Lukas Kovar (1), Hannah Britz (1), Yvonne Lydia Kohl (2), Robert Bals (3) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany, (2) Fraunhofer Institute for Biomedical Engineering, Sulzbach, Germany, (3) Department of Internal Medicine V - Pulmonology, Allergology, Respiratory Intensive Care Medicine, University of the Saarland, Homburg Saar, Germany
Introduction: Since nicotine is the pharmacologically active substance in tobacco responsible for addiction it also plays a distinctive role in causing smoking-induced diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer [1]. Hence, a better understanding of the pharmacokinetics of nicotine could help to derive new strategies of smoking cessation and to get a better understanding of the involvement of nicotine in pathophysiological processes.
Objectives:
- Development and evaluation of a whole-body physiologically-based pharmacokinetic (PBPK) model of nicotine including its main metabolite cotinine after intravenous (i.v.) administration in healthy volunteers
- Model adjustment for a smoking population to predict the pharmacokinetics in smokers
Methods: A parent-metabolite PBPK model of nicotine and cotinine was built in PK-Sim® (Version 7.4.0) as part of the Open Systems Pharmacology Suite [2]. Firstly, a cotinine model was established with data from i.v. administration in healthy volunteers. Subsequently, the model was complemented by the parent compound nicotine using i.v. plasma concentration-time profiles. Physicochemical parameters as well as plasma profiles of nicotine after i.v. single dose (SD) and multiple dose administration (range 15 µg/kg to 288 µg/kg) and mean profiles of cotinine after i.v. SD administration (range 5 mg to 20 mg) were obtained from published literature. Moreover, nicotine and cotinine fractions excreted to urine were available for several dosing regimens. For PBPK model building, 14 plasma profiles out of 5 clinical studies (46 study participants, all nonsmokers) were split into an internal (6 plasma profiles, 4 fractions excreted to urine) and an external (8 plasma profiles) dataset. When necessary, parameters were estimated based on the internal dataset. Model evaluation was performed with the external dataset by comparing observed and predicted plasma profiles. Conclusively, the model was adjusted for smokers and used to predict 10 different plasma profiles of nicotine and cotinine in smokers.
Results: About 75% of administered nicotine is metabolized to cotinine in the liver, mainly via CYP2A6 metabolism, representing the major route of elimination of nicotine [3]. Therefore, the model includes this important CYP2A6 metabolism of nicotine. The corresponding Michaelis-Menten constant KM was fitted within the range of literature values to 34 µM, the catalytic rate constant kcat was fitted to 14.69 min-1. Additionally, nicotine is cleared by an unspecific hepatic clearance (0.51 min-1, first order kinetics) and glomerular filtration (GFR fraction of 1.00). The elimination routes are consistent with published literature [3]. For cotinine itself, the model contains an unspecific hepatic metabolism (0.03 min-1, first order kinetics) and a glomerular filtration (GFR fraction of 0.07). The final model was capable to precisely describe and predict all profiles of the internal and external dataset with a mean area under the plasma concentration-time curve (AUC) ratio (AUC predicted / AUC observed) of 1.0, 0.61 and 1.10 for all nicotine, cotinine metabolite and administered cotinine profiles, respectively. Moreover, the individual AUC ratios of all plasma profiles were within twofold range. Fraction excreted to urine of nicotine and cotinine were accurately described (mean ratio (predicted vs. observed): 1.30 and 1.00 for nicotine and cotinine). According to published data, nicotine clearance in smokers appears to be about 15% lower compared to nonsmokers [4] resulting in higher AUC values. After adjusting the model to physiological characteristics of smokers like an increased haematocrit and lower nicotine metabolic capacity, the PBPK model was also able to predict this increase in AUC (AUC ratio: 1.02 and 0.73 for nicotine and cotinine).
Conclusions: The successfully developed whole-body PBPK model of nicotine and its main metabolite cotinine including CYP2A6 metabolism was able to predict nicotine and cotinine plasma profiles of different dosing regimens for both smokers and nonsmokers in an excellent way. The i.v. administration has been implemented successfully in the model which will be augmented by inhalative and transdermal administration processes. This may help finding more successful strategies of smoking cessation in order to decrease tobacco addiction and improving the understanding of nicotine involvement in pathophysiological processes.
References:
[1] Benowitz NL: Nicotine Addiction. N. Engl. J. Med. 362: 2295-2303, 2010.
[2] Eissing T, Kuepfer L, Becker C, Block M, Coboeken K, Gaub T, Goerlitz L, Jaeger J, Loosen R, Ludewig B, Meyer M, Niederalt C, Sevestre M, Siegmund H, Solodenko J, Thelen K, Telle U, Weiss W, Wendl T, Willmann S, Lippert J: A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks. Front Physiol (2011) 2: 4.
[3] Hukkanen J, Jacob P, Benowitz NL: Metabolism and disposition kinetics of nicotine. Pharmacol Rev 2005; 57: 79–115.
[4] Benowitz NL, Jacob P: Nicotine and cotinine elimination pharmacokinetics in smokers and nonsmokers. Clin Pharmacol Ther 1993; 53: 316–323.
Reference: PAGE 28 (2019) Abstr 8929 [www.page-meeting.org/?abstract=8929]
Poster: Drug/Disease Modelling - Absorption & PBPK