Drug-discovery scientists have been accustomed to having limited knowledge about the hundreds or thousands of compounds that project teams consider during their research. Now that high throughput property assays are available, a new source of information for more informed decisions is available. Typically, pharmaceutical profiling data are used to predict absorption, distribution, metabolism, excretion, and toxicity (ADMET). However, it can be applied more broadly to better plan and interpret discovery experiments. A compound must successfully pass the battery of discovery experiments to be considered for human experiments. Property information can provide improved insights for these experiments.
Figure 15.2 illustrates some of the discovery bioactivity experiments in which a test compound must be successful to advance. If erroneous activity or selectivity data are generated or misinterpreted, the SAR will mislead the project team. SAR is a central strategy of drug-discovery research. If the activity assays are affected by properties in addition to target protein interaction, then the SAR will be a composite of multiple variables. Table 15.1 lists some of the potential effects on SAR from lack of property data application in planning and interpretation of drug-discovery bioassays.
Property data from pharmaceutical profiling is not exclusively for optimizing PK. It can be considered as part of the multivariate ensemble of data (e.g., MW, chirality, purity, IC50, LD50) that is available to research teams for application to any drug-discovery experiment.
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