A Comprehensive Physiologically Based Pharmacokinetic Model for Predicting Vildagliptin Pharmacokinetics: Insights into Dosing in Renal Impairment

Abstract

Physiologically based pharmacokinetic (PBPK) modeling is of great importance in the field of medicine. This study aims to construct a PBPK model, which can provide reliable drug pharmacokinetic (PK) predictions in both healthy and chronic kidney disease (CKD) subjects. To do so, firstly a review of the literature was thoroughly conducted and the PK information of vildagliptin was collected. PBPK modeling software, PK-Sim^®, was then used to build and assess the IV, oral, and drug-specific models. Next, the average fold error, visual predictive checks, and predicted/observed ratios were used for the assessment of the robustness of the model for all the essential PK parameters. This evaluation demonstrated that all PK parameters were within an acceptable limit of error, i.e., 2 fold. Also to display the influence of CKD on the total and unbound AUC (the area under the plasma concentration-time curve) and to make modifications in dose, the analysis results of the model on this aspect were further examined. This PBPK model has successfully depicted the variations of PK of vildagliptin in healthy subjects and patients with CKD, which can be useful for medical practitioners in dosage optimization in renal disease patients.

Keywords: chronic kidney disease; physiologically based pharmacokinetic model; type 2 diabetes; vildagliptin.

Figure 1

Algorithm outlining the strategic approach to achieve results. IV: intravenous, PBPK: Physiologically based pharmacokinetic model, CKD: Chronic Kidney disease, R_pre/obs ratio: Predicted value/observed value.

Figure 2

Comparison of observed and predicted plasma concentration vs. time profiles after the intravenous administration of 25 mg dose of vildagliptin [30], (…): Values of reported data, (—): Values of simulated data, (- - -): highest and lowest values, (. . .): 5th and 95th percentile.

Figure 3

Comparison of observed and predicted plasma concentration vs time profiles of vildagliptin at oral doses (in mg) of (a) 25 [35], (b) 50 [30], (c) 50 [36], (d) 100 [37], and (e) 200 [38], respectively. (…): Values of reported data, (—): Values of simulated data, (- - -): highest and lowest values, (. . .): 5th and 95th percentile.

Figure 4

Comparison of mean R_pre/obs ratios for (a) peak plasma concentration (C_max) (b) area under the curve from time 0 to infinity (AUC_0–∞), and (c) clearance (CL) between healthy and diseased (CKD) patients. The red line shows the mean along with the 95% confidence interval (C.I).

Figure 5

Observed and predicted concentration vs time profiles after administrating vildagliptin orally in CKD patients at doses (in mg) of (a) 50 mg [36] (Moderate RI) and (b) 50 mg [36] (Severe RI), RI: renal impairment. (…): Values of reported data, (—): Values of simulated data, (- - -): highest and lowest values, (. . .): 5th and 95th percentile.

Figure 6

Box plots showing, the simulated AUC_0–∞ and AUC_{0–∞ (unbound)} with 5–95th percentiles after orally giving 50 mg dose of vildagliptin in both the healthy and renal failure populations (a,b). Dosage reduction for moderate and severe renal failure is suggested in (c,d) for comparison with healthy exposure. AUC_{0–∞ (unbound)}: area under the curve from time 0 to infinity unbound, AUC_0–∞: area under the curve from time 0 to infinity bound, CKD: Chronic kidney disease.

Figure 7

PBPK model development strategy. ADME: absorption, distribution, metabolism, and elimination, pKa: dissociation rate constant, f_u: unbound fraction, B:P: blood to plasma ratio, V_max: maximum velocity of reaction, K_m: concentration of substrate at half of the maximum velocity, DPP-4: dipeptidyl peptidase-4, PK: Pharmacokinetics.