Pharmacovigilance: Emerging trends, ongoing challenges, AI and future outlook in pharmacovigilance

 Corresponding author: Aniket A. Singh Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Pharmacovigilance: Emerging trends, ongoing challenges, AI and future outlook in pharmacovigilance Aniket A. Singh *, Mohit M. Jadhav, Trushal S. Singh, Swastik R. Mishra, Vaibhavi B. Sange and Kajal R. Yadav Department of Pharmaceutics, Siddhi’s Institute of Pharmacy Nandgaon, Tal Murbad Dist. Thane-421401. (Affiliated to Dr Babasaheb Ambedkar Technological University Lonere Raigad). World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 Publication history: Received on 19 July 2025; revised on 27 August 2025; accepted on 01 September 2025 Article DOI: https://doi.org/10.30574/wjbphs.2025.23.3.0785 Abstract Pharmacovigilance, which involves the science and activities surrounding the identification, evaluation, understanding, and prevention of adverse drug reactions or related issues, is fundamentally associated with efficient information management. The crucial role of information within pharmacovigilance includes the gathering, analysis, and distribution of data to ensure maximum patient safety. A strong information system is essential for pharmacovigilance, facilitating the acquisition of reports on adverse events from healthcare providers, patients, and other stakeholders. The development of data sources highlights the importance of integrating electronic health records, wearable technologies, and real-world evidence to enrich the information available for analysis. Cutting edge technologies like artificial intelligence and machine learning are revolutionizing pharmacovigilance by streamlining signal detection and facilitating predictive modeling, allowing for thorough examination of extensive datasets to uncover potential safety issues and support regulatory decision making. Additionally, this abstract explores the critical nature of structured information exchange among regulatory bodies, pharmaceutical firms, and healthcare practitioners. Prompt and transparent dissemination of safety information enables a proactive approach to emerging risks and aids in crafting effective risk management strategies. The ongoing assessment of information management methods is necessary to navigate the complexities of contemporary healthcare and pharmaceutical advancements. In summary, these abstract highlights the interconnectedness of pharmacovigilance and information management, illustrating that the effective use of diverse and high-quality data is essential for enhancing drug safety practices and protecting public health. Keywords: Pharmacovigilance; Adverse Drug Reactions (ADRS); Drug Safety; Signal Detection; Regulatory Framework; ICH; WHO-UMC; AI 1. Introduction 1.1. Definition of Pharmacovigilance Pharmacovigilance is recognized by both the World Health Organization (WHO) and the International Council for Harmonization (ICH) as a critical scientific and regulatory field focused on promoting the safe and effective utilization of medications. While their definitions align broadly, each organization underscores different components of this area. The WHO (2024) describes pharmacovigilance as “the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other issues linked to medications.” This definition captures a wide-ranging perspective on public health, addressing not only adverse drug reactions (ADRs) but also medication errors, issues of ineffective treatments, counterfeit and substandard drugs, and the long-term impacts of medical interventions in real world contexts [1]. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 61 Conversely, the ICH conveys a comparable foundational concept but frames it within regulatory boundaries, defining pharmacovigilance as “the science and activities concerning the detection, assessment, understanding, and prevention of adverse effects or any potential drug related issues, particularly as they pertain to the safety of medical products and the safeguarding of public health”. The WHO places emphasis on global surveillance through platforms like Vigias and adopts patient centered and public health strategies, whereas the ICH concentrates on aligning regulatory practices and incorporates pharmacovigilance into Risk Management Plans (RMPs), Periodic Safety Update Reports (PSURs), and Development Safety Update Reports (DSURs) to uphold uniform international standards [2]. Insights from recent years (2023–2025) indicate that both WHO and ICH see the importance of evolving pharmacovigilance beyond conventional reporting methods, integrating artificial intelligence for proactive signal detection, utilizing real world evidence (RWE) from electronic health records and patient feedback, and overseeing complex treatments such as biologics and advanced therapies. Collectively, these definitions highlight pharmacovigilance as an ever-evolving field, adapting to the complexities of contemporary therapeutics and global healthcare systems [3]. 