Muhammad Waqar Ashraf (1), Marko A. Peltoniemi (2), Pertti J. Neuvonen (3), Klaus T. Olkkola (4), Teijo I. Saari (1,2)
(1) Department of Anesthesiology and Intensive Care, University of Turku, Turku, Finland (2) Division of Perioperative Services, Intensive Care Medicine and Pain Management, Turku University Hospital, Turku, Finland. (3) Department of Clinical Pharmacology, University of Helsinki and HUSLAB, Helsinki University Central Hospital, Helsinki, Finland. (4) Department of Anesthesiology, Intensive Care and Pain Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Objectives:
Low dose S-Ketamine has been shown to be useful as an adjuvant in pain medicine (1). However oral S-ketamine has a low bioavailability and cytochrome P450 inhibitors can significantly change the exposure of S-ketamine, as demonstrated by in-vitro (2) and in-vivo studies (3). Ticlopidine is a mechanism-based inhibitor of CYP2B6, the enzyme responsible (≥60%) for the metabolism of S-ketamine to S-norketamine in humans (2). Simultaneous use can, therefore, increase S-ketamine drug concentrations and exposure and may cause problems in pain therapy. Accurate prediction of the drug-drug interaction (DDI) between these two drugs can help to devise precise dosing schemes of S-ketamine for pain relief. The objectives of this study are 1) Development of a physiologically based pharmacokinetic model to characterize the complex pharmacokinetics (PK) of orally administered S-ketamine, its primary metabolite S-norketamine, and ticlopidine 2) Development of DDI model to account for the dynamic mechanism based interaction between S-Ketamine and Ticlopidine.
Methods:
S-ketamine, S-norketamine, and ticlopidine data were gathered from five randomized, placebo-controlled, crossover studies (3-7). Nonlinear mixed effects modeling was performed with NONMEM software (version 7.3.0), and Perl-Speaks-NONMEM (PsN) and R based scripts were used for model coordination and evaluation. A semi-mechanistic structural model was developed with differential equations for gut-wall, portal vein, and liver, alongside a two (ticlopidine and S-norketamine) or three (S-ketamine) compartmental mammillary model. Well-stirred clearance models were used to describe first pass in the gut-wall and liver metabolism. After developing functional models for each drug, a DDI model based on Palacharla et al. 2017 (2) was developed using S-ketamine concentration-time data from individuals who had undergone a 6-day ticlopidine pre-dosing (7). The equations used for specifying DDI model used a competitive as well as a non-competitive component. It was assumed that ticlopidine pre-dosing completely abolishes CYP2B6 functionality, which permitted us to change the extent of CYP2B6 inhibition dynamically using DDI model equations (≥60%), in favor of an adequate model fit (2). Constants required for calculating variables of DDI model were obtained directly from the literature (ticlopidine KI = 0.57 µM (8), ticlopidine KINACT = 18/hr (8), S-ketamine fraction metabolized by CYP2B6 = 0.60 (2), physiological degradation rate of CYP2B6 = 0.022/hr (8), Ticlopidine IC50 CYP2B6 = 0.04 µM (10)). Additionally, an exponential between-subject-variability (BSV) and a proportional residual variability (RV) model was used in the final model for all three substances.
