-
Clinical Therapeutics Dec 2018Pharmacovigilance (PV) is a relatively new discipline in the pharmaceutical industry. Having undergone rapid growth over the past 2 decades, PV now touches many other... (Review)
Review
PURPOSE
Pharmacovigilance (PV) is a relatively new discipline in the pharmaceutical industry. Having undergone rapid growth over the past 2 decades, PV now touches many other disciplines in the research and development enterprise. With its growth has come a heightened awareness and interest in the medical community about the roles that PV plays. This article provides insights into the background and inner workings of PV.
METHODS
This narrative review covers the core PV activities and other major areas of the pharmaceutical enterprise in which PV makes significant contributions.
FINDINGS
Drug safety monitoring activities were organized by the US Food and Drug Administration and academic medical centers in the early 1950s in response to growing concern over the occurrence of aplastic anemia and other blood dyscrasias associated with the use of chloramphenicol. This experience was codified in the 1962 Kefauver-Harris Amendments to the Federal Food, Drug and Cosmetic Act as adverse event evaluation and reporting requirements. The ensuing decades have seen the development of core PV functions for pharmaceutical companies: case management, signal management, and benefit-risk management. A broader scope of PV has developed to include the following major activities: support of patient safety during the conduct of clinical trials through assuring proper use of informed consent and institutional review boards (ethics committees); selection of the first safe dose for use in humans, based on pharmacologic data obtained in animal studies; development of the safety profile for proper use of a new molecular entity and appropriate communication of that information to the range of relevant stakeholders; attendance to surveillance activities through a set of signal management processes; monitoring the manufactured product itself through collaborative activities with manufacturing professionals; management of benefit-risk to assure appropriate use in medical care after marketing; and maintenance of inspection readiness as a corporate cultural process.
IMPLICATIONS
The extent and pace of change promise to accelerate with the integration of biomedical informatics, analytics, artificial intelligence, and machine learning. This progress has implications for the development of the next generation of PV professionals who will need to be trained in entirely new skill sets to lead continued improvements in the safe use of pharmaceuticals.
Topics: Animals; Drug Industry; Humans; Patient Safety; Pharmacovigilance
PubMed: 30126707
DOI: 10.1016/j.clinthera.2018.07.012 -
Revue Medicale Suisse Jan 2019The main pharmacovigilance updates in 2018 are reviewed. Quinolones : no longer recommended for mild or moderately severe infections. Denosumab in cancer patients:... (Review)
Review
The main pharmacovigilance updates in 2018 are reviewed. Quinolones : no longer recommended for mild or moderately severe infections. Denosumab in cancer patients: increased risk of new primary malignancies. Cyproterone : increased risk of meningioma at high dose. Saccharomyces boulardii : risk of fungemia in frail patients. Ulipristal : risk of hepatotoxicity. Daclizumab : early withdrawal from the market as risks clearly outweigh benefits. Interactions between boosted antiretrovirals and anti-P2Y12 : prasugrel appears as the best option. Neural tube defects in babies born to women treated with dolutegravir : a signal to investigate. Cobicistat-boosted antiretrovirals exposure is decreased during pregnancy. Fourth generation pills containing drospirenone : a greater propensity to prolong the QT interval than 2nd generation pills.
Topics: Drug-Related Side Effects and Adverse Reactions; Female; Humans; Pharmacovigilance; Pregnancy
PubMed: 30629378
DOI: No ID Found -
Pharmaceutical Medicine Oct 2022Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial...
INTRODUCTION
Artificial intelligence through machine learning uses algorithms and prior learnings to make predictions. Recently, there has been interest to include more artificial intelligence in pharmacovigilance of products already in the market and pharmaceuticals in development.
OBJECTIVE
The aim of this study was to identify and describe the uses of artificial intelligence in pharmacovigilance through a systematic literature review.
METHODS
Embase and MEDLINE database searches were conducted for articles published from January 1, 2015 to July 9, 2021 using search terms such as 'pharmacovigilance,' 'patient safety,' 'artificial intelligence,' and 'machine learning' in the title or abstract. Scientific articles that contained information on the use of artificial intelligence in all modalities of patient safety or pharmacovigilance were reviewed and synthesized using a pre-specified data extraction template. Articles with incomplete information and letters to editor, notes, and commentaries were excluded.
RESULTS
Sixty-six articles were identified for evaluation. Most relevant articles on artificial intelligence focused on machine learning, and it was used in patient safety in the identification of adverse drug events (ADEs) and adverse drug reactions (ADRs) (57.6%), processing safety reports (21.2%), extraction of drug-drug interactions (7.6%), identification of populations at high risk for drug toxicity or guidance for personalized care (7.6%), prediction of side effects (3.0%), simulation of clinical trials (1.5%), and integration of prediction uncertainties into diagnostic classifiers to increase patient safety (1.5%). Artificial intelligence has been used to identify safety signals through automated processes and training with machine learning models; however, the findings may not be generalizable given that there were different types of data included in each source.
