The Role of the USA Food and Drug Administration in Clinical Research

2014 ◽  
pp. 117-143
Author(s):  
Stephen P. Glasser ◽  
Carol M. Ashton ◽  
Nelda P. Wray
Keyword(s):  
Clinical Research ◽  
Drug Administration ◽  
Food And Drug Administration ◽  
The Usa

scholarly journals Poly-ADP-ribose polymerase inhibitor use in ovarian cancer: expanding indications and novel combination strategies

2019 ◽  
Vol 29 (5) ◽  
pp. 956-968 ◽  
Author(s):  
Emily Hinchcliff ◽  
Shannon Neville Westin ◽  
Graziela Dal Molin ◽  
Christopher J LaFargue ◽  
Robert L. Coleman
Keyword(s):  
Ovarian Cancer ◽  
Drug Administration ◽  
Food And Drug Administration ◽  
Parp Inhibitors ◽  
Parp Inhibition ◽  
Combination Therapies ◽  
Combination Strategies ◽  
Polymerase Inhibitor

The use of poly(ADP-ribose) polymerase (PARP) inhibition is transforming care for the treatment of ovarian cancer, with three different PARP inhibitors (PARPi) gaining US Food and Drug Administration approval since 2014. Given the rapidly expanding use of PARPi, this review aims to summarize the key evidence for their use and therapeutic indications. Furthermore, we provide an overview of the development of PARPi resistance and the emerging role of PARPi combination therapies, including those with anti-angiogenic and immunotherapeutic agents.

scholarly journals Predicting Mortality with Applied Machine Learning: Can We Get There?

2019 ◽  
Vol 8 (1) ◽  
pp. 115-119
Author(s):  
Emily S. Patterson ◽  
C.J. Hansen ◽  
Theodore T. Allen ◽  
Qiwei Yang ◽  
Susan D. Moffatt-Bruce
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Real Time ◽  
Drug Administration ◽  
Patient Population ◽  
Food And Drug Administration ◽  
Hospital Environment ◽  
Clinical Settings ◽  
Applied Machine Learning

There is growing interest in using AI-based algorithms to support clinician decision-making. An important consideration is how transparent complex algorithms can be for predictions, particularly with respect to imminent mortality in a hospital environment. Understanding the basis of predictions, the process used to generate models and recommendations, how to generalize models based on one patient population to another, and the role of oversight organizations such as the Food and Drug Administration are important topics. In this paper, we debate opposing positions regarding whether these algorithms are ‘ready yet’ for use today in clinical settings for physicians, patients and caregivers. We report voting results from participating audience members in attendance at the conference debate for each of these positions obtained real-time from a smartphone-based platform.

The Statistics of Nutrition Sampling and Analysis

1974 ◽  
Vol 57 (5) ◽  
pp. 1181-1189
Author(s):  
Howard R Roberts
Keyword(s):  
Drug Administration ◽  
Natural Variation ◽  
Food And Drug Administration ◽  
Nutrient Content ◽  
Nutrition Labeling ◽  
Critical Importance ◽  
Analytical Methodology ◽  
Labeling Regulations ◽  
Analytical Variation

Abstract It is widely recognized that there is natural variation in the nutrient content of food products. When one attempts to measure nutrient levels, another source of variation becomes apparent—that inherent in the measurement process itself. Analytical variation is, of course, apparent when different methods are used but can also occur with the same method because of differences among laboratories and/or analysts. Both in its own right and more especially with regard to evaluating compliance with labeling regulations, method variability is of critical importance. In order to appreciate the role of analytical methodology, the nutrition labeling regulations and the procedures by which the Food and Drug Administration will assess compliance must first be thoroughly understood. This paper is directed toward that understanding.

scholarly journals Higher negative emotions in response to cigarette pictorial warning labels predict higher quit intentions among smokers

2019 ◽  
pp. tobaccocontrol-2019-055116 ◽  
Author(s):  
Yachao Li ◽  
Bo Yang ◽  
Daniel Owusu ◽  
Lucy Popova
Keyword(s):  
Drug Administration ◽  
Food And Drug Administration ◽  
Negative Emotions ◽  
Warning Labels ◽  
Mediation Analyses ◽  
Quit Smoking ◽  
Intentions To Quit ◽  
Quit Intentions ◽  
Different Levels

BackgroundCigarette pictorial warning labels (PWLs) could produce stronger quit intentions than text-only warning labels (TWLs) due to greater emotional arousal. Yet, it remains unclear whether PWLs that elicit different levels of emotions produce different outcomes. To better understand the role of negative emotions in the effects of PWLs, this study developed two sets of PWLs arousing different emotional levels (high vs low) but equally high on informativeness and compared them to each other and to the current TWLs.MethodsAdult US smokers (n=1503) were randomised to view nine high-emotion-arousing or low-emotion-arousing PWLs or TWLs. After each label, participants reported the negative emotions they felt while looking at the label. After seeing all the labels, participants reported their intentions to quit smoking. Mediation analyses tested whether message condition influenced quit intentions indirectly through negative emotions.ResultsCompared with TWLs, PWLs produced higher levels of negative emotions (b=0.27, SE=0.04, p<0.001). Compared with low-emotion arousing PWLs, high-emotion-arousing PWLs produced higher levels of negative emotions (b=0.24, SE=0.07, p<0.001). Higher negative emotions predicted stronger quit intentions (b=0.20, SE=0.03, p<0.001). Negative emotions mediated the effects of PWLs versus TWLs and high-emotion-arousing versus low- emotion-arousing PWLs on quit intentions.ConclusionsThe results provide additional evidence for negative emotions as the mechanism through which PWLs motivate smokers to consider quitting. The findings call on the Food and Drug Administration to design and implement high-emotion-arousing cigarette warning labels.

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