Taeheon Kim1#, Woojin Jung2#, Sangeun Cho1, Gwanyoung Kim1*, Jung-woo Chae2,3*, Hwi-yeol Yun2,3*
1 Life science institute, Daewoong Pharmaceutical, Yongin, South Korea 2 College of Pharmacy, Chungnam National University, Daejeon, South Korea 3 Bio-AI Convergence Research Center, Chungnam National University, South Korea #Those of authors contributed equally as first author *Those of authors contributed equally as correspondence.
Background: Niclosamide, a potent anthelmintic agent, has been found to be effective against COVID-19 in recent studies [1-3], and a few formulation research has been performed to overcome the limitations of the drug in systemic exposure [4]. In this study, niclosamide was formulated as a long-release intramuscular injection aimed for the virus in lungs. To conduct pharmacokinetic study, a hamster model was chosen, which has been utilized in several COVID-19 infection studies. The study investigated the optimal doses and effective durations of the drug to provide a valid antiviral effect in a Syrian hamster animal model, considering the major concern of COVID-19 in lung tissues [5].
Methods: Pharmacokinetic (PK) analyses were performed using NONMEM and PsN. Doses of 55, 96, 128, and 240 mg/kg were tested, and each group consisted of five hamsters. Two different types of PK models—linear models with partition coefficients and power-law distributed models [6-9], were built to describe the drug concentration relationship between the plasma and lungs of hamsters. Drug absorption is separated into two phases of fast and slow which is modeled with Weibull-type absorption rate [10], and for distribution space, the lung compartment with physiological parameters (lung volume and blood flow) was added onto two compartment model. For the models, numerical and visual diagnostics such as basic goodness of fits, visual predictive checks, and bootstrap results [11] were evaluated. After final model selection, the optimal dose was determined based on drug concentrations above IC50 which was decided in in-vitro study for COVID-19 inhibition. The simulations were performed at doses ranging from 240 to 1500 mg/kg, in increments of 5 mg/kg. Based on the simulated lung tissue drug concentration profiles, the success ratio was calculated for various durations above the IC50 for each dose. The durations were tested from 24 to 120 hours in 24-hour increments. The optimal dose was determined as the dosage at which over 90% of the simulated cases satisfied the lung tissue concentration above IC50 in the maximum possible duration.
Results: The power-law-based model not only exhibited a better numerical performance by 56.33 points in the OFV score (-200.84 and -256.33 for linear and power type model, respectively) but also the better agreement was shown in basic goodness of fits and visual predictive check. Both model’s robustness was confirmed in bootstrap model as final parameters well placed between interval estimates. As a final model, power-law-based model was chosen for dose optimization. The optimization based on the prediction of lung exposure was performed iteratively at various drug doses; the minimal required dose was expected to be approximately 1115 mg/kg.
Conclusions: The development of a power-law-based PK model was successful for intramuscular niclosamide and adequately described the nonlinearities expressed in this study. This method is expected to be applicable for investigating the drug disposition of certain formulations in the lungs. The optimal predicted effective dose was estimated to be approximately 1115 mg/kg. However, considering the maximum dose allowed for the animal species, further experiments required to select the reasonable human dose.
References:
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Reference: PAGE 32 (2024) Abstr 11206 [www.page-meeting.org/?abstract=11206]
Poster: Drug/Disease Modelling - Infection