DNA, drugs and chariots: on a decade of pharmacogenomics at the US FDA.
ABSTRACT Over the past 10 years, the US FDA has become a strong pharmacogenomics advocate as part of its mission to both protect and advance public health by enabling innovations that make medicines safer to use and more effective. The agency has evolved its advocacy cautiously on a foundation of science-based information from novel programs, such as the Voluntary Genomics Data Submission initiative, and on careful regulatory assessment of the extraordinary advances in clinical pharmacogenomics that have supported the update of drug labels with genetic information. This commentary goes into detail on the evolution of these achievements. However, many challenges remain for pharmacogenomics, and they will continue to evolve, and all stakeholders must work together. As the decade draws to a close, we have presented four major areas that need to be addressed collectively to assure that pharmacogenomics continues to mature over the next 10 years into a science that is essential to the practice of medicine.
SourceAvailable from: Soranun Chantarangsu
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ABSTRACT: Pharmacogenomics knowledge and technologies, which couple genomics information with pharmaceutical drug response, have been promised to revolutionise both drug development and prescription. One notable promise of pharmacogenomics is the potential to contribute to some of the Millennium Development Goals (MDGs), namely to increase justice in global health by incentivising public research laboratories and pharmaceutical companies to develop drugs for populations (e.g., in low- and middle-income countries) that have been neglected by the traditional drug development model. To evaluate the credibility of this promise, we examined - both quantitatively and qualitatively - those scientific papers indexed in PubMed and published between 1997 and 2010, with a view to describing the major orientations and tendencies characterising the development of pharmacogenomics research. Our results demonstrate that pharmacogenomics research has focused on three major non-communicable categories of disease: cancer, depression and other psychological disorders and cardiovascular and coronary heart disease. Few publications - and thus, by extension, little scientific interest - concerned orphan diseases, infectious diseases or maternal health, indicating that pharmacogenomics research over the last decade has replicated the well-known 90/10 ratio in drug development. As such, we argue that research in the field of pharmacogenomics has failed in its promise to contribute to the MDGs by reducing global health inequalities.Global Public Health 02/2014; 9(3):312-324. DOI:10.1080/17441692.2014.887137 · 0.92 Impact Factor
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ABSTRACT: Cancer is a complex disease that is strongly associated with defects in signal transduction pathways. A systematic understanding of signal transduction networks in cancer cells will make it possible to develop alternative therapeutic strategies. However, this understanding relies, at least in part, on the detection of endogenous protein–protein interactions (PPIs) that participate in these networks. The aim of this study is to find intervention strategies under the assumptions of inhibiting cell proliferation and activating cell apoptosis. We utilized 115 endogenous HeLa cell PPIs validated by in situ proximity ligation assay and information from on-line databases to reconstruct a cell-type specific signal transduction network. The CellNetAnalyzer were used to analyze the reconstructed network. The computational results from the minimal intervention set (MIS) showed that at least five proteins (AKT, DVL1, EGFR, IGF1R, and PDGFR) inhibited simultaneously in the reconstructed network were required for achieving the goals of drug interventions. By searching the IPA database, the proteins in the MIS, except DVL1, are single targets inhibited by clinic trial drugs to suppress cancer cells. The results suggest that a combination drug treatment may be more efficacious than a single-drug approach. Experiments should be carried out to validate the computational results in future research.Journal of the Taiwan Institute of Chemical Engineers 08/2014; 45(6). DOI:10.1016/j.jtice.2014.07.006 · 2.64 Impact Factor