1B. Pharm. Gawande Collage of Pharmacy S. Kherda, Maharashtra, India.
2M. Pharm, Department of Pharmaceutics, Rajarshi Shahu College of Pharmacy, Buldhana, Maharashtra, India
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.
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.
Figure 1: AI-driven strategies in infectious disease pharmacology for overcoming barriers, predicting PK/PD, combating resistance, and optimizing drug discovery and therapy.
The Figure 1 highlights the role of AI in infectious disease pharmacology. It showcases how AI overcomes biological barriers, predicts pharmacokinetics/pharmacodynamics (PK/PD), and addresses antimicrobial resistance. The integration accelerates drug discovery and supports precision therapeutic strategies for global health improvement.
2. Infectious Disease Pharmacology
2.1 Overview of antimicrobial agents
Pharmacological management of infectious diseases relies on a broad spectrum of antimicrobial agents, which are broadly categorized into antibiotics, antivirals, antifungals, and antiparasitics. Antibiotics primarily target bacterial pathogens by disrupting essential cellular processes such as cell wall synthesis, as seen with β-lactams, protein synthesis inhibition through aminoglycosides and macrolides, DNA replication interference by fluoroquinolones, or the blockade of metabolic pathways by agents like sulfonamides [14]. In contrast, antivirals are designed to interfere with viral replication at various stages, including blocking viral entry through fusion inhibitors, inhibiting nucleic acid synthesis with nucleoside analogues used in HIV and hepatitis, or suppressing viral protease activity with protease inhibitors applied in HIV and HCV treatment [15]. Notably, the introduction of direct-acting antivirals (DAAs) has revolutionized the management of hepatitis C, offering cure rates exceeding 95% [16]. Antifungal therapy, on the other hand, exploits the structural and metabolic differences between fungal and mammalian cells, targeting pathways such as ergosterol biosynthesis with azoles, disrupting cell membrane integrity with polyenes, or inhibiting cell wall glucan synthesis using echinocandins [17]. Similarly, antiparasitic agents remain indispensable in the treatment of conditions like malaria, leishmaniasis, and schistosomiasis. Artemisinin-based combination therapies (ACTs) are central to malaria management, while benzimidazoles are commonly employed for helminthic infections and nitroimidazoles for protozoal diseases [18]. Despite these significant advancements, the rapid emergence of antimicrobial resistance continues to pose a formidable challenge, underscoring the need for ongoing innovation in pharmacological strategies.
2.2 Pharmacokinetics (PK) and pharmacodynamics (PD) in infectious diseases
The interplay between pharmacokinetics (PK), which encompasses drug absorption, distribution, metabolism, and excretion, and pharmacodynamics (PD), which describes the relationship between drug concentration and microbial killing or inhibition, forms the foundation of effective antimicrobial therapy [19]. Key PK/PD indices are critical for optimizing dosing strategies; for instance, β-lactams rely on the duration of time above the minimum inhibitory concentration (MIC), aminoglycosides are best guided by the peak-to-MIC ratio, and agents such as fluoroquinolones and glycopeptides are optimized through the area under the concentration–time curve to MIC ratio (AUC/MIC) [20]. These relationships, however, are significantly influenced by host-pathogen interactions. In critically ill patients, for example, altered drug clearance and changes in distribution can complicate dosing regimens and impact therapeutic outcomes [21]. To address such challenges, therapeutic drug monitoring (TDM) has become an essential tool, particularly for antimicrobials with narrow therapeutic windows such as vancomycin and aminoglycosides, as it ensures clinical efficacy while minimizing toxicity [22]. Moreover, advances in population pharmacokinetic modeling and physiologically based pharmacokinetic (PBPK) models have expanded the ability to predict drug exposure across diverse patient groups, including children, pregnant women, and individuals with organ dysfunction [23]. By integrating PK/PD knowledge into clinical practice, clinicians can deliver tailored therapies that maximize efficacy, improve patient safety, and reduce the risk of antimicrobial resistance development.
