TY - JOUR

T1 - Utilizing drug-target-event relationships to unveil safety patterns in pharmacovigilance

AU - Hauser, Alexander Sebastian

AU - Kooistra, Albert Jelke

AU - Sverrisdóttir, Eva

AU - Sessa, Maurizio

PY - 2020

Y1 - 2020

N2 - 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 modelling 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.

AB - 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 modelling 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.

U2 - 10.1080/14740338.2020.1780208

DO - 10.1080/14740338.2020.1780208

M3 - Journal article

C2 - 32510245

VL - 19

SP - 961

EP - 968

JO - Expert Opinion on Drug Safety

JF - Expert Opinion on Drug Safety

SN - 1474-0338

ER -