Donepezil brain and blood pharmacokinetic modeling after nasal film and oral solution administration in mice
Christos Kaikousidis†, Paraskevi Papakyriakopoulou†, Aristides Dokoumetzidis, Georgia Valsami
Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, 15771, Athens, Greece_______† These authors contributed equally to this work
Introduction: The pharmacokinetic (PK) model development for nasal delivery is a challenging process without adequate literature reports. Intravenous PK models have been proposed based on the fast absorption from olfactory nerve [1]. However, to describe adequately the intranasal (IN) absorption, all the transport events occurring on nasal cavity, such as olfactory, trigeminal, and systemic absorption, should be considered [2]. In this work, compartmental pharmacokinetic model was applied to describe donepezil blood and brain PK data, after IN and Per Os (PO) administration of nasal film and oral solution, respectively. The developed model aims to describe the drug flow on both occasions and quantify the direct nose-to-brain (NTB) and systemic distribution.
Objectives:
- To develop a compartmental PK model describing PO blood and brain data.
- To model IN dataset (blood and brain) using parameters estimated in PO modeling and explain the double concentration peak observed in IN brain PK profile.
- To carry out a combined fitting of the model to the data describing both PO and IN administrations. .
Methods: The PK modeling was performed in four stages. Initially the PO data for the blood were studied individually to get a first estimate for the parameters later used in the combined PO model, which modeled both compartments (CMTs) simultaneously. In second modeling stage the blood volume of distribution was used as a fixed parameter in the total model to avoid identifiability issues. For this stage, classic multi-compartmental models were tested. In the third stage, the IN dataset was modeled using parameters estimated in the PO modeling stages (transfer from blood to brain, elimination from brain and blood), as prior knowledge. Various combinations were tested, including transit CMTs [3], to explain the double peak phenomenon probably attributed to a second stage of slower, but direct to brain, absorption. In the last stage, a combined fitting was carried out describing both PO and IN administrations, using the models from the previous stages and the parameters as initial estimates to further validate our modeling strategy. The above methods were implemented using both mean and raw data. Additive and proportional residual error models were tested for each case. The entire analysis was was carried out in the software NONMEM [4]. Model evaluation was achieved using the objective function value at each modeling stage and the visual inspection of fit. Predicted versus Observed as well as Residuals versus Time were studied to evaluate the goodness of fit and the bias of the predictor.
Results: The fitting of blood and brain PO data revealed a three-CMT model with first order absorption, linear elimination from the central CMT and additional elimination from brain. The standard errors are relatively low especially in the mean-data estimation, which are below 22% indicating a precise estimation. In the case of IN administration, the best fit was observed by a zero-order double absorption in the blood and brain CMT [5] with Tlag (first concentration peak, t=15 min) and an extra transit CMT into the brain. This transit CMT manage to adequately describes the late absorption that leads to the second concentration peak observed (t=60 min). It is hypothesized that trigeminal nerve, whose length requires longer time to be traveled, is responsible for this phenomenon [6]. Furthermore, in both models -describing the mean and raw data- the estimated parameters have similar values which means the process is robust. In the final modeling stage, a total fitting was performed involving the two previous models. The rates of blood to brain transfer and elimination were considered identical in both modes of administration, been estimated using the whole raw data alongside with the rest of the parameters. The estimated parameter values were close to the results of previous models with the exception of the transit to brain absorption rate, which was significantly different in the total fit case.
Conclusions: The compartmental modeling of blood and brain PK profiles manage to adequately interpret the absorption processes governing the NTB delivery. The developed IN model describing a double absorption phenomenon, in bloodstream and brain respectively, as well as proposes two different rates (fast and slow) for NTB delivery, may be valuable for drugs with good blood-brain-barrier penetration, but extensive hepatic metabolism. The understanding of donepezil pharmacokinetics following IN administration of nasal film, will favor the incorporation of this dosage form in nasal delivery strategy.
References:
[1] Rompicherla SKL, et al. Pharmacokinetic and pharmacodynamic evaluation of nasal liposome and nanoparticle based rivastigmine formulations in acute and chronic models of Alzheimer's disease. Naunyn Schmiedebergs Arch Pharmacol. 2021 Aug;394(8):1737-1755.
[2] Arora P, et al. Permeability issues in nasal drug delivery. Drug Discov Today. 2002 Sep 15;7(18):967-75.
[3] Kadakia E, et al. Mathematical Modeling and Simulation to Investigate the CNS Transport Characteristics of Nanoemulsion-Based Drug Delivery Following Intranasal Administration. Pharm Res. 2019 Mar 28;36(5):75.
[4] Beal SL et al. 1989-2011. NONMEM Users Guides. Icon Development Solutions, Ellicott City, Maryland, USA.
[5] Stevens J, et al. Systemic and direct nose-to-brain transport pharmacokinetic model for remoxipride after intravenous and intranasal administration. Drug Metab Dispos. 2011 Dec;39(12):2275-82.
[6] Lochhead J, et al. Intranasal delivery of biologics to the central nervous system. Adv Drug Deliv Rev. 2012;64(7):614-28.