Evaluation of tolvaptan-associated hepatic disorder using different national pharmacovigilance databases.

Tolvaptan-associated hepatic disorder is a rare, but lethal adverse event; however, the precise risk and time of onset remain unclear. This study aimed to characterize the severity, time­to­onset, and outcomes of hepatic disorder based on patient age and sex. Patient data were acquired from the Japanese Adverse Drug Event Report database (JADER) and the JAPIC AERS database, which consists of the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) processed by the Japan Pharmaceutical Information Center. Hepatic disorder was classified as severe or nonsevere. Tolvaptan use was associated with hepatic disorder in analyses using the FAERS [Severe hepatic disorder: reporting odds ratio (ROR) 4.93, 95% confidence interval (CI) 4.33‒5.61; information component (IC) 2.11, 95% CI 1.92‒2.29; nonsevere hepatic disorder: ROR 6.78, 95% CI 6.01‒7.65; IC 2.51, 95% CI 2.33‒2.68] and the JADER (severe hepatic disorder: ROR 4.21, 95% CI 3.57‒4.97; IC 1.86, 95% CI 1.63‒2.10; nonsevere hepatic disorder: ROR 4.27, 95% CI 3.68‒4.95; IC 1.83, 95% CI 1.62‒2.04). A time­to­onset analysis revealed that the median onset time was significantly longer in patients aged < 60 years compared with patients aged ≥ 60, regardless of the severity (FAERS: severe hepatic disorder 7 vs. 58 days, p < 0.0001; nonsevere hepatic disorder 8 vs. 52.5 days, p < 0.0001; JADER: severe hepatic disorder 9.5 vs. 32 days, p = 0.0017; nonsevere hepatic disorder 9 vs. 89 days, p < 0.0001). Severe outcomes were observed, regardless of the severity of hepatic disorder. Patients should be monitored for liver function based on age to prevent fatal outcomes.

Association of Clopidogrel with Interstitial Lung Disease: Gaining Insight Through the Japanese Pharmacovigilance Database.

Background: The P2Y12 receptor inhibitors clopidogrel and prasugrel are widely used. Clopidogrel and prasugrel have different metabolic pathways, but whether their adverse event (AE) profiles differ significantly is unclear. Objective: This study aimed to compare the possible AEs induced by clopidogrel and prasugrel and to assess the rank-order of their AEs submitted to a spontaneous reporting database. Materials and Methods: Data were extracted from the Japanese Adverse Drug Event Report database (JADER). Reports of AEs associated with clopidogrel and prasugrel were analyzed to calculate the reporting odds ratios (RORs) and 95% confidence intervals (CIs). Results: Based on 5869 reports for clopidogrel (69.6%, men) and 513 reports for prasugrel (74.1%, men), 703 and 135 different AEs were identified, respectively. Bleeding complications including hemorrhage were commonly reported for both clopidogrel and prasugrel. As for AEs related to clopidogrel, unexpected AEs such as interstitial lung disease (227 reports; ROR, 1.77; 95% CI, 1.49-2.10), abnormal hepatic function (137 reports; ROR, 1.27; 95% CI, 1.07-1.51), and hepatocellular injury (96 reports; ROR, 120.0; 95% CI, 94.9-151.8) ranked at relatively high positions based on the number of occurrences, unlike prasugrel. Conclusion: This analysis of the national pharmacovigilance database highlights distinct AE profiles for clopidogrel and prasugrel. Unexpected AEs associated with clopidogrel were identified, providing valuable insights for clinical monitoring and patient safety.

Comprehensive Analysis of Drug-Induced Parkinson-like Events.

Specific drugs are well known to have the capacity to induce Parkinson-like symptoms. Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Pharmaceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with the associated drug trends and characteristics. Our findings indicate that similar to Parkinson's disease, age-related differences affect the onset of these reported events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, the side effects can vary depending on the site of action. These insights underline the importance of the swift identification of the drugs suspected of causing these events and the implementation of measures to reduce their side effects.

Estimating Adverse Events Associated With Herbal Medicines Using Pharmacovigilance Databases: Systematic Review and Meta-Analysis.

