quantitative modeling of the effect of renal impairment …10.1007... · web...

2013_Sept_29_Hsu_Renal_TransporterPBPK_SupplementalSupplementary Materials for

Towards Quantitative Modeling of the Effect of Renal Impairment and Probenecid Inhibition

on Kidney Uptake and Efflux Transporters using PBPK

Vicky Hsu, Manuela de L T Vieira, Ping Zhao, Lei Zhang, Jenny Huimin Zheng, Anna Nordmark,

Eva Gil Berglund, Kathleen M. Giacomini, and Shiew-Mei Huang

1. PBPK models for substrates

1.1 Oseltamivir Carboxylate

Drug-dependent parameters for oseltamivir carboxylate (OC, active metabolite of oseltamivir phosphate), including logP, compound type,

acid/base pKas, blood-to-plasma partition ratio, and fraction unbound in plasma, were obtained from Parrott et al [1]. Distribution

parameters Vss and tissue-to-plasma partition coefficients were predicted using methods published by Rodgers et al based on drug

physicochemical and blood binding properties [2,3]. OC has an absolute bioavailability of ~80% after oral administration of oseltamivir

phosphate, and it is exclusively renally cleared, with significant contribution by active secretion [4]. To independently model OC PK after

oral administration of oseltamivir phosphate, conversion to OC was assumed with the appearance of plasma OC approximated by fraction

absorbed (fa, used to estimate the extent of conversion) and by a first order absorption rate constant (ka, used to estimate rate of

conversion). Fa was therefore derived from OC’s bioavailability, while ka was optimized based on a single oral dose study [4].

Due to simultaneous availability of both plasma and urine over time profiles, the healthy, control arm (n=5) of an oseltamivir renal

impairment multiple dose study [5] was used as a reference for obtaining transporter-mediated intrinsic clearances (CLint,T) for the net

basolateral uptake transporter “Tup,b” (based on plasma data) and the net apical efflux transporter “Teff,a” (based on urine data). The CLint,T

by “Tup,b” and oral absorption lag time were derived simultaneously via parameter estimation based on plasma profile observed from a

multiple dose renal impairment study design of oseltamivir phosphate administered as 100 mg single dose (day 1), 100 mg twice daily

(days 2 to 5), and 100 single dose (day 6) (Figure S1a). Since basolateral and apical passive diffusion clearances were assumed to be

negligible, a sensitivity analysis on a range of CLint,T by “Teff,a” was conducted and compared to the observed urine profile. The results

showed a “Teff,a” CLint,T base value of 0.001 µL/min/106 cells may sufficiently recover the observed cumulative fraction of drug excreted

unchanged over time, with a higher-fold value used in the model to assure complete efflux of drug into urine (Figure S4a). This final

model was then evaluated against separate observed clinical studies not used during mechanistic kidney model development for

performance verification (Figure S1b).

Hsu_Renal_TransporterPBPK_Supplemental_Final 1

2013_Sept_29_Hsu_Renal_TransporterPBPK_Supplemental

Figure S1a Simulated OC Cp-t profile following 100 mg oseltamivir phosphate PO multiple dose (single dose on day 1, twice daily on days 2 to 5, single dose on day 6), with observed data points from reference RI normal study [5].

Figure S1b Performance of simulated OC Cp-t profile following 150 mg oseltamivir phosphate PO single dose (5 virtual trials, n=18 each) against observed data from separate studies [4,6,7,8]

1.2 Cidofovir

Drug dependent parameters are summarized in Table 1 in the main text. The cidofovir model was developed using the population

representative of the healthy volunteer virtual population, assuming drug PK is similar between healthy volunteers and HIV patients (see

below).

Human pharmacokinetic data are available in HIV patients receiving intravenous infusion of cidofovir over 1 hour [9]. Mean values of

systemic clearance (CL), renal clearance (CLr), urinary excretion over time profile, and volume of distribution at steady state (Vss) from

this PK study, along with the mean unbound fraction in plasma (fu,p) form the basis of developing cidofovir PBPK model. Based on

physicochemical parameters provided to the PBPK software (Table 1), Vss was predicted, along with a modification of an empirical Hsu_Renal_TransporterPBPK_Supplemental_Final

0

200

400

600

800

1000

1200

0 12 24 36 48 60 72 84 96

Time (h)

Syst

emic

Con

cent

ratio

n (n

g/m

L)

Simulated 5th percentile 95th percentileBA study observed Hill observed Jhee observedSnell observed

Oseltamivir Carboxylate

0

100

200

300

400

500

600

700

0 24 48 72 96 120

Time (h)

Syst

emic

Con

cent

ratio

n (n

g/m

L)

2

2013_Sept_29_Hsu_Renal_TransporterPBPK_Supplementalscaling factor (Kp Scalar, Table 1) to be 0.49 L/kg using the algorithm by Rodgers et al [2,3]. Optimization of Kp scalar was performed

for all tissues equally. The predicted Vss value matches the Vss observed in HIV patients receiving 1, 3, and 10 mg/kg intravenous infusion

[9].

