Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption

Abstract

Chronic kidney disease (CKD) has significant effects on renal clearance (CL r) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CL r for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CL r for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CL r in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CL r for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CL r generated unacceptable CL r predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CL r of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CL r through CKD progression without any empirical scaling factors and can be used for CL r predictions prior to assessment of drug disposition in renal impairment. Chronic kidney disease (CKD) is a progressive illness that is pathologically heterogeneous 1 and systemic. 2 It is mainly characterized by declining functional nephron mass and glomerular filtration rate (GFR). 3 As such, GFR is a critical index for CKD diagnosis, progression, and classification. 4 Clinically, patients with severe (stage 4, GFR ~ 15-29 mL/min) and end-stage (stage 5, GFR < 15 mL/min) CKD are at high risk for comorbidities, polypharmacy, and adverse drug reactions, 5-8 necessitating careful medication management due to dramatically altered pharmacokinetics (PK) because of CKD. As a result, clinical characterization of drug

Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system

“…

Figure 5 Simulation of morphine and M6G renal clearance and plasma concentration–time profiles in chronic kidney disease patients. The renal clearance (CL r in mL/min) of morphine (red solid curve) and M6G (red dashed curve) was simulated at multiple stages of chronic kidney disease (CKD) reflected by varying glomerular filtration rate (GFR in mL/min) using novel in silico adaptive CKD model 15 together with experimentally determined permeability and active secretion via VPT-MPS (panel a ). The plasma morphine concentration–time profile was simulated after intravenous dosing of morphine (shown in red solid curves) in end stage kidney disease (ESKD) patients in comparison to the observed data 13 (panel b ).

…”

“…Further, using the mechanistic kidney model-integrated full body parent-metabolite PBPK model 11 , plasma concentration–time profile of morphine and M6G can be predicted in healthy subjects. Finally, due to the increased risk of opioid overdose in people with CKD 12 – 14 , the developed model can be extrapolated 15 to simulate systemic disposition of morphine and M6G in varying stages of CKD. This is the first study to demonstrate the combination of a VPT-MPS and mechanistic PBPK modeling for the quantitative prediction of kidney handling and plasma disposition of drugs in healthy subjects and people with CKD.…”

3D cell culture models: Drug pharmacokinetics, safety assessment, and regulatory consideration

“…In addition, once the in vitro data are generated, PBPK modeling can be further used to predict how renal clearance of the drug of interest declines in renal impairment or with progression of chronic kidney disease. 92 Taken together the combination of the proximal tubule on a chip system with PBPK modeling (Figure 4b ) provides great promise to predict renal clearance, that offers hope to bring renal clearance predictions to par with predictions of metabolic, hepatic clearance.…”

Bridging the gap betweenin silicoandin vivo:modeling opioid disposition in a kidney proximal tubule microphysiological system

“…After verification of morphine and M6G models in healthy subjects, morphine and M6G disposition was also simulated in chronic kidney disease (CKD) patients using our published adaptive kidney model of progressive CKD (15). First, the kidney clearance of morphine and M6G was simulated across varying stages of CKD with GFR values ranging from 3 to 120 mL/min.…”

“…Further, using the mechanistic kidney model-integrated full body parentmetabolite PBPK model (11), plasma concentration-time profile of morphine and M6G can be predicted in healthy subjects. Finally, due to the increased risk of opioid overdose in people with CKD (12)(13)(14), the developed model can be extrapolated (15) to simulate systemic disposition of morphine and M6G in varying stages of CKD. This is the first study to demonstrate the combination of a VPT-MPS and mechanistic PBPK modeling for the quantitative prediction of kidney handling and plasma disposition of drugs in healthy subjects and people with CKD.…”