Drug Safety Surveillance Research Articles

Integrating electronic health records with other data sources for postmarket drug safety signal identification: a review

Electronic health records (EHRs) have emerged as resources for both the identification of adverse drug events (ADEs) and general population health surveillance, however questions remain around how best to utilize EHR data for drug safety signal identification. While the majority of signal identification research has utilized spontaneous reports and health insurance claims, these data also have limitations. Pharmacovigilance approaches combining EHR data with other data sources have the potential to address many of the shortcomings of individual sources. This mini-review seeks to provide an overview of some of the recent research leveraging EHR data in combination with spontaneous reports, claims data, and other pharmacovigilance data sources for drug safety signal identification. Studies have shown that combining EHR data with these and other sources is often beneficial compared to the use of a single source alone, however the synergism or friction introduced is insufficiently explored in current literature. Our review explores how EHR data benefits signal identification when used in combination with other sources, what methods have been applied, and what considerations have previously been noted. Finally, we identify gaps in current research and highlight important considerations for future work using multiple real world data sources for drug safety surveillance.

Safety study on adverse events of zanubrutinib based on WHO-VigiAccess and FAERS databases.

The study endeavors to elucidate AE signals associated with zanubrutinib data from the WHO-VigiAccess and FAERS databases. The aim is to furnish robust scientific evidence to inform clinical practice and regulatory decisions. We meticulously extracted all AE breports tied to zanubrutinib from the both databases, encompassing the period from the drug's market introduction until 30 April 2024. Retrospective quantitative analysis employing ROR, PRR, and BCPNN methodologies were utilized to analysis. The investigation unveiled 1,304 reports from WHO-VigiAccess and 1,141 reports from FAERS related to zanubrutinib. Among the top 30 reported PTs in both databases, those not recorded in the drug label included pyrexia, dizziness, and death. In the FAERS database, signals for zanubrutinib were detected across 19 SOCs. Systems not covered in the drug label included reproductive system and breast disorders, metabolism and nutrition disorders, ear and labyrinth disorders, among others. Unmentioned signals included intestinal perforation, onychoclasis, night sweats, etc. This study confirms common AEs of zanubrutinib and identifies new ones, highlighting the need for careful monitoring and personalized treatment. It also emphasizes the importance of ongoing drug safety surveillance and patient management to maximize therapeutic benefits and minimize risks.

Developing large language models to detect adverse drug events in posts on X

ABSTRACT Adverse drug events (ADEs) are one of the major causes of hospital admissions and are associated with increased morbidity and mortality. Post-marketing ADE identification is one of the most important phases of drug safety surveillance. Traditionally, data sources for post-marketing surveillance mainly come from spontaneous reporting system such as the Food and Drug Administration Adverse Event Reporting System (FAERS). Social media data such as posts on X (formerly Twitter) contain rich patient and medication information and could potentially accelerate drug surveillance research. However, ADE information in social media data is usually locked in the text, making it difficult to be employed by traditional statistical approaches. In recent years, large language models (LLMs) have shown promise in many natural language processing tasks. In this study, we developed several LLMs to perform ADE classification on X data. We fine-tuned various LLMs including BERT-base, Bio_ClinicalBERT, RoBERTa, and RoBERTa-large. We also experimented ChatGPT few-shot prompting and ChatGPT fine-tuned on the whole training data. We then evaluated the model performance based on sensitivity, specificity, negative predictive value, positive predictive value, accuracy, F1-measure, and area under the ROC curve. Our results showed that RoBERTa-large achieved the best F1-measure (0.8) among all models followed by ChatGPT fine-tuned model with F1-measure of 0.75. Our feature importance analysis based on 1200 random samples and RoBERTa-Large showed the most important features are as follows: “withdrawals”/”withdrawal”, “dry”, “dealing”, “mouth”, and “paralysis”. The good model performance and clinically relevant features show the potential of LLMs in augmenting ADE detection for post-marketing drug safety surveillance.

