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Medicina Clinica Mar 2020An adverse drug reaction (ADR) is defined as a response to a medicinal product which is noxious and unintended. ADRs are an important cause of morbidity and mortality... (Review)
Review
An adverse drug reaction (ADR) is defined as a response to a medicinal product which is noxious and unintended. ADRs are an important cause of morbidity and mortality and increase health costs. The pharmacovigilance systems allow the identification and prevention of the risks associated with use of a drug, especially of recently marketed drugs; they detect signals from data of the global ADR register and also support decisions taken by regulatory agencies in different countries. Only a few drugs are withdrawn from the market, mainly due to hepatotoxicity. Spontaneous notification of ADR is the cheapest, simplest and most used method to recognize new safety drug problems, under-reporting being its main limitation. The future of pharmacovigilance and ADRs will include a higher involvement of patients, doctors, health authorities and pharmaceutical companies, and the use of new technologies.
Topics: Adverse Drug Reaction Reporting Systems; Drug-Related Side Effects and Adverse Reactions; Forecasting; Humans; Pharmacovigilance
PubMed: 31771857
DOI: 10.1016/j.medcli.2019.08.007 -
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 -
Therapie 2021
Topics: Adverse Drug Reaction Reporting Systems; Bismuth; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmacovigilance
PubMed: 33218671
DOI: 10.1016/j.therap.2020.11.001 -
Sante Publique (Vandoeuvre-les-Nancy,... 2023This article focuses on the hierarchical structure of users of a synthetic progestin, Homodeor and its effects on the construction of a pharmacovigilance plan by a...
This article focuses on the hierarchical structure of users of a synthetic progestin, Homodeor and its effects on the construction of a pharmacovigilance plan by a French health agency, at a time when an institutional desire is being expressed to work more closely with all patient associations. This case study is mainly on a qualitative survey led by interviewing agents, health professionals and user representatives, which aimed to explore the relationships and representations developed around this issue. Despite the diversity of progestin use, a hierarchy between the different user groups was gradually established. The pharmacovigilance measures were designed for a specific group of patients, presented as the ideal users of the drug. The case of Homodeor makes it possible to highlight the dynamics of competition between groups of patients, and more broadly, the challenges of taking minority groups into account in health policies in the light of their development context.
Topics: Humans; Progestins; Health Personnel; Surveys and Questionnaires; Health Facilities; Pharmacovigilance
PubMed: 37336747
DOI: 10.3917/spub.hs2.0049 -
Therapie 2022
Topics: Adverse Drug Reaction Reporting Systems; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmacovigilance
PubMed: 35581019
DOI: 10.1016/j.therap.2022.03.001 -
Clinical Therapeutics Jul 2022Current nonspecific causality assessment tools lack the assessment of drug-induced acute kidney injury (DIAKI). We recently published an editorial letter for developing... (Review)
Review
PURPOSE
Current nonspecific causality assessment tools lack the assessment of drug-induced acute kidney injury (DIAKI). We recently published an editorial letter for developing a specific causality assessment tool for DIAKI. The purpose of the present review was to suggest the possible required parameters and outline the path to developing a kidney-specific causality assessment tool (KSCAT).
METHODS
A stepwise approach for developing a KSCAT is important as this will be first version of this new tool. Thus, as a first step, we performed a screening of previously published articles on nonspecific and liver-specific causality assessment tools to define possible parameters. The suggested parameters for KSCAT fall into 3 categories: (1) drug-related; (2) kidney-related; and (3) terminology. A tri-polar method was then created that consists of definitive adverse drug reactions (ADRS), terminology, and without ADRS to suggest that the new KSCAT be efficient, specific, user friendly, and less time-consuming. Finally, a pyramid model is suggested to offer the perspectives of experts in the fields of pharmacovigilance, pharmacoepidemiology, and nephrology, as well as decision makers, while developing a KSCAT.
FINDINGS
Causality assessment tools, either nonspecific or organ-specific, fall into 3 categories: (1) expert judgment; (2) algorithms; and (3) probabilistic methods. None of the current causality assessment tools is sufficient for assessing the causality of kidney-related ADRs and for screening the expanded definition of ADR included in European Union Directive 2010/84/EU.
IMPLICATIONS
The causal relationship between drug(s) and DIAKI may be difficult and may not be assessed appropriately with the use of nonspecific tools or approaches. The aim of this article was to reiterate the need for KSCAT development and to propose the associated steps by stating the main principles: namely, the definition of ADR, suggested parameters to be included in the KSCAT, and integration of technology. Our ultimate desire is to invite experts to develop this new tool using an interdisciplinary approach and to benefit from our review in pursuing the next steps. The development of a KSCAT should start with regular and interdisciplinary consortium meetings of experts; the tool should then be tested for its usability, specificity, and practicality; and, finally, it should be used in real-life pharmacovigilance practices, as well as in research by health authorities, regulators, decision-makers, scientists, and clinicians. A KSCAT would support the provision of reliable and reproducible measures of the relationship likelihood in suspected cases of ADR to overcome uncertainty and provide a standardized approach.
Topics: Adverse Drug Reaction Reporting Systems; Causality; Drug-Related Side Effects and Adverse Reactions; Humans; Kidney; Pharmacovigilance
PubMed: 35725506
DOI: 10.1016/j.clinthera.2022.05.008 -
Therapie 2023
Topics: Humans; Artificial Intelligence; Automation; Drug-Related Side Effects and Adverse Reactions; Pharmacovigilance
PubMed: 36577617
DOI: 10.1016/j.therap.2022.11.003 -
British Journal of Clinical Pharmacology Feb 2023Drug-related adverse reactions are among the main reasons for harm to patients under care worldwide and even their deaths. The pharmacovigilance system has been proven... (Review)
Review
Drug-related adverse reactions are among the main reasons for harm to patients under care worldwide and even their deaths. The pharmacovigilance system has been proven to be an effective method of avoiding or alleviating such adverse events. In 2019, after two decades of implementation of the drug-related adverse reaction reporting system, China formally implemented a pharmacovigilance system with the Pharmacovigilance Quality Management Standards and a series of supporting technical documents created to improve the safety of medication given to patients. China's pharmacovigilance system has faced many problems and challenges during its implementation. This spontaneous reporting system is the main source of data for China's medication vigilance activities, but it has not provided sufficiently powerful evidence for regulatory decision-making. In conformity with the health-centred drug regulatory concept, the Chinese government has accelerated the speed of examination and approval of urgently needed clinical drugs and orphan drugs along with the requirement to improve the safety supervision of these drugs after their listing. China's marketing authorization holders (MAHs) must strengthen their pharmacovigilance capabilities as the primary responsible departments for drug safety. Chinese medical schools generally lack professional courses on pharmacovigilance. The regulatory authorities have recognized such problems and have made efforts to improve the professional capacity of pharmacovigilance personnel and to strengthen cooperation with stakeholders through the implementation of an action plan of medication surveillance and the establishment of a patient-based adverse events reporting system and active surveillance systems, which will help China bridge the gap to bring its pharmacovigilance practice up to standards.
Topics: Humans; Pharmacovigilance; Adverse Drug Reaction Reporting Systems; Drug and Narcotic Control; China; Drug-Related Side Effects and Adverse Reactions
PubMed: 35165914
DOI: 10.1111/bcp.15277 -
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 -
Therapie 2021
Topics: France; Humans; Nitrous Oxide; Pharmacovigilance; Substance-Related Disorders
PubMed: 32005483
DOI: 10.1016/j.therap.2020.01.001