DNA, drugs and chariots: On a decade of pharmacogenomics at the US FDA
Office of Clinical Pharmacology, Center for Drug Evaluation & Research, US FDA, HFD-850, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA. Pharmacogenomics
(Impact Factor: 3.22).
04/2010; 11(4):507-12. DOI: 10.2217/pgs.10.16
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.
Available from: Daniel Lynch
- "Such a system should identify the genetic components that have sufficient data to support clinical or diagnostic utility, present evidence-based interpretations of genetic results in the context of particular drugs, provide clear recommendations for the application of specific results, and highlight areas with gaps in knowledge that need further investigation. The outcome of such a critical appraisal should guide further studies aimed both at addressing the specific gaps in knowledge about a gene’s effects on a specific drug (termed a ‘drug-gene pair’) and at validating further the predictive biomarkers, thus allowing therapeutics and diagnostics developers and regulators to make meaningful risk-benefit assessments that will pave the way to clinical adoption of the PGx guidelines . This requires a multifaceted approach that includes routine integration of PGx in the design and outcomes analysis of clinical drug trials; retrospective studies that link patient health outcomes with medical/medication histories, gleaned through self-reported or EMR data [12,13]; and prospective, population-based, comparative effectiveness research [14,15]. "
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ABSTRACT: Implementation of pharmacogenomics (PGx) in clinical care can lead to improved drug efficacy and reduced adverse drug reactions. However, there has been a lag in adoption of PGx tests in clinical practice. This is due in part to a paucity of rigorous systems for translating published clinical and scientific data into standardized diagnostic tests with clear therapeutic recommendations. Here we describe the Pharmacogenomics Appraisal, Evidence Scoring and Interpretation System (PhAESIS), developed as part of the Coriell Personalized Medicine Collaborative research study, and its application to seven commonly prescribed drugs.
Available from: sciencedirect.com
- "Pharmacogenomics plays an important role in identifying drug responders and non-responders, avoiding adverse events, and optimizing drug dose  . Recently, the U.S. Food and Drug Administration (FDA) has become a strong pharmacogenomics advocate in an effort to make drugs safer and more effective  . In order to improve the quality of alreadymarketed drugs, the FDA has updated certain drug labels to include PGx information. "
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ABSTRACT: Personalized medicine is to deliver the right drug to the right patient in the right dose. Pharmacogenomics (PGx) is to identify genetic variants that may affect drug efficacy and toxicity. The availability of a comprehensive and accurate PGx-specific drug-gene relationship knowledge base is important for personalized medicine. However, building a large-scale PGx-specific drug-gene knowledge base is a difficult task. In this study, we developed a bootstrapping, semi-supervised learning approach to iteratively extract and rank drug-gene pairs according to their relevance to drug pharmacogenomics. Starting with a single PGx-specific seed pair and 20 million MEDLINE abstracts, the extraction algorithm achieved a precision of 0.219, recall of 0.368 and F1 of 0.274 after two iterations, a significant improvement over the results of using non-PGx-specific seeds (precision: 0.011, recall: 0.018, and F1: 0.014) or co-occurrence (precision: 0.015, recall: 1.000, and F1: 0.030). After the extraction step, the ranking algorithm further improved the precision from 0.219 to 0.561 for top ranked pairs. By comparing to a dictionary-based approach with PGx-specific gene lexicon as input, we showed that the bootstrapping approach has better performance in terms of both precision and F1 (precision: 0.251 vs. 0.152, recall: 0.396 vs. 0.856 and F1: 0.292 vs. 0.254). By integrative analysis using a large drug adverse event database, we have shown that the extracted drug-gene pairs strongly correlate with drug adverse events. In conclusion, we developed a novel semi-supervised bootstrapping approach for effective PGx-specific drug-gene pair extraction from large number of MEDLINE articles with minimal human input.
Available from: Soranun Chantarangsu
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