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Abstract

Infectious diseases continue to impose a major global health burden despite remarkable advances in vaccines, diagnostics, and antimicrobial agents. Pharmacology has played a pivotal role in combating these conditions through the development of antibiotics, antivirals, antifungals, and antiparasitics, guided by pharmacokinetic (PK) and pharmacodynamic (PD) principles. However, the rapid rise of antimicrobial resistance (AMR), coupled with economic disincentives, regulatory hurdles, and limited innovation, has slowed the progress of novel drug discovery. Against this backdrop, artificial intelligence (AI) has emerged as a transformative tool with applications spanning the drug discovery pipeline, clinical pharmacology, and healthcare delivery. AI-driven platforms have accelerated antimicrobial discovery, exemplified by the identification of halicin, and have enabled rapid drug repurposing during the COVID-19 pandemic. In addition, AI enhances PK/PD modeling, therapeutic drug monitoring, and dose optimization across diverse patient populations. Its integration with genomics, proteomics, and microbiome data fosters precision medicine, while predictive modeling offers new strategies for tracking and managing AMR. Within clinical settings, AI-powered diagnostic and decision support systems enhance accuracy, stewardship, and outbreak preparedness. Furthermore, AI has extended applications to neglected tropical diseases, supporting innovation in drug discovery, diagnostics, and vector control in resource-limited regions. Despite these advances, challenges remain regarding data quality, interpretability, ethical and regulatory frameworks, and real-world integration. Future directions point toward synergistic use of AI with systems biology and pharmacology to enable predictive, personalized, and globally equitable therapeutics. This review underscores AI’s potential to reshape infectious disease pharmacology and address pressing global health challenges.

Keywords

Infectious diseases, Antimicrobial resistance, Artificial intelligence, Machine learning, Precision medicine, Drug discovery, Clinical decision support

Introduction

Infectious diseases remain a leading cause of global morbidity and mortality, particularly in low- and middle-income countries. Despite major advances in vaccines, diagnostics, and antimicrobial therapy, diseases such as tuberculosis, malaria, HIV/AIDS, and emerging viral infections like COVID-19 continue to impose significant public health and economic burdens worldwide [1,2]. The World Health Organization (WHO) estimates millions of deaths annually from preventable and treatable infections, highlighting the urgent need for more effective interventions [3]. Pharmacology has historically been central in combating infectious diseases through the discovery and optimization of antimicrobial agents. Antibiotics, antivirals, antifungals, and antiparasitic drugs have transformed survival rates and reduced transmission [4]. Clinical pharmacology provides insights into pharmacokinetics and pharmacodynamics, guiding dosing strategies and therapeutic optimization [5]. Rational drug design, drug repurposing, and combination therapies have further expanded treatment options [6]. However, the efficacy of pharmacological interventions faces growing challenges. Antimicrobial resistance (AMR) has become one of the most pressing global health threats, driven by misuse of antimicrobials, genetic adaptation in pathogens, and slow pace of novel drug development [7]. Treatment failures and relapses complicate management, particularly in multidrug-resistant tuberculosis and resistant Gram-negative infections [8]. The pipeline for new antimicrobial agents remains insufficient compared to rising demand, largely due to high costs, long timelines, and low commercial incentives [9]. Artificial Intelligence (AI) has recently emerged as a transformative approach to address these challenges. AI methods, particularly machine learning (ML) and deep learning (DL), enable accelerated drug discovery, optimization of pharmacological properties, and prediction of resistance evolution [10,11]. AI-powered approaches are being applied to identify novel drug candidates, repurpose existing compounds, optimize clinical trial design, and enhance personalized treatment strategies in infectious diseases [12]. Moreover, AI integrates large datasets—from genomics, proteomics, chemical libraries, and electronic health records—to provide actionable insights at unprecedented speed and scale [13]. This review aims to provide a comprehensive overview of the intersection between infectious disease pharmacology and AI applications. It highlights the global impact of infectious diseases, the pharmacological role in their management, the major challenges such as AMR and treatment failures, and how AI offers novel opportunities to revolutionize drug discovery, development, and clinical application.

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Sakshi Nagre
Corresponding author

B. Pharm, Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.

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Vaishnavi Dole
Co-author

B. Pharm, Gawande Collage of Pharmacy S. Kherda, Maharashtra , India.

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Asmita Kharat
Co-author

B. Pharm, Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.

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Rutuja Kharat
Co-author

B. Pharm ,Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.

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Sachin Musadwale
Co-author

B. Pharm, Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.

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Pooja Solanke
Co-author

B. Pharm. Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.

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Rohan Sonone
Co-author

B. Pharm. Gawande Collage of Pharmacy S. Kherda, Maharashtra, India

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Shivshankar Nagrik
Co-author

M. Pharm, Department of Pharmaceutics, Rajarshi Shahu College of Pharmacy, Buldhana, Maharashtra, India

Sakshi Nagre*, Vaishnavi Dole, Asmita Kharat, Rutuja Kharat, Sachin Musadwale, Pooja Solanke, Rohan Sonone, Shivshankar Nagrik, Proteomics in Personalized Cancer Therapy: Advances, Applications, and Future Perspectives, Int. J. Sci. R. Tech., 2025, 2 (9), 01-12. https://doi.org/10.5281/zenodo.17016374

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