Results:
The semi-mechanistic PBPK model described adequately the pharmacokinetics of S-ketamine, S-norketamine and ticlopidine, as demonstrated by plausible parameter estimates and standard goodness-of-fit (GOF) plots. Our modeling results indicate that, 1) first order absorption rate constants were adequate to account for the absorption of S-ketamine and ticlopidine (ketKA = 1.71 /hr (%RSE = 17.5%), tclKA = 3.3 /hr (fixed), 2) gutwall clearance, plays a minor role in the metabolism of S-ketamine and ticlopidine (i.e. ketCLint,gw = 0.07 L/hr (42%), tclCLint,gw = 0 L/hr (fixed) in comparison to liver (ketCLint.h = 312 L/hr (14%), norkCLint,h = 74 L/hr (8%), tclCLint,h = 261 L/hr (154%)), 3) A three compartment model (V1 = 15.5 L (40%), CL1 = 297 L (11%), V2 = 101 L (9%), CL2 = 22.6 L (6%), V3 = 183 L (4%)) adequately described S-ketamine disposition kinetics, while a two compartmental model was implemented for S-norketamine (V1 = 85.3 L (5%), CL1 = 21 L (11.7%), V2 = 87.7 L (8.3%)) and Ticlopidine (V1 = 95.4 L (42%), CL1 = 30.1 L (30%), V2 = 5193 L (61%)). 4) Finally, the DDI model could adequately harness the complex mechanism based DDI between ticlopidine and S-ketamine. A 70% inhibition of S-ketamine clearance (signifying that the fraction of S-ketamine metabolized by CYP2B6 = 0.70) resulted in a good model fit. The final model was further evaluated with prediction-corrected visual predictive checks and normalized prediction distribution errors, both of which proved the appropriateness of the final model.
Conclusions:
The semi-mechanistic DDI model developed in the study adequately describes S-ketamine, S-norketamine and ticlopidine pharmacokinetic data and inter-individual variability in healthy human volunteers, and also accounts for the dynamic DDI between S-ketamine and ticlopidine.
References:
[1]. Radvansky, B.M., Shah, K., Parikh, A., Sifonios, A.N., Le, V. & Eloy, J.D. 2015, “Role of Ketamine in Acute Postoperative Pain Management: A Narrative Review”, BioMed Research International, vol. 2015.
[2]. Palacharla, R.C., Nirogi, R., Uthukam, V., Manoharan, A., Ponnamaneni, R.K. & Kalaikadhiban, I. 2017, “Quantitative in vitro phenotyping and prediction of drug interaction potential of CYP2B6 substrates as victims”, Xenobiotica, , pp. 1-13.
[3]. Hagelberg, N.M., Peltoniemi, M.A., Saari, T.I., Kurkinen, K.J., Laine, K., Neuvonen, P.J. & Olkkola, K.T. 2010, “Clarithromycin, a potent inhibitor of CYP3A, greatly increases exposure to oral S-ketamine”, European Journal of Pain, vol. 14, no. 6, pp. 625-629.
[4]. Peltoniemi, M.A., Saari, T.I., Hagelberg, N.M., Laine, K., Kurkinen, K.J., Neuvonen, P.J. & Olkkola, K.T. 2012, “Rifampicin has a Profound Effect on the Pharmacokinetics of Oral S-Ketamine and Less on Intravenous S-Ketamine”, Basic and Clinical Pharmacology and Toxicology, vol. 111, no. 5, pp. 325-332.
[5]. Peltoniemi, M.A., Saari, T.I., Hagelberg, N.M., Laine, K., Neuvonen, P.J. & Olkkola, K.T. 2012, “S-ketamine concentrations are greatly increased by grapefruit juice”, European journal of clinical pharmacology, vol. 68, no. 6, pp. 979-986.
[6]. Peltoniemi, M.A., Saari, T.I., Hagelberg, N.M., Laine, K., Neuvonen, P.J. & Olkkola, K.T. 2012, “St John’s wort greatly decreases the plasma concentrations of oral S-ketamine”, Fundamental and Clinical Pharmacology, vol. 26, no. 6, pp. 743-750.
[7]. Peltoniemi, M.A., Saari, T.I., Hagelberg, N.M., Reponen, P., Turpeinen, M., Laine, K., Neuvonen, P.J. & Olkkola, K.T. 2011, “Exposure to Oral S-ketamine is unaffected by itraconazole but greatly increased by ticlopidine”, Clinical pharmacology and therapeutics, vol. 90, no. 2, pp. 296-302.
[8]. Obach, R.S., Walsky, R.L. & Venkatakrishnan, K. 2007, “Mechanism-based inactivation of human cytochrome P450 enzymes and the prediction of drug-drug interactions”, Drug Metabolism and Disposition, vol. 35, no. 2, pp. 246-255.
Reference: PAGE 27 (2018) Abstr 8442 [www.page-meeting.org/?abstract=8442]
Poster: Drug/Disease Modelling - Absorption & PBPK