ADME -type descriptor

On the basis ofthe Cottesman data set and a set of259 compounds compiled from the literature, we explored the performance of several classification methods combined with different descriptor sets. These include simple ADME-type descriptors (log P, number of rotatable bonds, number of H-bond donors, and acceptors),... 

One of the simplest and most common ways to evaluate a molecule for ADME properties is a qualitative examination of its basic descriptor values such as molecular weight (MW), ClogP for lipophilicity, polar surface area (PSA), counts of hydrogen bond donors and acceptors (HBD, HBA), and count of rotatable bonds (RB). This type of approach popularized by Lipinski s famous Rule of 5 was published a decade ago [6]. Lipinski et al. established cutoffs for MW (500), ClogP (5), HBA (10), and HBD (5). These cutoffs were based on the 90th percentile of distributions of molecules in the World Drug Index having USAN or INN names. The Rule of 5 considers a violation of any two of these cutoffs to be an alert for poor absorption or permeability. 

In addition to the VolSurf treatment of the GRID fields, the information from the MIF can also be transformed to obtain a pharmacophoric type of representation, which is useful in the modeling of metabolic stability, cytochrome inhibition or even the direct study of the ADME related proteins (Fig. 10.3). The Almond software [17] transforms the MIF into a distance-based representation of the molecule interaction. These parameters describe the geometry of the interaction and QSAR models can be derived where the interaction with a protein is essential. Detailed information on these descriptors is presented elsewhere in this book. 

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. 

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