Pharmacometric Modeling of the Impact of Azelastine Nasal Spray on SARS-CoV-2 Viral Load and Related Symptoms in COVID-19 Patients.
Christiane DingsPeter MeiserFrank HolzerMichael FlegelDominik SelzerEszter NagyRalph MösgesJens Peter KlußmannThorsten LehrPublished in: Pharmaceutics (2022)
The histamine-1 receptor antagonist azelastine was recently found to impact SARS-CoV-2 viral kinetics in a Phase 2 clinical trial (CARVIN). Thus, we investigated the relationship between intranasal azelastine administrations and viral load, as well as symptom severity in COVID-19 patients and analyzed the impact of covariates using non-linear mixed-effects modeling. For this, we developed a pharmacokinetic (PK) model for the oral and intranasal administration of azelastine. A one-compartment model with parallel absorption after intranasal administration described the PK best, covering both the intranasal and the gastro-intestinal absorption pathways. For virus kinetic and symptoms modeling, viral load and symptom records were gathered from the CARVIN study that included data of 82 COVID-19 patients receiving placebo or intranasal azelastine. The effect of azelastine on viral load was described by a dose-effect model targeting the virus elimination rate. An extension of the model revealed a relationship between COVID-19 symptoms severity and the number of infected cells. The analysis revealed that the intranasal administration of azelastine led to a faster decline in viral load and symptoms severity compared to placebo. Moreover, older patients showed a slower decline in viral load compared to younger patients and male patients experienced higher peak viral loads than females.
Keyphrases
- sars cov
- respiratory syndrome coronavirus
- clinical trial
- end stage renal disease
- newly diagnosed
- ejection fraction
- induced apoptosis
- single cell
- double blind
- randomized controlled trial
- oxidative stress
- patient reported outcomes
- study protocol
- high resolution
- endoplasmic reticulum stress
- signaling pathway
- single molecule
- deep learning
- placebo controlled