Early View
ORIGINAL ARTICLE

An artificial intelligence algorithm for co-clustering to help in pharmacovigilance before and during the COVID-19 pandemic

Alexandre Destere

Alexandre Destere

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France

Search for more papers by this author
Giulia Marchello

Giulia Marchello

Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France

Search for more papers by this author
Diane Merino

Diane Merino

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Nouha Ben Othman

Nouha Ben Othman

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Alexandre O. Gérard

Alexandre O. Gérard

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Thibaud Lavrut

Thibaud Lavrut

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Delphine Viard

Delphine Viard

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Fanny Rocher

Fanny Rocher

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Search for more papers by this author
Marco Corneli

Marco Corneli

Université Côte d'Azur, Inria, Maison de la Modélisation des Simulations et des Interactions (MSI), MAASAI team, Nice, France

Search for more papers by this author
Charles Bouveyron

Charles Bouveyron

Université Côte d'Azur, Inria, CNRS, Laboratoire J.A. Dieudonné, Maasai team, Nice, France

Search for more papers by this author
Milou-Daniel Drici

Corresponding Author

Milou-Daniel Drici

Department of Pharmacology and Pharmacovigilance Center, Université Côte d'Azur Medical Centre, Nice, France

Correspondence

Milou-Daniel Drici, Department of Pharmacology and Pharmacovigilance Center of Nice, University Hospital of Nice, 06 000 Nice, France.

Email: [email protected]

Search for more papers by this author
First published: 08 February 2024

Funding information: No funding was received for this study.

Abstract

Aims

Monitoring drug safety in real-world settings is the primary aim of pharmacovigilance. Frequent adverse drug reactions (ADRs) are usually identified during drug development. Rare ones are mostly characterized through post-marketing scrutiny, increasingly with the use of data mining and disproportionality approaches, which lead to new drug safety signals. Nonetheless, waves of excessive numbers of reports, often stirred up by social media, may overwhelm and distort this process, as observed recently with levothyroxine or COVID-19 vaccines. As human resources become rarer in the field of pharmacovigilance, we aimed to evaluate the performance of an unsupervised co-clustering method to help the monitoring of drug safety.

Methods

A dynamic latent block model (dLBM), based on a time-dependent co-clustering generative method, was used to summarize all regional ADR reports (n = 45 269) issued between 1 January 2012 and 28 February 2022. After analysis of their intra and extra interrelationships, all reports were grouped into different cluster types (time, drug, ADR).

Results

Our model clustered all reports in 10 time, 10 ADR and 9 drug collections. Based on such clustering, three prominent societal problems were detected, subsequent to public health concerns about drug safety, including a prominent media hype about the perceived safety of COVID-19 vaccines. The dLBM also highlighted some specific drug–ADR relationships, such as the association between antiplatelets, anticoagulants and bleeding.

Conclusions

Co-clustering and dLBM appear as promising tools to explore large pharmacovigilance databases. They allow, ‘unsupervisedly’, the detection, exploration and strengthening of safety signals, facilitating the analysis of massive upsurges of reports.

CONFLICT OF INTEREST STATEMENT

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.