Mathematical Modeling of Andrographolide Therapy Effects and Immune Response in In Vivo Dynamics of SARS-CoV-2 Infection
Journal article
Authors/Editors
Strategic Research Themes
Publication Details
Author list: Panittavee Yarnvitayalert and Teerapol Saleewong
Publisher: MDPI
Publication year: 2025
Volume number: 17
Issue number: 891
Start page: 1
End page: 16
Number of pages: 16
ISSN: 19994915
eISSN: 1999-4915
Abstract
This study explores the viral dynamics of SARS-CoV-2 infection within host cells by incorporating the pharmacological effects of andrographolide—a bioactive compound extracted from Andrographis paniculata, renowned for its antiviral, anti-inflammatory, and
immunomodulatory properties. Through the application of mathematical modeling, the
interactions among the virus, host cells, and immune responses are simulated to provide a
comprehensive analysis of viral behavior over time. Two distinct models were employed to
assess the impact of varying andrographolide dosages on viral load, target cell populations,
and immune responses. One model revealed a clear dose–response relationship, whereas
the other indicated that additional biological or pharmacological factors may modulate
drug efficacy. Both models demonstrated stability, with basic reproductive numbers (R0)
suggesting the potential for viral propagation in the absence of effective therapeutic interventions. This study emphasizes the significance of understanding the pharmacokinetics
(PK) and pharmacodynamics (PD) of andrographolide to optimize its therapeutic potential.
The findings also underscore the necessity for further investigation into the compound’s
absorption, distribution, metabolism, and excretion (ADME) characteristics, as well as
its prospective applications in the treatment of not only COVID-19 but also other viral
infections. Overall, the results lay a foundational framework for future experimental research and clinical trials aimed at refining andrographolide dosing regimens and improving
patient outcomes.
Keywords
Andrographolide, COVID-19, Mathematical modeling, pharmacokinetic, viral dynamic model