2.3 Drug development challenges and limitations
The development of novel anti-infective drugs continues to face a range of persistent obstacles that hinder progress. One of the foremost scientific challenges lies in the high genetic plasticity of pathogens, which allows them to rapidly develop resistance and thereby shorten the effective lifespan of newly developed agents [24]. Alongside these scientific barriers, economic disincentives also play a significant role. Unlike drugs for chronic diseases, antimicrobials generally provide lower returns on investment because of their short treatment courses, discouraging pharmaceutical companies from pursuing extensive research and development in this field [25]. Regulatory and clinical trial hurdles further compound the problem, as proving superiority over existing therapies often requires complex and expensive trials involving large patient populations [26]. This has contributed to stagnation in the drug development pipeline. While there has been notable progress in certain areas—such as the development of direct-acting antivirals (DAAs) and new treatments for COVID-19, as well as some emerging antifungal agents—true breakthroughs in antibiotic discovery remain rare, with only a limited number of novel classes approved in recent decades [27]. Adding to these challenges are global inequities, as access to essential medicines remains severely limited in many low-income regions, worsening the burden of infectious diseases and undermining antimicrobial stewardship programs [28]. Collectively, these barriers highlight the urgent need for innovative strategies, including AI-driven approaches, to accelerate drug discovery, optimize development processes, and promote equitable access to anti-infective therapies worldwide.
3. Artificial Intelligence in Biomedical Sciences
3.1 Basics of AI, machine learning (ML), and deep learning (DL)
Artificial Intelligence (AI) refers to computational systems designed to mimic aspects of human intelligence such as learning, reasoning, and decision-making. Within AI, machine learning (ML) represents algorithms that improve their performance with experience and exposure to data. ML can be categorized into supervised learning (prediction from labeled data), unsupervised learning (clustering and pattern detection), and reinforcement learning (optimization through trial-and-error interactions) [29]. A more advanced subset, deep learning (DL), utilizes multi-layer artificial neural networks that automatically extract complex features from large datasets. DL has achieved breakthroughs in image analysis, natural language processing, and predictive modeling, enabling new possibilities in biomedicine and pharmacology [30]. In infectious diseases, DL models have been applied to predict protein–ligand interactions, identify new antimicrobial scaffolds, and analyze complex genomic data [31].
3.2 Role of AI in drug research and healthcare
Artificial intelligence (AI) is playing a transformative role in both drug research and clinical healthcare, reshaping the way infectious diseases are managed. In drug discovery and development, AI models have accelerated the identification of novel compounds by enabling the rapid screening of massive chemical libraries in silico, thereby reducing experimental workload and time. Deep learning approaches have already demonstrated success, as seen in the discovery of novel antibiotics such as halicin, which functions through previously unknown mechanisms [32]. Beyond discovery, AI is also proving invaluable in drug repurposing, where the analysis of large-scale biomedical data has facilitated the identification of existing drugs with potential activity against emerging pathogens, a strategy that was particularly impactful during the COVID-19 pandemic [33]. Furthermore, the integration of AI with genomics, microbiome data, and electronic health records has advanced the field of precision medicine, enabling antimicrobial therapies to be tailored to the unique characteristics of both the patient and the pathogen [34]. Within healthcare delivery, AI is enhancing infectious disease diagnostics through innovations such as rapid image-based identification of malaria parasites and tuberculosis bacilli. It also strengthens outbreak surveillance and provides predictive modeling for epidemic spread, contributing to more effective public health responses [35]. Collectively, these applications not only accelerate drug development timelines but also enhance therapeutic decision-making, ultimately improving outcomes in both individual patient care and population-level disease management.
Figure 2: Circular representation of AI’s role in drug research and healthcare, highlighting its applications in drug discovery, repurposing, precision medicine, and healthcare delivery.
Figure 2, illustrates how AI accelerates drug discovery, identifies repurposing opportunities, and supports precision medicine. It also enhances healthcare delivery through improved diagnostics and patient management. Collectively, these applications transform infectious disease treatment and global health outcomes.