CONCLUSION
Artificial intelligence allows for the processing and analysis of large amounts of data and can be applied to various disease states. The automation and machine learning models can optimize pharmacovigilance processes and provide a more efficient way to analyze information relevant to safety, although more research is needed to identify if this optimization has an impact on the quality of safety analyses. It is expected that its use will increase in the near future, particularly with its role in the prediction of side effects and ADRs.
Topics: Artificial Intelligence; Drug-Related Side Effects and Adverse Reactions; Humans; Machine Learning; Pharmaceutical Preparations; Pharmacovigilance
PubMed: 35904529
DOI: 10.1007/s40290-022-00441-z -
F1000Research 2019Considering that marketed drugs are not free from side effects, many countries have initiated pharmacovigilance programs. These initiatives have provided countries with... (Review)
Review
Considering that marketed drugs are not free from side effects, many countries have initiated pharmacovigilance programs. These initiatives have provided countries with methods of detection and prevention of adverse drug reactions at an earlier stage, thus preventing harm occurring in the larger population. In this review, examples of drug withdrawals due to effective pharmacovigilance programs have been provided with details. In addition, information concerning data mining in pharmacovigilance, an effective method to assess pharmacoepidemiologic data and detecting signals for rare and uncommon side effects, is also examined, which is a method synchronized with information technology and advanced electronic tools. The importance of policy framework in relation to pharmacovigilance is discussed in detail, and country experiences upon implementation of pharmacovigilance policies is highlighted.
Topics: Adverse Drug Reaction Reporting Systems; Data Mining; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmacovigilance
PubMed: 32161643
DOI: 10.12688/f1000research.21402.1 -
Drug Safety May 2022With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner,... (Review)
Review
With the rapid development of artificial intelligence (AI) technologies, and the large amount of pharmacovigilance-related data stored in an electronic manner, data-driven automatic methods need to be urgently applied to all aspects of pharmacovigilance to assist healthcare professionals. However, the quantity and quality of data directly affect the performance of AI, and there are particular challenges to implementing AI in limited-resource settings. Analyzing challenges and solutions for AI-based pharmacovigilance in resource-limited settings can improve pharmacovigilance frameworks and capabilities in these settings. In this review, we summarize the challenges into four categories: establishing a database for an AI-based pharmacovigilance system, lack of human resources, weak AI technology and insufficient government support. This study also discusses possible solutions and future perspectives on AI-based pharmacovigilance in resource-limited settings.
Topics: Artificial Intelligence; Databases, Factual; Health Personnel; Humans; Pharmacovigilance; Technology
PubMed: 35579814
DOI: 10.1007/s40264-022-01170-7 -
Drug Safety Oct 2019Pharmacovigilance currently faces several unsolved challenges. Of particular importance are issues concerning how to ascertain, collect, confirm, and communicate the... (Review)
Review
Pharmacovigilance currently faces several unsolved challenges. Of particular importance are issues concerning how to ascertain, collect, confirm, and communicate the best evidence to assist the clinical choice for individual patients. Here, we propose that these practical challenges partially stem from deeper fundamental issues concerning the epistemology of pharmacovigilance. After reviewing some of the persistent challenges, recent measures, and suggestions in the current pharmacovigilance literature, we support the argument that the detection of potential adverse drug reactions ought to be seen as a serendipitous scientific discovery. We further take up recent innovations from the multidisciplinary field of serendipity research about the importance of networks, diversity of expertise, and plurality of methodological perspectives for cultivating serendipitous discovery. Following this discussion, we explore how pharmacovigilance could be systematized in a way that optimizes serendipitous discoveries of untargeted drug effects, emerging from the clinical application. Specifically, we argue for the promotion of a trans-disciplinary responsive network of scientists and stakeholders. Trans-disciplinarity includes extending the involvement of stakeholders beyond the regulatory community, integrating diverse methods and sources of evidence, and enhancing the ability of diverse groups to raise signals of harms that ought to be followed up by the network. Consequently, promoting a trans-disciplinary approach to pharmacovigilance is a long-term effort that requires structural changes in medical education, research, and enterprise. We suggest a number of such changes, discuss to what extent they are already in process, and indicate the advantages from both epistemological and ethical perspectives.
