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A big data approach to the concordance of the toxicity of pharmaceuticals in animals and humans
یک رویکرد داده های بزرگ برای تطابق سمیت داروها در حیوانات و انسانها-2018 Although lack of efficacy is an important cause of late stage attrition in drug development the
shortcomings in the translation of toxicities observed during the preclinical development to
observations in clinical trials or post-approval is an ongoing topic of research. The concordance between
preclinical and clinical safety observations has been analyzed only on relatively small data sets, mostly
over short time periods of drug approvals. We therefore explored the feasibility of a big-data analysis on
a set of 3,290 approved drugs and formulations for which 1,637,449 adverse events were reported for
both humans animal species in regulatory submissions over a period of more than 70 years. The events
reported in five species – rat, dog, mouse, rabbit, and Cynomolgus monkey - were treated as diagnostic
tests for human events and the diagnostic power was computed for each event/species pair using
likelihood ratios.
The animal-human translation of many key observations is confirmed as being predictive, such as QT
prolongation and arrhythmias in dog. Our study confirmed the general predictivity of animal safety
observations for humans, but also identified issues of such automated analyses which are on the one
hand related to data curation and controlled vocabularies, on the other hand to methodological changes
over the course of time.
Keywords: predictivity of animal studies, concordance, adverse event, predictive value, translation, signal detection, big data, risk assessment |
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