BACKGROUND: Herbal medicines (HMs) are extensively used by consumers/patients worldwide. However, their safety profiles are often poorly reported and characterized. Previous studies have documented adverse events (AEs) associated with HMs, such as hepatotoxicity, renal failure, and allergic reactions. However, the prevalence rate of AEs related to HMs has been reported to be low. To date, no systematic review and meta-analysis has comprehensively analyzed the AEs of HMs using published data acquired from pharmacovigilance (PV) databases. OBJECTIVE: This study aimed to (1) estimate the reporting rate of the AEs of HMs using PV databases and (2) assess the detailed data provided in AE reports. METHODS: In this systematic review and meta-analysis, MEDLINE/PubMed, SCOPUS, EMBASE, and CINAHL were systematically searched for relevant studies (until December 2023). The DerSimonian-Laird random effects model was used for pooling the data, and subgroup analyses, the meta-regression model, and sensitivity analysis were used to explore the source of heterogeneity. Crombie's checklist was used to evaluate the risk of bias (ROB) of the included studies. RESULTS: In total, 26 studies met the eligibility criteria. The reporting rate of the AEs of HMs ranged considerably, from 0.03% to 29.84%, with a median overall pooled estimate of 1.42% (IQR 1.12%-1.72%). Subgroup analyses combined with the meta-regression model revealed that the reporting rate of the AEs of HMs was associated with the source of the reporter (P=.01). None of the included studies provided full details of suspected herbal products, only the main ingredients were disclosed, and other potentially harmful components were not listed. CONCLUSIONS: This systematic review and meta-analysis highlighted risks related to HMs, with a wide range of reporting rates, depending on the source of the reporter. Continuous efforts are necessary to standardize consumer reporting systems in terms of the reporting form, education, and follow-up strategy to improve data quality assurance, aiming to enhance the reliability and utility of PV data for monitoring the safety of HMs. Achieving effective monitoring and reporting of these AEs necessitates collaborative efforts from diverse stakeholders, including patients/consumers, manufacturers, physicians, complementary practitioners, sellers/distributors, and health authorities. TRIAL REGISTRATION: PROSPERO (Prospective International Register of Systematic Reviews) CRD42021276492.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review.

INTRODUCTION: Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility. AREAS COVERED: The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times. EXPERT OPINION: Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

Adverse drug reaction signal detection methods in spontaneous reporting system: A systematic review.

BACKGROUND: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system. METHODS: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method. RESULTS: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods. CONCLUSION: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events.

Psychotherapeutic drug-induced life-threatening arrhythmias: A retrospective analysis using the Japanese adverse drug event report database.

Background: Drug-induced life-threatening ventricular arrhythmias including torsade de pointes (TdP), ventricular tachycardia (VT), and ventricular fibrillation (VF) are serious cardiac side effects. Psychotherapeutic drugs are known to be risk factors for arrhythmias. The aim of this study was to evaluate psychotherapeutic drugs associated with life-threatening ventricular arrhythmias using the Japanese Adverse Drug Event Report (JADER) database. Methods: From the JADER database (April 2004 to September 2022), cases of TdP, VT, VF, and QT prolongation in patients taking psychotherapeutic drugs as 'suspected drugs' were extracted. The adjusted reported odds ratio (aROR) was calculated to identify potential drugs involved in combined TdP/VF/VT or combined QT prolongation/TdP. Results: Of the 4,530,772 cases reported, life-threatening arrhythmia-related adverse events were reported in 1760 cases (QT prolongation 1261, TdP 192, VF 108, VT 199) among 909 patients; 58.9% of patients were female, and the highest incidence was among patients aged 80-89 years (18.6%), followed by patients aged 70-79 years (15.4%). The highest aROR for TdP/VF/VT was found for trazodone (17.1), followed by sulpiride (10.8), haloperidol (9.8), donepezil (9.1), and fluvoxamine (7.9). The highest aROR for QT prolongation/TdP was found for guanfacine (87.8), followed by sultopride (60.1), escitalopram (21.0), trazodone (12.8), and donepezil (9.3). Conclusions: This study showed that typical antipsychotics, antidepressants, and antidementia drugs were associated with life-threatening arrhythmia-related adverse events in a Japanese clinical setting. These events were more frequent in women and elderly individuals.

