1 Introduction
2 Methods
2.1 Model Drugs
Parameter | Oseltamivir carboxylate | Cidofovir | Cefuroxime |
---|---|---|---|
Renal clearance by active secretion (%) | 62 | 39 | 55 |
Molecular weight (g/mol) | 312.4a
| 279.2a
| 424.4a
|
LogP
| −2.1b
| −1.5a
| −0.9a
|
Compound type | Ampholyteb
| Monoprotic acidc
| Monoprotic acida
|
Acid pKa | 3.6b
| 6.9c
| 3.15a
|
Base pKa | 8.2b
| N/A | N/A |
B/P
| 0.60b
| 0.98c
| 0.56c
|
f
u,p
| 0.97b
| 0.90 (0.56 in severe RI)d
| 0.67e
|
Vss (L/kg) | 0.44f
| 0.49f
| 0.20f
|
Kp scalar | 1.0 | 1.5 (optimized based on serum concentration–time IV profile)g
| 0.7 (optimized based on serum concentration–time IV profile)g
|
CLiv (L/h) | 19.0h
| 12.8d
| 11.0e
|
CLr (L/h) | 19.0h
| 11.4d
| 11.0e
|
Hepatic elimination (liver S9 intrinsic clearance) | N/A | 0.41 (retrograde analysis; sensitivity analysis to match with S9) | N/A |
CLint,T (μL/min/106 cells) by Tup,b
| 12.0 (optimized based on plasma concentration–time profile)g
| 3.33 (optimized based on serum concentration–time profile)g
| 9.62 (optimized based on serum concentration–time profile)g
|
CLint,T (μL/min/106 cells) by Teff,a
| 1 (>0.001 based on urine data)g
| 20 (>0.2 based on urine data)g
| 10 (>0.1 based on urine data)g
|
f
a
| 0.80h
| N/A | N/A |
ka (1/h) | 0.15 (optimized)g
| N/A | N/A |
Lag time (h) | 0.60 (optimized)g
| N/A | N/A |
2.1.1 Negligible Passive Diffusion for Highly Hydrophilic Drugs
2.1.2 Use of ‘Global’ Basolateral Uptake and Apical Efflux Transporters
2.1.3 Same Transporter Activity for Each Functional Proximal Tubular Cell in Patients with Severe Renal Impairment
2.2 The Inhibitor Drug
2.3 Simulation of Renal Impairment
2.4 Simulation of Renal Drug–Drug Interaction
2.5 Simulation of Potential Nephrotoxicity
2.6 PBPK Simulation Design
2.7 Approximation of the Standard Deviation of the Observed Mean AUC Ratio
3 Results
3.1 Can PBPK Modelling Describe Kidney Drug Transport for Compounds that Undergo Active Renal Secretion?
3.2 Can Changes in Transporter Activity by Severe Renal Impairment be Derived Using PBPK Modelling?
3.3 Can the In Vivo Inhibition Potency of Probenecid on Renal Transporters Be Derived Using PBPK Modelling?
3.4 Can PBPK Modelling Be Used to Evaluate the Role of Renal Transporters on Drug Exposure in Kidney Cells, With or Without Co-administration of a Transporter Inhibitor?
Hypothetical simulation | Maximum amount of cidofovir in kidney cells (mg) |
---|---|
Without probenecid | 0.023 |
With probenecid (using Ki = 1 μM) | |
Net basolateral uptake onlya
| 0.001 |
Net apical efflux onlya
| 0.656 |
Uptake and efflux | 0.014 |