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PURPOSE: Marketing authorization holders (MAHs) are expected to provide high-quality periodic safety update reports (PSURs) on their pharmaceutical products to health authorities (HAs). We present a novel instrument aiming at improving quality of PSURs based on standardized analysis of PSUR assessment reports (ARs) received from the European Union HAs across products and therapeutic areas. METHODS: All HA comments were classified into one of three categories: "Request for regulatory actions," "Request for medical and scientific information," or "Data deficiencies." The comments were graded according to their impact on patients' safety, the drug's benefit-risk profile, and the MAH's pharmacovigilance system. RESULTS: A total of 476 comments were identified through the analysis of 63 PSUR HA ARs received in 2013 and 2014; 47 (10%) were classified as "Requests for regulatory actions," 309 (65%) as "Requests for medical and scientific information," and 118 (25%) comments were related to "Data deficiencies." The most frequent comments were requests for labeling changes (35 HA comments in 19 ARs). The aggregate analysis revealed commonly raised issues and prompted changes of the MAH's procedures related to the preparation of PSURs. CONCLUSION: The authors believe that this novel instrument based on the evaluation of PSUR HA ARs serves as a valuable mechanism to enhance the quality of PSURs and decisions about optimization of the use of the products and, therefore, contributes to improve further the MAH's pharmacovigilance system and patient safety.

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The effectiveness of a signal detection process integrating traditional and data-mining techniques was evaluated retrospectively in the real-world setting of a drug safety department at a large pharmaceutical company. To this effect, annual metrics on all signals detected internally or externally for all approved Novartis drugs were reviewed from January 1, 2007, to December 31, 2011. Timeliness (ie, the ability of the signal detection process to detect signals prior to any regulator) was taken as a main component of effectiveness. Over this 5-year period, 568 (about 17%) of the 3481 signals submitted by the safety management team at the signal escalation boards were identified as new or changing signals. Of these 568 signals, 53 (10%) were detected first by health authorities (a quarter of which were class signals). In conclusion, the signal detection process at Novartis Pharmaceuticals could detect at least 9 of 10 signals prior to them being detected by health authorities.

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PURPOSE: The detection of safety signals with medicines is an essential activity to protect public health. Despite widespread acceptance, it is unclear whether recently applied statistical algorithms provide enhanced performance characteristics when compared with traditional systems. Novartis has adopted a novel system for automated signal detection on the basis of disproportionality methods within a safety data mining application (Empirica™ Signal System [ESS]). ESS uses two algorithms for routine analyses: empirical Bayes Multi-item Gamma Poisson Shrinker and logistic regression (LR). METHODS: A model was developed comprising 14 medicines, categorized as "new" or "established." A standard was prepared on the basis of safety findings selected from traditional sources. ESS results were compared with the standard to calculate the positive predictive value (PPV), specificity, and sensitivity. PPVs of the lower one-sided 5% and 0.05% confidence limits of the Bayes geometric mean (EB05) and of the LR odds ratio (LR0005) almost coincided for all the drug-event combinations studied. RESULTS: There was no obvious difference comparing the PPV of the leading Medical Dictionary for Regulatory Activities (MedDRA) terms to the PPV for all terms. The PPV of narrow MedDRA query searches was higher than that for broad searches. The widely used threshold value of EB05 = 2.0 or LR0005 = 2.0 together with more than three spontaneous reports of the drug-event combination produced balanced results for PPV, sensitivity, and specificity. CONCLUSIONS: Consequently, performance characteristics were best for leading terms with narrow MedDRA query searches irrespective of applying Multi-item Gamma Poisson Shrinker or LR at a threshold value of 2.0. This research formed the basis for the configuration of ESS for signal detection at Novartis.

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