Experience has shown that it is most efficient to intervene early in the discovery process. This is when discovery teams traditionally have the least amount of information available for making decisions. Informed decisions at this stage have the greatest leverage on productive downstream work. Correct choice of one or more lead series at this stage can make later work more successful. Conversely, lack of information at this stage can lead to investment of time and resources in a compound series that is later found to have a fatal flaw. This is demoralizing for the team, wasteful of resources, and leads to later entry into the clinical market.
Working at early stages can, however, impose additional constraints on the pharmaceutical profiling program. Typically, very little compound is available at this point and activity measurements usually have the highest priority. Thus, profiling methods must use only milligram-level quantity of each compound. Methods must also have rapid throughput, in order to provide the discovery team with data in a time frame that is consistent with the fast-paced decisions of early discovery. Profiling methods must be high capacity, because hundreds of compounds are often evaluated for each project team and most pharmaceutical organizations have many early-discovery projects underway at a given time. Finally, large amounts of data are generated and must be effectively delivered to discovery scientists and archived.
These requirements have lead to the following analytical solutions. Assays for low amounts of compounds must typically have highly sensitive detection schemes. Rapid throughput often relies on robotic methods. The large number of compounds has led to parallel high-density analysis formats, such as 96-well plates. To handle the large amounts of data, special software, databases, and visualization methods have been developed.
It is also advantageous to include assays that cover diverse physicochemical and physiological properties. This multivariate approach allows the survey of diverse property space to (1) measure the contribution of each property against the pharmaceutical hurdles, and (2) develop multivariate analysis models. These are discussed in later sections.
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