ADME and Toxicity Profiling

Major reasons for drug candidates to fail in early clinical phases are unpropitious pharmaco-kinetic properties (such as a lack of bioavailability) or toxicity. Therefore, estimation of these properties is an important part of a combinatorial library profile. An increasing number of publications [6, 42-45] demonstrate the importance of ADME parameters (ADME = absorption, distribution, metaboHsm, and ehmina-tion). Several commercial software packages are available. The ways in which ADME parameters are derived are similar in most available software products. In a first step, simple descriptors are calculated and in a second step, these descriptors are correlated with experimental data [46]. The parameters must describe properties that are important for pharmaco-kinetics (lipophilicity, size, and polarity of molecules, etc.). Standard correlation methods can be used, because the type of correlation has only secondary effects on the results. Generally, the predictivity of classification methods (the output from which can only be good or bad ) is slightly better compared to a quantitative correlation. 

In spite of the high interest in the parameters and in spite of the fact that more and more software companies offer products to calculate them, reliable calculation of ADME properties remains difficult The results are only acceptable if the molecular structures of the training set (for which experimental data are known) and the structures of interest are similar. Typically, the methods achieve a high degree of predictivity ( 90% correct classification) within the training data. Nevertheless, most software packages fail completely in realistic tests [47]. 

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