Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

ABSTRACT

Introduction

Signal detection is the most pivotal activity of signal management to guarantee that drugs maintain a positive risk-benefit ratio during their lifetime on the market. Signal detection is based on the systematic evaluation of available data sources, which have recently been extended in order to improve timely and comprehensive signal detection of drug safety problems.

Areas covered

In recent years, attempts have been made to incorporate pharmacological data for the prediction of safety signals. Previous studies have shown that data on the pharmacological targets of drugs are predictive of post-marketing adverse events. However, current approaches limit such predictions to adverse events expected from the interaction of a drug with the main pharmacological target and do not take off-target interactions into consideration.

Expert opinion

The authors propose the application of predictive modeling techniques utilizing pharmacological data from public databases for predicting drug-target-event relationships deriving from main- and off-target binding and from which potential safety signals can be deduced. Additionally, they provide an operative procedure for the identification of clinically relevant subgroups for predicted safety signals.

KEYWORDS:

• Pharmacoepidemiology
• pharmacometrics
• bioinformatics
• cheminformatics
• system pharmacology
• drug safety
• pharmacovigilance

Article highlights

Data sources used to perform signal detection have been extended to achieve timely and comprehensive signal detection.

Attempts have been made to predict adverse events from the interaction of a drug with the main pharmacological target. However, off-target interactions have not been considered.

Off-targets are a crucial aspect of the safety profile of drugs.

Availability of experimental data for off-targets is often limited. However, predictive modelling techniques can be applied to complement the available data.

The authors propose a framework that uses publicly available data to predict unknown safety signals deriving from main- and off-target binding.

This box summarizes key points contained in the article.

Acknowledgments

A.S. Hauser, A.J. Kooistra, E. Sverrisdóttir and M. Sessa are members of the Personalised Medicine Research Cluster at the Department of Drug Design and Pharmacology at the University of Copenhagen. Additionally, M. Sessa is a member of the Metabolism and Inflammation Research Cluster and coordinator of the Geriatric Pharmacoepidemiology Team.

Author contributions

M. Sessa had the preliminary idea of this framework and all authors contributed to its development. All authors drafted the paper, revised it critically for intellectual content, approved the final version to be published, and agreed to be accountable for all aspects of the work. A.S. Hauser and M. Sessa performed the data analysis and interpreted the results.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

M. Sessa was supported by a grant from the Novo Nordisk Foundation to the University of Copenhagen [NNF15SA0018404] and Helsefonden [20-B-0059]. A.S. Hauser would like to gratefully acknowledge funding from the Lundbeck Foundation [R278-2018-180].