The research is partially funded by a US National Institutes of Health (NIH) grant from the National Institute of General Medical Sciences and the recent findings were published in the journal Nature Biotechnology.
The researchers used 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 scientists believe, the methodology could be applied to a wide range of medications and help Roche, and others, better understand drug metabolism, and subsequently drug toxicity.
According to the study 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.
Another study using the computational genetic analysis method was also recently published, in the journal Anesthesiology. In this study, a mouse model of narcotic drug addiction was analyzed, and the findings identified a novel method for treating the disorder.