Predicting lung exposure of intramuscular niclosamide as an antiviral agent: Power‐law based pharmacokinetic modeling

Abstract

Niclosamide, a potent anthelmintic agent, has emerged as a candidate against COVID‐19 in recent studies. Its formulation has been investigated extensively to address challenges related to systemic exposure. In this study, niclosamide was formulated as a long‐acting intramuscular injection to achieve systemic exposure in the lungs for combating the virus. To establish the dose–exposure relationship, a hamster model was selected, given its utility in previous COVID‐19 infection studies. Pharmacokinetic (PK) analysis was performed using NONMEM and PsN. Hamsters were administered doses of 55, 96, 128, and 240 mg/kg with each group comprising five animals. Two types of PK models were developed, linear models incorporating partition coefficients and power‐law distributed models, to characterize the relationship between drug concentrations in the plasma and lungs of the hamsters. Numerical and visual diagnostics, including basic goodness‐of‐fit and visual predictive checks, were employed to assess the models. The power‐law‐based PK model not only demonstrated superior numerical performance compared with the linear model but also exhibited better agreement in visual diagnostic evaluations. This phenomenon was attributed to the nonlinear relationship between drug concentrations in the plasma and lungs, reflecting kinetic heterogeneity. Dose optimization, based on predicting lung exposure, was conducted iteratively across different drug doses, with the minimum effective dose estimated to be ~1115 mg/kg. The development of a power‐law‐based PK model proved successful and effectively captured the nonlinearities observed in this study. This method is expected to be applicable for investigating the drug disposition of specific formulations in the lungs.