in future studies using specific CYP2C19 genotypes.
474 475 476 477 478 479 480 481
5. Conclusion In conclusion, a PBPK model was applied to predict the clinical PK profiles and to assess the DDI potential of voriconazole when administered concurrently with multiple doses of PPIs. The results suggested that the simulated plasma concentration–time profiles both of voriconazole and PPIs by PBPK models corresponded to the observed profiles. Omeprazole is the most potent CYP2C19 inhibitor, and the PK values of voriconazole increased to various degrees when administered with PPIs (omeprazole > esomeprazole > lansoprazole > rabeprazole). However, dosage adjustments for voriconazole are unnecessary because the dose remained in the voriconazole therapeutic range. Funding: None. Competing interests: None declared. Ethical approval: Not required.
482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498
References [1] Mikus G, Schöwel V, Drzewinska M, Rengelshausen J, Ding R, Riedel KD. Potent cytochrome P450 2C19 genotype-related interaction between voriconazole and the cytochrome P450 3A4 inhibitor ritonavir. Clin Pharmacol Ther 2006;80:126–35. [2] Quehl P, Hollender J, Schüürmann J, Brossette T, Maas R, Jose J. Co-expression of active human cytochrome P450 1A2 and cytochrome P450 reductase on the cell surface of Escherichia coli. Microb Cell Fact 2016;15:1–15. [3] Niece KL, Boyd NK, Akers KS. In vitro study of the variable effects of proton pump inhibitors on voriconazole. Antimicrob Agents Chemother 2015;59:5548– 54. [4] Noubarani M, Keyhanfar F, Motevalian M, Mahmoudian M. Improved HPLC method for determination of four PPIs, omeprazole, pantoprazole, lansoprazole and rabeprazole in human plasma. J Pharm Pharm Sci 2010;13:1–10. [5] Li XQ, Andersson TB, Ahlström M, Weidolf L. Comparison of inhibitory effects of the proton pump-inhibiting drugs omeprazole, esomeprazole, lansoprazole, pantoprazole, and rabeprazole on human cytochrome P450 activities. Drug Metab Dispos 2004;32:821–7. [6] Wood N, Tan K, Purkins L, Layton G, Hamlin J, Kleinermans D, et al. Effect of omeprazole on the steady-state pharmacokinetics of voriconazole. Br J Clin Pharmacol 2003;56:45–50. [7] Poirier A, Funk C, Scherrmann JM, Lavé T. Mechanistic modeling of hepatic transport from cells to whole body: application to napsagatran and fexofenadine. Mol Pharm 2009;6:1716–33. [8] Zhu L, Yang J, Zhang Y, Wang Y, Zhang J, Zhao Y, et al. Prediction of pharmacokinetics and penetration of moxifloxacin in human with intraabdominal infection based on extrapolated PBPK model. Korean J Physiol Pharmacol 2015;19:99–104. [9] Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 2006;95:1238–57. [10] Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J 2011;13:519–47. [11] Theurillat R, Zimmerli S, Thormann W. Determination of voriconazole in human serum and plasma by micellar electrokinetic chromatography. J Pharm Biomed Anal 2010;53:1313–18. [12] Roche VF. The chemically elegant proton pump inhibitors. Am J Pharm Educ 2006;70:101. [13] Harding VD. Pharmaceutical formulations containing voriconazole. US Patent US6632803. 2003. [14] Wu C, Sun L, Sun J, Yang Y, Ren C, Ai X, et al. Profiling biopharmaceutical deciding properties of absorption of lansoprazole enteric-coated tablets using gastrointestinal simulation technology. Int J Pharm 2013;453:300–6. [15] Shi S, Klotz U. Proton pump inhibitors: an update of their clinical use and pharmacokinetics. Eur J Clin Pharmacol 2008;64:935–51. [16] Chiba K, Shimizu K, Kato M, Nishibayashi T, Terada K, Izumo N, et al. Prediction of inter-individual variability in the pharmacokinetics of CYP2C19 substrates in humans. Drug Metab Pharmacokinet 2014;29:379–86. [17] Chan TS, Yu H, Moore A, Khetani SR, Kehtani SR, Tweedie D. Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585
[18]
[19]
[20] [21]
[22]
[23]
[24]
[25]
[26]
[27] [28]
by cytochrome P450 using a novel hepatocyte model, HepatoPac. Drug Metab Dispos 2013;41:2024–32. Wu F, Lu G, Zhao P, Jamei M, Huang SM, Bashaw ED, et al. Predicting nonlinear pharmacokinetics of omeprazole enantiomers and racemic drug using physiologically based pharmacokinetic modeling and simulation: application to predict drug/genetic interactions. Pharm Res 2014;3:1919–29. Yanni SB, Annaert PP, Augustijns P, Ibrahim JG, Benjamin DK Jr, Thakker DR. In vitro hepatic metabolism explains higher clearance of voriconazole in children versus adults: role of CYP2C19 and flavin-containing monooxygenase 3. Drug Metab Dispos 2010;38:25–31. Jones HM, Parrott N, Jorga K, Lavé T. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 2006;45:511–42. Kita T, Sakaeda T, Aoyama N, Sakai T, Kawahara Y, Kasuga M, et al. Optimal dose of omeprazole for CYP2C19 extensive metabolizers in anti-Helicobacter pylori therapy: pharmacokinetic considerations. Biol Pharm Bull 2002;25:923–7. Abelö A, Andersson TB, Antonsson M, Naudot AK, Skånberg I, Weidolf L. Stereoselective metabolism of omeprazole by human cytochrome P450 enzymes. Drug Metab Dispos 2000;28:966–72. Kim KA, Kim MJ, Park JY, Shon JH, Yoon YR, Lee SS, et al. Stereoselective metabolism of lansoprazole by human liver cytochrome P450 enzymes. Drug Metab Dispos 2003;31:1227–34. Miura M, Satoh S, Tada H, Habuchi T, Suzuki T. Stereoselective metabolism of rabeprazole-thioether to rabeprazole by human liver microsomes. Eur J Clin Pharmacol 2006;62:113–17. Hassan-Alin M, Andersson T, Niazi M, Röhss K. A pharmacokinetic study comparing single and repeated oral doses of 20 mg and 40 mg omeprazole and its two optical isomers, S-omeprazole (esomeprazole) and R-omeprazole, in healthy subjects. Eur J Clin Pharmacol 2005;60:779–84. Andersson DT, Hassan-Alin M, Hasselgren G, Röhss K, Weidolf L. Pharmacokinetic studies with esomeprazole, the (S)-isomer of omeprazole. Clin Pharmacokinet 2001;40:411–26. Vlase DL, Popa A, Neag M, Muntean D, Leucuta SE. Effect of fluoxetine on the pharmacokinetics of lansoprazole. Clin Drug Investig 2011;31:727–33. Pascual A, Calandra T, Bolay S, Buclin T, Bille J, Marchetti O. Voriconazole therapeutic drug monitoring in patients with invasive mycoses improves efficacy and safety outcomes. Clin Infect Dis 2008;46:201–11.