In order to define clearance pathways for cidofovir PBPK model, several steps were taken sequentially.

(1). A mean CL of 12.8 L/h, a mean CLr of 11.4 L/h were calculated from the above mentioned cidofovir study at three infusion doses

(Cundy, 1995). The mean CL and CLr values of each dose were 130 and 129, 152 and 129, and 148 and 124 mL/h/kg, respectively, and

the mean values of fraction excreted in the urine at 24 hours were 98.5, 83.9, and 94.7%, respectively, at 1, 3, and 10 mg/kg (mean body

weight of these doses were 89.2, 76.5, 67.3 kg, respectively).

(2). The residual, non renal clearance of cidofovir was calculated using the retrograde function of the PBPK software. Mean CL and CLr

of 12.8 L/h and 11.4 L/h were used, and liver was assumed responsible for the non renal clearance. This retrospectively derived hepatic

intrinsic clearance is based on the activity in human liver microsomes [10]. The intrinsic clearance value for microsomes was further

translated to the value that is based on human S9 fraction in the PBPK software using default protein concentration in mg per gram liver

for both subcellular fractions (39.8 and 120.8 mg/g for microsomes and S9, respectively). The residual hepatic CL was assigned to S9

because metabolic enzyme-specific and pathway-specific information was not available for cidofovir. In addition, microsomal protein

concentration (39.9 mg/g) is fixed in the software, whereas the value can be altered for S9, providing flexibility of further defining S9

protein concentration in specific populations. The resulting intrinsic clearance will be responsible for extrapolating in vivo, non renal

clearance of cidofovir when the model is used for simulations.

(3). The apparent CLr was further defined in the mechanistic kidney module of the PBPK software (Figure 1 of the main text). Assuming

passive diffusion is negligible (major assumptions of the main text), the non filtrational clearance of cidofovir was defined using a net

basolateral uptake (Tup,b) and a net apical efflux (Teff,a) clearance terms. Parameter estimation was conducted on the intrinsic clearance of

uptake transporter at the tubular cell level (CLint,T in µL/min/106 cells) using plasma data at all three doses (1, 3, and 10 mg/kg intravenous

infusion). Briefly, the PBPK model of cidofovir was used as structural model. In the absence of detailed patient information, mean

demographics such as mean body weight (89, 77, and 67 kg), mean age of 39 years and mean serum creatinine level of 70 mol/L for all

dose groups, and the gender of male were defined for each dose level. The mean plasma concentration time data set of each dose was

simultaneously used to estimate CLint,T by the net uptake transporter (Tup,b). A maximum a posteriori estimation method was used along

with both proportional and additive errors. The initial estimate of uptake CLint,T was 3.0 L/min/106 cells. A uniform distribution was

assumed. A total of 10 iterations were used. The individual and population predicted (IPRED and PPRED) concentration time profiles

were plotted against the observed data (Figure S2a, upper panel). The observed versus model predicted concentrations (IPRED and

PPRED) are shown in

Hsu_Renal_TransporterPBPK_Supplemental_Final

0

5000

10000

15000

20000

25000

-3.0 2.0 7.0 12.0

Sub

Plas

ma

((ng/

mL)

)

Time (Hours)

10 mg/kg

Observe IPRED PPRED

0

1000

2000

3000

4000

0.0 2.0 4.0 6.0 8.0

Sub

Plas

ma

((ng/

mL)

)

Time (Hours)

1 mg/kg

Observe IPRED PPRED

0

2000

4000

6000

8000

-3.0 2.0 7.0 12.0

Sub

Plas

ma

((ng/

mL)

)

Time (Hours)

3 mg/kg

Observe IPRED PPRED

3


Figure S2a Upper panel: Individual and population predicted (IPRED and PPRED) cidofovir concentration-time profiles using PBPK model with CLint,T of renal uptake transporter estimated from the mean plasma PK data (observed) of three doses (Cundy, 1995) [9]. Panels from left to right are for 1, 3, and 10 mg/kg doses, respectively; Lower panel: Observed versus model predicted cidofovir concentrations (IPRED and PPRED) using PBPK model.