1.2. Importance of Drug Safety in Public Health The safety of medications is a vital component of public health. While drugs are crucial for preventing and treating illnesses, they also come with risks, including adverse drug reactions (ADRs) and other related complications that can negatively impact patient health and put additional pressure on healthcare systems. Recent data suggest that ADRs contribute to 5-10% of hospital admissions worldwide and are a significant cause of illness and death, especially among at risk groups like children, the elderly, and individuals with multiple health issues. Maintaining drug safety is essential for preserving the balance between the risks and benefits of medications, which in turn helps to sustain patient trust and fosters responsible medicine use. On a broader scale, robust pharmacovigilance systems play a crucial role in the early identification of safety concerns, thus allowing regulatory bodies to take swift actions such as altering product labels, initiating recalls, or limiting use to mitigate potential widespread harm. Additionally, monitoring drug safety is critical in managing the introduction of new therapeutic options, including biologics, biosimilars, gene therapies, and herbal medications, since the long term impacts of these treatments may only become apparent after they have been approved. The incorporation of artificial intelligence, real world evidence (RWE), and patient focused reporting systems has amplified the significance of drug safety as a proactive, data driven approach to public health. By minimizing healthcare expenses associated with ADRs, enhancing adherence to treatment, and maintaining public trust in regulatory frameworks, drug safety steadily remains a fundamental aspect of effective healthcare delivery and contributes to global health resilience [4]. 1.3. History of Pharmacovigilance The development of pharmacovigilance reflects an increasing awareness that drug safety must extend beyond the confines of clinical trials to safeguard public health in everyday situations. This field began to emerge in response to major drug disasters, particularly the thalidomide incident of the early 1960s, which resulted in significant birth defects and highlighted the necessity for rigorous post marketing monitoring. Consequently, countries such as the United States and the United Kingdom established systems for monitoring adverse drug reactions (ADRs), leading to the formation of the WHO Programme for International Drug Monitoring in 1968 and the establishment of VigiBase by the Uppsala Monitoring Centre currently the largest repository of Individual Case Safety Reports (ICSRs) worldwide [1]. During the 1980s and 1990s, pharmacovigilance evolved to encompass risk benefit assessments and signal detection techniques, aided by advancements in computer technologies and statistical disproportionality analysis. The 2000s saw the standardization of international practices through the International Council for Harmonisation (ICH), introducing guidelines like E2E (Pharmacovigilance Planning) and E2C(R2) (Periodic Benefit Risk Evaluation Reports) that embedded risk management into regulatory processes. In recent years, the field has expanded to include biologics, biosimilars, vaccines, herbal medicines (often referred to as phytovigilance), and innovative therapies such as gene and cell products [2]. Between 2023 and 2025, technological innovations including artificial intelligence, the incorporation of real-world evidence (RWE), and patient focused reporting systems have revolutionized pharmacovigilance, transforming it into a proactive, predictive discipline capable of detecting safety signals more efficiently and accurately. Thus, pharmacovigilance has transitioned from a reactive ADR reporting system to a comprehensive, data informed framework that enhances regulatory decision making and fosters public health resilience [5]. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 62 1.4. Global burden of ADRs Adverse drug reactions (ADRs) pose a major challenge to public health worldwide, resulting in considerable morbidity, mortality, and economic consequences. Recent estimates from the World Health Organization (WHO) indicate that ADRs lead to about 5-10% of global hospital admissions and account for 10-15% of complications among hospitalized patients, particularly in lowand middle-income countries where pharmacovigilance systems are still maturing [6]. ADRs are increasingly recognized as a leading cause of mortality, ranking among the top ten causes in various healthcare systems, with an estimated annual economic impact exceeding USD 50 billion from extended hospital stays and lost productivity. At risk populations such as children, the elderly, and those with multiple health issues face heightened risks due to polypharmacy and insufficient safety data from clinical trials [7,8]. This highlights the need for robust pharmacovigilance, as clinical trials often have limited sample sizes and do not adequately represent high risk individuals. Modern pharmacovigilance combines real world evidence, artificial intelligence for signal detection, and patient reported outcomes to enhance the early identification of risks and inform regulatory decisions, thereby bolstering public confidence in healthcare and ensuring the responsible use of medications [9,10]. Objective Figure 1 Objectives of Pharmacovigilance The primary aim of pharmacovigilance is to ensure the safe and effective use of medications while safeguarding public health. As stated by the World Health Organization (WHO, 2024), key goals include. Identifying new or previously unrecognized adverse drug reactions (ADRs) that might not have been apparent during clinical trials, Evaluating the occurrence and severity of known reactions, and examining the mechanisms, risk factors, and long-term impacts of medications across various populations. The International Council for Harmonization builds on these objectives within regulatory settings, highlighting the necessity of incorporating pharmacovigilance into Risk Management Plans (RMPs), Periodic Benefit Risk Evaluation Reports (PBRERs), and Development Safety Update Reports (DSURs) to enhance global safety monitoring. Contemporary pharmacovigilance also strives to avert harm by facilitating prompt regulatory interventions, such as label changes, safety warnings, or drug withdrawals, while effectively conveying risks to healthcare professionals and patients to support well informed decisions [1,6] Recent developments have expanded these goals to tackle the intricacies of emerging therapies like biologics, gene, and cell therapies, alongside herbal medicines, by integrating real world evidence (RWE) and patient reported outcomes into safety assessments. Furthermore, artificial intelligence and machine learning are being leveraged to anticipate potential safety concerns earlier, thus evolving pharmacovigilance into a more proactive field. Together, these objectives ensure that pharmacovigilance acts as an adaptive system for detecting, assessing, preventing, and Communicating drug related risks to protect both individual patients and the wider community [5]. 2. Regulatory framework The global framework for pharmacovigilance is orchestrated through collaborative systems aimed at standardizing drug safety oversight and enhancing public health on an international scale. Central to this effort is the World Health Organization Uppsala Monitoring Centre (WHO UMC), which manages the WHO Programme for International Drug Monitoring. Established in 1968, this initiative now comprises over 170 member countries and utilizes VigiBase, the World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 63 largest database of Individual Case Safety Reports (ICSRs) worldwide, to facilitate global signal detection and timely recognition of new safety issues. Additionally, the International Council for Harmonisation (ICH) formulates uniform guidelines to ensure consistent pharmacovigilance across regulatory domains. Notable guidelines include ICH E2E for risk management during product development and ICH E2C(R2) to standardize safety reporting after marketing. The Council for International Organizations of Medical Sciences (CIOMS) supplements these efforts with recommendations that address scientific, regulatory, and public health needs, ensuring pharmacovigilance operates as a cohesive global discipline [5,11]. In India, the oversight of pharmacovigilance is primarily managed by the Drug Controller General of India (DCGI), the Central Drugs Standard Control Organization (CDSCO), and the Pharmacovigilance Programme of India (PvPI). These entities work together to ensure drug safety, from approval through to post marketing monitoring. The DCGI, under the Ministry of Health and Family Welfare, plays a crucial role in approving new medications, monitoring clinical trials, and enforcing regulations in accordance with the New Drugs and Clinical Trials Rules (NDCTR) of 2019. The CDSCO acts as the central authority that requires pharmaceutical firms to submit Periodic Safety Update Reports (PSURs) and adhere to Risk Management Plans (RMPs) following international standards. Established in 2010, the PvPI, coordinated by the Indian Pharmacopoeia Commission (IPC), functions as the national pharmacovigilance network, gathering and analyzing safety reports and providing data for global signal detection. Recent advancements focus on digital pharmacovigilance, enhancing drug safety and regulatory decision making [11,12]. Figure 2 Regulatory framework of Pharmacovigilance The U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) serve as the cornerstone of the pharmacovigilance regulatory framework in the United States and the European Union. Both agencies implement extensive systems for the continuous assessment of drug safety throughout a product’s lifecycle. In the U.S., the FDA manages pharmacovigilance through the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER), utilizing tools like the FDA Adverse Event Reporting System (FAERS) and MedWatch for spontaneous reports, along with active monitoring via the Sentinel Initiative. The FDA also enforces Risk Evaluation and Mitigation Strategies (REMS), Periodic Safety Update Reports (PSURs), and Risk Management Plans (RMPs) to address and mitigate risks. Meanwhile, the EMA oversees pharmacovigilance through the Pharmacovigilance Risk Assessment Committee (PRAC) and the EudraVigilance database. Both organizations have recently enhanced their methods to include real world evidence, driven signal detection, and patientfocused reporting systems to better identify safety issues early. 