7
[29] Ogawa R, Echizen H. Drug–drug interaction profiles of proton pump inhibitors. Clin Pharmacokinet 2010;49:509–33. [30] Lefebvre RA, Flouvat B, Karolac-Tamisier S, Moerman E, Ganse EV. Influence of lansoprazole treatment on diazepam plasma concentrations. Clin Pharmacol Ther 1992;52:458–63. [31] Cavanaugh J, Winters E, Cohen A, Locke C, Braeckman R. Lack of effect of lansoprazole on steady state warfarin metabolism. Gastroenterology 1991;100:A40. [32] Ogilvie BW, Yerino P, Kazmi F, Buckley DB, Rostami-Hodjegan A, Paris BL. The proton pump inhibitor, omeprazole, but not lansoprazole or pantoprazole, is a metabolism-dependent inhibitor of CYP2C19: implications for coadministration with clopidogrel. Drug Metab Dispos 2011;39:2020– 33. [33] Norgard NB, Mathews KD, Wall GC. Drug–drug interaction between clopidogrel and the proton pump inhibitors. Expert Rev Clin Pharmacol 2014;155:1266– 74. [34] Blume DH, Donath F, Warnke A, Schug BS. Pharmacokinetic drug interaction profiles of proton pump inhibitors. Drug Saf 2006;29:769–84. [35] Cheer SM, Prakash A, Faulds D, Lamb HM. Pantoprazole: an update of its pharmacological properties and therapeutic use in the management of acidrelated disorders. Drugs 2003;63:101–32. [36] Scott SA, Sangkuhl K, Gardner EE, Stein CM, Hulot JS, Johnson JA, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450-2C19 (CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol Ther 2011;90:328–32. [37] Scott SA, Sangkuhl K, Shuldiner AR, Hulot JS, Thorn CF, Altman RB, et al. PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 19. Pharmacogenet Genomics 2012;22:159–65. [38] Hicks JK, Swen JJ, Thorn CF, Sangkuhl K, Kharasch ED, Ellingrod VL, et al. Clinical Pharmacogenetics Implementation Consortium guideline for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants. Clin Pharmacol Ther 2013;93:402–8. [39] Wu J, Jia LT, Shao LM, Chen JM, Zhong DD, Xu S, et al. Drug–drug interaction of rabeprazole and clopidogrel in healthy Chinese volunteers. Eur J Clin Pharmacol 2013;69:179–87.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621
Contents lists available at ScienceDirect
International Journal of Antimicrobial Agents j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / i j a n t i m i c a g
Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole
1
Q2
2 3 4
Q1 Fang Qi a, Liqin Zhu b,*, Na Li a, Tingyue Ge a, Gaoqi Xu a, Shasha Liao a a
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
b
Basic Medical College, Tianjin Medical University, 22# Qixiangtai Road, Heping District, Tianjin 300070, China Department of Pharmacy, Tianjin First Central Hospital, 24#Fukang Road, Nankai District, Tianjin 300192, China
A R T I C L E
I N F O
Article history: Received 1 August 2016 Accepted 25 November 2016 Keywords: Voriconazole Proton pump inhibitors PPI PBPK model Drug–drug interactions
A B S T R A C T
This study aimed to determine the influence of proton pump inhibitors (PPIs) on the pharmacokinetics of voriconazole and to characterise potential drug–drug interactions (DDIs) between voriconazole and various PPIs (omeprazole, esomeprazole, lansoprazole and rabeprazole). Using adjusted physicochemical data and the pharmacokinetic (PK) parameters of voriconazole and PPIs, physiologically based pharmacokinetic (PBPK) models were built and were verified in healthy subjects using GastroPlusTM to predict the plasma concentration–time profiles of voriconazole and PPIs. These models were then used to assess potential DDIs for voriconazole when administered with PPIs. The results indicated the PBPK model-simulated plasma concentration–time profiles of both voriconazole and PPIs were consistent with the observed profiles. In addition, the DDI simulations suggested that the PK values of voriconazole increased to various degrees when combined with several PPIs. The area under the plasma concentration– time curve for the time of the simulation (AUC0–t) of voriconazole was increased by 39%, 18%, 12% and 1% when co-administered with omeprazole, esomeprazole, lansoprazole and rabeprazole, respectively. Omeprazole was the most potent CYP2C19 inhibitor tested, whereas rabeprazole had no influence on voriconazole (omeprazole > esomeprazole > lansoprazole > rabeprazole). However, in consideration of the therapeutic concentration range, dosage adjustment of voriconazole is unnecessary regardless of which PPI was co-administered. © 2017 Published by Elsevier B.V.
