Integrating state-space modeling, parameter estimation, deep learning, and docking techniques in drug repurposing: a...

Area of Science:

• * Virology and Molecular Biology
• * Computational Drug Discovery
• * Systems Biology

Background:

• * Emerging SARS-CoV-2 variants pose significant challenges for diagnostics and therapeutics.
• * Drug repurposing is crucial for rapid development of effective treatments.
• * Understanding virus-host protein interactions is key for therapeutic intervention.

Purpose of the Study:

• * To identify critical regulatory proteins targeted by SARS-CoV-2.
• * To investigate drug repurposing strategies for COVID-19 management.
• * To evaluate therapeutic interventions for SARS-CoV-2-induced cytokine storms.

Main Methods:

• * Mathematical modeling and parameter estimation to analyze regulatory protein responses.
• * Proportional-Integral-Derivative (PID) controllers for target identification.
• * Deep learning and molecular docking for drug-target and drug-drug interaction analysis.
• * Case study on COVID-19 cytokine storm focusing on Angiotensin-converting enzyme 2 (ACE2).

Main Results:

• * Activating ACE2 is a promising therapeutic strategy; AT1R inhibition showed less efficacy.
• * Deep learning and molecular docking identified Lomefloxacin and Fostamatinib as safe for concurrent use.
• * These drugs demonstrated no significant thermodynamic interactions for managing COVID-19 cytokine storms.

Conclusions:

• * ACE2 activation can mitigate SARS-CoV-2-induced lung injury and inflammation.
• * The integrated approach accelerates the discovery of safe and effective treatments for viral variants.
• * This framework enhances drug repurposing for rapid therapeutic development against pandemics.