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Correction to: Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks

van Stekelenborg, John; Kara, Vijay; Haack, Roman; Vogel, Ulrich; Garg, Anju; Krupp, Markus; Gofman, Kate; Dreyfus, Brian; Hauben, Manfred; Bate, Andrew
PMID: 36689132
ISSN: 1179-1942
CID: 5419482

Correction to: Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review (Drug Safety, (2022), 45, 5, (477-491), 10.1007/s40264-022-01176-1)

Kompa, Benjamin; Hakim, Joe B.; Palepu, Anil; Kompa, Kathryn Grace; Smith, Michael; Bain, Paul A.; Woloszynek, Stephen; Painter, Jeffery L.; Bate, Andrew; Beam, Andrew L.
Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review, written by Benjamin Kompa, Joe B. Hakim, Anil Palepu, Kathryn Grace Kompa, Michael Smith, Paul A. Bain, Stephen Woloszynek, Jeffery L. Painter, Andrew Bate, Andrew L. Beam, was originally Online First without Open Access. After publication in volume 45, issue 5, page 477"“491 the author decided to opt for Open Choice and to make the article an Open Access publication. With the author(s)"™ decision to opt for Open Choice the copyright of the article changed on 27 January 2023 to © The Author(s) 2023 and the article is forthwith distributed under a Creative Commons Attribution NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article"™s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article"™s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by- nc/4. 0/". The original article has been corrected.
SCOPUS:85148597284
ISSN: 0114-5916
CID: 5445672

Individual Case Safety Report Replication: An Analysis of Case Reporting Transmission Networks

van Stekelenborg, John; Kara, Vijay; Haack, Roman; Vogel, Ulrich; Garg, Anju; Krupp, Markus; Gofman, Kate; Dreyfus, Brian; Hauben, Manfred; Bate, Andrew
INTRODUCTION:The basis of pharmacovigilance is provided by the exchange of Individual Case Safety Reports (ICSRs) between the recipient of the original report and other interested parties, which include Marketing Authorization Holders (MAHs) and Health Authorities (HAs). Different regulators have different reporting requirements for report transmission. This results in replication of each ICSR that will exist in multiple locations. Adding in the fact that each case will go through multiple versions, different recipients may receive different case versions at different times, potentially influencing patient safety decisions and potentially amplifying or obscuring safety signals inappropriately. OBJECTIVE:The present study aimed to investigate the magnitude of replication, the variability among recipients, and the subsequent divergence across recipients of ICSRs. METHODS:Seven participating TransCelerate Member Companies (MCs) queried their respective safety databases covering a 3-year period and provided aggregate ICSR submission statistics for expedited safety reports to an independent project manager. As measured in the US Food and Drug Administration (FDA)'s Adverse Event Reporting System (FAERS), ICSR volume for these seven MCs makes up approximately 20% of the total case volume. Aggregate metrics were calculated from the company data, specifically: (i) number of ICSR transmissions, (ii) average number of recipients (ANR) per case version transmitted, (iii) a submission selectivity metric, which measures the percentage of recipients not having received all sequential case version numbers, and (iv) percent of common ISCRs residing in two or more MAH databases. RESULTS:The analysis reflects 2,539,802 case versions, distributed through 7,602,678 submissions. The overall mean replication rate is 3.0 submissions per case version. The distribution of the ANR replication measure was observed to be very long-tailed, with a significant fraction of case versions (~ 12.4% of all transmissions) being sent to ten or more HA recipients. Replication is higher than average for serious, unlisted, and literature cases, ranging from 3.5 to 6.1 submissions per version. Within the subset of ICSR versions sent to three recipients, a significant degree of variability in the actual recipients (i.e., HAs) was observed, indicating that there is not one single combination of the same three HAs predominantly receiving an ICSR. Submission selectivity increases with the case version. For case version 6, the range of the submission selectivity for the MAHs ranges from ~ 10% to over 50%, with a median of 30.2%. Within the participating MAHs, the percentage of cases that reside within at least two safety databases is approximately 2% across five databases. Further analysis of the data from three MAHs showed percentages of 13.4%, 15.6%, and 27.9% of ICSRs originating from HAs and any other partners such as other MAHs and other institutions. CONCLUSION:Replication of ICSRs and the variation of available safety information in recipient databases were quantified and shown to be substantial. Our work shows that multiple processors and medical reviewers will likely handle the same original ICSR as a result of replication. Aside from the obvious duplicate work, this phenomenon could conceivably lead to differing clinical assessments and decisions. If replication could be reduced or even eliminated, this would enable more focus on activities with a benefit for patient safety.
PMID: 36565374
ISSN: 1179-1942
CID: 5409452