38 1. Introduction
39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
A physiologically based pharmacokinetic (PBPK) model is a mathematical model that integrates anatomical and physiological parameters of the body, physicochemical properties of a drug, and formulation properties of drug products to predict in vivo absorption, distribution, metabolism and excretion of compounds in the clinic [1]. The PBPK model has been developed as a tool to predict clinical pharmacokinetic (PK) profiles and to evaluate drug–drug interactions (DDIs). Cytochrome P450s (CYPs) are the main drugmetabolising enzymes expressed in the human liver and are involved in the metabolism of many drugs. Inhibition of CYPs can result in pronounced changes in drug pharmacokinetics and may lead to DDIs [2]. Voriconazole, a novel triazole antifungal agent, is known to exhibit highly variable non-linear pharmacokinetics and is metabolised in the liver, primarily through CYP2C19 and, to a lesser
55 56 57 58 59
Q3
* Corresponding author. Department of Pharmacy, Tianjin First Central Hospital, 24#Fukang Road, Nankai District, Tianjin 300192, China. E-mail address: [email protected] (L. Zhu).
extent, through CYP3A4 and CYP2C9 [3]. Voriconazole interacts with an exhaustive list of medications that can significantly impact plasma concentrations. Proton pump inhibitors (PPIs) are considered a key advancement in the treatment of acid-related gastrointestinal disorders and five PPIs are currently available on the market (omeprazole, esomeprazole, lansoprazole, pantoprazole and rabeprazole). PPIs are of particular interest because they are widely used medications that undergo CYP450-dependent metabolism by CYP2C19, CYP3A4 and CYP2C9, which makes these drugs competitive inhibitors of voriconazole [4,5]. There are currently no studies examining DDIs associated with the concurrent use of voriconazole and PPIs. Thus, it is unclear how a suitable PPI to administer with voriconazole in the clinic is chosen. A previous study reported that omeprazole increased the plasma concentration of voriconazole [6]. However, there are a limited number of studies reporting how other PPIs alter voriconazole PK parameters. Furthermore, PPIs may vary in the extent to which they increase the plasma concentration of voriconazole because individual PPIs differ in their inhibition of CYP450 enzymes. The objective of this study was to use a PBPK model to predict PK profiles and to assess the DDI potential of voriconazole when co-administered with multiple doses of PPIs.
http://dx.doi.org/10.1016/j.ijantimicag.2016.11.025 0924-8579/© 2017 Published by Elsevier B.V.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
2
82 83 84 85 86 87 88 89 90 91
2. Materials and methods
146
n
i Vmax i =1 K + C t ,u
CL int,u = ∑
All PBPK models and simulations of drug interactions were performed using GastroPlusTM v.9.0 (Simulations Plus Inc., Lancaster, CA). Each model was constructed and refined to match the observed maximum plasma concentration (Cmax) and area under the plasma concentration–time curve (AUC) values for the object drug. The Population Estimates for Age-Related (PEAR) human physiology model was used to assume that the typical study subject was a 30-year-old American healthy male weighing 75 kg.
92 2.1. Structure and validation of the voriconazole model 93 94 The voriconazole PBPK model was composed of 14 tissue com95 partments, including heart, lung, brain, adipose, muscle, skin, spleen, 96 reproductive organs, gastrointestinal tract, liver, kidney, yellow 97 marrow, red marrow and the rest of the body. Each compartment 98 was defined by an associated tissue blood flow rate, volume and 99 tissue-to-plasma partition coefficient (Kp) that were predicted using 100 established tissue composition-based models [7–9]. Intestinal ab101 sorption of voriconazole was simulated using the default Advanced 102 Compartmental Absorption and Transit (ACAT) model in fasted states. 103 The model was developed using physicochemical and PK proper104 ties. The following basic physicochemical parameters were used to 105 construct the model: the acid dissociation constant (pKa); solubil106 ity at one or more pH values; effective permeability (Peff); dose; 107 formulation; molecular weight; blood/plasma concentration ratio 108 (Rbp); fraction unbound in plasma (fup); and partition coefficient (logP 109 or logD) at one or more pH values. These parameters were initial110 ly required to run simulations with GastroPlusTM (depicted in Table 1). 111 The key parameters were obtained from published literature and 112 113 Q4 DrugBank (https://www.drugbank.ca/). Several parameters were acquired using the ADMET PredictorTM (Simulations Plus Inc.), which 114 is an inbuilt module within GastroPlusTM. Adjusted plasma fup values 115 were used in the models. 116 The recombinant enzyme and kinetic inputs [maximum reac117 tion velocity (Vmax) and Michaelis–Menten constant (Km)] data were 118 obtained from the published literature [4,19]. The apparent Km and 119 Vmax values of CYP2C19, CYP3A4 and CYP2C9 for voriconazole were 120 as follows: 9.3 ± 3.6 μM and 40 ± 13.9 pmol/min/pmol for CYP2C19; 121 834.7 ± 182.2 μM and 32.2 ± 28.4 pmol/min/pmol for CYP3A4; and 122 20 μM and 0.056 pmol/min/pmol for CYP2C9. Moreover, CYP2C19, 123 CYP3A4 and CYP2C9 contribute 85.0%, 14.5% and 0.5%, respective124 ly, of the total metabolism of voriconazole. The drug absorption, 125 distribution, metabolism and excretion processes were described 126 by a set of differential equations available in the GastroPlusTM soft127 ware. In this model, the intrinsic unbound metabolic clearance (CLint,u) 128 by CYP enzymes and the systemic plasma clearance (CLpb) were cal129 culated using Eq. 1 and Eq. 2. 130
CL pb
(1)
i m
147 148
⎛ ⎞ ⎜ ⎟ CL int,u = R bp × Q ⎜ ⎟ ⎜ CL int,u + Q R bp ⎟ ⎜⎝ fup ⎟⎠
(2) 149
i where K m is the Michaelis–Menten constant of voriconazole for the i is the maximum rate of meith metabolising CYP isozyme, Vmax tabolism for the ith CYP isozyme, Ct,u is the unbound tissue drug concentration, Rbp is the blood/plasma concentration ratio, Q is tissue blood flow, and fup is the fraction unbound in plasma. After the base PBPK model was constructed, a simulation was performed with an initial dose of 400 mg. The predicted plasma concentration–time curves were validated using data from an experimental human study of volunteers who received a single dose of 400 mg voriconazole. The overall accuracy of the predicted PK parameters was assessed from the fold-error (difference between predicted and observed in vivo values), and the prediction was considered successful if the fold-error was <2 [20].
