Abstract

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

Highlights

  • Pre-marketing randomized clinical trials typically focus on establishing the safety and efficacy of a single drug rather than investigating drug-drug interactions; patients who use drugs other than the one under investigation are usually excluded.unlike pre-marketing trials, it is common to use multiple drugs for treatment post-marketing

  • The comitant signal score (CSS) exhibited slightly lower accuracy for detecting drug-drug interaction signals in comparison with the Ω shrinkage measure. These results suggest that the CSS might be a more suitable method for detecting drug-drug interaction signals using proportional reporting ratio (PRR) instead of the combination risk ratio (CRR)

  • The CRR proposed by Susuta et al is based on PRR, which facilitates the detection of drug-drug interaction signals, and makes it easy to understand the fluctuations in drug-drug interactions due to a single drug in terms of signal intensity [10]

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Summary

Introduction

Unlike pre-marketing trials, it is common to use multiple drugs for treatment post-marketing. Attention should be paid to the adverse events caused by a given drug, and to those arising as a result of interactions between two or more drugs. The proportion of adverse events caused by drug-drug interactions was estimated to be approximately 30% of the unexpected adverse events [1]. The use of a spontaneous reporting system is believed to be beneficial for the early detection of druginduced adverse events post-marketing. Spontaneous reporting systems do not include the number of drug users; the incidence of adverse events cannot be calculated, and instead, unknown adverse events are searched for using safety signals [2]

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