3.3 Data-driven approaches in pharmacology
Pharmacology is increasingly evolving into a data-driven discipline, with artificial intelligence (AI) at the forefront of managing and interpreting complex biological datasets. One of the key areas of application is pharmacokinetic and pharmacodynamic (PK/PD) modeling, where AI algorithms can predict drug behavior across diverse patient populations while accounting for variability in factors such as age, organ function, and genetic background [36]. In parallel, the rise of genomics, proteomics, transcriptomics, and metabolomics has generated enormous volumes of biological data, and AI—particularly deep learning techniques—facilitates the integration of these datasets to uncover novel drug targets and mechanisms of resistance [37]. Another important application lies in the analysis of real-world evidence, including electronic health records, pharmacovigilance databases, and global prescription data. By applying machine learning to these sources, researchers can more effectively detect adverse drug reactions and optimize dosing regimens in clinical practice [38]. Furthermore, AI significantly enhances high-throughput screening by boosting the efficiency of chemical and biological screening platforms, thereby accelerating the transition from hit identification to lead compound selection in anti-infective drug development [39]. Together, these advances mark a paradigm shift in pharmacology, moving away from traditional empirical strategies toward predictive, personalized, and precision-based therapeutics.
4. Applications Of AI In Infectious Disease Pharmacology
4.1 Drug Discovery & Repurposing
AI has emerged as a critical driver of antimicrobial innovation, offering powerful tools to accelerate the discovery of both novel agents and new applications for existing drugs. In the search for novel antimicrobials and antivirals, deep learning algorithms are capable of screening millions of chemical structures in silico to identify molecules with potential antimicrobial activity. A landmark example of this approach was the discovery of halicin, a new antibiotic with a unique mechanism of action that has demonstrated efficacy against multidrug-resistant bacteria [40]. Similarly, AI has been instrumental in advancing antiviral research by analyzing viral protein structures and predicting compounds capable of inhibiting viral replication [41]. Beyond novel discoveries, AI also plays a central role in drug repurposing, significantly reducing the time and cost of development. This was particularly evident during the COVID-19 pandemic, when AI-driven platforms rapidly identified existing drugs such as remdesivir and baricitinib as potential therapeutics [42]. Moreover, the combination of network pharmacology with machine learning algorithms enabled accelerated screening of FDA-approved drugs for off-label antiviral applications, thereby expanding therapeutic options in an urgent global health crisis [43].
4.2 PK/PD Modeling & Dose Optimization
AI plays a crucial role in optimizing pharmacokinetics (PK) and pharmacodynamics (PD) by enabling more precise and individualized therapeutic strategies. Through predictive modeling, machine learning integrates patient-specific variables such as age, organ function, and comorbidities with drug concentration data to forecast absorption, distribution, metabolism, and excretion [44]. This approach allows for the simulation of multiple dosing scenarios without the need for exhaustive clinical trials, thereby streamlining drug development and clinical decision-making. In practice, AI-driven PK/PD models have been successfully applied in tuberculosis therapy to optimize rifampicin exposure and in HIV therapy to refine antiretroviral dosing regimens [45]. Additionally, Bayesian machine learning algorithms are increasingly used to support therapeutic drug monitoring, particularly for antimicrobials with narrow therapeutic indices such as vancomycin and aminoglycosides, ensuring effective treatment while minimizing the risk of toxicity [46].
4.3 Antimicrobial Resistance (AMR) Prediction
The global antimicrobial resistance (AMR) crisis has underscored the importance of predictive modeling as a tool to anticipate and counter resistance threats. AI technologies are increasingly employed to analyze genomic sequences of pathogens, enabling the detection of resistance genes and the prediction of emerging resistance phenotypes [47]. In addition to genomic analysis, natural language processing has been applied to mine scientific literature and surveillance databases, uncovering resistance patterns that might otherwise remain hidden [48]. Beyond tracking trends, machine learning models are capable of forecasting the evolution of resistant strains by incorporating factors such as selective pressures, patterns of drug use, and genetic variation. For instance, AI has been successfully used to predict fluoroquinolone resistance in Mycobacterium tuberculosis and to model beta-lactamase mutations in Escherichia coli [49]. This predictive power not only guides the design of new drugs but also informs antimicrobial stewardship policies, ultimately supporting more effective strategies to combat resistance.