3. Methods of pharmacovigilance 3.1. Spontaneous Reporting System Spontaneous Reporting Systems (SRS) serve as a fundamental and extensively utilized approach in pharmacovigilance, aimed at identifying potential adverse drug reactions (ADRs) once medications are available to the public. These World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 64 systems depend on the voluntary submission of Individual Case Safety Reports (ICSRs) by healthcare professionals, patients, and pharmaceutical firms to various national or regional databases, including the FDA Adverse Event Reporting System (FAERS) in the U.S., EudraVigilance in the EU, and the WHO Uppsala Monitoring Centre’s VigiBase. SRS are valuable for early signal detection but face challenges like underreporting and data quality variability. Recent technological advancements have improved their functionality for better monitoring and signal detection in drug safety [13]. 3.2. Active surveillance Active surveillance is a proactive strategy in pharmacovigilance that seeks to identify and assess adverse drug reactions (ADRs) by systematically gathering safety data from specific groups. Unlike spontaneous reporting systems that rely on voluntary input, active surveillance employs structured methods to enhance the accuracy and completeness of drug risk detection. Techniques such as Cohort Event Monitoring (CEM) follow patients on particular medications to document adverse events, while Prescription Event Monitoring (PEM) analyzes safety outcomes via prescription data and follow ups. Technological advancements from 2023 to 2025, including the use of electronic health records and machine learning, have improved the effectiveness of active surveillance in identifying safety trends and risks at the population level. 3.3. Observational study Observational studies play a vital role in pharmacovigilance by assessing the link between drug use and adverse drug reactions (ADRs) in everyday clinical environments. Unlike randomized controlled trials, which have stringent guidelines and often exclude high risk groups, observational studies align more closely with actual clinical practice, revealing rare, delayed, or population specific safety concerns. The two main types include cohort studies, where drug exposed groups are monitored over time against unexposed ones to determine ADR incidence and risk, and case control studies, which compare prior drug exposures of individuals with specific ADRs to those without. Recent innovations have improved these studies by integrating electronic health records and other databases for extensive analysis. Additionally, machine learning and Bayesian modeling help address confounding variables, enhancing causal inference. Despite potential biases, well-structured observational studies offer critical evidence for signal validation and regulatory decisions. 3.4. Randomized Controlled Trials Randomized Controlled Trials (RCTs) are a vital tool in pharmacovigilance, primarily assessing drug safety and efficacy prior to market introduction while also providing useful post marketing safety insights. By allocating participants randomly to treatment or control groups, RCTs reduce bias and yield strong evidence on drug adverse event relationships. They facilitate systematic safety data collection, helping identify common short term adverse drug reactions (ADRs) and dose response links. Nonetheless, RCTs have notable limitations, such as small, homogenous participant groups that often exclude vulnerable populations. Recent developments have introduced adaptive trial designs and pragmatic RCTs to expand patient demographics and improve safety monitoring. 3.5. Real World Evidence Real World Evidence (RWE) has become essential in pharmacovigilance, offering valuable insights into drug safety and efficacy in everyday clinical settings. Unlike randomized controlled trials (RCTs), which involve restricted environments and selective populations, RWE comes from real world data (RWD) sources like electronic health records (EHRs), insurance claims, and patient registries. This data aids in identifying rare, long term, and specific adverse drug reactions (ADRs) not typically seen during preapproval studies. Regulatory bodies like the FDA and EMA are increasingly utilizing RWE for signal detection and risk management, integrating advanced techniques like machine learning to improve safety monitoring [5,14]. 3.6. Quantitative Signal Detection Quantitative signal detection methods are essential in pharmacovigilance, employing statistical approaches to uncover potential links between medications and adverse drug reactions (ADRs) within extensive safety databases. These techniques are crucial for analyzing data from spontaneous reporting systems such as FAERS, EudraVigilance, and VigiBase, where manual data review is unfeasible. Disproportionality analysis, including tools like Proportional Reporting Ratios (PRR) and Bayesian methods, helps identify significant drug event pairs. Recent innovations in machine learning and AI have enhanced the detection of safety signals and drug interactions, facilitating regulatory agencies’ efforts to streamline pharmacovigilance processes. