Adverse Events in the Digital Age: Finding the Sharpest Tool in the Box

Giovanni, Robert Di; Cochrane, Andrew; Lewis, Donna

doi:10.1007/s43441-021-00337-1

Cited by 2 publications

(5 citation statements)

References 4 publications

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“…Tables 3 and 4 list some important features to help assess such AE data analysis software. This is of primary importance also from a regulatory point of view, as it becomes challenging to “handle” an increasing abundance of tailored digital tools (or methods) 106 . Furthermore, we believe that some promising approaches to advance molecular AE mining will be achieved from the greater use of annotation benchmarks regarding biomedical concepts and mechanisms.…”

Section: Discussionmentioning

confidence: 99%

Advancing drug safety science by integrating molecular knowledge with post‐marketing adverse event reports

Soldatos¹,

Kim

Schmidt

et al. 2022

CPT Pharmacom & Syst Pharma

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Promising drug development efforts may frequently fail due to unintended adverse reactions. Several methods have been developed to analyze such data, aiming to improve pharmacovigilance and drug safety. In this work, we provide a brief review of key directions to quantitatively analyzing adverse events and explore the potential of augmenting these methods using additional molecular data descriptors. We argue that molecular expansion of adverse event data may provide a path to improving the insights gained through more traditional pharmacovigilance approaches. Examples include the ability to assess statistical relevance with respect to underlying biomolecular mechanisms, the ability to generate plausible causative hypotheses and/or confirmation where possible, the ability to computationally study potential clinical trial designs and/or results, as well as the further provision of advanced features incorporated in innovative methods, such as machine learning. In summary, molecular data expansion provides an elegant way to extend mechanistic modeling, systems pharmacology, and patient-centered approaches for the assessment of drug safety. We anticipate that such advances in real-world data informatics and outcome analytics will help to better inform public health, via the improved ability to prospectively understand and predict various types of drug-induced molecular perturbations and adverse events. | 541 MOLECULAR KNOWLEDGE FOR ADVANCED DRUG SAFETY (inter-)national regulatory authorities worldwide. Major instances of such safety data collections include: • The Sentinel Initiative of the US Food and Drug Administration (FDA), 2,3 • The FDA Adverse Event Reporting System (FAERS; formerly AERS), maintained by the FDA, 4 • The Vaccine Adverse Event Reporting System (VAERS), maintained by the FDA, co-managed by the Centers for Disease Control and Prevention, 5 • The European database of suspected adverse drug reaction reports (EudraVigilance), maintained by the European Medicines Agency (EMA), 6 and • VigiBase -the global database of individual case safety reports (ICSRs), maintained by the World Health Organization's (WHO) Uppsala Monitoring Center (UMC). 7How to cite this article: Soldatos TG, Kim S, Schmidt S, Lesko LJ, Jackson DB. Advancing drug safety science by integrating molecular knowledge with post-marketing adverse event reports. CPT

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Section: Discussionmentioning

confidence: 99%

Advancing drug safety science by integrating molecular knowledge with post‐marketing adverse event reports

Soldatos¹,

Kim

Schmidt

et al. 2022

CPT Pharmacom & Syst Pharma

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“…Moreover, if these data are owned by or are accessible to the manufacturer we believe a clear regulation or guidance is needed, as mentioned in our previous publication, 11 because the data may require screening for potential AEs.…”

Section: Background—pain Points Within Big Datamentioning

confidence: 99%

“…Regulations currently provide detail on how to validate the technology, 8 but insufficient detail in terms of the responsibilities of the MAH for human‐curated screening of various “…internet or digital media data sources…” 9 . An appropriate guideline setting down a framework delineating the regulatory expectations for the management of these safety data is required 11 . proposed WHEN to screen those data for potential AEs while regulatory requirements are not clear or up to date.…”

Section: Introductionmentioning

confidence: 99%

“…9 An appropriate guideline setting down a framework delineating the regulatory expectations for the management of these safety data is required. 11 proposed WHEN to screen those data for potential AEs while regulatory requirements are not clear or up to date. In this paper, we focus on WHERE to find knowledge and potential AEs from such digitally collected raw data.…”

mentioning

confidence: 99%

“…Therefore, we propose that both clinical trial and nontrial settings should be covered, with the caveat that tools used for collection must be validated to ensure the data collected are reliable. Moreover, if these data are owned by or are accessible to the manufacturer we believe a clear regulation or guidance is needed, as mentioned in our previous publication, 11 because the data may require screening for potential AEs.…”

mentioning

confidence: 99%

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Adverse events in the digital age and where to find them

Giovanni

Cochrane

Parker

et al. 2022

Pharmacoepidemiology and Drug

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Exponential growth of health‐related data collected by digital tools is a reality within pharmaceutical and medical device research and development. Data generated through digital tools may be categorized as relevant to efficacy and/or safety. The enormity of these data requires the adoption of new approaches for processing and evaluation. Recognition of patterns within the safety data is vital for sponsors seeking regulatory approval for their new products. Nontraditional data sources may contain relevant safety information; early evaluation of these data will help to determine the product safety profile. Advanced technologies have allowed the development of digital tools to screen these data, which in some situations are classified as software as a medical devices and subject to clinical evaluation and post‐marketing surveillance. Artificial intelligence may help to reduce or even eliminate noise from within these data, allowing safety experts to focus on the most pertinent evidence. We propose a data typology and provide considerations on how to define adverse events within different types of data, even where no human reporter exists. Proposals are made for the automation of screening processes. We consider validation aspects to support solutions that are proven to produce reliable results, and to deliver trusted outputs to stakeholders.

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Adverse Events in the Digital Age: Finding the Sharpest Tool in the Box

Cited by 2 publications

References 4 publications

Advancing drug safety science by integrating molecular knowledge with post‐marketing adverse event reports

Advancing drug safety science by integrating molecular knowledge with post‐marketing adverse event reports

Adverse events in the digital age and where to find them

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