Digital biomarkers for post-licensure safety monitoring

Garcia-Gancedo, Luis; Bate, Andrew
Post-licensure safety data form the cornerstone of safety surveillance. However, such data have some limitations related to the subjectiveness of reporting and recording, primary purpose of the collected data, or heterogeneity. Routine capture of richer data would in part help mitigate these limitations, enabling earlier, more reliable safety insights. Digital health tools that remotely acquire health-related information are increasingly available and used by patients and the wider population. However, they are rarely used for pharmacovigilance purposes. Here, we review different cases that reveal the opportunities and challenges of using these technologies for enhanced safety assessment in routine healthcare delivery. We believe such approaches will advance our understanding of the safety of drugs and vaccines in the future.
PMID: 36108916
ISSN: 1878-5832
CID: 5336422

Black Swan Events and Intelligent Automation for Routine Safety Surveillance

Kjoersvik, Oeystein; Bate, Andrew
Effective identification of previously implausible safety signals is a core component of successful pharmacovigilance. Timely, reliable, and efficient data ingestion and related processing are critical to this. The term 'black swan events' was coined by Taleb to describe events with three attributes: unpredictability, severe and widespread consequences, and retrospective bias. These rare events are not well understood at their emergence but are often rationalized in retrospect as predictable. Pharmacovigilance strives to rapidly respond to potential black swan events associated with medicine or vaccine use. Machine learning (ML) is increasingly being explored in data ingestion tasks. In contrast to rule-based automation approaches, ML can use historical data (i.e., 'training data') to effectively predict emerging data patterns and support effective data intake, processing, and organisation. At first sight, this reliance on previous data might be considered a limitation when building ML models for effective data ingestion in systems that look to focus on the identification of potential black swan events. We argue that, first, some apparent black swan events-although unexpected medically-will exhibit data attributes similar to those of other safety data and not prove algorithmically unpredictable, and, second, standard and emerging ML approaches can still be robust to such data outliers with proper awareness and consideration in ML system design and with the incorporation of specific mitigatory and support strategies. We argue that effective approaches to managing data on potential black swan events are essential for trust and outline several strategies to address data on potential black swan events during data ingestion.
PMCID:9112242
PMID: 35579807
ISSN: 1179-1942
CID: 5277452

Artificial Intelligence Based on Machine Learning in Pharmacovigilance: A Scoping Review