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

A real-world drug safety surveillance study of lenvatinib from the FAERS database

ABSTRACT Background There is a need to determine lenvatinib-associated real-world adverse events (AEs) as its adverse effects may result in its discontinuation. Research design and methods Lenvatinib-associated AEs were analyzed and quantified and risk signals from the first quarter of 2015 to the fourth quarter of 2023 were detected through data mining. Potential targets for lenvatinib-associated cholecystitis, cholangitis, and hepatic encephalopathy were identified by data mining. Result 68 Preferred Terms (PTs) with an important imbalance were kept. Unexpected AEs, such as immune-mediated hepatitis, portal vein thrombosis and adrenal insufficiency were associated with the use of lenvatinib use. Lenvatinib alone was more strongly associated with adrenal insufficiency than lenvatinib and pembrolizumab combination. Hepatic encephalopathy was more strongly correlated with drug use when Lenvatinib was administered to male patients with hepatocellular carcinoma. Most AEs occurred during the first month after treatment, with a median onset time of 41 days. FGFR4, PDGFRA, and KIT (Lenvatinib targets) are potentially linked to cholecystitis, cholangitis, and hepatic encephalopathy. Conclusions We identified Lenvatinib-associated AEs and discovered new AEs that will be useful for clinical monitoring and risk assessment.

Short-Term Training, a Useful Approach for Sustainable Pharmacovigilance Knowledge Development in Tanzania, Kenya, Ethiopia and Rwanda.

Continuous professional development among stakeholders involved in drug safety monitoring and surveillance is imperative in strengthening pharmacovigilance (PV) systems. The "Pharmacovigilance infrastructure and post-marketing surveillance system capacity building for regional medicine regulatory harmonization in East Africa" (PROFORMA) project aims to enhance the national PV infrastructure, post-marketing surveillance systems and clinical trial regulatory capabilities in Ethiopia, Tanzania, Kenya and Rwanda. To achieve this, training, including short-term training (STT) activities, at various levels is required. This article aims to describe the experiences of the authors during the development and implementation of STT in an attempt to improve the PV training landscape of these countries. To identify gaps, a baseline assessment of PV teaching and practices at the national medicines regulatory authorities (NMRAs) and medical universities was conducted. Five successive training sessions, tailored to each country's specific needs and regulatory environments, were conducted; three focusing on fundamental concepts in PV and two dedicated to training-of-trainers courses. The training targeted staff from PV units of the NMRAs and medical universities. Enabling participation from all four countries in the same training fostered cross-country learning and collaboration. The contribution of STT to university education and the operational methodologies within NMRAs are explored, showcasing the impact on knowledge transfer and skill development in each country. In conclusion, by investing strategically in STT activities and fostering partnerships with academic institutions and NMRAs, we demonstrated a sustainable approach to PV capacity strengthening in resource-limited settings. The success of this model underscores its potential for adoption and replication across the African continent, offering a valuable framework for strengthening drug safety regulation and ultimately protecting public health.

Post-marketing drug safety surveillance of enfortumab vedotin: an observational pharmacovigilance study based on a real-world database.

Enfortumab vedotin (EV) is an antibody-drug conjugate (ADC) that has been approved by the FDA for patients with locally advanced or metastatic urothelial carcinoma (UC). This study presents a comprehensive pharmacovigilance analysis of the post-marketing safety profile of EV in the real-world based on the US Food and Drug Administration Adverse Event Reporting System (FAERS). Adverse event (AE) reports regarding EV between January 2020 and December 2023 were obtained from the FAERS database. The standardized MedDRA query (SMQ) narrow search AEs on the preferred term (PT) level were used. Disproportionality analysis was performed to identify the AE signals for EV with the reporting odds ratio (ROR), proportional reporting ratio (PRR), multi-item gamma Poisson shrinker (MGPS), and Bayesian confidence propagation neural network (BCPNN). A total of 2,216 reports regarding EV were included in the present study. SMQ analysis results indicated that a stronger strength signal was found in severe cutaneous adverse reactions, retroperitoneal fibrosis, and peripheral neuropathy. A total of 116 significant disproportionality PTs referring to 14 system organ classes (SOCs) were retained by disproportionality analysis, with 49 PTs not listed on the EV drug label. Frequently reported EV-related AEs included rash, peripheral neuropathy, decreased appetite, alopecia, and pruritus. The time to onset of the majority of EV-related AEs was within 30 days (66.05%), with only 0.73% events occurring after 1 year. The disproportionality analysis highlights that dermatologic toxicity and peripheral neuropathy were the major AEs induced by EV. The potential AEs not listed on the drug label were mainly related to gastrointestinal, hepatic, and pulmonary events. Further research is needed to confirm and explore the EV-related AEs in clinical practice.

Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling.

The accurate identification and timely updating of adverse reactions in drug labeling are crucial for patient safety and effective drug use. Postmarketing surveillance plays a pivotal role in identifying previously undetected adverse events (AEs) that emerge when a drug is used in broader and more diverse patient populations. However, traditional methods of updating drug labeling with new AE information have been manual, time consuming, and error prone. This paper introduces the LabelComp tool, an innovative artificial intelligence (AI) tool designed to enhance the efficiency and accuracy of postmarketing drug safety surveillance. Utilizing a combination of text analytics and a trained Bidirectional Encoder Representations from Transformers (BERT) model, the LabelComp tool automatically identifies changes in AE terms from updated drug labeling documents. Our objective was to create and validate an AI tool with high accuracy that could enable researchers and FDA reviewers to efficiently identify safety-related drug labeling changes. Our validation study of 87 drug labeling PDF pairs demonstrates the tool's high accuracy, with F1 scores of overall performance ranging from 0.795 to 0.936 across different evaluation tiers and a recall of at least 0.997 with only one missed AE out of 483 total AEs detected, indicating the tool's efficacy in identifying new AEs. The LabelComp tool can support drug safety surveillance and inform regulatory decision-making. The publication of this tool also aims to encourage further community-driven enhancements, aligning with broader interests in applying AI to advance regulatory science and public health.

Advancing Drug Safety Surveillance: A Review of AI Techniques and Strategies for Improving Pharmacovigilance and Adverse Event Reporting

Advancing Drug Safety Surveillance: A Review of AI Techniques and Strategies for Improving Pharmacovigilance and Adverse Event Reporting

Statin-Associated Liver Dysfunction and Muscle Injury: epidemiology, Mechanisms, and Management Strategies.

Surveillance of drug safety is an important aspect in the routine medical care. Adverse events caused by real-world drug utilization has become one of the leading causes of death and an urgent issue in the field of toxicology. Cardiovascular disease is now the leading cause of fatal diseases in most countries, especially in the elderly population who often suffer from multiple diseases and need long-term multidrug therapy. Among which, statins have been widely used to lower bad cholesterol and regress coronary plaque mainly in patients with hyperlipidemia and atherosclerotic cardiovascular diseases (ASCVD). Although the real-world benefits of statins are significant, different degrees and types of adverse drug reactions (ADR) such as liver dysfunction and muscle injury, have a great impact on the original treatment regimens as well as the quality of life. This review describes the epidemiology, mechanisms, early identification and post-intervention of statin-associated liver dysfunction and muscle injury based on the updated clinical evidence. It provides systematic and comprehensive guidance and necessary supplement for the clinical safety of statin use in cardiovascular diseases.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects.

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

A simulation-based comparison of drug-drug interaction signal detection methods.

Several statistical methods have been proposed to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance, which mainly relies on spontaneous reporting system database. Most previous studies have applied statistical models to real world data, but it is not clear which method outperforms the others. We aimed to assess the performance of various detection methods by implementing simulations under various conditions. We reviewed proposed approaches to detect signals indicating drug-drug interactions (DDIs) including the Ω shrinkage measure, the chi-square statistic, the proportional reporting ratio, the concomitant signal score, the additive model and the multiplicative model. Under various scenarios, we conducted a simulation study to examine the performances of the methods. We also applied the methods to Korea Adverse Event Reporting System (KAERS) data. Of the six methods considered in the simulation study, the Ω shrinkage measure and the chi-square statistic with threshold = 2 had higher sensitivity for detecting the true signals than the other methods in most scenarios while controlling the false positive rate below 0.05. When applied to the KAERS data, the two methods detected one known DDI for QT prolongation and one unknown (suspected) DDI for hyperkalemia. The performance of various signal detection methods for DDI may vary. It is recommended to use several methods together, rather than just one, to make a reasonable decision.

