Assessment of a Severity-Based Algorithm to Detect Signals of Severe Drug-Induced Liver Injury Using Spontaneous Reporti
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ORIGINAL RESEARCH ARTICLE
Assessment of a Severity-Based Algorithm to Detect Signals of Severe Drug-Induced Liver Injury Using Spontaneous Reporting Database Komal Gupte-Singh1 • Hu Li1 • Jonathan Lucas Swain1 • Yingkai Cheng1 Arie Regev1
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Published online: 30 November 2016 Ó Springer International Publishing Switzerland 2016
Abstract Background Severe drug-induced liver injury (DILI) is a major safety concern in pharmacovigilance. It would be beneficial to develop a robust method for early detection of hepatic safety signals. Objective The aim of this study was to assess the performance of a severity-based algorithm in detecting signals of severe DILI. Method We assessed a method using preferred terms (PTs) listed in the Medical Dictionary for Regulatory Activities. PTs were grouped into three categories based on severity and relatedness of liver injury (category A: typical for severe DILI; category B: atypical for DILI regardless of severity; category C: severity not specified regardless of etiology). The disproportionality of these categories was then analyzed using the US Food and Drug Administration’s (FDA’s) Adverse Event Reporting System based on the empirical Bayesian geometric mean (EBGM). A lower bound of the 90% confidence interval of the EBGM (EB05) [2 for category A in two continuous quarters was used to define a signal of severe DILI. Selected drugs with (positive controls) and without (negative controls) known hepatotoxicity were tested. Time to initiation of regulatory action (none, withdrawal, or boxed warning) was compared and contrasted using the severity-based algorithm and a previously published algorithm. Results Our method detected signals of severe DILI in the fourth quarter (EB05 = 8.83), twenty third quarter (EB05 = 2.14), and tenth quarter (EB05 = 3.14) for & Arie Regev [email protected] 1
Eli Lilly and Company, Lilly Corporate Center, 893 S Delaware Street, Indianapolis, IN 46285, USA
troglitazone, leflunomide, and telithromycin, respectively, after launch in the US market. Compared with the previously published algorithm, the severity-based algorithm proposed in our study identified a signal of severe DILI in the same quarter (troglitazone and telithromycin) or earlier (leflunomide) for the positive controls. For the negative controls, no signal for severe hepatotoxicity was detected. Conclusions The results indicate that the severity-based algorithm is sensitive in the early detection of severe hepatotoxicity and can add value to routine pharmacovigilance. Further studies are needed to demonstrate the effectiveness of this method.
Key Points The present study devised a method to improve early detection of severe hepatotoxicity using preferred terms (PTs) listed in the Medical Dictionary for Regulatory Activities. Results indicate that the algorithm is useful in early detection of severe hepatotoxicity and may add value to routine pharmacovigilance.
1 Introduction Drug-induced liver injury (DILI) remains a major unresolved issue for the health care system and the drug industry
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