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 65 3.7. Phytovigilance Phytovigilance is a distinct area within pharmacovigilance that focuses on assessing the safety of herbal medicines and plant-based products. Unlike traditional medications, herbal remedies feature intricate compositions and variable active ingredients, posing specific safety concerns. Many herbal products are utilized without thorough pre marketing assessments, highlighting the need for post marketing surveillance to identify adverse drug reactions and interactions with conventional drugs. Phytovigilance employs methods similar to pharmacovigilance while also utilizing tailored assessment tools for herbal products. Initiatives by organizations like the Pharmacovigilance Programme of India and WHO Uppsala Monitoring Centre aim to enhance monitoring efforts amid challenges like underreporting and regulatory inconsistencies [15]. Figure 3 Methods of pharmacovigilance 3.8. Vaccine Pharmacovigilance Vaccine pharmacovigilance is a dedicated branch of pharmacovigilance that focuses on monitoring, evaluating, and preventing adverse events following immunization (AEFI) to guarantee vaccine safety throughout their use. Unlike traditional drugs, vaccines are often given to healthy individuals in large groups, rendering even infrequent adverse events critical to public health. Monitoring occurs through various methods, including spontaneous reporting systems like VAERS in the U.S. and Surveillance in the EU, as well as active surveillance through initiatives such as the Vaccine Safety Datalink (VSD). Recent advancements have integrated machine learning and Bayesian methods to improve signal detection, while global efforts led by WHO enhance data sharing to bolster public trust in vaccination programs. 4. Challenges In Pharmacovigilance Pharmacovigilance faces numerous challenges, particularly in terms of data integrity, underreporting, inadequate resources, regulatory shortcomings, and technological complexities, especially in lowand middle-income countries (LMICs) that are adopting artificial intelligence. A critical issue is the underreporting of adverse drug reactions (ADRs), with research suggesting that only 5-10% of these events are documented in spontaneous reporting frameworks, particularly for non-severe incidents. This problem is fueled by a lack of awareness among healthcare providers, time limitations, and insufficient access to reporting mechanisms, leading to low quality data that hampers effective signal detection. In LMICs, challenges are intensified by limited financial and human resources, poor national coordination, dependence on external funding, high staff turnover, and minimal integration between public and private healthcare systems within formal pharmacovigilance frameworks [16]. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 66 Figure 4 Challenges in pharmacovigilance Technological and infrastructural issues also significantly impede progress. Creating and sustaining efficient pharmacovigilance databases is challenging in regions with inadequate IT support, variable data standards, and inconsistent terminology. AI driven pharmacovigilance encounters further difficulties, such as the lack of well annotated data, variations in clinical language, and the need to integrate complex data from various sources. Additionally, the use of AI and machine learning raises concerns about data quality, potential algorithmic biases, the opacity of “black box” models, and compliance with data privacy and regulatory requirements. These technologies demand rigorous validation, clear explanation frameworks, and human supervision to ensure they are used safely and ethically within regulatory frameworks [17,18]. Cultural and organizational dynamics additionally complicate pharmacovigilance systems. The presence of numerous overlapping safety organizations can lead to unclear responsibilities and inefficient use of resources. High turnover rates for trained pharmacovigilance personnel and limited career advancement opportunities hinder retention. There is also a hesitance among professionals used to traditional systems to embrace digital advancements [19]. Furthermore, integrating pharmacovigilance education into healthcare training programs and enhancing awareness among patients, providers, and regulators represents a significant gap. In LMICs, limited patient involvement and a lack of understanding about the importance of pharmacovigilance in public health contribute to behaviors like selfmedication and the underreporting of ADRs, particularly in traditional medicine sectors. Fostering a culture of reporting and embedding pharmacovigilance into everyday healthcare practice is crucial for the sustainability of these systems [20]. 