We are seeking input from the scientific community on challenges, opportunities and gaps in pharmacogenomics. Please help us shape future programs in pharmacogenomics by responding to the recently published request for information (RFI). The RFI asks for your input on several topics, such as:
- Critical technological advances that can be applied to pharmacogenomics problems;
- Specific tools, resources, methods and approaches needed to enhance our molecular, genetic and biological mechanistic understanding;
- Ways to advance clinical implementation for improving health care outcomes, including safety, effectiveness, time and costs;
- Synergies that might come from a research network;
- Types of scientific endeavors that would best be funded by R01 grants in the field; and
- Additional interfaces and interactions that should be developed by NIH with other funders or organizations.
Please take the time to comment on any or all of the above topics between now and the May 17 deadline. You may respond as individuals or groups. Working together, we can help advance this research area of great scientific interest and immediate health relevance.
There remains an unfortunate chasm between the two halves of systems biology: the high-throughput “-omics” half and the dynamic modeling half. Pharmacogenomics is particularly sensitive to this chasm because pharmaceutical development is almost always more effective when we have a solid mechanistic understanding. Programs aimed at bridging the chasm between statistical and mechanistic approaches might leverage the strengths of both and help us avoid developing drugs with fundamentally flawed mechanisms of action.
Thanks for this feedback. Your comments have been collected for the RFI. NIGMS agrees that both association/statistical approaches and mechanistic evidence/modeling are essential to understanding completely how drugs act, and both types of information are necessary to predict drug effects.
Dysregulation of proteins is the key to diseases. There is no reason to justify that the focus should be on genomics instead of proteomics to study pharmacological efficacy. Given the fact that many new blockbuster drugs target enzymes that regulate protein modification enzymes, these enzyme substrates should be good biomarkers to understand mechanisms of drug actions and to stratify patients. And these biomarkers cannot be identified by genomics.
If microarray or RNA-seq is useful for identifying protein biomarkers, proteomics will be more powerful as two thirds of proteins’ RNA do not correlate well with protein expression.
I believe proteomics is mature now for hunting for pharmacology study. And pharmacogenomics is a biased term.
We agree that protein expression is critical. Interesting comment about pharmacogenomics being a dated term. We are thinking about that and considering the many other (beyond genomics) aspects of a mechanistic understanding essential to predicting and personalizing medicine. Keep the comments coming—and please also submit them via the RFI form.