CONCLUSION

As the reader can see, there are two major approaches using numerous technologies to identify and validate targets: “top-down” and “bottom-up.” Choice of approach depends on the knowledge of the disease indication at hand, and in some cases both approaches may apply. Irrelevant of the approach or approaches, key is information technologies. Pharmaceutical companies’ biggest concern in terms of drug discovery in the postgenomic era is data integration, particularly as it relates to target validation. The plea is for better visualization and data mining tools as well as better interface of databases [166]. At present, there is no quick way to validate a target. Indeed, there is no universal definition of what a validated target is. All of the data being generated on a genome-wide scale must be captured and integrated with other data and with information derived by classical, hypothesis-driven biological experimentation to provide the picture from which completely new targets will emerge. This is a daunting bioinformatics challenge [167]. However, one thing is clear: target validation has become a term that is often used but not rigorously applied. The evolution of truly validated targets is a much slower process than target identification. At present, even large drug discovery organizations in the biggest pharmaceutical companies are not capable of producing more than a handful of wellvalidated targets each year [2]. Thus, the technologies described in this chapter must be applied such that the number of disease gene targets identified is rigorously validated to improve the quality of drug discovery while providing an increased number of better validated targets because even the best validated target doesn’t guarantee lead compounds for development. Targets are only truly validated when a successful drug (not compound), working through that mechanism, has been found.