Molecular ADME property

In the following section, the calculation of the VolSurf parameters from GRID interaction energies will be explained and the physico-chemical relevance of these novel descriptors demonstrated by correlation with measured absorption/ distribution/metabolism/elimination (ADME) properties. The applications will be shown by correlating 3D molecular structures with Caco-2 cell permeabilities, thermodynamic solubilities and metabolic stabilities. Special emphasis will be placed on interpretation of the models by multivariate statistics, because a rational design to improve molecular properties is critically dependent on an understanding of how molecular features influence physico-chemical and ADME properties. 

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. 

Robust peptide-derived approaches aim to identify a small drug-like molecule to mimic the peptide interactions. The primary peptide molecule is considered in these approaches as a tool compound to demonstrate that small molecules can compete with a given interaction. A variety of chemical, 3D structural and molecular modeling approaches are used to validate the essential 3D pharmacophore model which in turn is the basis for the design of the mimics. The chemical approaches include in addition to N- and C-terminal truncations a variety of positional scanning methods. Using alanine scans one can identify the key pharmacophore points D-amino-acid or proline scans allow stabilization of (i-turn structures cyclic scans bias the peptide or portions of the peptide in a particular conformation (a-helix, (i-turn and so on) other scans, like N-methyl-amino-acid scans and amide-bond-replacement (depsi-peptides) scans aim to improve the ADME properties." ... 

VolSurfwas initially validated on oral absorption [16, 17] and blood-brain-barrier permeation [18] models (see belovi ). VolSurf has continued to be developed to improve in silico predictions for ADME properties, although its use has also been extended to receptor-based evaluation of binding affinity [19, 20], While other soft-ivare tools for ADME modeling are available (see, e.g., [21]), the MIF-based collection of sofhvare and models available from Molecular Discovery (MD) is both extensive and ivell validated by the private sector. Three programs from MD, VolSurf, MetaSite and Almond, are particularly suited for rapid evaluation of large compound sets [22] in connection ivith ADME/Tox related properties ... 

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]. 

ADME properties have been recognized as a major reason for the failure of dmg candidates thus, several efforts are performed to develop molecular descriptors and models able to predict ADME properties of dmg candidates before their synthesis. Bibliographic references to theoretical aspects and QSAR studies on ADME properties are reported in the thematic bibliography. 

Property filters usually are binary variables assuming a value equal to 1, if the molecule shows a specific property (drug-likeness, ADME properties, and toxicities) and equal to zero otherwise. These filters are not comprised of many molecular descriptors and a threshold or a range of values is associated to each descriptor together with a condition on the descriptor value if the... 

In addition to the cited references, a thematic bibliography with almost 5,000 entries is available. Here, bibliographic references have been collected for some 70 topics of general interest. These references are additional references to those already quoted in the main text of the book. Topics are listed in alphabetic order, from ADME properties to Wiener index . Selection of the topics was based on the most frequent keywords encountered in publications about molecular descriptors and related research fields. 

A recent survey [84,85] shows that failures in clinical testing of new molecular entities can be attributed to issues related to the absorption, distribution, metabolism, and excretion (ADME) of compounds. Researchers are nowadays quite aware of the fact that poor ADME properties are at least as unwanted as lack of efficacy or selectivity. Therefore it is desirable to identify indicators that link the chemical and ADME property space these indicators can guide the synthesis of potent compounds with improved ADME profiles. As mentioned before, one of the first attempts to correlate... 

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