A Pharmacovigilance Study of Drug-Induced Glaucoma Utilizing the Japanese Adverse Event Reporting System.

Purpose: Clinically, glaucoma is a serious problem because it is asymptomatic until a relatively late stage in most cases, which can lead to delays in the diagnosis and treatment of the disease. The purpose of this study was to clarify the rank-order of the association of glaucoma with the causative drugs using a spontaneous reporting system database. Methods: Data were extracted from the Japanese Adverse Drug Event Report database of the Pharmaceuticals and Medical Devices Agency (Japan). Based on reports of glaucoma caused by all drugs, we calculated the reporting odds ratio (ROR) and 95% confidence interval (CI) for glaucoma. Results: Among 609 reports of adverse events corresponding to glaucoma (46%, women), the most frequently implicated drug were steroids (prednisolone, betamethasone sodium phosphate, triamcinolone acetonide, and fluorometholone), pregabalin, ranibizumab, crizotinib, tacrolimus hydrate, darbepoetin alfa, and foscarnet sodium hydrate. Among 207 reports involved in angle-closure glaucoma (86%, women), anticholinergic drug and antidepressants ranked high and showed signals. Signals were also detected in bromazepam (ROR, 69.7; 95% CI, 30.9-157.5), oral brotizolam (ROR, 16.6; 95% CI, 6.18-44.8), and oral milnacipran hydrochloride (ROR, 22.8; 95% CI, 8.46-61.4) for angle-closure glaucoma. Conclusion: A national pharmacovigilance database enabled us to identify the drugs that frequently induce glaucoma. The likelihood of the reporting of glaucoma varied among the drugs, which should be used carefully in clinical practice to avoid it.

Pituitary-Related Adverse Events and Onset Patterns Caused by Immune Checkpoint Inhibitors: Analysis Using the Japanese Adverse Drug Event Report Database.

Background and Objectives: One type of immune-related adverse event caused by immune checkpoint inhibitors (ICIs) is pituitary-related adverse events. The management of pituitary-related adverse events is important because they can be fatal if not treated promptly. Therefore, this study was conducted to investigate the onset of pituitary-related adverse events using the Japanese Adverse Drug Report (JADER) database. Materials and Methods: Cases registered in the JADER database from 2004 to 2019 were used. The target drugs were ipilimumab, nivolumab, pembrolizumab, avelumab, atezolizumab, and durvalumab, and the target adverse events were the high-level terms "Anterior pituitary hypofunction," "Anterior pituitary hyperfunction," "Posterior pituitary disorder," and "Pituitary neoplasm" in the Medical Dictionary for Regulatory Activities, Japanese version (MedDRA/J). The information component (IC) was used for signal detection and IC delta (ICΔ) was used for women-related signals. Onset timing and patterns were analyzed using the Weibull distribution. Results: Signals were detected with ipilimumab, nivolumab, pembrolizumab, and atezolizumab in "Anterior pituitary hypofunction," with ICs and 95% credible intervals (95%CrI) of 5.53 (5.30-5.69), 4.96 (4.79-5.08), 4.04 (3.76-4.25), and 2.40 (1.53-3.00). Significant signals were detected in women, except for atezolizumab. Additionally, the time of onset was classified as the wear-out failure type. Inverse signals were detected with ipilimumab and nivolumab in "Posterior pituitary disorder," with ICs (95%CrI) of -1.24 (-2.80--0.26), and -0.89 (-1.64--0.37). Conclusions: Anterior pituitary hypofunction is likely to occur with the long-term administration of ipilimumab, nivolumab, and pembrolizumab. Further investigation is needed to determine the differences in the tendencies to detect signals in the anterior and posterior pituitaries between ipilimumab and nivolumab.

