The FDA IND safety reporting Final Rule (21CFR 312.32) applies to all human drugs and biological products being studied under an Investigational New Drug (IND). A sponsor must file an IND safety report for any serious...
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The FDA IND safety reporting Final Rule (21CFR 312.32) applies to all human drugs and biological products being studied under an Investigational New Drug (IND). A sponsor must file an IND safety report for any serious unexpected suspected adverse reaction (SUSAR) of a medicinal product being investigated. Some events may be obviously drug-related (e.g., agranulocytosis, anaphylactic reaction, drug-induced hepatic injury, Stevens-Johnson Syndrome). For serious adverse events that are not interpretable as individual occurrences, additional processes and procedures need to be employed for identifying and assessing risks in the accumulating safety data. The approaches shared in this manuscript apply principally to safety reporting of events that are anticipated to occur in the patient population-regardless of study participation. For these events, the study sponsor should periodically review the data in the aggregate and make a judgment as to whether there is a reasonable possibility of an event having been caused by the study drug rather than the underlying condition of the patient or a concomitant therapy. Factors cited for consideration are the size and consistency of the difference in event frequency between the test and control groups, supportive preclinical findings, evidence of a dose response relationship, plausible mechanism of action, known class effect and occurrence of other related adverse events. Examples are provided that demonstrate the flexibility sponsors have in meeting the spirit of the Final Rule;some combination and variation of methods from the examples could be employed. The important thing, as expressed by Jacqueline Corrigan-Curay (Director of the Office of Medical Policy, Center for Drug Evaluation and Research, FDA), is to have a thoughtful process;a system in place to look for clinically important imbalances, applying the best clinical and quantitative judgment, while maintaining trial integrity (Ball et al. in Interdisciplinary aggregate as
Computational models of social learning and decision-making provide mechanistic tools to investigate the neural mechanisms that are involved in understanding other people. While most studies employ explicit instructio...
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Computational models of social learning and decision-making provide mechanistic tools to investigate the neural mechanisms that are involved in understanding other people. While most studies employ explicit instructions to learn from social cues, everyday life is characterized by the spontaneous use of such signals (e.g., the gaze of others) to infer on internal states such as intentions. To investigate the neural mechanisms of the impact of gaze cues on learning and decision-making, we acquired behavioural and fMRI data from 50 participants performing a probabilistic task, in which cards with varying winning probabilities had to be chosen. In addition, the task included a computer-generated face that gazed towards one of these cards providing implicit advice. Participants' individual belief trajectories were inferred using a hierarchical Gaussian filter (HGF) and used as predictors in a linear model of neuronal activation. During learning, social prediction errors were correlated with activity in inferior frontal gyrus and insula. During decision-making, the belief about the accuracy of the social cue was correlated with activity in inferior temporal gyrus, putamen and pallidum while the putamen and insula showed activity as a function of individual differences in weighting the social cue during decision-making. Our findings demonstrate that model-based fMRI can give insight into the behavioural and neural aspects of spontaneous social cue integration in learning and decision-making. They provide evidence for a mechanistic involvement of specific components of the basal ganglia in subserving these processes. (C) 2020 The Authors. Published by Elsevier Ltd.
Evaluation of the safety profile of medicines is moving from a more reactive approach, where safety experts and statisticians have been primarily focusing on the review of clinical trial data and spontaneous reports, ...
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Evaluation of the safety profile of medicines is moving from a more reactive approach, where safety experts and statisticians have been primarily focusing on the review of clinical trial data and spontaneous reports, to a more proactive endeavor with cross-functional teams strategically evolving their understanding of the safety profile. They do this by anticipating the ultimate benefit-risk profile and its related risk management implications from the start of development. The proposed approach is based on assessments of integrated program-level safety data. These data stem from multiple sources such as preclinical information;clinical and spontaneous adverse event reports;epidemiological, real-world, and registry data;as well as, potentially, data from social media. Blended qualitative and quantitative evaluations allow integration of data from diverse sources. Adding to this, a collaborative multidisciplinary view, which is focused on continuous learning and decision-making via diverse safety management teams, ensures that companies look at their growing safety database and associated risk management implications from every relevant perspective. This multifaceted and iterative approach starts early in the development of a new medicine, continues into the post-marketing setting, and wanes as the product matures and the safety profile becomes more well understood. Not only does this satisfy regulatory requirements but, crucially, it provides the healthcare system and treated patients with a better understanding of the drug's safety profile.
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