Pharmacogenomics: how patterns of genomic and transcriptomic variation determine the response to drugs and chemicals
Urs A. Meyer
Biozentrum of the University of Basel, Switzerland
Urs A. Meyer homepage
Genomics, transcriptomics, proteomics and metabolomics are revolutionizing the study of disease processes and the development and rational use of drugs. These technologies increasingly enable medicine to redefine and classify diseases and make reliable assessments of the individual risk to acquire a particular disease, and raise the number and specificity of drug targets. In the human genome sequence, we investigate variations between populations and between individuals that contribute to health and disease or altered drug responses. Approximately 50 relatively common (>1 %) gene variants or alleles (polymorphisms) are the best studied individual risk factors for adverse drug reactions and drug ineffectiveness, including genes for drug-metabolizing enzymes, drug transporters, drug receptors and ion-channels. This is the field of pharmacogenomics and I will discuss examples of polymorphisms of drug response. A recent development in defining genetic variation are high-through-put systems testing for >1
million single nucleotide polymorphisms (SNPs) or the HapMap project.
We also start to understand other aspects of phenotypic diversity by learning what determines the expression of genes in different cells at different times into different RNAs and proteins and thereby different function. One initial approach is to study overall gene expression in cells or tissues with microarrays that contain probes for every possible mRNA variant. For example, we have studied how drugs influence their own metabolism and transport by inducing the transcription of multiple genes including those involved in drug disposition. In the future, pharmacogenomics will be influenced by rapid and low cost whole genome sequencing, epigenetic influences on gene expression and systems biology models of multifactorial and multigenic drug responses.
The take-home message of my talk is: To explain patterns of variation in disease or drug effects, a combination of genomic, transcriptomic and ultimately proteomic and functional information is required.
Saturday, October 27, 15:40