2.2. Structure and validation of the proton pump inhibitor models The PBPK models constructed for PPIs focused on voriconazole, and the relevant physicochemical parameters are listed in Table 1. When no information was available for a specific input parameter, default settings proposed by the program were used. The enzymatic clearance values were calculated in human liver microsomes using the enzyme kinetic parameters estimated from experiments performed with cDNA-expressed enzymes. The values were adjusted to fit plasma concentration–time profiles and are summarised in Table 2. The contributions of metabolism by CYP2C19, CYP3A4 and CYP2C9 are listed in Table 2. After the models were constructed, the experimental observed data were loaded to verify the accuracy of the basic models, and the fold-errors of the predicted PK parameters were required to be <2. 2.3. Quantitative prediction of drug–drug interactions The DDIs between voriconazole and PPIs in virtual healthy volunteers were simulated using GastroPlusTM. The in vitro inhibition studies of the PPIs (omeprazole, esomeprazole, lansoprazole and rabeprazole) on human P450 enzymes were previously described and compared based on Li et al [5]. The concentration that causes 50% inhibition of the marker reaction (IC50) and the unbound in vitro inhibition constant (Ki) were obtained from the published literature and are listed in Table 2. The intensity of inhibition for PPIs can be judged by [I]/Ki values, where [I] is the simulated Cmax (using
131 132 133
Table 1 Summary of proton pump inhibitors’ input physicochemical parameters in simulations [10–18].
134
Parameter
Voriconazole
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
135 136 137 138 139 140 141 142
Molecular weight (g/mol) Dosage form Base pKa Solubility (mg/mL) Peff (cm/s × 10–4)b Rbp fup (%) logP
349.32 Tablet 1.76 0.2 4.5 1 42 1.8
345.42 Tablet 4.06 0.359a 12 0.59 5 2.23b
345.42 Capsule 4.06 0.353a 1.87 0.59 5 2.23
369.37 Capsule 3.83 0.044 7.15 0.74 3 2.11
359.45 Capsule 4.53 0.336a 1.49 0.75 4 2.3
143 144 145
pKa, acid dissociation constant; Peff, effective permeability; Rbp, blood/plasma concentration ratio; fup, fraction unbound in plasma; logP, partition coefficient. a From DrugBank (https://www.drugbank.ca/). b Estimated by ADMET PredictorTM.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
191 192
Table 2 Summary of proton pump inhibitors’ input enzyme kinetic parameters in simulations [5,21–24].
193
CYP450 enzyme/kinetic parametera
194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
2C19 Vmax Km Ki Fraction of metabolism (%) 3A4 Vmax Km Ki Fraction of metabolism (%) 2C9 Vmax Km Ki Fraction of metabolism (%)
209 210
3
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
162 1.75 2.40 ± 0.05 91.1
990 6.01 7.90 ± 0.5 71.5
405 15.1 0.74 ± 0.09 81.6
110 5.1 ± 0.2 18.8 ± 1.3 33.3
170 22 41.9 ± 5.9 7.4
740 262 46.6 ± 6.8 28.3
83.1 65.5 IC50 > 200 16.6
264 12 50.7 ± 6.4 33.7
82 57 16.4 ± 3.0 1.5
770 107 81.5 ± 72.7 0.2
11.2 82.3 20.8 ± 3.3 1.8
33 1.9 51.0 ± 9.4 26.4
Vmax, maximum reaction velocity; Km, Michaelis–Menten constant; Ki, unbound in vitro inhibition constant; IC50, concentration causing 50% inhibition of the marker reaction. a Km and Ki are expressed as μM; Vmax is expressed as pmol/min/pmol of P450.
211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
a typical dose of each PPI). A PBPK model incorporating competitive inhibition was used to simulate the inhibitory effects of the different strength inhibitors on the plasma concentration–time profiles of the substrate voriconazole. Dynamic simulations of voriconazole plasma concentration–time profiles taken with and without PPIs were run with the DDI Module within GastroPlusTM. The dose, dose interval and duration of administration of the substrate and inhibitors were set based on the US Food and Drug Administration (FDA) drug instructions and the regimens for clinical routine medications. All virtual subjects received oral voriconazole using a 400 mg twice-daily loading dose (Day 1), which was followed by a 200 mg twice-daily maintenance dose regimen (Days 2–10). Once-daily oral omeprazole (40 mg), esomeprazole (40 mg), lansoprazole (30 mg) or rabeprazole (20 mg) was coadministered with the morning dose of voriconazole on Days 1–10. 3. Results
Q5 3.1. Physiologically based pharmacokinetic model and verification A full-body mechanistic PBPK model for voriconazole was constructed based on the physiological and enzyme kinetic parameters using GastroPlusTM software. The parameters were optimised to obtain acceptable correlations between the in silico simulations and in vivo data. The Kp values of voriconazole were predicted using established tissue composition-based models and the results are shown in Table 3. The in vivo data were loaded to verify the predictive accuracy. The simulations and verification of the plasma concentration–
240 241 242 243
time curves for intravenous (i.v.) (2 h) voriconazole and oral voriconazole at a dose of 400 mg are shown in Fig. 1a,b. The predicted and observed PK parameters with prediction accuracy are summarised in Table 4. The simulated i.v. and oral plasma concentration–time profiles of voriconazole obtained from PBPK modelling corresponded well with the observed profiles. In addition, the predicted PK parameters were reasonably consistent (<2fold error) with the observed values. The physicochemical, physiological and enzyme kinetic parameters for each PPI were entered into the PBPK model to produce plasma concentration–time curves (Fig. 1c–f). The predicted plasma concentration data were validated using data obtained from the literature [4,25–27]. The predicted and observed PK parameters and fold-error values are summarised in Table 4. Fig. 1 shows that the simulated profiles for PPIs are qualitatively similar to the observed data. All of the fold-errors of the predicted and observed PK parameters were <2, which indicated that the models were successful and accurately simulated the DDIs for voriconazole and PPIs.