Skin Toxicities Associated with Botulin Toxin Injection for Aesthetic Procedures: Data from the European Spontaneous Reporting System.

Botulinum toxin is a protein deriving from the bacteria Clostridium botulinum and it is widely used for the treatment of a variety of muscle hyperactivity syndromes and for cosmetic indications. Having a long-lasting effect, Botulinum toxin type A (BTA) is one of the most botulin toxin products used. Even if BTA has shown benefits in reducing the vertical lines between the eyebrows, Adverse Drug Reactions (ADRs) have been experienced as well, of which the most common ones are headache and drooping eyelids. In addition, since other local and systemic risks have been identified, a non-interventional post-authorization safety study (PASS) has been started. The aim of the present study was to report cases of skin toxicity associated with this drug, considering Individual Case Safety Reports (ICSRs) existing on the Eudravigilance website. Among 1464 ICSRs sent to the EV database, 718 ICSRs, including 5154 PTs, reported BTA as a suspected drug associated with cutaneous toxicity. The majority of patients experiencing BTA-induced skin toxicity were female (92.1%) belonging mostly to the age group of 18-64 years. The most serious criteria, when reported, were "Other Medically Important Condition" and "Caused/prolonged hospitalization", although the outcome was mainly reported as "Unknown". The most reported PTs, related to skin disorders, were: "Erythema", "Rash", "Pruritus", "Urticaria", "Swelling face", "Brow ptosis", "Eyelid ptosis", "Injection site pain", and "Angioedema". Considering that in most ICSRs, ADRs related to skin disorders were symptoms of hypersensitivity reactions which in some conditions could be life-threatening, further studies are required to better define the safety profile of BTA used for aesthetic procedures.

Progressive multifocal leukoencephalopathy analyzed using the Japanese Adverse Drug Event Report database.

BACKGROUND: Progressive multifocal leukoencephalopathy (PML) has been reported as the development of drugs with immunomodulatory properties, such as anticancer, immunosuppressive, and biological agents, has accelerated. To clarify an incidence profile of drug-associated PML in real-world clinical practice, we analyzed reported patients with PML using the Japanese Adverse Drug Event Report (JADER) database. METHODS: We analyzed PML reports extracted from the JADER database based on the preferred term of "progressive multifocal leukoencephalopathy" from between 2004 and 2021. This was a retrospective, observational study. We evaluated the effects of causative drugs, underlying diseases, and the age of the patients on the annual number of PML reports. RESULTS: The JADER database contained 773,966 reports published between April 2004 and March 2022, from which we identified 361 PML events. These PML events may include multiple counts of the same case reported by different pathways and patients diagnosed with probable or possible PML. The number of PML reports and reporting ratios have gradually increased over the past decade. The annual number of PML reports associated with biologics, immunosuppressants, and antineoplastic drugs showed an increasing trend. Females aged ≥30 years showed an increase in PML reports; in contrast, there the number of reports for males aged ≥50 years increased. CONCLUSIONS: The number of PML reports and reporting ratios have gradually increased in the past decade in Japan, and it considered that it was related to change in the treatment of malignancies and autoimmune diseases, and the increasing use of biologics, immunosuppressive agents, and antineoplastic agents.

Analysis of Corticosteroid-Induced Glaucoma Using the Japanese Adverse Drug Event Reporting Database.

Glaucoma is the most common cause of blindness, which significantly reduces quality of life. Most glaucoma cases are primary glaucoma; nevertheless, many patients suffer from glaucoma caused by drugs, such as corticosteroids. A comprehensive review of the risks associated with corticosteroid-induced glaucoma is limited. Therefore, we used the Japanese Adverse Drug Event Reporting Database (JADER) published by the Pharmaceuticals and Medical Devices Agency (PMDA) to analyze the risk factors associated with glaucoma and the trends and characteristics of corticosteroid-induced glaucoma. We did not find sex or age differences associated with the onset of glaucoma. Hierarchical clustering and principal component analysis revealed that triamcinolone acetonide and betamethasone sodium phosphate, which are used around the eyes in Japan, are more likely to induce intraocular pressure (IOP) elevation compared with other corticosteroids. Increased IOP is a direct cause of glaucoma. Based on these findings, it may be necessary to limit or avoid the use of these corticosteroids.