5. Advances and Innovations in Pharmacovigilance 5.1. Artificial Intelligence in PV Artificial Intelligence (AI) represents a segment of computer science dedicated to creating systems capable of executing tasks that generally necessitate human intellect, such as analyzing data, recognizing patterns, and making decisions. It has become a vital element in individuals’ everyday lives and is increasingly utilized in scientific exploration, healthcare, and pharmacovigilance (PV). As previously highlighted, PV is a sophisticated field that demands the gathering and examination of extensive data from diverse sources to identify adverse drug reactions (ADRs). The task of processing and analyzing such large datasets is intricate and resource demanding, akin to finding a “needle in a haystack.” [21].With the continuous rise in data volume, there is a heightened interest in incorporating AI technologies into PV systems [22]. AI, through machine learning (ML) and natural language processing (NLP), holds great promise for enhancing various aspects of healthcare sciences, particularly in pharmacovigilance (PV) [23,24]. The increasing amount of PV data has reached a level where manual analysis is becoming less feasible. AI can aid in PV by streamlining processes, enhancing data quality and accuracy, shortening case processing times, and managing complex datasets [25,26]. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 67 Numerous studies have explored the role of AI in PV, yielding encouraging outcomes. For instance, research by Dsouza et al. Systematically reviewed data from 13 studies that utilized ML to forecast adverse drug reactions (ADRs). Their findings indicate that ML algorithms can effectively enhance ADR prediction by more efficiently identifying associations between drugs and events, thereby minimizing the need for manual data handling. Furthermore, AI technologies like NLP can significantly shorten processing durations, improve precision, and lessen the burden on PV systems and their practitioners [27]. The incorporation of ML and NLP into PV frameworks has demonstrated a marked improvement in the efficiency and accuracy of ADR detection, reducing the dependence on manual processes [22]. Figure 5 Pharmacovigilance innovations With the expanding research and rising implementation of AI models, the future of photovoltaic technology seems to lean towards increased automation and improved data analysis abilities [28]. While AI holds promise for improving pharmacovigilance (PV), there are several challenges that need to be overcome. One major issue is that AI relies heavily on the quality and comprehensiveness of the data it uses. If the datasets are incomplete or not well maintained, this can result in erroneous detection of adverse drug reactions (ADRs). Training AI with datasets that lack certain ADRs or have underreported cases can result in flawed predictions and overlooked signals [29]. Incorporating AI into pharmacovigilance (PV) could lead to substantial advantages for both patients and healthcare professionals by enhancing the collection and examination of data. Utilizing machine learning (ML) and natural language processing (NLP), AI can gather, process, and analyze information at an unmatched pace, boosting the speed of data evaluation, predictive precision, and the overall effectiveness of adverse drug reaction (ADR) identification. Despite certain limitations, integrating AI with human expertise can help address these obstacles. Given the rapid technological Advancements of the past decade, it is essential for data driven fields like PV to adopt AI to expand their capabilities [22]. 5.2. Mechanic Learning for Signal Detection Recent efforts have explored machine learning (ML)-based extraction methods, from BERT-based models to LLM-driven systems like OnSIDES and AskFDALabel [30].Although these methods can scale, they face challenges related to the specific mappings and hierarchical connections within MedDRA [31,32].To tackle these issues, we present PVLens, an automated platform that retrieves safety information from FDA Structured Product Labels (SPLs) and aligns terminology with MedDRA, RxNorm, and SNOMED CT, utilizing dictionary-driven NLP and UMLS tools to analyze SPL XML data [33].while integrating Safety-Related Label-Ing Changes (SrLC) [34]. PVLens effectively reduces false negatives (FNs), decreasing the chances of overlooking critical adverse events (AEs). While some false positives (FPs) may arise, they can be quickly resolved, making this method far more efficient than manually examining each SPL. This approach greatly enhances efficiency compared to conventional manual label assessments, which are often slow and inconsistent. Our performance metrics are in line with those of MITRE and FDA evaluations of NLP methods for AE extraction, which indicated a maximum F1 score of 79% for MedDRA coding, whereas PVLens achieved 88.2% [35]. World Journal of Biology Pharmacy and Health Sciences, 2025, 23(03), 060-073 68 5.3. Patient centric PV Patient-focused pharmacovigilance has rapidly evolved with the advent of mobile applications and online platforms that allow patients to report adverse events directly. These tools facilitate the collection of data on symptom severity, timing, and functional impact, demonstrating an increase in reporting volumes and capturing outcomes that are often overlooked by healthcare professionals. Additionally, analyzing social media, online forums, and search trends provides timely insights into medication usage, off-label treatments, adverse effects, and their effects on quality of life. Research indicates that social media data can enhance traditional monitoring systems, aiding in early detection and hypothesis development. However, it is essential to apply effective noise reduction, use pharmacovigilance-specific natural language processing, ensure privacy, and validate findings against clinical data before taking regulatory actions. Overall, integrating mobile patient reporting and social media analysis with robust quality control measures and connections to real-world evidence enhances traditional pharmacovigilance practices [36,37,38]. 