Table 3 Tissue-to-plasma partition coefficients (Kp values) of voriconazole calculated using established tissue composition-based models.
244
Tissue/organ
Kp value
245 246 247 248 249 250 251 252 253 254 255 256 257
Lung Adipose Muscle Liver Spleen Heart Brain Kidney Skin Reproductive organs Red marrow Yellow marrow Rest of body
0.58 2.55 1.31 1.98 1.35 1.48 3.00 1.34 1.64 1.35 3.19 2.55 1.36
258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276
3.2. Drug–drug interaction simulation with omeprazole The [I]/Ki values, presented in Table 5, reveal a potential for PPIs to inhibit the metabolism of voriconazole. A dynamic DDI simulation was performed to predict the effect of omeprazole on voriconazole using a single dose of 40 mg of omeprazole in the PBPK model for 24 h. Omeprazole is a competitive inhibitor of CYP2C19. The plasma concentration–time curves of voriconazole at baseline and following DDI are shown in Fig. 2. The model-predicted ratios of voriconazole Cmax and AUC0–t (AUC for the time of the simulation) with omeprazole co-administration were 1.09 and 1.11 (Table 6). Referring to clinical experience and the study of Wood et al [6], the increases were considered unlikely to be of clinical significance. As expected, when continuing to simulate the DDI with omeprazole in regular multiple doses over 10 days, the predicted PK values of voriconazole were higher when co-administered with omeprazole. Furthermore, the ratios were significantly higher than the ratios observed in the single-dose regimen. The ratios of Cmax and AUC0–t values were ca. 1.14 and 1.39, respectively (Table 6). These results indicate that there is a significant influence on voriconazole. However, the Cmax of voriconazole was <5.5 mg/mL (Fig. 3). This finding indicated that the dosage remained within the therapeutic concentration range of voriconazole (1.0–5.5 mg/L) [28]. The increases were considered unlikely to be of clinical relevance and did not warrant dosage adjustments.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
4
303 304
Table 4 Observed and simulated pharmacokinetic parameters of voriconazole and proton pump inhibitors (PPIs).
305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325
Voriconazole 400 mg i.v.
Voriconazole 400 mg oral
Omeprazole 40 mg oral
Esomeprazole 40 mg oral
Lansoprazole 30 mg oral
Rabeprazole 20 mg oral
Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error Observed Predicted Fold-error
Cmax (μg/mL)
Tmax (h)
AUC0–inf (μg · h/mL)
AUC0–t (μg · h/mL)
4.08 3.95 1.03 2.59 2.49 1.04 0.93 0.87 1.06 0.79 0.82 1.03 0.60 0.61 1.01 0.25 0.24 1.02
2.15 2.00 1.07 1.60 1.92 1.20 0.53 0.48 1.10 1.50 1.36 1.10 3.033 2.96 1.02 1.10 0.88 1.24
28.70 30.71 1.07 22.27 23.92 1.07 1.49 1.77 1.19 2.48 3.04 1.23 2.42 2.76 1.14 0.62 0.91 1.47
25.09 27.12 1.08 20.18 21.07 1.04 1.29 1.77 1.37 2.36 3.04 1.29 2.42 2.76 1.14 0.60 0.91 1.66
Cmax, maximum plasma concentration; Tmax, time to Cmax; AUC0–inf, area under the plasma concentration–time curve from 0 h to infinity; AUC0–t, area under the plasma concentration–time curve for the time of the simulation; i.v., intravenous.
326 327 328 329 330 331 332 333
3.3. Drug–drug interaction simulation with other multiple doses of proton pump inhibitors The voriconazole PBPK model was used to predict DDIs caused by esomeprazole, lansoprazole and rabeprazole. A dynamic simulation was conducted for the co-administration of multiple doses of esomeprazole (capsule, 40 mg once daily) and a regular dose of
voriconazole. The results are shown in Table 6 and Fig. 3. The simulation revealed that the Cmax and AUC0–t values were increased within a narrow range and the ratios were 1.08 and 1.18, respectively. Co-administration of multiple doses of lansoprazole (enteric coating capsule, 30 mg once daily) was also simulated and the predicted Cmax and AUC0–t ratios for voriconazole were 1.06 and 1.19, respectively (Table 6). The influence of lansoprazole was smaller than esomeprazole, which indicates there is no significant clinical drug interaction. For rabeprazole, the result showed that there was no appreciable change in voriconazole plasma exposure.
340 341 342 343 344 345 346 347 348 349 350
Table 5 Estimated mean [I]/Ki values for each enzyme by proton pump inhibitors. CYP450 enzyme 2C19 3A4 2C9
351 352
Omeprazole
Esomeprazole
Lansoprazole
Rabeprazole
353 354 355
0.36 0.02 0.05
0.10 0.02 0.01
0.82 <0.01 0.03
0.01 <0.01 <0.01
356 357 358
[I]/Ki value
334
359
335 336 337 338 339
Fig. 1. Observed (symbols) and physiologically based pharmacokinetic (PBPK) modelsimulated (lines) plasma concentration–time profile of voriconazole and proton pump inhibitors: (a) 400 mg voriconazole intravenous; (b) 400 mg voriconazole oral; (c) 40 mg omeprazole oral; (d) 40 mg esomeprazole oral; (e) 30 mg lansoprazole oral; and (f) 20 mg rabeprazole oral.
