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The relatively new field of bioinformatics has rapidly emerged to manage the massive amount of data generated from genomics and proteomics analyses into useful information for disease diagnosis and drug discovery. Presently, 90% of the drugs sold in the more than $300 billion annual global pharmaceutical drug market treat the symptoms of disease and not the underlying causes. Less than 700 of the proteins encoded by the human genome have been specifically targeted by the biopharmaceutical industry for the discovery and development of these drugs. But with the completion of the human genome project, there are now over 5000 interesting potential drug targets.

However, the industry does not have the capacity to fully exploit all of these targets. It costs typically US$ 1 billion over a 10 year period to identify a drug against a single target and bring it successfully to the market place. This high cost mostly reflects the enormous failure rate at the various stages of the drug discovery, development and testing process. The most common and expensive setback associated with experimental drugs is their unforeseen side-effects in late stage human clinical trials. It has been estimated that the pharmaceutical industry misdirects more than $5 billion annually in screening for compounds against targets that are inappropriate.
Substantial reductions in these costs could be achieved with the identification of the more promising drug targets and markers of toxicity. If inappropriate drug targets and drug candidates could be eliminated earlier in the drug discovery process, this would save billions of dollars. This recognition of potential cost savings by the pharmaceutical industry has driven the proteomics market.

While large databases exist with the nucleotide (and predicted protein) sequences of genes and the mRNA expression levels of these genes from gene microarray analyses, very little information is currently available about the actual levels and activation states of their encoded proteins. One of the major initiatives of Kinexus has been to collect data about the expression and phosphorylation states of hundreds of cell signalling proteins in hundreds of tissue and cell lysates from humans and experimental animal models. Only with such a critical mass of proteomics data can the true potential of bioinformatics be realized and applied to map out the architecture and composition of cell signalling networks and define the most reliable biomarkers of disease and appropriate therapeutic drug targets for the treatment of these diseases.