Preventability of Adverse Drug Reactions Related to Antibiotics: An Assessment Based on Spontaneous Reporting System.

BACKGROUND: Antibiotics are commonly used in both outpatient and inpatient settings and are responsible for the majority of adverse drug reaction (ADR) reports. We aimed to characterize spontaneously reported ADRs associated with antibiotics and assessing the preventability of these ADRs in a Vietnamese setting. MATERIALS AND METHODS: We conducted a retrospective descriptive study based on ADRs related to antibiotics spontaneously reported by healthcare workers to the National Pharmacovigilance Database of Vietnam (NPDV) between June 2018 and May 2019. The characteristics of included reports were descriptively analyzed. The preventability of reported ADRs was assessed using a standardized preventability scale. We identified the leading causes and described the characteristics associated with preventable ADRs (pADRs). RESULTS: We included 6385 antibiotic-related reports from a total of 12,056 reports submitted to the NPDV during the study period. Beta-lactam antibiotics, mostly broad-spectrum with parenteral route, were suspected in the majority cases. The most commonly reported pADRs were allergic reactions, mostly classified under skin and subcutaneous tissue disorders. Of all included cases, 537 cases (8.4%) were deemed as associated with pADRs. Major causes of pADRs include potentially inappropriate prescribing (352/537, 65.5%) and re-administration of antibiotics causing prior allergy/allergies (99/537, 18.4%). The majority of pADRs involved the use of beta-lactam antibiotics with inappropriate indications. CONCLUSION: ADRs related to antibiotic use represent more than half of ADRs spontaneously reported in Vietnam. Approximately one in every ten reported cases is associated with pADRs. The majority pADRs can be prevented through simple improvement in antibiotic prescribing practices.

[Utilization of Big Data with a Focus on Administrative Claims Database].

With the development of information technology, patient information is stored as electronic data, and huge amounts of such data are collected every day. Such a collection compiled over the course of clinical practice is called real-world data and is expected to be used for evaluating drug efficacy and safety. Real-world data such as health insurance association-based administrative claims databases, pharmacy-based dispensing databases, and spontaneous reporting system databases are mainly used in pharmaceutical research. Among them, claims databases are used for various observational studies such as studies on nationwide prescription trends, pharmacovigilance studies, and studies on rare diseases due to their large sample size. Although the nature of omics data is different from that of real-world data, it has become accessible on cloud platforms and are being used to broaden the scope of research in recent years. In this paper, we introduce a method for generating and further testing hypotheses through integrated analysis of real-world data and omics data, with a focus on administrative claims databases.

Identifying Crude Drugs in Kampo Medicines Associated with Drug-Induced Liver Injury Using the Japanese Adverse Drug Event Report Database: A Comprehensive Survey.

The current study aimed to identify the crude drugs associated with drug-induced liver injury (DILI) in 148 Kampo medicines prescribed throughout Japan using the Japanese Adverse Drug Event Report (JADER) database, a large-scale spontaneous reporting system in Japan. First, we tabulated the number of DILI reports from the report-based dataset and the background information from the patient-based dataset. Thereafter, we combined the 126 crude drugs into 104 crude drug groups to examine multicollinearity. Finally, the reporting odds ratios (RORs), 95% confidence intervals, p values for Fisher's exact test, and number of reports were calculated for each crude group to identify those associated with DILI. Notably, the number of adverse event reports for DILI (63,955) exceeded that for interstitial lung disease (51,347), the most common adverse event. In total, 78 crude drug groups (90 crude drugs) were reported to have an ROR > 1, a p < 0.05, and ≥10 reported cases. Our results highlight DILI as an essential issue, given that it was among the most frequently reported adverse drug reactions. We were able to clearly identify the crude drugs associated with DILI, which could help manage adverse drug reactions attributed to Kampo medicines and crude drugs.

Analysis of Appendicitis Cases in the Japanese Adverse Drug Event Report (JADER) Database.