5.4. Pharmacogenomics The rising incorporation of pharmacogenomics (PGx) into pharmacovigilance (PV) is increasingly significant, as it sheds light on the variations in individual reactions to adverse drug reactions (ADRs) and enhances proactive risk management strategies. Recent evidence drawn from real-world data (RWE) and preemptive testing suggests that prescribing medications based on genetic variants, particularly CYP and HLA, could lower the frequency of ADRs and minimize hospitalizations across various drug classes. The addition of genomic data to safety databases helps identify patterns of genetically driven ADRs that might otherwise appear unusual. By incorporating PGx into PV methods, personalized risk assessments can lead to enhanced drug labeling, resulting in tailored risk reduction strategies, including dosage adjustments or alternative therapies. There is also a pressing need to create PGx-specific data signals for further epidemiological study. However, challenges remain, such as ensuring that high-quality genetic data are routinely accessible, establishing standardized phenotyping algorithms, maintaining data privacy, and necessitating larger cohorts for meaningful RWE analysis. Despite these hurdles, studies and regulatory efforts from 2021 to 2024 increasingly indicate that PGx could play a crucial role in advancing safer, more personalized treatment options [39]. 6. Pharmacovigilance in Special Population Monitoring drug safety for pediatric, geriatric, and pregnant or breastfeeding populations poses specific methodological and practical challenges, primarily due to the limited representation of these groups in clinical trials before drug approval. Each demographic displays unique pharmacokinetic, pharmacodynamic, and exposure characteristics that influence risk factors. In children, variations in drug absorption, metabolism, and excretion, along with off-label usage and weight-based dosing methodologies, increase the possibility of dosing mistakes and atypical adverse drug reactions (ADRs). Pediatric Individual Case Safety Reports (ICSRs) represent a minor portion of spontaneous reports and necessitate proactive oversight and tailored causality evaluations to identify critical clinical patterns [40,41]. Older adults experience higher risks and diverse ADRs due to the complexities associated with polypharmacy, multiple chronic illnesses, and age-induced alterations in drug metabolism. Current pharmacovigilance efforts are increasingly leveraging vast electronic health record (EHR) databases, assessments of frailty, and specialized pharmacoepidemiologic methods to detect inappropriate medications and drug interactions within this group [42,43]. In contrast, pregnant and breastfeeding women frequently face exclusion from clinical research, creating a significant gap in evidence. As a result, contemporary pregnancy pharmacovigilance utilizes registries, linked mother-infant databases, and focused monitoring in lower-income regions to evaluate outcomes related to mothers, fetuses, and newborns, thereby fostering safer prescribing practices [44]. Rare diseases and orphan medications pose unique challenges in pharmacovigilance due to their limited patient populations, diverse clinical presentations, and expedited approval processes that often depend on scant pre-market data. These factors render conventional signal detection methods inadequate. Thus, regulators and sponsors are increasingly turning to disease registries, global data sharing collaborations, adaptive post marketing studies, and innovative statistical techniques (such as Bayesian methods and N-of-1 analyses) to gather safety information across various regions and institutions [45]. Recent reviews and regulatory documents emphasize the importance of customized pharmacovigilance strategies for orphan drugs, which should integrate centralized registries, patient reported outcomes, long term monitoring, and collaborative governance to identify rare or delayed adverse effects while maintaining patient access to treatment [46,47]. Biologics and their biosimilars entail unique pharmacovigilance (PV) demand due to factors such as immunogenicity, variability linked to manufacturing, and impurities related to the product, which can result in varied safety profiles, including hypersensitivity and immune related adverse reactions. Consequently, PV post marketing focuses on meticulous traceability (including brand name and batch numbers), comprehensive immunogenicity monitoring, and