Denise Tuerk, Nina Hanke, Thorsten Lehr
Clinical Pharmacy, Saarland University, Saarbruecken, Germany
Introduction: The measurement of biomarkers (e.g. in blood or urine) and their evaluation can provide valuable information about physiological processes and pathophysiological changes. The biomarker creatinine is an endogenous breakdown product of creatine phosphate from muscle and protein metabolism. As it is solely excreted unchanged by the kidneys, creatinine is a commonly measured biomarker to assess renal function [1], although it is not exclusively removed by glomerular filtration. Tubular secretion accounts for 10-40% of the renal excretion of creatinine [2] and different transporters of the solute carrier family have been shown to facilitate creatinine transport in vitro [3,4]. Increased serum creatinine levels are an indicator for reduced renal function. Due to the frequent prescription of the antibiotic trimethoprim (global consumption of about 5*109 standard units in 2010 [5]), investigation of its drug-drug interaction (DDI) potential is clinically relevant. Trimethoprim is a potent inhibitor of organic cation transporters (OCT) and of multidrug and extrusion proteins (MATE) [3,6]. During trimethoprim treatment, an increase in serum creatinine is observed which is not related to impaired renal function. This pharmacokinetic interaction is caused by the inhibition of tubular secretion of creatinine. Physiologically based pharmacokinetic (PBPK) modeling is a valuable tool to investigate the underlying mechanisms of the observed interaction.
Objectives:
- To build and evaluate a whole-body PBPK model of creatinine
- To predict the DDI of creatinine with the perpetrator drug trimethoprim and to investigate the involved mechanisms
Methods: A whole-body PBPK model of creatinine was developed using the modeling software PK-Sim® and MoBi® (Version 8.0) [7]. Drug-dependent parameters were obtained from literature, as well as plasma concentration-time profiles after intravenous administration of creatinine (2 studies) and baseline creatinine measurements (12 studies) in healthy subjects. Model evaluation was performed by comparison of predicted to observed plasma concentrations. As a quantitative measure of the model performance, the mean relative deviation (MRD) of the predicted values was calculated for all observed plasma concentrations. To predict the trimethoprim-creatinine DDI, a previously developed PBPK model of trimethoprim [8] was coupled to the creatinine model, using literature values to describe the competitive inhibition of OCT2 and MATE1 by trimethoprim. The DDI model performance was evaluated by comparison of predicted to observed creatinine plasma concentration ratios (creatinine plasma concentration during DDI / baseline creatinine plasma concentration) and calculation of geometric mean fold errors (GMFEs) of the predicted ratios for 8 interaction studies from literature.
Results: The final creatinine model applies an endogenous synthesis in muscle cells and renal clearance by passive glomerular filtration and tubular secretion via OCT2 and MATE1. The plasma concentrations of creatinine are very well described with all simulated concentrations within the 2-fold acceptance limits compared to observed values. The MRD over all creatinine studies is 1.07 (range 1.00-1.41). Application of the developed creatinine model for DDI prediction results in plasma concentration-time profiles before and during trimethoprim co-administration that accurately match the observed profiles. Predicted compared to observed DDI concentration ratios are all within 1.25-fold of the observed values showing a low overall GMFE of 1.14 (range 1.04-1.22), indicating a good predictive performance of the PBPK models.
Conclusions: A whole-body PBPK model of creatinine has been successfully established. The model includes synthesis in muscle cells as well as the transport processes responsible for tubular secretion and it precisely describes the plasma concentrations of endogenous creatinine. The effect of the OCT and MATE perpetrator drug trimethoprim on creatinine pharmacokinetics was also successfully predicted. Potential future applications of the model include the prediction of DDIs with other OCT and MATE perpetrator drugs and the description of creatinine concentrations in chronic kidney disease.
References:
[1] Seegmiller JC, Eckfeldt JH, Lieske JC. Challenges in measuring glomerular filtration rate: a clinical laboratory perspective. Adv Chronic Kidney Dis. (2018) 25(1):84-92.
[2] Levey AS, Perrone RD, Madias NE. Serum creatinine and renal function. Annu Rev Med. (1988) 39(1):465-90.
[3] Lepist EI, Zhang X, Hao J, Huang J, Kosaka A, Birkus G, Murray BP, Bannister R, Cihlar T, Huang Y, Ray AS. Contribution of the organic anion transporter OAT2 to the renal active tubular secretion of creatinine and mechanism for serum creatinine elevations caused by cobicistat. Kidney Int. (2014) 86(2):350-7.
[4] Mathialagan S, Rodrigues AD, Feng B. Evaluation of renal transporter inhibition using creatinine as a substrate in vitro to assess the clinical risk of elevated serum creatinine. J Pharm Sci. (2017) 106(9):2535-41.
[5] Van Boeckel TP, Gandra S, Ashok A, Caudron Q, Grenfell BT, Levin SA, Laxminarayan R. Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet Infect Dis (2014) 14(8):742-50.
[6] U.S. Food and Drug Administration. Drug development and drug interactions: table of substrates, inhibitors and inducers. https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/DrugInteractionsLabeling/ucm093664.htm. (2017).
[7] https://www.open-systems-pharmacology.org
[8] Tuerk D, Hanke N, Lehr T. Physiologically-based pharmacokinetic modeling of the CYP2C8 perpetrator trimethoprim. PAGE 28 (2019) Abstr 8904.
Reference: PAGE () Abstr 9315 [www.page-meeting.org/?abstract=9315]
Poster: Drug/Disease Modelling - Absorption & PBPK