Study of adverse drug reactions associated with antiepileptic drugs: a pharmacovigilance study using spontaneous reporting system

Background: More than 25 antiepileptic drugs (AEDs) are available in the Indian market to treat epilepsy of which many have similar efficacy but differ in their tolerability and are associated with many adverse drug reactions (ADRs). ADRs are one of the most common causes of death and clinical trials are not sufficient to uncover all the ADRs, hence post-marketing surveillance or pharmacovigilance is necessary. The aim of the study was to analyze the ADRs of AEDs by spontaneous reporting system under Pharmacovigilance Program of India (PvPI).Methods: Suspected ADR reporting forms provided by PvPI were used to collect the data from healthcare professionals of Madras Medical College and Rajiv Gandhi Government General Hospital, Chennai.Results: A total of 77 ADRs from 61 reports were analysed of which 34 were male and 27 were female patients and maximum were in the middle-aged adult group (N=44). Majority of the ADRs were related to skin and subcutaneous disorders (N=55) and most implicated ADR was found to be maculopapular rash (N=12) associated with phenytoin. Most of the ADRs were non-serious (N=42) and were probable category (N=45) as per WHO-UMC scale.Conclusions: Monitoring ADRs in patients using antiepileptic drugs is a matter of importance; hence a robust pharmacovigilance practice is essential.

COVID Vaccine and Cardiovascular Signals: An Analysis of Vaccine Adverse Event Reports (Preprint)

UNSTRUCTURED Adverse events (AEs) following COVID vaccination have been intensely monitored. In our study, we analyzed data from a spontaneous reporting system - Vaccine Adverse Event Reporting System and detected signals of AEs following administration of COVID vaccines. We identified several cardiovascular and inflammatory-related AEs that demonstrated high odds ratio. We demonstrated our system can serve as a complementary system to identify and monitor AEs outside of pre-defined outcomes routinely monitored by existing databases or projects.

Assessment of the Safety Signal for the Abuse Potential of Pregabalin and Gabapentin Using the FAERS Database and Big Data Search Analytics

Introduction: The latest decade, an emerging issue has been the abuse potential of the gabapentinoids pregabalin and gabapentin. The aim of our study was to assess this safety signal combining two different methods of surveillance: search analytics big data and the FDA spontaneous reporting system database.Methods: Analysis of big data and the FAERS was used to detect pregabalin's and gabapentin's abuse potential in comparison with two controls, clonazepam and levetiracetam, and further, the correlation between these domains was investigated. Data from the United States between 2007 and 2020Q2 were analyzed.Results: The FAERS analysis revealed the following pattern of signals: clonazepam > pregabalin ≥ gabapentin > levetiracetam, for both the primary term “drug abuse and dependence” and the secondary terms (withdrawal, tolerance, overdose). The Google domain pattern was slightly different: clonazepam ≥ gabapentin ≥ pregabalin≥ levetiracetam. A monotonic correlation was found between FAERS and Google searches for gabapentin (r = 0.558; p < 0.001), pregabalin (r = 0.587; p < 0.001), and clonazepam (r = 0.295; p = 0.030).Conclusion: Our results revealed that there is preliminary evidence of a safety signal for the abuse potential of pregabalin and gabapentin. Analysis of the FAERS database, supplemented by big data search analytics, suggests that there is potential of using these methods as a supplementary tool to detect drug abuse-related safety signals in pharmacovigilance.