4.4 Personalized & Precision Therapy
AI is driving a shift from generalized treatment protocols toward highly individualized therapeutic strategies by integrating complex patient-specific data into clinical decision-making. One key application lies in the combination of host genomic information, immune status, and microbiome composition, where AI models are able to predict treatment responses and assess the likelihood of adverse events [50]. In infectious diseases such as HIV, AI-guided algorithms incorporate viral resistance profiles alongside patient pharmacogenomic data to determine the most effective antiretroviral combinations, ensuring optimal outcomes while minimizing failure rates [51]. A similar approach has been applied to the management of sepsis, where AI-driven models integrate patient-specific biomarkers to guide personalized antibiotic therapy, thereby enhancing precision in treatment and improving survival outcomes [52].
4.5 Clinical Decision Support Systems (CDSS)
AI is increasingly being embedded into clinical workflows through clinical decision support systems (CDSS), where it plays a central role in both diagnosis and therapeutic decision-making. In diagnostics, AI-powered image recognition tools have demonstrated accuracy comparable to expert clinicians in identifying malaria from blood smears and detecting tuberculosis from chest radiographs [53]. Complementing this, natural language processing techniques enhance real-time diagnosis by extracting and analyzing relevant information from electronic health records, thereby streamlining clinical evaluation [54]. On the therapeutic side, AI-integrated CDSS platforms provide tailored recommendations for empiric antimicrobial therapy by incorporating local resistance patterns and patient-specific characteristics. These systems are now being integrated into antimicrobial stewardship programs, where they have shown significant benefits in improving adherence to clinical guidelines and reducing inappropriate antibiotic use [55].
5. Case Studies & Recent Advances
5.1 AI-driven Antibiotic Discovery (Halicin and Beyond)
One of the most celebrated breakthroughs in AI-guided pharmacology is the discovery of halicin, a novel antibiotic discovered using a deep learning framework. Stokes et al. (2020) trained a neural network model on approximately 2,500 molecules with known antibacterial activity and then screened over 100 million compounds in chemical libraries. Halicin demonstrated potent activity against multidrug-resistant pathogens such as Acinetobacter baumannii and Clostridioides difficile, while operating via a novel mechanism involving disruption of bacterial electrochemical gradients [56]. This case exemplifies how AI accelerates identification of non-traditional antibiotic scaffolds, bypassing limitations of conventional empirical screening. Other recent studies have extended this approach to discover inhibitors targeting resistance enzymes such as β-lactamases, and antivirals targeting viral polymerases [57]. These advances highlight AI’s role in rejuvenating the stagnant antibiotic pipeline.
5.2 AI in COVID-19 Drug and Vaccine Development
The COVID-19 pandemic served as a real-world testbed for demonstrating the power of AI in infectious disease pharmacology, highlighting its role in drug development, vaccine design, and public health management. In drug repurposing, AI algorithms rapidly identified potential therapeutics such as remdesivir, favipiravir, and baricitinib by employing molecular docking simulations, transcriptomic analyses, and network pharmacology models [58]. These AI-driven platforms significantly shortened the transition from discovery to clinical evaluation, allowing early-stage clinical trials to commence within months. In parallel, AI made vital contributions to vaccine development by analyzing SARS-CoV-2 genomic sequences to identify immunogenic epitopes that could guide mRNA vaccine design. Notably, BioNTech and Pfizer leveraged AI-enabled immunoinformatics to refine epitope selection for their BNT162b2 mRNA vaccine, ensuring both efficacy and safety [59]. Machine learning also played an important role in optimizing vaccine manufacturing pipelines, improving stability and scalability to meet global demand [60]. Beyond therapeutics and vaccines, AI tools were instrumental in epidemiological modeling, providing critical insights into disease spread, resource allocation, and the projected outcomes of public health interventions [61]. Collectively, these applications underscore AI’s pivotal role in accelerating the development of both therapeutic and preventive measures during global health emergencies.
