title: Pharmacogenomics and cardiovascular drugs: need for integrated biological system with phenotypes and proteomic markers.
abstract: Personalized medicine is based on a better knowledge of biological variability, considering the important part due to genetics. When trying to identify involved genes and their products in differential cardiovascular drug responses, a five-step strategy is to be followed: 1) Pharmacokinetic-related genes and phenotypes (2) Pharmacodynamic targets, genes and products (3) Cardiovascular diseases and risks depending on specific or large metabolic cycles (4) Physiological variations of previously identified genes and proteins (5) Environment influences on them. After summarizing the most well-known genes involved in drug metabolism, we will take as example of drugs, the statins, considered as very important drugs from a Public-Health standpoint, but also for economical reasons. These drugs respond differently in human depending on multiple polymorphisms. We will give examples with common ApoE polymorphisms influencing the hypolipemic effects of statins. These drugs also have pleiotropic effects and decrease inflammatory markers. This illustrates the need to separate clinical diseases phenotypes in specific metabolic pathways, which could propose other classifications, of diseases and related genes. Hypertension is also a good example of clinical phenotype which should be followed after various therapeutic approaches by genes polymorphisms and proteins markers. Gene products are under clear environmental expression variations such as age, body mass index and obesity, alcohol, tobacco and dietary interventions which are the first therapeutical actions taken in cardiovascular diseases. But at each of the five steps, within a pharmacoproteomic strategy, we also need to use available information from peptides, proteins and metabolites, which usually are the gene products. A profiling approach, i.e., dealing with genomics, but now also with proteomics, is to be used. In conclusion, the profiling, as well as the large amount of data, will more than before render necessary an organized interpretation of DNA, RNA as well as proteins variations, both at individual and population level.
authors.affiliation: Inserm U525 Equipe 4, Université Henri Poincaré Nancy I, 30 rue Lionnois Faculté de Pharmacie, 54000 Nancy, France. gerard.siest@pharma.uhp-nancy.fr
authors.forename: Gérard
authors.initials: G
authors.lastname: Siest
authors.forename: Jean-Brice
authors.initials: JB
authors.lastname: Marteau
authors.forename: Sandy
authors.initials: S
authors.lastname: Maumus
authors.forename: Hind
authors.initials: H
authors.lastname: Berrahmoune
authors.forename: Elise
authors.initials: E
authors.lastname: Jeannesson
authors.forename: Anastasia
authors.initials: A
authors.lastname: Samara
authors.forename: Anne-Marie
authors.initials: AM
authors.lastname: Batt
authors.forename: Sophie
authors.initials: S
authors.lastname: Visvikis-Siest
mesh_terms: D002317:Cardiovascular Agents / Q000378:metabolism / Q000493:pharmacokinetics / Q000627:therapeutic use*; D002318:Cardiovascular Diseases / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism; D005838:Genotype; D006801:Humans; D008954:Models, Biological; D010597:Pharmacogenetics / Q000379:methods* / Q000639:trends; D010641:Phenotype; D040901:Proteomics / Q000379:methods* / Q000639:trends
chemical_list: D002317:Cardiovascular Agents