Application of the U.S. Food and Drug Administration's Sentinel Routine Querying Tools to the Taiwan Sentinel Data Model-formatted National Health Insurance Research Database.

The U.S. Food and Drug Administration's Sentinel System is a leading distributed data network for drug safety surveillance in the world. The National Health Insurance Research Database (NHIRD) in Taiwan was converted into the Taiwan Sentinel Data Model (TSDM) based on the Sentinel Common Data Model (SCDM) version 6.0.2. The goal of this study was to investigate the feasibility of applying the same study designs, analytic choices, and analytic tools as used by the U.S. Sentinel System to examine the same drug-outcome associations in the TSDM-formatted NHIRD. Four known drug-outcome associations previously examined by the U.S. Sentinel System were selected as the use cases: (1) use of angiotensin-converting enzyme inhibitors (ACEIs) and risk of angioedema, (2) use of warfarin and risk of gastrointestinal bleeding, (3) use of oral clindamycin and risk of Clostridioides difficile infection (CDI), and (4) use of glyburide and risk of serious hypoglycemia. We followed the same study designs and analytic choices used by the U.S. Sentinel System and applied the Sentinel Routine Querying Tools to answer the same study questions within the TSDM-formatted NHIRD. The results showed that ACEIs were associated with a non-significant increase in risk of angioedema compared to beta-blockers (hazard ratio [HR]: 1.21; 95% confidence interval [CI]: 0.89-1.64); warfarin was associated with a higher risk of gastrointestinal bleeding compared to statins (HR: 1.72; 1.50-1.98); glyburide was associated with an increased risk of hypoglycemia compared to glipizide (HR: 1.61, 1.30-2.00). We were unable to evaluate the association between oral clindamycin and risk of CDI due to the low event number. Our study demonstrated that it was feasible to directly apply the publicly available Sentinel Routine Querying Tools within the TSDM-formatted NHIRD. However, sources of heterogeneity other than design and analytic differences should be carefully considered when comparing the results between the two systems.

Epidemiological surveillance of drug safety using cumulative sequential analysis in electronic healthcare data.

Methods for safety signal detection in electronic healthcare data analysing data sequentially are being developed to meet the limitations of spontaneous reporting systems. This study aims to provide an overview of the literature on sequential analysis of electronic healthcare data and describe the development and testing of a novel epidemiological surveillance system. We searched Medline, Embase, PubMed, Scopus, Web of Science, and the Cochrane Library applying similar in- and exclusion criteria as those of a previous systematic review. The proposed system consisted of repeated cohort studies and was tested in an emulated prospective setting. Two signal evaluations were performed with several sensitivity analyses and a target trial emulation. In the literature, 11 studies analysed the data sequentially of which two applied traditional epidemiological methods. Epidemiological surveillance of several exposures and outcomes can be successfully conducted with the newly proposed sequential analysis of electronic healthcare data. Signal evaluation studies confirmed the results of the system. Very few studies in the literature analysed data at multiple time points, although this seems to be a prerequisite for testing the methods in a realistic setting. We demonstrated the feasibility of a sequential surveillance system using electronic healthcare data.

A simplified similarity-based approach for drug-drug interaction prediction.

Drug-drug interactions (DDIs) are a critical component of drug safety surveillance. Laboratory studies aimed at detecting DDIs are typically difficult, expensive, and time-consuming; therefore, developing in-silico methods is critical. Machine learning-based approaches for DDI prediction have been developed; however, in many cases, their ability to achieve high accuracy relies on data only available towards the end of the molecule lifecycle. Here, we propose a simple yet effective similarity-based method for preclinical DDI prediction where only the chemical structure is available. We test the model on new, unseen drugs. To focus on the preclinical problem setting, we conducted a retrospective analysis and tested the models on drugs that were added to a later version of the DrugBank database. We extend an existing method, adjacency matrix factorization with propagation (AMFP), to support unseen molecules by applying a new lookup mechanism to the drugs' chemical structure, lookup adjacency matrix factorization with propagation (LAMFP). We show that using an ensemble of different similarity measures improves the results. We also demonstrate that Chemprop, a message-passing neural network, can be used for DDI prediction. In computational experiments, LAMFP results in high accuracy, with an area under the receiver operating characteristic curve of 0.82 for interactions involving a new drug and an existing drug and for interactions involving only existing drugs. Moreover, LAMFP outperforms state-of-the-art, complex graph neural network DDI prediction methods.

