Clinical trials are an important source of safety data that contribute to the totality of safety information available to generate evidence for regulators, sponsors, payers, physicians, and patients. This work is a result of the efforts of the American Statistical Association Biopharmaceutical Section Safety Working Group.

This randomized, controlled trial demonstrated that lorcaserin used in conjunction with standard cessation counseling was associated with dose-related increases in smoking cessation and prevention of associated weight gain. To our knowledge, this is the first demonstration in humans of a potential role of 5-HT2C agonism in the modulation of central neurological circuits involved with reward.

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Objective: To identify an early treatment milestone that optimizes sensitivity and specificity for predicting 5% weight loss at Week (W) 52 in patients with and without type 2 diabetes on lorcaserin or placebo. Methods: Post hoc area under the curve for receiver operating characteristic analyses of data from three phase 3 trials comparing lifestyle modification1placebo with lifestyle modification1lorcaserin. A total of 6897 patients (18-65 years; BMI, 30-45 or 27-29.9 kg/m 2 with 1 comorbidity) were randomized to placebo or lorcaserin 10 mg bid. Changes (baseline to W52) in cardiometabolic parameters were assessed. Results: Response (5% weight loss from baseline) at W12 was a strong predictor of W52 response. Lorcaserin patients with a W12 response achieved mean W52 weight losses of 10.6 kg (without diabetes) and 9.3 kg (with diabetes). Proportions achieving 5% and 10% weight loss at W52 were 85.5% and 49.8% (without diabetes), and 70.5% and 35.9% (with diabetes). Lorcaserin patients who did not achieve a W12 response lost 3.2 kg (without diabetes) and 2.8 kg (with diabetes) at W52. Responders had greater improvements in cardiometabolic risk factors than the modified intent-to-treat (MITT) population, consistent with greater weight loss. Conclusions: 5% weight loss by W12 predicts robust response to lorcaserin at 1 year.

Signal detection has traditionally relied on spontaneous reporting databases that have been available worldwide for decades. However, current regulatory guidelines and ease of reporting have increased the size of these databases exponentially over the last few years. With such large databases, data-mining tools using disproportionality analysis and helpful graphics are often used to detect potential signals. Although the data sources have many limitations, analyses of these data have been successful at identifying safety signals postmarketing. Experience analyzing these dynamic data is useful in understanding the potential and limitations of analyses with new data sources such as social media, claims, or electronic medical records data.