Appendicitis is one of the most common abdominal surgical emergencies worldwide; however, its causes remain poorly understood. The Japanese Adverse Drug Event Report (JADER) database is a spontaneous reporting system (SRS) that can be utilized to analyze the safety signals of adverse events. In this study, we investigated the association between drug use and the onset of appendicitis using the JADER database. We first used the reporting odds ratio (ROR) as the signal and found signals for appendicitis, perforated appendicitis, and complicated appendicitis for 23, 9, and 1 drug, respectively. To investigate the level of hazard over time in drug-associated appendicitis, the Weibull shape parameter ß was calculated using a Weibull plot, which revealed drug-dependent patterns for changes in the risk of appendicitis over time for the eight drugs. Furthermore, logistic regression analysis was performed to account for the influence of age, sex, and primary disease, and a significant association was detected between two drugs and appendicitis. Several types of drugs, such as antitumor, antirheumatic, and anti-inflammatory drugs, were included in our analyses; however, only clozapine, which is used for patients with schizophrenia, was commonly identified in these analyses. The resulting data suggest that certain drugs may be associated with appendicitis and may require adequate attention.

Application of tree-based machine learning classification methods to detect signals of fluoroquinolones using the Korea Adverse Event Reporting System (KAERS) database.

BACKGROUND: Safety issues for fluoroquinolones have been provided by regulatory agencies. This study was conducted to identify signals of fluoroquinolones reported in the Korea Adverse Event Reporting System (KAERS) using tree-based machine learning (ML) methods. RESEARCH DESIGN AND METHODS: All adverse events (AEs) associated with the target drugs reported in the KAERS from 2013 to 2017 were matched with drug label information. A dataset containing label-positive and -negative AEs was arbitrarily divided into training and test sets. Decision tree, random forest (RF), bagging, and gradient boosting machine (GBM) were fitted on the training set with hyperparameters tuned using five-fold cross-validation and applied to the test set. The ML method with the highest area under the curve (AUC) scores was selected as the final ML model. RESULTS: Bagging was selected as the final ML model for gemifloxacin (AUC score: 1) and levofloxacin (AUC: 0.9987). RF was selected in ciprofloxacin, moxifloxacin, and ofloxacin (AUC scores: 0.9859, 0.9974, and 0.9999 respectively). We found that the final ML methods detected additional signals that were not detected using the disproportionality analysis (DPA) methods. CONCLUSIONS: The bagging-or-RF-based ML methods performed better than DPA and detected novel AE signals previously unidentified using the DPA methods.

Artificial Intelligence in Pharmacovigilance and COVID-19.

The history of pharmacovigilance started back 169 years ago with the death of a 15- year-old girl, Hannah greener. However, the Thalidomide incident of 1961 brought a sharp change in the pharmacovigilance process, with adverse drug reaction reporting being systematic, spontaneous, and regulated timely. Therefore, continuous monitoring of marketed drugs was essential to ensure the safety of public health. Any observed adverse drug reaction detected by signals was to be reported by the health profession. Moreover, signal detection became the primary goal of pharmacovigilance based on reported cases. Among various methods used for signal detection, the Spontaneous Reporting System was most widely preferred; although, it had the limitation of "under- reporting". Gradually, the World Health Organization collaborating centre and "Uppsala Monitoring Centre" were established in 1978 for international monitoring of drugs. The centre was responsible for operating various databases like vigiflow, vigibase, vigilyze, and vigiaccess. Recently, huge data could be generated through spontaneous reporting linked with computational methods, such as Bayesian Framework, E-Synthesis. Furthermore, drug safety surveillance at an early stage prior to the official alerts or regulatory changes was made possible through social media. In addition, India created a National Pharmacovigilance Program, and Schedule Y of the Drug and Cosmetic Act 1945 was reviewed and amended in 2005. The collaboration of Information Technology and Pharmaceutical Company can further enhance the awareness regarding artificial intelligence in pharmacovigilance, which was in its infancy until 2017. Artificial intelligence helps improve the quality and accuracy of information much quickly.