ADME parameters

Ekins S, Waller CL, Swaan PW, Crucian G, Wrighton SA, Wikel JH. Progress in predicting human ADME parameters in silica. J Pharmacol Toxicol Methods 2000 44 251-72. 

S. A., and Wikel, J. H., Progress in predicting human ADME parameters in silico, J. Pharmacol. Toxicol. 

It is important to understand the need for the multiple assays that are now routinely performed by most pharmaceutical companies to measure various absorption distribution metabolism and excretion (ADME) parameters to determine the pharmacokinetic (PK) properties of new chemical entities (NCEs). The goal of new drug discovery is to find NCEs that have the appropriate... 

WiKEL, J.H. Progress in predicting human ADME parameters in silico. 

Ekins, S., Waller, C.L., Swaan, P.W., Cruciani, G., Wrighton, S.A., Wikel, J.H. Progress in predicting human ADME parameters in silico. J. Pharmacol. Toxicol. Methods 2000, 44, 251-72. 

The SA protocol has also been successfully applied to optimize drug-like properties or ADME parameters while maximizing diversity and/or predicted... 

The disadvantages of CD include (i) strict correlation between the structure of the guest molecule and the cavity size of the CD molecule (ii) limited solubility of CD in water, and thus limited the maximum concentrations this approach can achieve and (iii) CDs can signiLcantly modify absorption-distribution-metabolism-excretion/elimination (ADME) parameters if the binding constant K is too high, and thus limited the amount of free drug for absorption (Miller et al., 2006). 

Pharmacology and drug disposition describing pharmacological effects and mechanisms of action and any known adsorption, distribution, metabolism, and excretion (ADME) parameters. 

Pharmacokinetics (PK) can be a major source of variability in the dose response relationship. It manifests itself in interindividual differences in the plasma concentration-time profile of a drug. Factors which lead to variability in the ADME parameters are therefore of importance in understanding overall variability in PK. 

A previous volume in our series, edited in 2003 by Han van de Waterbeemd, Hans Lennernas, and Per Artursson, was dedicated to summarize the current status in the estimation of relevant ADME parameters. This volume emerged as a top-seller in our series indicating the high impact of this topic in modern drug research. 

ADME parameters. Sufficient solubility in aqueous medium for absorption and blood transport has to be combined with sufficient lipophilicity for passage through cell membranes. If an active compound is too hydrophilic and at the same time contains a carboxylic acid group, for instance, conversion to a simple ester will facilitate absorption. Once in the blood, unspecific esterases will catalyse hydrolysis to the active carboxylic acid form. Such an ester is an instance of a prodrug. 

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. 

Initially, the focus in lead optimization was on improving the in vitro activity. Now, it has been reahzed that other compound properties, such as like solubility and ADME parameters, are of equal importance and should therefore be addressed as early as possible. 

Calculation of drug-likeness parameters (like ADME parameters and Li-pinski s rule [43]). 

Computational methods are essential in processing the vast data streams from sequencing efforts, stiTictural infomiation, protein analysis and differential gene expression. Computational approaches serve to integrate the research, development and discovery phases. An example of such efforts is the study of daig-likeness with respect to ADME parameters, in particular BBB penetration. 

Furthermore, prediction of human efficacy and DMPK/toxidty properties from preclinical studies is perhaps the ultimate goal of drug discovery. A common practice in the pharmaceutical industry is to establish in vitro—in vivo correlation for an ADME parameter in preclinical studies, then find the species that correlates to humans. If such animal-to-human in vitro correlation could be established, then the human in vitro information could be used to predict the clinical ADME outcome (Fig. 5.4). On the other hand, if the in vivo correlation for a parameter between animal and human is well understood, then animal in vivo data could be used to predict human outcomes. In vitro assays play an invaluable role in these processes. Examples of such predictions often include human efficacy and pharmacokinetic properties (e.g., target inhibition,... 

In a recent classification study, scaffolds taken from natural products were subjected to a two-step analysis in order to identify a diverse set of natural product scaffolds with preferred ADME-properties." Molecular diversity analysis and selection of natural product scaffolds were carried out using cell-based partitioning and the BCUT metrics." To approximate ADME parameters, rule-of-five " calculations were carried out. A set of about 50 natural product scaffolds fulfilled the desired diversity and ADME criteria. ... 

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