5.3 Applications in Neglected Tropical Diseases (NTDs)
AI’s applications are increasingly extending into the field of neglected tropical diseases (NTDs), which have historically faced underfunding and limited drug innovation. In drug discovery, machine learning algorithms are being applied to identify promising compounds against pathogens such as Trypanosoma cruzi (the causative agent of Chagas disease), Leishmania donovani (responsible for leishmaniasis), and Plasmodium falciparum (the most lethal malaria parasite) [62]. These computational approaches accelerate high-throughput screening and streamline hit-to-lead optimization, thereby reducing both time and cost in the drug development pipeline. In diagnostics, AI-driven image recognition tools are increasingly being deployed in low-resource settings to overcome barriers in healthcare access. For example, smartphone-based deep learning models have enabled automated detection of malaria-infected red blood cells and schistosomiasis eggs, providing scalable, low-cost diagnostic solutions that do not depend on highly trained personnel [63]. AI is also advancing vector control strategies by integrating climate, entomological, and epidemiological data into predictive models that can forecast transmission dynamics of vector-borne diseases. Such tools are being used to facilitate targeted interventions against mosquito-borne illnesses like dengue and Zika, improving the efficiency of public health programs [64]. By bridging critical gaps in both innovation and accessibility, AI holds the potential to transform the management of NTDs and contribute meaningfully to global health equity.
6. Challenges and Limitations
6.1 Data Quality and Bias
AI-driven models rely heavily on large, high-quality datasets to ensure accurate predictions. However, biomedical and pharmacological data often suffer from incompleteness, heterogeneity, and imbalance. For example, antimicrobial resistance (AMR) surveillance data are disproportionately collected from high-income countries, potentially biasing predictive models and limiting their global applicability [65]. Furthermore, clinical datasets may underrepresent vulnerable groups (e.g., pediatrics, elderly, or patients in low-resource settings), leading to algorithms that generalize poorly and risk exacerbating healthcare disparities [66]. Data curation and standardization remain major hurdles for AI applications in infectious diseases.
6.2 “Black Box” Issue in AI Interpretability
Many AI models, particularly deep learning algorithms, function as “black boxes” producing outputs without clear explanations of the decision-making process. This lack of interpretability raises skepticism among clinicians and regulatory bodies when AI is used for critical decision-making in infectious disease pharmacology [67]. For instance, an AI algorithm might recommend a novel antibiotic or dosing regimen, but without interpretability, physicians may hesitate to adopt the recommendation. To address this, efforts in explainable AI (XAI) aim to provide transparency by linking predictions to biological mechanisms or highlighting key features driving outcomes [68].
6.3 Regulatory, Ethical, and Privacy Concerns
The integration of AI into pharmacology and healthcare, while transformative, brings with it a range of complex regulatory and ethical challenges. One major concern lies in the regulatory frameworks governing drug development and clinical practice. Traditional drug approval processes were designed around conventional research pipelines and are not yet fully adapted to AI-accelerated discovery. Regulatory bodies such as the FDA and EMA are still in the process of developing appropriate guidelines to evaluate AI-derived therapeutics and clinical decision support systems [69]. Alongside regulatory gaps, issues of privacy and security present significant obstacles. Since AI depends on vast patient datasets—including genomic profiles and clinical records—ensuring compliance with stringent data protection regulations such as GDPR and HIPAA remains a critical challenge [70]. Ethical considerations further complicate the landscape, particularly when it comes to accountability and liability in cases where AI-generated recommendations lead to medical errors. Additionally, the potential for algorithmic bias within training datasets raises concerns about perpetuating or even worsening existing health inequities across populations [71]. Together, these challenges highlight the need for balanced governance frameworks that safeguard patient rights while enabling innovation.
6.4 Limited Integration into Real-World Clinical Practice
Despite encouraging research outcomes, the real-world integration of AI systems into infectious disease pharmacology has remained limited, largely due to a number of persistent barriers. Technical challenges are among the most significant, particularly the lack of interoperability between AI platforms and hospital electronic health record (EHR) systems, which hinders seamless adoption into clinical workflows [72]. Beyond technical limitations, clinical skepticism also plays a role, as many physicians remain cautious about depending on AI for treatment decisions in the absence of extensive validation through randomized clinical trials. Resource disparities further complicate implementation, with high-income healthcare systems better positioned to adopt AI tools, while low- and middle-income countries often face obstacles related to infrastructure, training, and sustainability [73]. Taken together, these challenges illustrate that while AI holds tremendous potential, its successful translation from research settings to bedside applications will require overcoming systemic, ethical, and logistical hurdles.
