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Open Access 05-05-2024 | Artificial Intelligence | Original Research

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches

Authors: Rosa De Abreu Ferreira, Sheng Zhong, Charlotte Moureaud, Michelle T. Le, Adrienne Rothstein, Xiaomeng Li, Li Wang, Meenal Patwardhan

Published in: Advances in Therapy | Issue 6/2024

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Abstract

Introduction

The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model’s capability of detecting safety signals earlier than humans.

Methods

Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy.

Results

For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review.

Conclusion

This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.
Literature
3.
go back to reference European Medicines Agency and Heads of Medicines Agencies. EMA/827661/2011 Rev1*. Guideline on good pharmacovigilance practices (GVP) Module IX—Signal management (Rev 1). 2017. European Medicines Agency and Heads of Medicines Agencies. EMA/827661/2011 Rev1*. Guideline on good pharmacovigilance practices (GVP) Module IX—Signal management (Rev 1). 2017.
7.
go back to reference International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. Medical Dictionary for Regulatory Activities (MedDRA). https://www.meddra.org/. Accessed 11 Mar 2024. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. Medical Dictionary for Regulatory Activities (MedDRA). https://​www.​meddra.​org/​. Accessed 11 Mar 2024.
8.
go back to reference Huyen C. Designing machine learning systems. O’Reilly Media, Inc; 2022. Huyen C. Designing machine learning systems. O’Reilly Media, Inc; 2022.
9.
go back to reference Trevor H, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. Berlin: Springer; 2016. Trevor H, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. Berlin: Springer; 2016.
13.
go back to reference Provost F. Machine Learning from Imbalanced Data Sets 101. In: Proceedings of the association for the advancement of artificial intelligence 2000 workshop on imbalanced data sets. 2000;68. pp. 1–3. Provost F. Machine Learning from Imbalanced Data Sets 101. In: Proceedings of the association for the advancement of artificial intelligence 2000 workshop on imbalanced data sets. 2000;68. pp. 1–3.
Metadata
Title
A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches
Authors
Rosa De Abreu Ferreira
Sheng Zhong
Charlotte Moureaud
Michelle T. Le
Adrienne Rothstein
Xiaomeng Li
Li Wang
Meenal Patwardhan
Publication date
05-05-2024
Publisher
Springer Healthcare
Published in
Advances in Therapy / Issue 6/2024
Print ISSN: 0741-238X
Electronic ISSN: 1865-8652
DOI
https://doi.org/10.1007/s12325-024-02870-5

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