Pharmacovigilance is Everyone's Concern: Let's Work It Out Together

Pharmacovigilance in Vaccines: Importance, Main Aspects, Perspectives, and Challenges-A Narrative Review

Pharmacovigilance plays a central role in safeguarding public health by continuously monitoring the safety of vaccines, being critical in a climate of vaccine hesitancy, where public trust is paramount. Pharmacovigilance strategies employed to gather information on adverse events following immunization (AEFIs) include pre-registration data, media reports, clinical trials, and societal reporting. Early detection of AEFIs during clinical trials is crucial for thorough safety analysis and preventing serious reactions once vaccines are deployed. This review highlights the importance of societal reporting, encompassing contributions from community members, healthcare workers, and pharmaceutical companies. Technological advancements such as quick response (QR) codes can facilitate prompt AEFI reporting. While vaccines are demonstrably safe, the possibility of adverse events necessitates continuous post-marketing surveillance. However, underreporting remains a challenge, underscoring the critical role of public engagement in pharmacovigilance. This narrative review comprehensively examines and synthesizes key aspects of virus vaccine pharmacovigilance, with special considerations for specific population groups. We explore applicable legislation, the spectrum of AEFIs associated with major vaccines, and the unique challenges and perspectives surrounding pharmacovigilance in this domain.

A smart hospital-driven approach to precision pharmacovigilance

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug's risks and adverse drug reactions. These goals can be achieved by using 'smart hospitals' as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

Adverse Drug Reaction Case Safety Practices in Large Biopharmaceutical Organizations from 2007 to 2017: An Industry Survey

INTRODUCTION

Drug safety remains a top global public health concern. An increase in the number of data sources available has increased the complexity of pharmacovigilance operations, so the US FDA has created draft guidance focusing on optimizing drug safety data for well-characterized medicines. However, to date, no data demonstrating changes in reports have been presented.

OBJECTIVES

This study provided data assessing changes in individual case safety reports (ICSRs) and aggregate reports (ARs) for large biopharmaceutical companies from 2007 to 2017. This study also evaluated current trends on the use of advanced machine and deep learning in order to process all data captured for ICSRs as well as opinions from industry thought leaders on creating a sustainable case-processing operation.

METHODOLOGY

Using data captured from Navitas Life Science's annual pvnet^® benchmark, we calculated workload indicators characterizing pharmacovigilance operations for large biopharmaceutical organizations. Workload indicators included the number of ICSRs by organization, the number of ARs, and the number and types of data sources used. We also conducted structured in-depth interviews with seven biopharmaceutical executives to discover the reasons for changes in workload indicators across time as well as current strategies for increasing efficiencies in drug safety reporting.

RESULTS

The median number of ICSRs increased from 84,960 cases in 2007 to over 200,000 cases in 2017; this increase was largely attributable to an increase in both nonserious cases and follow-up cases. Member companies reported using 12 ± 3 data sources for case identification. The number of ARs also increased from a median of 70 reports in 2007 to 258 reports in 2017. To address these increases, 61% of the biopharmaceutical organizations we surveyed planned to adopt machine learning for full ICSR processing; however, as of 2018, none of the organizations surveyed had mechanisms in place.

CONCLUSION

This study demonstrated that pharmacovigilance departments are currently burdened by ever-increasing case volumes. With increased guidance from regulatory agencies, as well as improvements in artificial intelligence and natural language processing, biopharmaceutical organizations must determine the most resource-efficient and sustainable methods to process the growing volume of cases.

Signal Management in Pharmacovigilance: A Review of Activities and Case Studies

PURPOSE

After nearly 12 decades of pharmaceutical catastrophes and the associated groundbreaking regulatory innovations, pharmacovigilance has come down to us in the present day as 3 interlocking core disciplines: case management, signal management, and benefit-risk management. This review provides a state-of-the-art introduction to the great variety of sources of safety information, both dependent on and independent of the Individual Case Safety Report (ICSR), and explains how this content undergoes management-system processes with globally accepted definitions, standards, and structures that make possible the ongoing safe use of a pharmaceutical product throughout its lifecycle. This occurs in the context of: (1) new products coming to market with new risks for drug-drug interactions, and (2) new global threats to safe manufacturing and distribution.

METHODS

This narrative review, reflective of the author's experience, uses a large-frame system of signal management developed by the Council for International Organizations of Medical Sciences VIII Working Group and modified by the author to include governance. A key feature of the review is the regular inclusion of relevant case studies to provide a backdrop of the unexpected, with resulting tragic outcomes, to the ever-evolving regulatory landscape.

FINDINGS

Regarding content, beyond the commonly appreciated sources of safety information that find their way into ICSRs are non-ICSR-based sources, including preclinical data, manufacturing data, findings from subject-matter experts who participate on data-monitoring committees, outside expert panels, advocacy groups, and independent investigator studies. Regarding process, it is important to recognize that governance is crucial in the effective conduct of signal management, in that subject-matter experts are essential to the scientific and medical aspects of decision making, and business and policy executives are essential in determining the final courses of action, as these decisions have implications for the company.

IMPLICATIONS

Signal management is an integral part of pharmacovigilance practices that strive to obtain all of the information necessary for maintaining the safety profiles of a company's pharmaceutical and biological products, to support favorable benefit-risk balances, and to ensure safe use by health care providers and their patients.

Big data, farmacoepidemiología y farmacovigilancia

Big data es un término que comprende un grupo de herramientas tecnológicas capaces de procesar conjuntos de datos heterogéneos extremadamente grandes, los cuales se recolectan de manera continua, están disponibles para ser usados y constituyen una fuente de evidencia científica.En el área de la farmacoepidemiología, los análisis generados a partir de estos conjuntos de datos pueden resultar en la obtención de terapias médicas más eficientes, con menor número de reacciones adversas y menos costosas. Asimismo, el uso de herramientas como el Text Mining o el Machine Learning también ha llevado a grandes avances en las áreas de farmacoepidemiología y farmacovigilancia, por lo que es probable que su empleo sea cada vez mayor.

Assessing Patient Participation Burden Based on Protocol Design Characteristics

BACKGROUND

Although a number of studies have quantitatively measured investigative site burden to administer increasingly complex protocol designs, robust scholarly research has not been performed to quantify the burden that patients face as participants in clinical trials.

METHODS

This paper presents the results of a cross-sectional pilot study conducted by the Tufts Center for the Study of Drug Development and ZS Associates among nearly 600 patients via an online survey conducted between February and March 2019. Respondents rated the perceived burden of 60 commonly administered protocol procedures. The association and relationship between overall patient burden-derived from aggregating mean perceived burden ratings for individual procedures-and performance (eg, screen failure and retention rates, clinical trial cycle times) for a cross-sectional sample of 137 individual protocols was assessed. Descriptive statistics, significance tests, and univariate analyses were performed.

RESULTS

Strong positive, statistically significant associations were observed between a composite measure of patient burden and protocol-specific design and performance measures, most notably study visits above the tolerable mean and the study conduct duration from first patient first visit to last patient last visit.

CONCLUSIONS

The study results suggest a new and viable approach to optimize protocol design and improve patient engagement.