Sibutramine[BSA]

Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs. We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug-event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows. We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest. Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.

Background: Adulterated herbal weight loss products with containing undeclared synthetic drugs are common and responsible for many serious health damages. Objectives: The purpose of the study was to determine five synthetic adulterants in eight common herbal weight loss supplements, which are currently sold in Iran markets, to verify their presence in supplements, without mentioning on the labels. Materials and Methods: Eight common herbal weight loss samples were obtained from the Iran pharmaceutical market after advertising in the Persian language on satellite channels and internet. Five pharmacological classes of drugs used for weight loss, namely sibutramine, phenolphthalein, phenytoin, bumetanide and rimonabant, were investigated and quantified by GC-MS for the first three and LC-MS for the last two medications. Results: The most undeclared ingredients, which were illegally added include sibutramine, phenolphthalein, bumetanide, and phenytoin in the original super slim, herbaceous essence, super slim green lean, and fat loss, supplements, respectively. Rimonabant was not found. Caffeine, pseudoephedrine, theobromine and amfepramone were also found in the supplements using GC-MS assay. Conclusions: Adulterated synthetic substances were detected in the herbal weight loss products. Health care professionals should make people aware of the risks of taking herbal weight-loss supplements.