Journal of Pharmacovigilance & Drug Safety

Adverse Events Following Vaccination with COVID-19 Vaccines: A Narrative Review

Abhinav Tiwary — 2024-02-08

Introduction: The Covid-19 pandemic has had a devastating effect globally. The rapid development of vaccines certainly helped in mitigating the damage by controlling the transmission of infection as well as by decreasing the incidence of severe disease. The rapid development process raised concerns over vaccines’ safety particularly its long-term safety. Study of AEFI of these vaccines is essential to guide choices for specific groups of population as well as to address concerns with regards to vaccine hesitancy.

Methods: Pubmed and Cochrane review database were used to access the literature. Data from pertinent official sources such as WHO, govt. of India and others were also used. All the relevant articles were organised using Zotero reference manager.

Results: The adverse reactions to various Covid 19 vaccines is mild to moderate in severity with no significant interference in daily activities of the recipient. Minor effects are similar in pregnant and non-pregnant population although some studies reported higher frequency of nausea and vomiting with Pfizer-BioNTech and Moderna vaccines. There is no unique pattern in cause of deaths among few vaccine related deaths that have been reported.

Conclusion: Vaccine has been the cornerstone in controlling Covid-19 pandemic and remains the key for preventing any future outbreaks. The vaccines are safe to administer and induces protection. It is vital to be aware of and keep monitoring the adverse events which shall help in better selection of vaccines for groups and subgroups of populations as well as to address the problem of vaccine hesitancy.

Keywords: Covid-19 vaccine, AEFI, Vaccine Hesitancy, Adverse effects

Artificial-Intelligence based Machine-Learning in Pharmacovigilance

Pragna Roy — 2024-02-08

Introduction:

Adverse drug responses (ADRs) pose a serious threat to healthcare, increasing the risk of death, morbidity, and medical expenses. The growing complexity and volume of healthcare data of ADR is driving the field of pharmacovigilance to evolve and integrating artificial intelligence (AI) techniques as Machine Learning (ML) has emerged as a potential answer. This article collectively addresses the evolving landscape of AI implementation on drug safety monitoring, emphasizing advancements, challenges, and opportunities

Objective:

The objective is to comprehensively examine the utilization of AI and ML techniques in pharmacovigilance, spanning topics such as distributed data networks, drug–drug interactions, ADR, real-time contextual intel, content formation

Method:

The review incorporates articles that were obtained from the databases of PubMed, Embase, Web of Science, and IEEE Xplore between the years 2000 and 2023 using keywords “artificial intelligence”; “machine learning”; “pharmacovigilance”

Result:

There is a significant shift towards advanced ML techniques, particularly deep learning, in pharmacovigilance. AI can predict and assess drug–drug interactions, emphasizing their intricate nature. It also structures data for pharmacovigilance from well-coordinated multi-databases but issues have been identified in distributed data networks. Although pharmacovigilance tasks and data sources now in use may not have been specifically created for causal inference, there is great potential for integrating machine learning with causal paradigms

Conclusion:

The collective findings underscore the promising advancements, persistent challenges, and future potential of AI and ML in enhancing pharmacovigilance practices. Standardization, interdisciplinary collaboration, and ongoing research efforts are crucial for realizing the full benefits of these technologies in ensuring drug safety and mitigating adverse events

Causality assessment of adverse drug reactions: A machine learning approach

Puneet Paliwal — 2024-02-08

Introduction: Tuberculosis, caused by Mycobacterium tuberculosis, has a reported incidence of 2.77 per 1 lakh population in 2022 with a mortality of 0.32 per million. Acquired Immunodeficency Syndrome is caused by Human Immunodeficiency virus (HIV) which is characterised by marked immune suppression resulting in opportunistic infections. There are various adverse drug reactions reported with the anti-tubercular and anti-retroviral therapy. The present study attempts for causality categorization by machine learning algorithm.

Materials and methods: The present study comprises of 60 cases of adverse drug reaction in patient on either anti-tubercular or anti-retroviral therapy. To predict the causality category, a neural network was designed with a single input layer, two middle layer and a output layer. The train model was assessed on test cases to find accuracy of prediction of causality category.

Results: Mean age of cases was 35.92 16.9 yrs. Mean weight of the cases were 50.36 12.8 kgs. The underlying disease were pulmonary tuberculosis 71.7%, MDR tuberculosis 11.7%, extra pulmonary tuberculosis 8.3% and PLHA 8.3%. Out of 60 cases of adverse drug reactions, 2 cases were hospitalised and 1 case died. Various adverse reactions noted were hepatitis (18.33%), peripheral neuropathy (16.67%), rashes (11.67%), vomiting (11.67%), itching (8.3%). Other rare reactions included visual disturbances, psychosis etc. Out of 60 cases,54 cases were of possible causality category and 6 cases were of probable category. The overall accuracy of trained neural network on test cases was 62.5%

Conclusion: Causality assessment can be done by machine learning algorithm, which may help in pharmacovigilance practices.

"Navigating the Landscape of Medical Device Failures: Challenges, Regulations, and Materiovigilance"

Syed Ziaur Rahman — 2024-02-08

Introduction: In the realm of healthcare, the significance of materiovigilance cannot be overstated. Materiovigilance, a term often overshadowed by pharmacovigilance, focuses on the surveillance and control of medical devices' safety and performance post-marketing. Its components include the systematic collection, analysis, and interpretation of data related to medical devices, aiming to enhance patient safety by decreasing adverse events associated and optimize device efficacy

Aim :The aim is to shed light on the crucial components of Materiovigilance and underscore the concerning lack of awareness in this vital domain of healthcare

Methodology: This literature review utilized databases like PUBMED, EMBASE, SCOPUS, and COCHRANE, employing keywords such as "materiovigilance"; "pharmacovigilance" ;"materiovigilance history"; and "awareness about materiovigilance"

Results: Critical components like vigilance reporting systems, risk assessment, and regulatory interventions play a pivotal role in ensuring medical device safety. It is evident that a significant lack of awareness, posing risks to patient safety. This research serves as a tool to bridge the awareness gap, emphasizing the need for understanding and active participation in Materiovigilance

Conclusion: Collaborative efforts among regulatory bodies, healthcare providers, and manufacturers can enhance understanding and awareness in Materiovigilance. This clarity and collaboration contribute to fostering Materiovigilance awareness, ensuring improved patient safety. By creating awareness, we aim to establish a safer healthcare environment, guaranteeing the efficacy and safety of medical devices globally

Pharmacovigilance in AYUSH Systems of Medicine

Galib . — 2024-02-09

The Sustainable Development Goals (SDGs), were adopted by all United Nations Member States in 2015 as a universal call to action to end poverty, protect the planet and ensure that all people enjoy peace and prosperity by 2030.¹Traditional Medicine (TM) can play a pivotal role in meeting these SDGs particularly of the SDG 3 (ensure healthy lives and promote wellbeing for all at all ages).