Fig. 2. Simulated mean plasma concentrations of single-dose voriconazole dosed alone or with concomitant omeprazole.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
360 361
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
362 363
Table 6 Model-predicted pharmacokinetic parameters and ratios for voriconazole given alone and with proton pump inhibitors.
364
Compound
365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383
Omeprazole Single-baseline DDI with omeprazole Ratio with omeprazole (10 days)-baseline DDI with omeprazole Ratio with omeprazole Esomeprazole (10 days)-baseline DDI with esomeprazole Ratio with esomeprazole Lansoprazole (10 days)-baseline DDI with lansoprazole Ratio with lansoprazole Rabeprazole (10 days)-baseline DDI with rabeprazole Ratio with rabeprazole
384 385
5
Fa (%)
F (%)
Cmax (μg/mL)
Tmax (h)
AUC0–inf (ng · h/mL)
AUC0–t (ng · h/mL)
99.97 99.97 1.00 99.95 99.95 1.00
77.28 82.47 1.07 79.97 84.74 1.06
2.49 2.72 1.09 3.42 3.88 1.14
1.92 2.00 1.04 14.00 14.00 1.00
23900 26600 1.11 368000 502000 1.36
21100 23400 1.11 363000 505000 1.39
99.95 99.95 1.00
79.97 84.32 1.05
3.42 3.60 1.08
14.00 14.00 1.00
368000 416000 1.18
363000 406000 1.18
99.95 99.95 1.00
79.97 81.00 1.01
3.42 3.62 1.06
14.00 14.04 1.00
368000 412000 1.12
363000 406000 1.19
99.95 99.95 1.00
79.97 80.62 1.01
3.42 3.43 1.00
14.00 14.00 1.00
368000 37100 1.01
363000 367000 1.01
Fa, absorption fraction; F, bioavailability; Cmax, maximum plasma concentration; Tmax, time to Cmax; AUC0–inf, area under the plasma concentration–time curve from 0 h to infinity; AUC0–t, area under the plasma concentration–time curve for the time of the simulation; DDI, drug–drug interaction.
386 387 388 389 390 391 392 393 394 395
4. Discussion This is the first study to compare the effects of different PPIs on the pharmacokinetics of voriconazole using a PBPK model. The results suggested that omeprazole was the most potent CYP2C19 inhibitor, and the PK values of voriconazole increased to various degrees when administered concurrently with PPIs. However, the increases are considered unlikely to be of clinical relevance. In addition, an in vitro study has indicated that lansoprazole was the most
potent inhibitor, which contrasts with the findings of the current study (omeprazole > esomeprazole > lansoprazole > rabeprazole). The interactions between voriconazole and PPIs can be interpreted as similar mechanisms of action, and there is less influence on absorption. Consequently, bioavailability did not change significantly. Omeprazole is almost completely metabolised in the liver into its pharmacologically inactive metabolites. As mentioned above, omeprazole has a high affinity for CYP2C19. Clinically available omeprazole is a racemic mixture of esomeprazole and R-omeprazole
396
397 398
Fig. 3. Simulated mean plasma concentrations of multidose voriconazole dosed alone or with (a) concomitant omeprazole (40 mg once daily), (b) esomeprazole (40 mg once daily), (c) lansoprazole (30 mg once daily) or (d) rabeprazole (20 mg once daily).
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
399 400 401 402 403 404 405 406 407
ARTICLE IN PRESS 6
408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473
F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
and there are subtle differences in the pharmacokinetics and metabolism of esomeprazole compared with racemic omeprazole. Previous in vitro experiments have suggested that esomeprazole has a lower systemic clearance than the omeprazole racemate [29]. In this study, administration of an equivalent dose of esomeprazole resulted in a higher AUC than the omeprazole racemate. According to Li et al [5], esomeprazole has a slightly weaker inhibitory potency on CYP2C19 than omeprazole, and esomeprazole is metabolised to a great extent by CYP3A4 (and concurrently less by CYP2C19) than the racemic form. That is to say, its elimination is less dependent on CYP2C19. Thus, esomeprazole has a reduced effect on the pharmacokinetics of voriconazole than omeprazole. A previous in vitro study showed lansoprazole was the most potent inhibitor of CYP2C19. Therefore, it is likely that lansoprazole inhibits CYP2C19 more than omeprazole (Table 5). However, lansoprazole does not cause clinically significant inhibition of CYP2C19, and several in vivo investigations have found only minor increases of CYP2C19 substrate serum levels. These changes are unlikely to cause clinically significant DDIs [30]. The results obtained from in vitro experiments may not translate to in vivo effects owing to confounding factors such as age, sex, nutritional status and other co-morbidities that may affect hepatic function. Omeprazole, having been on the market the longest, has undergone the most thorough investigation for DDIs. Omeprazole has always been known to inhibit the metabolism of diazepam in vivo, whereas lansoprazole has no interaction with the CYP2C19 substrate diazepam, which is similar to warfarin [30,31]. The lack of clinically relevant direct inhibition of CYP2C19 caused by lansoprazole is likely explained by its relatively short half-life and high plasma protein binding [17,32]. Although omeprazole also has a short half-life, inhibition of CYP2C19 by omeprazole is of clinical relevance because a significant amount of CYP2C19 activity may yet be ‘bound’ by omeprazole metabolism [33]. Moreover, the interaction between omeprazole and voriconazole is particularly complex. Ogilvie et al [32] presented that omeprazole, but not lansoprazole, was an irreversible inhibitor of CYP2C19. The results of this study showed that rabeprazole has significantly less DDIs than other PPIs (except pantoprazole). The main reason for the decrease in drug interaction may be related to its nonenzyme-catalysed degradation, which forms a thioether product. Thus, oxidative metabolism catalysed by CYP2C19 and CYP3A4 plays a minor role in its biotransformation, and rabeprazole has a low affinity for a range of CYP isoenzymes [34]. Rabeprazole was reported to be associated with no significant changes in the AUCs of theophylline, phenytoin, warfarin and diazepam in subjects whose CYP2C19 genotypes or phenotypes were unknown [29]. Limitations of this study include the lack of pantoprazole, which was due to missing data. Using GastroPlusTM software to simulate pantoprazole, enzymatic kinetic parameters are essential. However, there are limited published data available. Pantoprazole has a lower affinity for CYP2C19 and CYP3A4 than other PPIs, with Ki values of 14–69 μM for CYP2C19 and 22 μM for CYP3A4 [5], which implies that there is a slight effect on voriconazole. Although pantoprazole showed the highest CYP2C9 inhibition (with a Ki of 6 μM) of the five PPIs, it is possible that pantoprazole would have no effect on voriconazole. Voriconazole is partially metabolised by CYP2C9, and CYP2C19 is the major enzyme involved in the metabolism of voriconazole. There are several possible DDIs following pantoprazole co-administration with drugs metabolised via CYP2C9. In contrast to the other currently available PPIs, the initial metabolite of the drug undergoes phase II sulfate conjugation [35]. This conjugation reaction, a relatively non-saturable route of drug metabolism, is often considered to explain the reduced incidence of drug interactions caused by pantoprazole. CYP2C19 is a highly polymorphic pharmacogene and genetic variants in the CYP2C19 gene locus may alter CYP2C19 substrate
metabolism, which results in interindividual phenotypic variability [36–38]. The CYP2C19 genotype has been found to contribute to the pharmacokinetics and plasma concentration of voriconazole. The frequency of CYP2C19 poor metabolisers is ca. 3.52% in Europeans and 14% in Chinese [39]. Therefore, the influence of PPIs on voriconazole should be assessed in future studies using specific CYP2C19 genotypes.