Adverse events of epidiolex: A real-world drug safety surveillance study based on the FDA adverse event reporting system (FAERS) database

Epidiolex, the first FDA-approved drug with cannabis extract, treats Dravet and Lennox-Gastaut syndromes. Using data from the FAERS database between 2018 and 2023, this study analyzed 13,275 Epidiolex-related adverse events. Through computational methods (ROR, PRR, BCPNN, EBGM), we found that real-world adverse reactions largely align with those in Epidiolex's drug leaflet. However, Seizure cluster, Blood ketone body decrease, Cortical visual impairment, Hyperactive pharyngeal reflex, and Poverty of speech emerged as potential new side effects not previously listed, warranting further attention for drug safety.

An Explainable Framework for Predicting Drug-Side Effect Associations via Meta-Path-Based Feature Learning in Heterogeneous Information Network.

Side effects of drugs have gained increasing attention in the biomedical field, and accurate identification of drug side effects is essential for drug development and drug safety surveillance. Although the traditional pharmacological experiments can accurately detect the side effects of drugs, the identifying process is time-consuming, costly, and may lead to incomplete identification of side effects. With the expanding of various biomedical databases, many computational methods have been developed for the task of drug-side effect associations (DSAs) prediction. However, existing methods have the following three drawbacks: 1). multiple drug-related databases are not fully used; 2). the complex semantics among drugs and side effects are not effectively captured; 3). the explainability of the predicted DSAs is missed for most existing methods. Therefore, there is an urgent need to find a more effective method for predicting DSAs. To address these issues, we propose a novel meta-path-based graph neural network model for drug-side effect associations prediction (MPGNN-DSA). In MPGNN-DSA, a heterogeneous information network is first constructed by combining multiple biological datasets. Then, a meta-path-based feature learning module is utilized for learning high-quality representations of drugs and side effects by capturing the semantics contained in meta-paths of the constructed HIN. With the learned features, the prediction module is conducted to derive the predicted side effects for drugs. In addition, the explainability of the predicted DSAs can be provided as well with the semantics contained in meta-paths. We conduct comprehensive experiments, and the results demonstrate the effectiveness of MPGNN-DSA, suggesting that the proposed method will be a feasible solution to the task of DSAs prediction.

Transforming Pharmacovigilance Using Gen AI: Innovations in Aggregate Reporting, Signal Detection, and Safety Surveillance

Pharmacovigilance, the science of monitoring and evaluating drug safety, plays a crucial role in ensuring patient well-being and public health. With the advent of artificial intelligence (AI), specifically Gen AI, pharmacovigilance has witnessed a transformative shift. Gen AI's advanced capabilities in real-time signal detection, automated reporting, and data integration have significantly enhanced the efficiency and accuracy of drug safety monitoring and surveillance. This article explores the role of Gen AI in pharmacovigilance, emphasizing its potential to revolutionize aggregate reporting, signal detection, risk assessment, and safety surveillance. It delves into the challenges and considerations that come with adopting AI in pharmacovigilance, such as ethical and regulatory implications, data privacy and security concerns, and the need for algorithm transparency and interpretability. The article also discusses the future directions and opportunities for Gen AI in pharmacovigilance which include enhanced signal detection algorithms, personalized safety assessments, and predictive risk modelling and incorporation of emerging technologies like blockchain and IoT that can complement Gen AI and improve data security and real-time monitoring. Collaborative efforts and data sharing among stakeholders are essential for maximizing Gen AI's potential in pharmacovigilance. Public-private partnerships and global pharmacovigilance networks can accelerate the adoption of AI technologies and drive innovation in drug safety monitoring. In conclusion, Gen AI presents a transformative opportunity for pharmacovigilance, promising safer medications and improved patient outcomes. Embracing responsible AI adoption, addressing ethical considerations, and encouraging further research are key to unlocking the full potential of Gen AI in advancing drug safety and public health.