(4). The PBPK model of cidofovir in healthy volunteers was further developed to include CLint,T of net apical efflux transport (Teff,a) in the

kidney. A sensitivity analysis was conducted using urine accumulation data from the study by Cundy et al [9] The values of 20, 0.2, 0.02

and 0.002 L/min/106 cells for the efflux CLint,T were tested manually in the PBPK model to predict urinary collection of cidofovir over

time. The simulated urinary drug accumulation-time profiles under different efflux CLint,T after 3 mg/kg intravenous infusion of cidofovir

were compared to the mean profiles of each dose (Figure S2b). Although the observed mean drug accumulation profile of 3 mg/kg dose

group appeared to have a lower total accumulation at 24 hours and a slightly slower appearance than those of 1 and 10 mg/kg groups, a

CLint,T of >0.2 L/min/106 cells can provide reasonable description of the urinary collection of cidofovir. As the model can not further

confirm a value of >0.2 L/min/106 cells, we chose 20 L/min/106 cells in the final model to ensure effective accumulation of the drug in

the urine compartment.

Figure S2b Simulated (at 3 mg/kg intravenous infusion of cidofovir over 1 hour) and observed cidofovir accumulation in the urine. Values are efflux intrinsic clearance (in L/min/106 cells) used for PBPK model of cidofovir. Observed data are the mean urinary drug accumulation for each intravenous dose [9].

1.3 Cefuroxime

Cefuroxime physicochemical parameters were obtained from Chemspider (http://www.chemspider.com; Royal Society of Chemistry,

Cambridge, United Kingdom), the protein binding affinity from ex vivo data [11] and the blood/plasma ratio was in silico predicted based

on the drug physicochemical properties using ADMET PredictorTM (Simulation Plus®, Lancaster, CA, USA). The Vss and organ partition


-5,000

0

5,000

10,000

15,000

20,000

25,000

0 5,000 10,000 15,000 20,000 25,000

Obs

erva

tions

Individual Predictions

Individual predictions vs observations (ng/mL)

IPRED/Obs Identity Linear (IPRED/Obs)

-5,000

0

5,000

10,000

15,000

20,000

25,000

0 5,000 10,000 15,000 20,000 25,000

Obs

erva

tions

Population Predictions

Population predictions vs observations (ng/mL)

PPRED/Obs Identity Linear (PPRED/Obs)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 4 8 12 16 20 24

3 mg/kg10 mg/kg1 mg/kgSimulated_20Simulated_0.2Simulated_0.02Simulated_0.002

Time (hr)

%do

se re

cove

red

4

2013_Sept_29_Hsu_Renal_TransporterPBPK_Supplementalcoefficients were predicted by the method of Rodgers et al [2,3] based on the drug physicochemical properties and blood binding affinity.

Sensitivity analysis was performed on the value of Kp scalar to adequately adjust the predicted Vss, to the average value of 0.19 L/kg

observed in healthy subjects after IV dosing [11]. Optimization of Kp scalar was performed for all tissues equally. Cefuroxime is

exclusively renally eliminated: 99% of dose administered is recovered unchanged in 24h urine. The contribution of net secretion of the

total clearance is around 45% in a healthy adult [11]. The role of basolateral uptake transporter in the tubular secretion of cefuroxime is

demonstrated by the in vivo observed 40% reduction on cefuroxime’s clearance by competitive inhibition with probenecid [12] and the

increased in cefuroxime AUC by 27% by co-administration of the OAT substrate NX-059 [13]. The intrinsic clearance of a renal

basolateral uptake transporter, “Tup,b”, was estimated (software PE function) using intravenous plasma data of three ascending doses (0.25,

0.5 and 1g IV bolus) [11]. The basal and apical passive diffusion clearance was assumed to be negligible as a function of the drug

hydrophilicity and observed net secretion. Sensitivity analysis of the range of values of the intrinsic clearance of a renal apical efflux

transporter, “Teff,a”, was conducted based on urine data over time from three ascending doses (0.25, 0.5 and 1g IV bolus) [11]. A value of

Teff,a Clint,T (µL/min/106cells) higher than 0.1 was able to recover the cumulative fraction of drug excreted unchanged in urine over 12

hours (Figure S4c), with a higher-fold value of 10 being used in the model to assure complete efflux of drug into urine.