Kompa, Benjamin; Hakim, Joe B; Palepu, Anil; Kompa, Kathryn Grace; Smith, Michael; Bain, Paul A; Woloszynek, Stephen; Painter, Jeffery L; Bate, Andrew; Beam, Andrew L
INTRODUCTION:Artificial intelligence based on machine learning has made large advancements in many fields of science and medicine but its impact on pharmacovigilance is yet unclear. OBJECTIVE:The present study conducted a scoping review of the use of artificial intelligence based on machine learning to understand how it is used for pharmacovigilance tasks, characterize differences with other fields, and identify opportunities to improve pharmacovigilance through the use of machine learning. DESIGN:The PubMed, Embase, Web of Science, and IEEE Xplore databases were searched to identify articles pertaining to the use of machine learning in pharmacovigilance published from the year 2000 to September 2021. After manual screening of 7744 abstracts, a total of 393 papers met the inclusion criteria for further analysis. Extraction of key data on study design, data sources, sample size, and machine learning methodology was performed. Studies with the characteristics of good machine learning practice were defined and manual review focused on identifying studies that fulfilled these criteria and results that showed promise. RESULTS:The majority of studies (53%) were focused on detecting safety signals using traditional statistical methods. Of the studies that used more recent machine learning methods, 61% used off-the-shelf techniques with minor modifications. Temporal analysis revealed that newer methods such as deep learning have shown increased use in recent years. We found only 42 studies (10%) that reflect current best practices and trends in machine learning. In the subset of 154 papers that focused on data intake and ingestion, 30 (19%) were found to incorporate the same best practices. CONCLUSION:Advances from artificial intelligence have yet to fully penetrate pharmacovigilance, although recent studies show signs that this may be changing.
PMID: 35579812
ISSN: 1179-1942
CID: 5284222

Use of Real World Data and Evidence in Drug Development of Medicinal Products Centrally Authorized in Europe in 2018-2019

Eskola, Sini Marika; Leufkens, Hubertus Gerardus Maria; Bate, Andrew; De Bruin, Marie Louise; Gardarsdottir, Helga
Real World Data and Evidence (RWD/RWE) are considered to have a great potential to complement, in some cases replace, the evidence generated through randomized controlled trials. By tradition, use of RWD/RWE in the post-authorization phase is well known, while published evidence of use in the pre-authorization phase of medicines development is lacking. The primary aim of this study was to identify and quantify the role of potential use of RWD/RWE (RWE signatures) during the pre-authorization phase, as presented in initial marketing authorization applications of new medicines centrally evaluated with a positive opinion in 2018-2019 (n=111) by the European Medicines Agency (EMA). Data for the study was retrieved from the evaluation overviews of the European Public Assessment Reports (EPARs), which reflect the scientific conclusions of the assessment process and is accessible through the EMA website. RWE signatures were extracted into an RWE Data Matrix including 11 categories divided over five stages of the drug development lifecycle. Nearly all EPARs included RWE signatures for the discovery (98.2%) and life-cycle management (100.0%). Half of them included RWE signatures for the full development phase (48.6%) and for supporting regulatory decisions at the registration (46.8%), while over third (35.1%) included RWE signatures for the early development. RWE signatures were more often seen for orphan and conditionally approved medicines. Oncology, hematology and anti-infectives stood out as therapeutic areas with most RWE signatures in their full development phase. The findings bring unprecedented insights about the vast use of RWD/RWE in drug development supporting the regulatory decision making.
PMID: 34689334
ISSN: 1532-6535
CID: 5042162

Data Mining and Other Informatics Approaches to Pharmacoepidemiology

Chapter by: Bate, Andrew; Trifiro, Gianluca; Avillach, Paul; Evans, Stephen J. W.
in: PHARMACOEPIDEMIOLOGY by
pp. 675-700
ISBN: 978-1-119-41341-7
CID: 4893382

The International Society for Pharmacoepidemiology's Comments on the Core Recommendations in the Summary of the Heads of Medicines Agencies (HMA) - EMA Joint Big Data Task Force

Pottegård, Anton; Klungel, Olaf; Winterstein, Almut; Huybrechts, Krista; Hallas, Jesper; Schneeweiss, Sebastian; Evans, Stephen; Bate, Andrew; Pont, Lisa; Trifirò, Gianluca; Smith, Meredith; Bourke, Alison
PMID: 31642154
ISSN: 1099-1557
CID: 4180602

Association Between Immune-Related Adverse Events During Anti-PD-1 Therapy and Tumor Mutational Burden

Bomze, David; Hasan Ali, Omar; Bate, Andrew; Flatz, Lukas
PMID: 31436791
ISSN: 2374-2445
CID: 4046952