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Conference Papers Year : 2012

Uncertainties Using Genomic Information for Evidence-Based Decisions

Pasky Pascual
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For the first time, technology exists to monitor the biological state of an organism at multiple levels. It is now possible to detect which genes are activated or deactivated when exposed to a chemical compound; to measure how these changes in gene expression cause the concentrations of cell metabolites to increase or decrease; to record whether these changes influence the over-all health of the organism. By integrating all this information, it may be possible not only to explain how a person’s genetic make-up might enhance her susceptibility to disease, but also to anticipate how drug therapy might affect that individual in a particularized manner.But two related uncertainties obscure the path forward in using these advances to make regulatory decisions. These uncertainties relate to the unsettled notion of the term “evidence” — both from a scientific and legal perspective. From a scientific perspective, as models based on genomic information are developed using multiple datasets and multiple studies, the weight of scientific evidence will need to be established not only on long established protocols involving p-values, but will increasingly depend on still evolving Bayesian measures of evidentiary value. From a legal perspective, new legislation for the Food and Drug Administration has only recently made it possible to consider information beyond randomized, clinical trials when evaluating drug safety. More generally, regulatory agencies are mandated to issue laws based on a “rational basis,” which courts have construed to mean that a rule must be based, at least partially, on the scientific evidence. It is far from certain how judges will evaluate the use of genomic information if and when these rules are challenged in court.
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Dates and versions

hal-01518683 , version 1 (05-05-2017)





Pasky Pascual. Uncertainties Using Genomic Information for Evidence-Based Decisions. 10th Working Conference on Uncertainty Quantification in Scientific Computing (WoCoUQ), Aug 2011, Boulder, CO, United States. pp.1-14, ⟨10.1007/978-3-642-32677-6_1⟩. ⟨hal-01518683⟩
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