NUTLEY, N.J., May 8 /PRNewswire/ -- In findings published in Nature Biotechnology (May 2006, vol. 24 No. 5), Roche scientists present a new method for analyzing the metabolism of a commonly prescribed drug. Use of this mouse genetic analysis method may lead to a better understanding of how drugs are metabolized, which could facilitate more effective individualization of drug selection and dosing regimens in humans.
This research, which is partially funded by a National Institutes of Health (NIH) grant (1 R01 GM068885-01A1) from the National Institute of General Medical Sciences, utilized a computational method for mouse genetic analysis to identify factors that regulate the metabolism of warfarin, a widely used anticoagulant. The scientists discovered that this computational method can quickly identify genetic variants within drug metabolizing enzymes that contribute to different drug responses in mice and provides valuable information about genes that are likely to play a role in human drug metabolism. Therefore, the methodology could be applied to a wide range of medications and help Roche, as well as others, better understand drug metabolism, and subsequently drug toxicity. Steve Shafer, Stanford University Department of Anesthesia, was also an author on the study.
“This research and the computational method can help scientists and clinicians better understand the drugs they are using, as well as the diseases they target,” stated Gary Peltz, M.D., Ph.D., head of Genetics and Genomics at Roche in Palo Alto, California. “It can also be used to identify genetic susceptibility factors affecting drug-induced toxicity.” While the research is at an early stage, Peltz notes that the next step is to analyze other drugs of clinical importance, including one that induces liver toxicity.
According to the authors, pharmacogenomic data can influence drug development and clinical practice. They note that use of pharmacogenomic information has the potential to increase drug efficacy, reduce side effects and improve treatment outcomes for patients. Therefore, it is essential that scientists develop effective strategies to identify genetic factors affecting the metabolism or response to current and future therapies.
Similarly, another paper by Peltz and colleagues at Roche and Stanford was also published last week in Anesthesiology (vol. 104, No. 5, May 2006). In this paper (“A Genetic Analysis of Opioid-induced Hyperalgesia in Mice”), which also features the computational genetic analysis method, a mouse model of narcotic drug addiction was analyzed, and the findings identified a novel method for treating drug addiction.
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