PD and PK pathways of HMGCo A reductase inhibitors (Statins). (a) Cholesterol and lipoprotein transport: genes involved in mediating statin effects on hepatic cholesterol metabolism and consequent effects on plasma lipoprotein transport. Statins inhibit endogenous cholesterol production by competitive inhibition of HMG-CoA reductase (HMGCR), the enzyme that catalyzes conversion of HMG-CoA to mevalonate, an early rate-limiting step in cholesterol synthesis. This pathway delineates genes involved in statin pharmacogenomics, including genes involved in mediating the PD effects of statins on plasma lipoprotein metabolism and those involved in the PKs effects of the drug transport and metabolism. Note the effects of inhibition of HMG-CoA reductase on major aspects of hepatic cholesterol metabolism and selected gene products that can modulate the effects of statins on metabolism and transport of plasma lipoproteins. (b) PKs of Statins: representation of the superset of all genes involved in the transport, metabolism, and clearance of statin class drugs. This figure depicts a generalized view of the PKs of statins, representing the superset of all genes with a reported influence on statin transport and metabolism. Statins are dosed orally and enter the systemic circulation through intestinal cells both passively and by active transport via the ABC and SLC gene family transporters. The major organs of metabolism and elimination include the liver and, to a lesser extent, the kidney. Metabolism is catalyzed by enzymes of the CYP and UGT gene family. The main pathway of elimination is ABC-transporter-mediated biliary excretion. The more hydrophilic compounds (e.g., pravastatin) require active transport into the liver, are less metabolized by the CYP family, and exhibit more pronounced active renal excretion, whereas the less hydrophilic compounds are transported by passive diffusion and are better substrates for both CYP enzymes and transporters involved in biliary excretion. These figures are available at www.pharmgkb.org.

The home page of PharmGKB provides a straightforward schema for understanding pharmacogenomics. After drugs are delivered, PKs and PDs both involve sets of genes and lead to both efficacious and toxic effects. Variation in response can be related to the PK and PD genes by studying their variations in the human population. All data in the PharmGKB are indexed as being relevant to PK, PD, clinical outcomes (CO), genetic variation (GN), or functional assays at the molecular and cellular level (FN).

Browsing function in PharmGKB. PharmGKB allows users to browse the major classes of data (genetic variation in pharmacogenes, curated literature, drugs associated with genotype, phenotype, pathway or other information, pathways, diseases, and phenotype data files). The number of data objects in each category is displayed, and there is a full-text search capability to allow more focused searching.

Example of the PharmGKB gene variant browser: nitric oxide synthetase 3 (NOS3). NOS3 is involved in the angiotensin and vascular endothelial growth factor (agents inhibiting the vascular endothelial growth factor signaling pathway have been developed as a new class of anticancer agents) pathways and the response to a number of drugs. The genomic DNA is the thick bar, with exons marked in brown. SNPs in PharmGKB are shown above the genomic DNA with a graphical indication of minor allele frequency. The location of SNPs in dbSNP and jSNP are shown below the genomic DNA. The table shows the chromosomal position, with links to the Golden Path genome browser, dbSNP, and with links to detailed information about the alleles, assays, frequencies, and individual-level data.

Diastolic blood pressure response to metoprolol in hypertensive patients is predicted by ADRB1 diplotype. Diplotype is defined by the SNPs at codons 49 and 389. SR = Ser49Arg389; SG = Ser49Gly389; GR = Gly49Arg389. Reproduced from Clinical Pharmacology and Therapeutics 37, 44–52 (2003).

Flow chart for the functional evaluation of genes with replicated associations. Replicated genes from clinical trial populations are explored for functional basis of genetic effects, using gene expression studies, cellular models, and animal models (reprinted from: Pharmacogenomics J. 6, 311–326 (2006).

Impact of germline TPMT genotype on incidence of toxicity (upper). The lower graph indicated that when dose is individualized based on TMPT germline status, the cumulative incidence (CI) of relapse is not compromised.

Whole-genome approach to identify genes that predict survival. Genes whose expression predicted in vitro drug sensitivity also predicted probability of disease-free survival in St Jude patients (upper) and independent group of patients treated on Dutch/CoAll studies (lower).