Topics: Adverse Drug Reaction Reporting Systems; Drug-Related Side Effects and Adverse Reactions; Humans; Interdisciplinary Communication; Pharmacovigilance; Social Networking
PubMed: 31062194
DOI: 10.1007/s40264-019-00826-1 -
Drug Safety Jul 2019Pharmacovigilance has received much attention in Arab countries recently due to the development of new regulations. However, there are differences in the progression of... (Review)
Review
Pharmacovigilance has received much attention in Arab countries recently due to the development of new regulations. However, there are differences in the progression of pharmacovigilance systems by regulatory agencies in these countries because only some are able to meet the requirements for conducting pharmacovigilance activities. Only 45% of Arab countries are official members of the World Health Organization (WHO) Collaborating Centre for International Drug Monitoring. Countries such as Morocco, Tunisia, Saudi Arabia, Egypt, and Jordan are considered to be advanced pharmacovigilance countries, whereas other countries such as Libya, Yemen, and Palestine remain in the very early stages of implementing and developing pharmacovigilance systems. Countries such as Somalia, Djibouti, Mauritania, and Comoros Island have no pharmacovigilance system or culture. Asian Arab countries have some advantages over those in Africa because 50% of them are a part of the Gulf Cooperation Council (GCC), meaning that most of them can utilize similar approaches for the application of the majority of activities related to the healthcare system, including pharmacovigilance. Thus, participating in the GCC enables increased connections among these countries. However, one of the strengths in Africa is that Morocco is partnering with the WHO through the WHO Collaborating Center to enhance and strengthen pharmacovigilance across the Eastern Mediterranean Region and the Francophone and Arab countries. This partnership could have a role in enhancing the pharmacovigilance culture among African Arab countries. This review provides a general overview of the current situation regarding regulatory agencies related to pharmacovigilance in Arab countries.
Topics: Developing Countries; Drug-Related Side Effects and Adverse Reactions; Humans; Middle East; Pharmacovigilance; Population Surveillance
PubMed: 31006085
DOI: 10.1007/s40264-019-00807-4 -
Revue Medicale Suisse Jan 2012Main pharmacovigilance updates in 2011 are reviewed. Dronedarone: Serious cardio-vascular and hepatic adverse reactions for a questionable efficacy. Long-term proton... (Review)
Review
Main pharmacovigilance updates in 2011 are reviewed. Dronedarone: Serious cardio-vascular and hepatic adverse reactions for a questionable efficacy. Long-term proton pump inhibitors: A cause of hypomagnesemia. Bisphosphonates: A risk of atypical femoral fractures. Dasatinib: Cases of pulmonary arterial hypertension reported. Lenalidomide: A risk of second primary malignancies. Daptomycine: Cases of eosinophilic pneumonia reported. Tigecycline: Inferior to comparators. Drotrecogin alfa: Market withdrawal due to lack of efficacy. Nimesulide: More hepatotoxic than other NSAIDs. Topiramate: Evidence of teratogenicity (oral clefts). Valproate: Impaired cognitive development in addition to well-known teratogenicity. Antipsychotics in late pregnancy: A risk of neonatal complications.
Topics: Adverse Drug Reaction Reporting Systems; Drug-Related Side Effects and Adverse Reactions; Female; Hospitals, University; Humans; Pharmacovigilance; Switzerland
PubMed: 23185821
DOI: No ID Found -
Therapeutic Innovation & Regulatory... Mar 2021In this publication, the authors, having gained several decades of experience in Pharmacovigilance departments, both in pharmaceutical companies as well as in service... (Review)
Review
In this publication, the authors, having gained several decades of experience in Pharmacovigilance departments, both in pharmaceutical companies as well as in service providers companies, describe the reason why and the way how a guideline for outsourcing was created. While outsourcing in pharmacovigilance has become the rule rather than the exception in the last decade, a consensus guideline, based on best practice, appears to be missing. A group of committed professionals have given their time and knowledge to an iterative process of writing and reviewing drafts of such a guideline. One of the authors was closely involved with the writing as well as presenting the draft guidance at conferences. The other author was managing the process, including discussions, periodically reviewing the result, as well as responding to the reviewers' comments. In this publication, a high-level overview of the guideline is provided. The key message is that outsourcing in PV requires both a detailed preparation, including risk assessments of the various elements, as well as close contact with the service provider.
Topics: Outsourced Services; Pharmacovigilance
PubMed: 33118144
DOI: 10.1007/s43441-020-00229-w -
Expert Opinion on Drug Safety 2023Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to... (Review)
Review
INTRODUCTION
Artificial intelligence (AI) based tools offer new opportunities for pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored to preserve and strengthen medical and pharmacological expertise in drug safety.
AREAS COVERED
This work aims to describe PV tasks in which the contribution of AI and intelligent automation (IA) tools is required, in the context of a continuous increase of spontaneous reporting cases and regulatory tasks. A narrative review with expert selection of pertinent references was performed through Medline. Two areas were covered, management of spontaneous reporting cases and signal detection.
PERSPECTIVE
The use of AI and IA tools will assist a large spectrum of PV activities, both in public and private PV systems, in particular for tasks of low added value (e.g. initial quality check, verification of essential regulatory information, search for duplicates). Testing, validating, and integrating these tools in the PV routine are the actual challenges for modern PV systems, to guarantee high-quality standards in terms of case management and signal detection.
Topics: Humans; Artificial Intelligence; Pharmacovigilance; Drug-Related Side Effects and Adverse Reactions
PubMed: 37435796
DOI: 10.1080/14740338.2023.2227091