Denise Tuerk, Nina Hanke, Thorsten Lehr
Clinical Pharmacy, Saarland University, Saarbruecken, Germany
Introduction: Trimethoprim is applied alone or in combination with sulfamethoxazole to treat bacterial infections by inhibition of bacterial folic acid metabolism. Trimethoprim is a weak cytochrome P450 (CYP) 2C8 inhibitor and an inhibitor of multidrug and toxin extrusion (MATE) 1 and MATE2-K [1]. As trimethoprim is one of the most commonly prescribed antibiotics [2], the investigation of its drug-drug interaction (DDI) potential is clinically very relevant. If trimethoprim is co-administered with the CYP2C8 substrates repaglinide or pioglitazone, it increases the area under the curve (AUC) of those drugs by 61% and 42%, respectively [3,4]. In addition, during administration of trimethoprim an increase of serum creatinine was reported, probably due to inhibition of tubular secretion of creatinine [5]. Physiologically-based pharmacokinetic (PBPK) modeling is a valuable tool to quantitatively describe and predict the pharmacokinetics of trimethoprim and the effect of trimethoprim co-administration on the pharmacokinetics of victim drugs or endogenous substances.
Objectives:
- To build and evaluate a whole-body PBPK model of trimethoprim.
Methods: A whole-body PBPK model of trimethoprim was developed using the modeling software PK-Sim® and MoBi® (Version 7.4.0) [6]. Drug-dependent parameters (e.g. lipophilicity, solubility, acid dissociation constant) and plasma concentration-time profiles of 17 clinical studies of trimethoprim (intravenous and oral administration, dosing range 100 – 400 mg, single- and multiple dosing, individual and mean profiles) as well as fraction excreted to urine measurements were obtained from literature. The gathered plasma concentration-time profiles were divided into an internal (6 studies) and an external data set (11 studies), which were used for model building and model evaluation, respectively. Parameters that could not be informed from literature were optimized using the studies of the internal dataset. Model evaluation was performed by comparison of predicted to observed plasma concentration-time profiles, AUC values and maximum plasma concentrations (Cmax) of the external data set. As a quantitative measure of the model performance, the mean relative deviation (MRD) between predicted and observed values was calculated for all observed plasma concentrations. An MRD value below 2 signifies an adequate prediction. Furthermore, the quality of the model was characterized by AUC ratios (AUC predicted / AUC observed), Cmax ratios (Cmax predicted / Cmax observed) and the calculation of geometric mean fold absolute deviation (GMFE) values.
Results: The final trimethoprim model applies transport via P-glycoprotein, an unspecific hepatic clearance process, tubular secretion via MATE1 and MATE2-K and passive glomerular filtration. The studies of the internal and external data sets are well described and predicted, with 98% of all simulated plasma concentrations within the two-fold acceptance limits compared to observed values. The MRD over all trimethoprim studies is 1.65. AUC ratios and Cmax ratios show low GMFEs of 1.15 (range 1.04-1.39, n=17) and of 1.10 (range 1.01-1.34, n=17), respectively.
Conclusion: A whole-body PBPK model of trimethoprim has been successfully established. The model precisely describes and predicts plasma concentration-time profiles and fractions excreted to urine of trimethoprim over a wide dosing range. As a future application, the model can be coupled with CYP2C8 and MATE victim drugs, to investigate the DDI potential of new drugs or to predict the trimethoprim pharmacokinetics in vulnerable populations.
References:
[1] 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).
[2] 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.
[3] Niemi M, Kajosaari LI, Neuvonen M, Backman JT, Neuvonen PJ. The CYP2C8 inhibitor trimethoprim increases the plasma concentrations of repaglinide in healthy subjects. Br J Clin Pharmacol (2004) 57(4): 441-7.
[4] Tornio A, Niemi M, Neuvonen PJ, Backman JT. Thrimethoprim and the CYP2C8*3 allele have opposite effects on the pharmacokinetics of pioglitazone. Drug Metab Dispos (2008) 36(1): 73-80.
[5] Andreev E, Koopman M, Arisz L. A rise in plasma creatinine that is not a sign of renal failure: which drugs can be responsible? J Intern Med (1999) 246(3): 247-52.
[6] https://www.open-systems-pharmacology.org
Reference: PAGE 28 (2019) Abstr 8904 [www.page-meeting.org/?abstract=8904]
Poster: Drug/Disease Modelling - Absorption & PBPK