474 475 476 477 478 479 480 481
5. Conclusion In conclusion, a PBPK model was applied to predict the clinical PK profiles and to assess the DDI potential of voriconazole when administered concurrently with multiple doses of PPIs. The results suggested that the simulated plasma concentration–time profiles both of voriconazole and PPIs by PBPK models corresponded to the observed profiles. Omeprazole is the most potent CYP2C19 inhibitor, and the PK values of voriconazole increased to various degrees when administered with PPIs (omeprazole > esomeprazole > lansoprazole > rabeprazole). However, dosage adjustments for voriconazole are unnecessary because the dose remained in the voriconazole therapeutic range. Funding: None. Competing interests: None declared. Ethical approval: Not required.
482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498
References [1] Mikus G, Schöwel V, Drzewinska M, Rengelshausen J, Ding R, Riedel KD. Potent cytochrome P450 2C19 genotype-related interaction between voriconazole and the cytochrome P450 3A4 inhibitor ritonavir. Clin Pharmacol Ther 2006;80:126–35. [2] Quehl P, Hollender J, Schüürmann J, Brossette T, Maas R, Jose J. Co-expression of active human cytochrome P450 1A2 and cytochrome P450 reductase on the cell surface of Escherichia coli. Microb Cell Fact 2016;15:1–15. [3] Niece KL, Boyd NK, Akers KS. In vitro study of the variable effects of proton pump inhibitors on voriconazole. Antimicrob Agents Chemother 2015;59:5548– 54. [4] Noubarani M, Keyhanfar F, Motevalian M, Mahmoudian M. Improved HPLC method for determination of four PPIs, omeprazole, pantoprazole, lansoprazole and rabeprazole in human plasma. J Pharm Pharm Sci 2010;13:1–10. [5] Li XQ, Andersson TB, Ahlström M, Weidolf L. Comparison of inhibitory effects of the proton pump-inhibiting drugs omeprazole, esomeprazole, lansoprazole, pantoprazole, and rabeprazole on human cytochrome P450 activities. Drug Metab Dispos 2004;32:821–7. [6] Wood N, Tan K, Purkins L, Layton G, Hamlin J, Kleinermans D, et al. Effect of omeprazole on the steady-state pharmacokinetics of voriconazole. Br J Clin Pharmacol 2003;56:45–50. [7] Poirier A, Funk C, Scherrmann JM, Lavé T. Mechanistic modeling of hepatic transport from cells to whole body: application to napsagatran and fexofenadine. Mol Pharm 2009;6:1716–33. [8] Zhu L, Yang J, Zhang Y, Wang Y, Zhang J, Zhao Y, et al. Prediction of pharmacokinetics and penetration of moxifloxacin in human with intraabdominal infection based on extrapolated PBPK model. Korean J Physiol Pharmacol 2015;19:99–104. [9] Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci 2006;95:1238–57. [10] Benet LZ, Broccatelli F, Oprea TI. BDDCS applied to over 900 drugs. AAPS J 2011;13:519–47. [11] Theurillat R, Zimmerli S, Thormann W. Determination of voriconazole in human serum and plasma by micellar electrokinetic chromatography. J Pharm Biomed Anal 2010;53:1313–18. [12] Roche VF. The chemically elegant proton pump inhibitors. Am J Pharm Educ 2006;70:101. [13] Harding VD. Pharmaceutical formulations containing voriconazole. US Patent US6632803. 2003. [14] Wu C, Sun L, Sun J, Yang Y, Ren C, Ai X, et al. Profiling biopharmaceutical deciding properties of absorption of lansoprazole enteric-coated tablets using gastrointestinal simulation technology. Int J Pharm 2013;453:300–6. [15] Shi S, Klotz U. Proton pump inhibitors: an update of their clinical use and pharmacokinetics. Eur J Clin Pharmacol 2008;64:935–51. [16] Chiba K, Shimizu K, Kato M, Nishibayashi T, Terada K, Izumo N, et al. Prediction of inter-individual variability in the pharmacokinetics of CYP2C19 substrates in humans. Drug Metab Pharmacokinet 2014;29:379–86. [17] Chan TS, Yu H, Moore A, Khetani SR, Kehtani SR, Tweedie D. Meeting the challenge of predicting hepatic clearance of compounds slowly metabolized
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549
ARTICLE IN PRESS F. Qi et al. / International Journal of Antimicrobial Agents ■■ (2017) ■■–■■
550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585
[18]
[19]
[20] [21]
[22]
[23]
[24]
[25]
[26]
[27] [28]
