Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing

Schmider, Juergen; Kumar, Krishan; LaForest, Chantal; Swankoski, Brian; Naim, Karen; Caubel, Patrick

doi:10.1002/cpt.1255

Cited by 66 publications

(38 citation statements)

References 22 publications

(22 reference statements)

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“…Our aim is to provide the right type of safety data in the right format and context, thereby increasing the quality of the evidence available for scientific evaluation to inform decisions optimally. Ultimately, our views concerning PV systems are similar to those expressed by other authors in the field of biomedical sciences [ 53 – 55 ]. We believe that PV system owners will have to adapt to artificial intelligence by the progressive, intelligent adoption of new technologies, commencing with process automation.…”

Section: Discussionsupporting

confidence: 78%

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

Lewis

McCallum

2019

Ther Innov Regul Sci

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There are significant challenges and opportunities in deploying and utilizing advanced information technology (IT) within pharmacovigilance (PV) systems and across the pharmaceutical industry. Various aspects of PV will benefit from automation (e.g., by improving standardization or increasing data quality). Several themes are developed, highlighting the challenges faced, exploring solutions, and assessing the potential for further research. Automation of the workflow for processing of individual case safety reports (ICSRs) is adopted as a use case. This involves a logical progression through a series of steps that when linked together comprise the complete work process required for the effective management of ICSRs. We recognize that the rapid development of new technologies will invariably outpace the regulations applicable to PV systems. Nevertheless, we believe that such systems may be improved by intelligent automation. It is incumbent on the owners of these systems to explore opportunities presented by new technologies with regulators in order to evaluate the applicability, design, deployment, performance, validation and maintenance of advanced technologies to ensure that the PV system continues to be fit for purpose. Proposed approaches to the validation of automated PV systems are presented. A series of definitions and a critical appraisal of important considerations are provided in the form of use cases. We summarize progress made and opportunities for the development of automation of future systems. The overall goal of automation is to provide high quality safety data in the correct format, in context, more quickly, and with less manual effort. This will improve the evidence available for scientific assessment and helps to inform and expedite decisions about the minimization of risks associated with medicines.

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Section: Discussionsupporting

confidence: 78%

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

Lewis

McCallum

2019

Ther Innov Regul Sci

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“…14 When originated from industries, these developments appear to be mostly restricted to their sole products. 15 The underlying assumption of the present work is that a global knowledge database on drugs, enriched by supervised ML models trained on reporting data, could allow setting up a universal tool for preprocessing free text reported ADRs. This tool would increase efficiency in dealing with such information and improve capacities in drug surveillance.…”

mentioning

confidence: 99%

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions

Létinier

Jouganous²,

Benkebil

et al. 2021

Clin Pharma and Therapeutics

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Adverse drug reaction (ADR) reporting is a major component of drug safety monitoring; its input will, however, only be optimized if systems can manage to deal with its tremendous flow of information, based primarily on unstructured text fields. The aim of this study was to develop an automated system allowing to code ADRs from patient reports. Our system was based on a knowledge base about drugs, enriched by supervised machine learning (ML) models trained on patients reporting data. To train our models, we selected all cases of ADRs reported by patients to a French Pharmacovigilance Centre through a national web-portal between March 2017 and March 2019 (n = 2,058 reports). We tested both conventional ML models and deep-learning models. We performed an external validation using a dataset constituted of a random sample of ADRs reported to the Marseille Pharmacovigilance Centre over the same period (n = 187). Here, we show that regarding area under the curve (AUC) and F-measure, the best model to identify ADRs was gradient boosting trees (LGBM), with an AUC of 0.93 (0.92-0.94) and F-measure of 0.72 (0.68-0.75). This model was run for external validation showing an AUC of 0.91 and a F-measure of 0.58. We evaluated an artificial intelligence pipeline that was found able to learn how to identify correctly ADRs from unstructured data. This result allowed us to start a new study using more data to further improve our performance and offer a tool that is useful in practice to efficiently manage drug safety information.

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“…In putting this issue together, we have attempted to cover the very large waterfront of regulatory science, ranging from biomarker validation to novel approaches and acceptability of generation of clinical evidence and regulatory licensure frameworks that now include patient‐focused drug development . We also include regulatory science topics that can improve the effectiveness and efficiency of postmarketing pharmacovigilance, generic drug approval, and strategies to facilitate demonstration of value to support reimbursement . We include topics of global import to demonstrate the impact of geopolitical forces on drug regulation, including Brexit and harmonization of regulatory requirements …”

Section: Regulatory Science Advances Regulatory Policymentioning

confidence: 99%

Regulation and Innovation: Role of Regulatory Science in Facilitating Pharmaceutical Innovation

Honig¹,

Zhang

2019

Clin Pharma and Therapeutics

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Regulatory science is defined as science and research intended to inform decision making in a regulatory framework. This issue of Clinical Pharmacology & Therapeutics and the papers herein deal with the expansive topic of regulatory science and its role in fostering innovation and accelerating access to medicines. Regulatory health authorities, industry, and multiple stakeholders have a shared objective in advancing regulatory science to keep up with the ever‐increasing pace of biomedical science.

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Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing

Cited by 66 publications

References 22 publications

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities

Artificial Intelligence for Unstructured Healthcare Data: Application to Coding of Patient Reporting of Adverse Drug Reactions

Regulation and Innovation: Role of Regulatory Science in Facilitating Pharmaceutical Innovation

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