PK data and parameters from three ascending bolus doses in healthy male subjects [11] were used for model building (Figure S3a and

S3b). Cefuroxime PBPK model was verify by two independent data sets: Plasma concentration–time profile of cefuroxime 1.5 g infused

over 30 min [13] (Figure S3c) to a population of healthy young subjects, and 0.75 and 1.5 g infused over 20 min [14] (Figure S3d).

Figure S3 Performance of cefuroxime PBPK model by comparing the simulated Cp-t profile with observed data across different studies and dosing scenarios: a) 1.0 g cefuroxime IV bolus over 3 min [11] b) 0.25, 0.5 and 1.0g cefuroxime IV bolus over 3 min [11] c) 1.5g cefuroxime infusion over 30 min [13] and d) 0.75 and 1.5 g cefuroxime over 20 min infusion [14].Hsu_Renal_TransporterPBPK_Supplemental_Final 5


Figure S4 Simulated cumulative fraction of drug excreted unchanged for a) oseltamivir carboxylate, b) cidofovir, and c) cefuroxime.

2. PBPK model for probenecid

Drug-dependent parameters for probenecid are summarized in Table S1. In silico predictions included PubChem

(http://pubchem.ncbi.nlm.nih.gov; National Institutes of Health, Bethesda, MD, USA) and ADMET PredictorTM (Simulation Plus®,

Lancaster, CA, USA) for logP and blood-to-plasma partition coefficient, respectively. The remaining physicochemical and blood binding

properties were based on literature information [15,16,17]. In vivo clearance was calculated based on an observed clinical study (n=6) in

which a single low intravenous dose of 0.5 g was administered [18]. In terms of elimination, general renal clearance (in contrast to

mechanistic kidney model used in the model substrate drugs) was applied, such that approximately 10% of dose is excreted renally

unchanged [19]. Using the retrograde model to derive intrinsic clearances, 70% of the total clearance was assigned to a hepatic CYP, with

the remaining 20% assigned to a UGT enzyme [19]. Distribution parameters Vss and tissue-to-plasma coefficients were predicted using

methods published by Rodgers et al (Rodgers), and optimized using parameter estimation based on observed IV profile [18]. Oral

absorption parameters fa was estimated based on observed near complete oral absorption [15], and the ka was optimized to fit oral profiles

observed from two separate studies [18,20]. Due to known saturation at higher oral doses, Km and Vmax values were assigned to the

hepatic CYP enzyme and optimized according to the CYP CLint derived from the IV data of probenecid. Intravenous and oral probenecid

model results are shown in Figure S5. The main source of probenecid pharmacokinetic nonlinearity is due to saturable metabolism

process. Therefore, despite slight observed changes in fu,p, a constant fu,p value was assumed for probenecid (less than 10% change in fu,p in

the probenecid concentration range simulated if compared to observed fu,p changes) [18].

Table S1 Drug-dependent parameter summary table for probenecid

Parameters Probenecid

Molecular weight (g/mol) 285a

LogP 3.2b

Compound Type Monoprotic Acidc

Acid pKa 3.4c

Base pKa N/A

B/P 0.60d

Fu,p 0.10e

Vss (L/kg) 0.10 (predicted using method 2)f


a) Oseltamivir Carboxylate

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 6 12 18 24

Time (h)

Cum

ulat

ive

frac

tion

of s

ubst

rate

ex

cret

ed u

ncha

nged

b) Cidofovir

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 5 10 15 20

Time (h)C

umul

ativ

e fr

actio

n of

sub

stra

te

excr

eted

unc

hang

ed

c) Cefuroxime

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 2 4 6 8 10 12

Time (h)

Am

ount

of s

ubst

rate

exc

rete

d un

chan

ged

(mg)

