Factors Influencing Regulatory Decision-Making in Signal Management: Analysis Based on the Signals Identified from the FAERS.

PURPOSE: This study aimed to identify factors that influence the decision to take safety regulatory actions in routine signal management based on spontaneous reports. For this purpose, we analyzed the safety signals identified from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and related information. METHOD: From the signals that the FDA identified in the FAERS between 2008 1Q and 2014 4Q, we selected 216 signals for which regulatory action was or was not taken. Characteristics of the signals were extracted from the FAERS quarterly reports that give information about what signals were identified from the FAERS and what actions were taken for them, and the FAERS data released in the same quarter when the signal was published. Univariate and multivariable logistic regression analysis was used to assess the relationship between the characteristics of each of the signals and the decision on regulatory action. RESULT: As a result of the univariate logistic regression analysis, we selected 5 factors (positive rechallenge, number of cases accumulated in the last one-year period before the signal indication, previous awareness, serious outcome, risk for special populations) to include in the multivariable logistic regression model (p < 0.2). The multivariate logistic regression analysis showed that the number of cases accumulated in the last one-year period before the signal indication and previous awareness were associated with the regulatory action (p < 0.05). CONCLUSION: The present study showed that number of cases accumulated in the last one-year period before the signal indication and previous awareness potentially associated with the United States regulatory action. When assessing safety signals, we should be careful of the adverse events with a large number of cases accumulated rapidly in a short period. In addition, we should pay attention to new information on not only unknown risks but also previously identified and potential risks.

Significance of data mining in routine signal detection: Analysis based on the safety signals identified by the FDA.

PURPOSE: Data mining has been introduced as one of the most useful methods for signal detection by spontaneous reports, but data mining is not always effective in detecting all safety issues. To investigate appropriate situations in which data mining is effective in routine signal detection activities, we analyzed the characteristics of signals that the US Food and Drug Administration (FDA) identified from the FDA Adverse Event Reporting System (FAERS). METHODS: Among the signals that the FDA identified from the FAERS between 2008 1Q and 2014 4Q, we selected 233 signals to evaluate in this study. We conducted a disproportionality analysis and classified these signals into two groups according to the presence or absence of statistical significance in the reporting odds ratio (ROR). Then, we compared the two groups based on the characteristics of the suspected drugs and adverse events (AEs). RESULTS: Safety signals were most frequently identified for new drugs that had been on the market for less than 5 years, but some signals were still identified for old drugs (≥20 years), and most of them were statistically significant. The proportion of the signals for "serious" events was significantly higher in the group of nonsignals by ROR (Fisher's exact test, P = 0.032). CONCLUSIONS: Data mining was shown to be effective in the following situations: (1) early detection of safety issues for newly marketed drugs, (2) continuous monitoring of safety issues for old drugs, and (3) signal detection of nonserious AEs, to which little attention is usually given.

Association between infection and severe drug adverse reactions: an analysis using data from the Japanese Adverse Drug Event Report database.

PURPOSE: It has been reported recently that immune reactions are involved in the pathogenesis of certain types of adverse drug reactions (ADRs). We aimed to determine the associations between infections and drug-induced interstitial lung disease (DILD), rhabdomyolysis, Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), or drug-induced liver injury (DILI) using a spontaneous adverse drug event reporting database in Japan. METHODS: The reported cases were classified into three categories (anti-infectious drug group, concomitant infection group, and non-infection group) based on the presence of anti-infectious drugs (either as primary suspected drug or concomitant drug) and infectious disease. We assessed the association between four severe ADRs and the presence and seriousness of infection using logistic regression analysis. RESULTS: We identified 177,649 cases reported in the study period (2009-2013). Logistic regression analysis showed significant positive associations between infection status and onset of SJS/TEN or DILI (SJS/TEN: anti-infectious drug group: odds ratio (OR) 2.04, 95% CI [1.85-2.24], concomitant infection group: OR 2.44, 95% CI [2.21-2.69], DILI: anti-infectious drug group: OR 1.27, 95% CI [1.09-1.49], concomitant infection group: OR 1.25, 95% CI [1.04-1.49]), compared to the non-infection group. By contrast, there were negative or no associations between infection and DILD or rhabdomyolysis. A significantly positive association between infection and SJS/TEN seriousness (OR 1.48, 95% CI [1.10-1.98]) was observed. CONCLUSIONS: This study suggested that infection plays an important role in the development of SJS/TEN and DILI. For the patients with infection and/ or anti-infectious drugs, careful monitoring for severe ADRs, especially SJS/TEN, might be needed.

Isolation of RNA aptamers specific for the 3' X tail of HCV.

The 3' end of the HCV genome, designated as the 3' X tail, comprises an almost invariant 98-nucleotide sequence containing three highly conserved stem-loop structures (3' SL1, 3' SL2, and 3' SL3). Since these sequences are all critical for the initiation of negative-strand synthesis and essential for viral replication, they are attractive targets for novel anti-HCV drugs. To obtain effective RNA aptamers specific for the 3' X tail, and with the aim of developing novel inhibitors of HCV replication, we performed in vitro selection of aptamers with specificity for the 3' X tail. In vitro selection, namely SELEX (systematic evolution of ligands by exponential enrichment) is a useful strategy for isolating nucleic acid sequences from a randomized oligonucleotide pool that have a high affinity for a target molecule. After four selection cycles, a pool of the 3' X tail-specific RNA aptamers were obtained. This RNA pool included 39 clones that could be divided into three main classes (cSL1, cSL2, and cSL3) which harbor complementary sequences to the apical loops of 3' SL1, 3' SL2, and 3' SL3, respectively. Biochemical analyses are in progress to evaluate whether these RNA aptamers have the potential to block HCV replication.