7. FUTURE DIRECTIONS
7.1 AI-guided Next-Generation Drug Development
Future antimicrobial pharmacology will be shaped by AI-driven drug discovery pipelines that move beyond repurposing to de novo molecular design. Generative adversarial networks (GANs) and reinforcement learning models are increasingly applied to design molecules with optimal antimicrobial properties while minimizing toxicity [74]. Integration of AI with quantum computing and synthetic biology may enable rapid identification of non-traditional antimicrobial modalities, such as bacteriophage therapies, antimicrobial peptides, and CRISPR-based antimicrobials [75]. These approaches promise to expand the therapeutic arsenal against multidrug-resistant infections.
7.2 Real-Time Global Monitoring of Pathogens and Resistance
AI-enabled surveillance platforms will revolutionize global monitoring of infectious diseases and antimicrobial resistance (AMR). By integrating genomic sequencing, clinical records, and environmental data, AI can track the emergence of resistant strains in real time [76]. Cloud-based platforms like Nextstrain already provide dynamic visualization of pathogen evolution, and with AI integration, predictions of resistance hotspots and transmission dynamics will become more precise [77]. This real-time monitoring will strengthen global antimicrobial stewardship and guide drug development priorities.
7.3 AI in Predictive Pandemic Preparedness
The COVID-19 pandemic highlighted the need for AI-powered pandemic preparedness systems. AI models can simulate zoonotic spillover risks, forecast epidemic spread, and optimize supply chains for drugs and vaccines [78]. Combining epidemiological data with mobility and climate models, AI will allow governments to implement proactive interventions against emerging pathogens [79]. Future pandemic preparedness frameworks are likely to embed AI as a central component in early warning systems, ensuring faster response and reduced mortality.
7.4 Synergistic Approaches: AI, Traditional Pharmacology and Systems Biology
The most transformative advances will arise from synergistic integration of AI, traditional pharmacology, and systems biology. AI can model drug-host-pathogen interactions at a systems level, capturing complexities that conventional methods overlook [80]. By integrating multi-omics data (genomics, transcriptomics, proteomics, and metabolomics), AI can help construct personalized pharmacological profiles that enable precision dosing and therapeutic regimens [81]. The convergence of these approaches may usher in a new era of precision infectious disease pharmacology, where therapies are tailored not only to pathogens but also to individual host biology and microbiome composition.
CONCLUSION
Infectious disease pharmacology has long been central to improving global health outcomes, but its progress has been constrained by antimicrobial resistance, high development costs, and unequal access to therapies. The emergence of artificial intelligence (AI) provides a transformative opportunity to overcome these barriers by reshaping every stage of the drug development and clinical application pipeline. AI-driven methods accelerate drug discovery, streamline repurposing strategies, and enhance pharmacokinetic/pharmacodynamic modeling, enabling more precise dosing and personalized therapies. Its integration with multi-omics data, electronic health records, and real-world evidence further strengthens the foundations of precision medicine. Beyond discovery and treatment, AI supports antimicrobial resistance prediction, outbreak surveillance, and clinical decision support systems, offering valuable tools for public health preparedness. Nevertheless, the translation of these promising innovations into real-world practice remains limited by systemic barriers. Challenges related to data quality, algorithmic bias, interpretability, regulatory adaptation, and privacy protections continue to restrict large-scale adoption. Furthermore, disparities in infrastructure and resources create unequal opportunities for implementation, particularly in low- and middle-income countries where the burden of infectious diseases is greatest. Looking ahead, synergistic integration of AI with traditional pharmacology and systems biology is expected to usher in a new era of predictive and personalized infectious disease therapeutics. Real-time monitoring of resistance, AI-enabled pandemic preparedness, and equitable global deployment of AI-based tools will be pivotal in addressing future health crises. By fostering innovation, accessibility, and responsible governance, AI has the potential to redefine infectious disease pharmacology and contribute meaningfully to global health equity.
REFERENCE
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
10.5281/zenodo.17016374