6

2013_Sept_29_Hsu_Renal_TransporterPBPK_SupplementalKp scalar 0.30 (optimized)g

CLiv (L/h) 1.03h

CLr (L/h) 0.10i

CLint (µL/min/106 cells) by CYP 0.036 (IV, retrograde analysis)i

CYP Vmax (pmol/min/pmol of isoform) 0.816 (PO, optimized based on CYP CLint)g

CYP Km (µM) 22.8 (PO, optimized based on CYP CLint)g

CLint (µL/min/106 cells) by UGT 0.53 (retrograde analysis)i

fa 0.95 (PO)e

ka (1/h) 0.45 (PO, optimized)g

LogP, partition coefficient; pKa, dissociation constant; B/P, blood-to-plasma partition ratio; Fu,p, fraction unbound in plasma; Vss, volume of distribution at steady state; Kp, tissue-to-plasma partition coefficient; CLiv, in vivo clearance; CLr, renal clearance; CLint, in vitro intrinsic clearance; CYP, cytochrome p450 enzyme; Vmax, maximum rate of metabolite formation; Km, Michaelis-Menten constant; fa, fraction available from dosage form; ka, first-order absorption rate constant.aChemSpider (http://www.chemspider.com; Royal Society of Chemistry, Cambridge, United Kingdom)bPubChem (http://pubchem.ncbi.nlm.nih.gov; National Institutes of Health, Bethesda, MD, USA)cShore et al [17] dADMET PredictorTM (Simulation Plus®, Lancaster, CA, USA)eDayton et al [15]fRodgers et al [2,3]gOptimization involves manual or automated sensitivity analysis, or parameter estimation techniqueshEmanuelsson et al [18]iCunningham et al [19]

Figure S5 Simulated probenecid Cp-t profile following a) 0.5 g probenecid IV infusion over 15 m, with observed data [18], b) 1.0 g probenecid PO single dose, with observed data points [18,20], and c) 2.0 g probenecid PO single dose, with observed data points [18,20].


a) IV 0.5 g Probenecid

1

10

100

1000

0 6 12 18 24 30

Time (h)

Syst

emic

Con

cent

ratio

n (µ

g/m

L)

b) PO 1.0 g Probenecid

1

10

100

1000

0 8 16 24 32 40 48Time (h)

c) PO 2.0 g Probenecid

1

10

100

1000

0 8 16 24 32 40 48Time (h)

7

OC dose (23 h after probenecid dose)

Cidofovir dose (2 h after probenecid dose)


3. Probenecid dosing and predicted concentrations in drug-drug interaction (DDI) studies

3.1. OseltamivirOseltamivir-probenecid DDI study [6]: “single 150-mg dose of oseltamivir, administered during treatment with probenecid, in which 500 mg of probenecid was given every 6 h for 16 doses beginning 23 h before the dose of oseltamivir.” Predicted probenecid concentration at time of substrate dosing is provided below in Figure S6.

Figure S6 Predicted probenecid Cp-t profile in OC-probenecid DDI study [6].

3.2. CidofovirCidofovir-probenecid DDI study [9]: Cidofovir dosed “with concomitant oral probenecid given as a 4 g course (comprising 2 g at 2 h before a dose of cidofovir and 1 g at each of 2 and 8 h after the dose).” Predicted probenecid concentration at time of substrate dosing is provided below in Figure S7.

Figure S7 Predicted probenecid Cp-t profile in cidofovir-probenecid DDI study [9].

3.3. CefuroximeCefuroxime-probenecid DDI study [14]: “750 mg cefuroxime with 1 g of probenecid given orally 3 h before the cefuroxime infusion.” Predicted probenecid concentration at time of substrate dosing is provided below in Figure S8.


0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

0 12 24 36 48 60 72 84 96Syst

emic

Con

cent

ratio

n (n

g/m

L)

Time - Inhibitor (h)

Mean Values of Systemic concentration in plasma of Probenecid f-PBPK over Time

ISys 1

0

50

100

150

200

250

300

0 24 48 72 96 120 144 168

Syst

emic

Con

cent

ratio

n (m

g/L)



ISys 1

8

Cefuroxime dose (3 h after probenecid dose)


Figure S8 Predicted probenecid Cp-t profile in cefuroxime-probenecid DDI study [14].

4. PBPK model files

It is important to note that whenever possible, we always strive to use reliably measured parameters as a primary means of building PBPK

models. Only in situations in which parameters are not readily available do we rely on in silico prediction or parameter estimation to

derive a parameter value. All PBPK model files using Simcyp Simulator® (e.g.,”.cmp”, “.lbr”, and “.wks”) are available upon request to

the authors.

5. Simulated cellular cidofovir drug amount-time profile over 24 hours after intravenous infusion of drug

Figure S9 Amount of cidofovir in kidney cells vs. time profile following 3.0 mg/kg cidofovir IV infusion over 1 h with and without probenecid hypothetical inhibition(s).


0

10

20

30

40

50

60

70

80

90

0 4 8 12

Syst

emic

Con

cent

ratio

n (m

g/L)



ISys 1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 6 12 18 24Time (h)

w/o interaction net apical efflux only

net basolateral uptake only uptake and efflux

9


References

1. Parrott N, Davies B, Hoffman G, Koerner A, Lave T, Prinssen E, Theogaraj E, Singer T. Development of a physiologically based model for oseltamivir and simulation of pharmacokinetics in neonates and infants. Clin Pharmacokinet. 2011;50:613-23.

2. Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94:1259-76.

3. 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.

4. Study of the pharmacokinetics and absolute bioavailability of the neuraminidase inhibitor Ro 64-0796/Ro 64-0802 (Protocol NP15719). Clinical Pharmacology and Biopharmaceutics Review from Drugs@FDA(1999).

5. Multiple ascending oral dose study of the pharmacokinetics, tolerability, and safety of the neuraminidase inhibitor Ro 64-0796 in subjects with renal impairment. Clinical Pharmacology and Biopharmaceutics Review from Drugs@FDA(1999).

6. Hill G, Cihlar T, Oo C, Ho ES, Prior K, Wiltshire H, Barrett J, Liu B, Ward P. The anti-influenza drug oseltamivir exhibits low potential to induce pharmacokinetic drug interactions via renal secretion-correlation of in vivo and in vitro studies. Drug Metab Dispos. 2002;30:13-9.

7. Jhee SS, Yen M, Ereshefsky L, Leibowitz M, Schulte M, Kaeser B, Boak L, Patel A, Hoffman G, Prinssen EP, Rayner CR. Low penetration of oseltamivir and its carboxylate into cerebrospinal fluid in healthy Japanese and Caucasian volunteers. Antimicrob Agents Chemother. 2008;52:3687-93.

8. Snell P, Oo C, Dorr A, Barrett J. Lack of pharmacokinetic interaction between the oral anti-influenza neuraminidase inhibitor prodrug oseltamivir and antacids. Br J Clin Pharmacol. 2002;54:372-7.

9. Cundy KC, Petty BG, Flaherty J, Fisher PE, Polis MA, Wachsman M, Lietman PS, Lalezari JP, Hitchcock MJ, Jaffe HS. Clinical pharmacokinetics of cidofovir in human immunodeficiency virus-infected patients. Antimicrob Agents Chemother. 1995;39:1247-52.

10. Vieira Mde L, Zhao P, Berglund EG, Reynolds KS, Zhang L, Lesko LJ, Huang SM. Predicting drug interaction potential with a physiologically based pharmacokinetic model: a case study of telithromycin, a time-dependent CYP3A inhibitor. Clin Pharmacol Ther. 2012;91:700-8.

11. Foord RD. Cefuroxime: Human Pharmacokinetics. Antimicrob Agents Chemother. 1976;9:741-7.

12. Verhagen CA, Mattie H, van Strijen E. The renal clearance of cefuroxime and ceftazidime and the effect of probenecid on their tubular excretion. Br J Clin Pharmacol. 37, 1994;37:193-7.

13. Kagedal M, Nilsson D, Huledal G, Reinholdsson I, Cheng YF, Asenblad N, Pekar D, Borga O. A study of organic acid transporter mediated pharmacokinetic interaction between NXY-059 and cefuroxime. J Clin Pharmacol. 2007;47:1043-8.

14. Garton AM, Rennie RP, Gilpin J, Marrelli M, Shafran SD. Comparison of dose doubling with probenecid for sustaining serum cefuroxime levels. J Antimicrob Chemother. 1997;40:903-6.

15. Dayton PG, Yu TF, Chen W, Berger L, West LA, Gutman AB. The physiological disposition of probenecid, including renal clearance, in man, studied by an improved method for its estimation in biological material. J Pharmacol Exp Ther. 1963;140:278-86.

16. Dayton PG, Perel JM. The metabolism of probenecid in man. Ann NY Acad Sci. 1971;399-402.

17. Shore PA, Brodie BB, Hogben CA. The gastric secretion of drugs: a pH partition hypothesis. J Pharmacol Exp Ther. 1957;119:361-9.

18. Emanuelsson BM, Beermann B, Paalzow LK. Non-linear elimination and protein binding of probenecid. Eur J Clin Pharmacol. 1987;32:395-401.


2013_Sept_29_Hsu_Renal_TransporterPBPK_Supplemental19. Cunningham RF, Israili ZH, Dayton PG. Clinical pharmacokinetics of probenecid. Clin Pharmacokinet. 1981;6:135-51.

20. Selen A, Amidon GL, Welling PG. Pharmacokinetics of probenecid following oral doses to human volunteers. J Pharm Sci. 1982;71:1238-42.


quantitative modeling of the effect of renal impairment …10.1007... · web...

Documents