Hyunju Yang 1, Soyoung Lee 1, Jung-woo Chae 1,2, Hwi-yeol Yun 1,2, Taeheon Kim 3, Woojin Jung 4
1 College of Pharmacy, Chungnam National University (, Republic of Korea), 2 Department of Bio-AI convergence, Chungnam National University (, Republic of Korea), 3 Life Science Institute, Daewoong Pharmaceutical (, Republic of Korea), 4 Graduate School of Clinical Pharmacy, CHA University (, Republic of Korea)
Introduction: Niclosamide has demonstrated potent broad-spectrum antiviral activity against respiratory viruses, including SARS-CoV-2 and influenza [1, 2], but exhibits poor oral bioavailability due to low aqueous solubility and extensive hepatic metabolism via CYP1A2 and UGT1A1 [3]. Intranasal delivery may enable local protection directly at the portal of infection [4]; however, the highly vascularized nasal mucosa may also lead to unintended systemic absorption [5]. To address this, a physiologically based pharmacokinetic (PBPK) model incorporating an explicit nasal mucosa compartment was developed to predict drug exposure in the nasal mucosa and epithelial lining fluid (ELF), supporting dose optimization for local antiviral efficacy while minimizing systemic exposure [6, 7, 8].
Objectives:
– To develop a physiologically based pharmacokinetic model for nasal administration of niclosamide and evaluate its performance against in vivo pharmacokinetic data.
– To apply the model to predict human target-site exposure and assess whether local drug application leads to significant systemic exposure.
Methods: The development of the nasal physiologically based pharmacokinetic model for niclosamide was performed using PK-Sim® and MoBi®. In vivo pharmacokinetic data were obtained in male Sprague–Dawley rats (n = 5 per group) following intravenous (1 mg/kg), oral (5 mg/kg), and intranasal solution administration (0.5 and 1 mg/kg). Physicochemical properties obtained from literature and in vivo data. Regarding the liver metabolic parameters, rat values were estimated by fitting the model to in vivo data, whereas human values were retrieved directly from the literature [9, 10]. Model performance was comprehensively evaluated using geometric mean fold error (GMFE) for AUC and Cmax, goodness-of-fit plots, and visual predictive checks (VPCs).
Results: The nasal mucosa compartment was structurally based on the four-compartment architecture used in PK-Sim®, comprising epithelial lining fluid (ELF), intracellular, interstitial, and vascular sub-compartments. In the absence of nasal mucosa-specific physiological parameters, tissue composition ratios were derived from intestinal mucosa as a surrogate reference. Within this structure, the ELF compartment functions as a depot, from which drug transfer can occur via two pathways: transcellular transport into the intracellular space and paracellular transport directly into plasma. However, given the lack of direct experimental evidence supporting paracellular absorption for niclosamide in the nasal mucosa, only the transcellular pathway was implemented in the final model. The developed model successfully described the PK profiles, with geometric mean fold errors (GMFEs) of 2.16 for AUC and 2.49 for Cmax. The model predictions aligned with the observations closely in the goodness-of-fit (GOF) plots. In the population simulation, visual predictive checks (VPC) demonstrated that the model prediction intervals adequately encompassed the observed concentrations over time. Human simulations predicted nasal mucosa and ELF drug exposure across QD, BID, and TID dosing regimens. The predicted Cpeak was consistent across all regimens at 0.325 nmol/L in the nasal mucosa and 0.080 nmol/L in the ELF. In contrast, predicted systemic exposure was negligible, remaining in the picomolar range. These predictions enabled quantitative evaluation of the target-site versus systemic exposure trade-off following intranasal niclosamide administration.
Conclusions: The physiologically based pharmacokinetic model for nasal niclosamide was successfully established and validated against the observed PK data. This model allows for the prediction of local tissue exposure, facilitating the optimization of local therapeutic efficacy. Consequently, it serves as a valuable tool for guiding dose selection and designing clinical trials for intranasal niclosamide.
References:
[1] Xu J et al. (2020) Broad Spectrum Antiviral Agent Niclosamide and Its Therapeutic Potential. ACS Infect Dis.
[2] Weiss A et al. (2021) Niclosamide shows strong antiviral activity in a human airway model of SARS-CoV-2 infection and a conserved potency against the Alpha (B.1.1.7), Beta (B.1.351) and Delta variant (B.1.617.2). PLoS One.
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[4] Cairns DM et al. (2022) Efficacy of Niclosamide vs Placebo in SARS-CoV-2 Respiratory Viral Clearance, Viral Shedding, an Duration of Symptoms Among Patients With Mild to Moderate COVID-19: A Phase 2 Randomized Clinical Trial. JAMA Netw Open.
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[6] Wu X et al. (2022) Semi-PBPK Modeling and Simulation to Evaluate the Local and Systemic Pharmacokinetics of OC-01(Varenicline) Nasal Spray. Front Pharmacol.
[7] Kaulbach HC et al. (1993) Estimation of nasal epithelial lining fluid using urea as a marker. J Allergy Clin Immunol.
[8] Yang Z et al. (2019) Repurposing Niclosamide as a Novel Anti-SARS-CoV-2 Drug by Restricting Entry Protein CD147. Biomedicines.
[9] 1. Report of the task group on reference man. Annals of the ICRP. 1979;3(1-4):iii.
[10] Davies B et al. (1993) Physiological parameters in laboratory animals and humans. Pharm Res.
Reference: PAGE 34 (2026) Abstr 12149 [www.page-meeting.org/?abstract=12149]
Poster: Drug/Disease Modelling - Absorption & PBPK