by cytochrome P450 using a novel hepatocyte model, HepatoPac. Drug Metab Dispos 2013;41:2024–32. Wu F, Lu G, Zhao P, Jamei M, Huang SM, Bashaw ED, et al. Predicting nonlinear pharmacokinetics of omeprazole enantiomers and racemic drug using physiologically based pharmacokinetic modeling and simulation: application to predict drug/genetic interactions. Pharm Res 2014;3:1919–29. Yanni SB, Annaert PP, Augustijns P, Ibrahim JG, Benjamin DK Jr, Thakker DR. In vitro hepatic metabolism explains higher clearance of voriconazole in children versus adults: role of CYP2C19 and flavin-containing monooxygenase 3. Drug Metab Dispos 2010;38:25–31. Jones HM, Parrott N, Jorga K, Lavé T. A novel strategy for physiologically based predictions of human pharmacokinetics. Clin Pharmacokinet 2006;45:511–42. Kita T, Sakaeda T, Aoyama N, Sakai T, Kawahara Y, Kasuga M, et al. Optimal dose of omeprazole for CYP2C19 extensive metabolizers in anti-Helicobacter pylori therapy: pharmacokinetic considerations. Biol Pharm Bull 2002;25:923–7. Abelö A, Andersson TB, Antonsson M, Naudot AK, Skånberg I, Weidolf L. Stereoselective metabolism of omeprazole by human cytochrome P450 enzymes. Drug Metab Dispos 2000;28:966–72. Kim KA, Kim MJ, Park JY, Shon JH, Yoon YR, Lee SS, et al. Stereoselective metabolism of lansoprazole by human liver cytochrome P450 enzymes. Drug Metab Dispos 2003;31:1227–34. Miura M, Satoh S, Tada H, Habuchi T, Suzuki T. Stereoselective metabolism of rabeprazole-thioether to rabeprazole by human liver microsomes. Eur J Clin Pharmacol 2006;62:113–17. Hassan-Alin M, Andersson T, Niazi M, Röhss K. A pharmacokinetic study comparing single and repeated oral doses of 20 mg and 40 mg omeprazole and its two optical isomers, S-omeprazole (esomeprazole) and R-omeprazole, in healthy subjects. Eur J Clin Pharmacol 2005;60:779–84. Andersson DT, Hassan-Alin M, Hasselgren G, Röhss K, Weidolf L. Pharmacokinetic studies with esomeprazole, the (S)-isomer of omeprazole. Clin Pharmacokinet 2001;40:411–26. Vlase DL, Popa A, Neag M, Muntean D, Leucuta SE. Effect of fluoxetine on the pharmacokinetics of lansoprazole. Clin Drug Investig 2011;31:727–33. Pascual A, Calandra T, Bolay S, Buclin T, Bille J, Marchetti O. Voriconazole therapeutic drug monitoring in patients with invasive mycoses improves efficacy and safety outcomes. Clin Infect Dis 2008;46:201–11.
7
[29] Ogawa R, Echizen H. Drug–drug interaction profiles of proton pump inhibitors. Clin Pharmacokinet 2010;49:509–33. [30] Lefebvre RA, Flouvat B, Karolac-Tamisier S, Moerman E, Ganse EV. Influence of lansoprazole treatment on diazepam plasma concentrations. Clin Pharmacol Ther 1992;52:458–63. [31] Cavanaugh J, Winters E, Cohen A, Locke C, Braeckman R. Lack of effect of lansoprazole on steady state warfarin metabolism. Gastroenterology 1991;100:A40. [32] Ogilvie BW, Yerino P, Kazmi F, Buckley DB, Rostami-Hodjegan A, Paris BL. The proton pump inhibitor, omeprazole, but not lansoprazole or pantoprazole, is a metabolism-dependent inhibitor of CYP2C19: implications for coadministration with clopidogrel. Drug Metab Dispos 2011;39:2020– 33. [33] Norgard NB, Mathews KD, Wall GC. Drug–drug interaction between clopidogrel and the proton pump inhibitors. Expert Rev Clin Pharmacol 2014;155:1266– 74. [34] Blume DH, Donath F, Warnke A, Schug BS. Pharmacokinetic drug interaction profiles of proton pump inhibitors. Drug Saf 2006;29:769–84. [35] Cheer SM, Prakash A, Faulds D, Lamb HM. Pantoprazole: an update of its pharmacological properties and therapeutic use in the management of acidrelated disorders. Drugs 2003;63:101–32. [36] Scott SA, Sangkuhl K, Gardner EE, Stein CM, Hulot JS, Johnson JA, et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450-2C19 (CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol Ther 2011;90:328–32. [37] Scott SA, Sangkuhl K, Shuldiner AR, Hulot JS, Thorn CF, Altman RB, et al. PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 19. Pharmacogenet Genomics 2012;22:159–65. [38] Hicks JK, Swen JJ, Thorn CF, Sangkuhl K, Kharasch ED, Ellingrod VL, et al. Clinical Pharmacogenetics Implementation Consortium guideline for CYP2D6 and CYP2C19 genotypes and dosing of tricyclic antidepressants. Clin Pharmacol Ther 2013;93:402–8. [39] Wu J, Jia LT, Shao LM, Chen JM, Zhong DD, Xu S, et al. Drug–drug interaction of rabeprazole and clopidogrel in healthy Chinese volunteers. Eur J Clin Pharmacol 2013;69:179–87.
Please cite this article in press as: Fang Qi, et al., Influence of different proton pump inhibitors on the pharmacokinetics of voriconazole, International Journal of Antimicrobial Agents (2017), doi: 10.1016/j.ijantimicag.2016.11.025
586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621