Dr. Sagam Dinesh Reddy, Dr. Sharathchandra Nibhanpudi, MD,
Dr. Sunitha Patil, MD
Dr. Sagam Dinesh Reddy, MD
(Physician), DFM (Family Medicine), DIMCH/CCPMH (Community Mental Health), PGPN
(Pediatric Nutrition), AFIH (RLI Kanpur/DGFASLI-GOVT of India), LMR Hospital, G Konduru, NTR District,
Andhra Pradesh, India, 521229, ORCID: [0000-0001-7659-9441].
Dr. Sharathchandra Nibhanpudi, MD
(Pharmacology), Department
of Pharmacology, SVMC Tirupathi, Andhra Pradesh, India.
Dr. Sunitha Patil, MD
(Microbiology), Group Captain, Assistant Professor, Department of Microbiology, AFMC
Pune, Maharashtra, India.
Corresponding Author: Dr. Sagam Dinesh Reddy, LMR Hospital, G Konduru, NTR District,
Andhra Pradesh, India, 521229, ORCID: [0000-0001-7659-9441].
Citation:
Dr. Sagam Dinesh
Reddy, Dr. Sharathchandra Nibhanpudi, MD, Dr. Sunitha Patil, MD
(2025) AI-Driven Drug
Repurposing for Post-Viral Arthralgia: In
Vitro and In Vivo Validation of
Doxycycline, Sofosbuvir, and Pranlukast. Int J Fam Med Pub Health, 4(1);1-12.
Copyrights: © 2025, S.
Dinesh Reddy, Sharathchandra N, Sunitha P,. This is an open-access article
distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits
unrestricted use, distribution and reproduction in any medium, provided the
original author and source are credited.
Abstract
Post-viral arthralgia, particularly following
dengue and chikungunya infections, poses a significant clinical challenge due
to its persistent and debilitating nature, with limited therapeutic options
available. Our research aimed to address this gap by leveraging AI-driven drug
repurposing to identify effective treatments for post-viral arthralgia.
Utilizing advanced computational techniques, including machine learning models
and molecular docking studies, we analyzed vast datasets to predict and
validate the efficacy of existing drugs. Our methodological approach involved
training AI algorithms on specific biomolecules and conducting in vitro and in vivo assays to assess the anti-inflammatory effects of candidate
drugs. Our results identified doxycycline, sofosbuvir, and pranlukast as
promising candidates, demonstrating significant reductions in pro-inflammatory
cytokine levels and joint inflammation in treated animal models. These findings
suggest that AI-driven drug repurposing can efficiently identify novel
therapeutic uses for existing drugs, offering a faster and cost-effective
alternative to traditional drug discovery processes. The implications of our
study are substantial, providing new therapeutic options for managing
post-viral arthralgia and highlighting the potential of AI in addressing other
viral infections and inflammatory conditions. Our work underscores the
transformative potential of AI-driven drug repurposing in developing effective
treatments for post-viral arthralgia, marking a significant step forward in the
field. Future research should focus on clinical validation of these findings
and exploring the broader applications of AI in drug discovery.
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