Objective: To review and compare the phase 3 clinical trial evidence on the 4 new pharmacological agents approved for the management of overweight and obesity.
Data Sources: Searches were performed (from 1966 through January 2016) in PubMed/MEDLINE, Scientific Citation Index, and product package inserts to identify key phase 3 clinical trials that were used in the approval of each agent.
Study Selection And Data Extraction: Phase 3 clinical trials that listed end points of ≥5% and ≥10% weight loss benchmarks from baseline as well as total percentage of weight loss by participants were selected for the review.
Data Synthesis: No head-to-head trials have been identified between these agents at this point, which limits comparisons across agents. Phentermine/topiramate ER appeared to have the best overall average weight loss from baseline as well as highest percentages of patients achieving both ≥5% and ≥10% weight loss benchmarks, followed second by naltrexone/bupropion, and then liraglutide, with lorcaserin showing the lowest rates. Phentermine/topiramate ER completion rates were highest for both treatment and placebo groups, followed by liraglutide, with lorcaserin and naltrexone/bupropion showing similar completion rates, below that of the other 2 agents. Common side effects reported differed between agents, although the most common adverse events reported were gastrointestinal in nature, with liraglutide demonstrating the highest reported rates and lorcaserin demonstrating the lowest.
Conclusion: These 4 new pharmacological agents represent new options for the clinician to utilize when trying to manage the problem of obesity. No clear first-line agent has emerged, so treatment decisions should be based on patient-specific factors.
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http://dx.doi.org/10.1177/1060028016634351 | DOI Listing |
Diabetol Metab Syndr
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LCEA Laboratory, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco.
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College of Electrical and Information Engineering, Beihua University, Jilin, 132013, China.
Remote sensing images often suffer from the degradation effects of atmospheric haze, which can significantly impair the quality and utility of the acquired data. A novel dehazing method leveraging generative adversarial networks is proposed to address this challenge. It integrates a generator network, designed to enhance the clarity and detail of hazy images, with a discriminator network that distinguishes between dehazed and real clear images.
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