ADMET property

As discussed above, all ADMET aspects are dependent on each other and should all be considered when making predictions. Integrated analysis of different aspects of drug pharmacokinetic profiles is yet another future trend. Ultimately, drug ADMET properties should be predicted based on an integration of a compilation of in silico models reflecting different aspects of the process. 

The Jamieson paper reports the results of a number of studies, some successful, others not. Failures can be ascribed to the difficulties encountered in log P control. The first evident trouble concerns the choice of the lipophilicity descriptor many prefer log P, but this choice is questionable as has been outlined by Lombardo (see Chapter 16). Secondly, variations in lipophilicity profile influence not only hERG activity, but also target selectivity and also ADMET properties. Lipophilicity is a bulk property and its modification can involve different moieties of the molecules. Once the chemical modulation has been designed, but before moving to the bench, the research group should predict the consequences of this change on each step of the drug s action, but unfortunately this is not always done. 

Hansch and Leo [13] described the impact of Hpophihdty on pharmacodynamic events in detailed chapters on QSAR studies of proteins and enzymes, of antitumor drugs, of central nervous system agents as well as microbial and pesticide QSAR studies. Furthermore, many reviews document the prime importance of log P as descriptors of absorption, distribution, metabolism, excretion and toxicity (ADMET) properties [5-18]. Increased lipophilicity was shown to correlate with poorer aqueous solubility, increased plasma protein binding, increased storage in tissues, and more rapid metabolism and elimination. Lipophilicity is also a highly important descriptor of blood-brain barrier (BBB) permeability [19, 20]. Last, but not least, lipophilicity plays a dominant role in toxicity prediction [21]. 

Lipophilicity correlations with ADMET properties are often promising when marketed or well-characterized compounds are utihzed in the training set Even when structurally diverse sets of marketed dmgs are chosen, the common denomi-... 

Pharmacokinetics and toxicity have been identified as important causes of costly late-stage failures in drug development. Hence, physicochemical as well as ADMET properties need to be fine-tuned even in the lead optimization phase. Recently developed in silica approaches will further increase model predictivity in this area to improve compound design and to focus on the most promising compounds only. A recent overview on ADME in silica models is given in Ref [128]. 

Clark, D.E. and Grootenhuis, P.D. Progress in computational methods for the prediction of ADMET properties. Curr. Opin. Drug. Disc. Dev. 2002, 5, 382-390. 

Norrnder, U. (2006) Bergstrom CAS prediction of ADMET properties. ChemMedChem, 1, 920-937. 

Gola, J., Obrezanova, O., Champness, E. and Segall, M. (2006) ADMET property prediction The state of the art and current challenges. QSAR and Combinatorial Science, 25, 1172-1180. 

In vitro cell-based models have been developed to aid in the evaluation of ADMET properties of compounds to explore the influences of species differences. CaCo-2 cells, for example, constitute an immortalized line of heterogeneous human epithelial colorectal adenocarcinoma cells widely used to predict the absorption rates of candidate drug compounds across the intestinal epithelial cell barrier. Drug absorption rates are determined 21 days after CaCo-2 cell seeding to allow for monolayer formation and cell differentiation. 

When assessing the potential routes of administration, one must consider the physicochemical properties of the drug, its ADMET properties, the therapeutic indication, and the patient population, some of which are discussed below. Table 1 provides a list of some of those factors that must be addressed when determining the most favorable route of administration and the subsequent formulation for delivery. Ideally the route of administration and subsequent formulation will be optimized after identifying critical design parameters to satisfy the needs of patients and health care professionals alike while maintaining the safety and efficacy of the product. 

The cooperative relationship between the structural features of molecules and many physiological processes makes artificial neural network models a frequent choice for predicting the ADMET properties of drug candidates. ... 

SVM s have been shown to have superior performance to ANN s in non-linear modeling studies such as optical character recognition. In our hands, initial applications of SVM s to the prediction of ADMET properties from molecular structure indicate that their overall error performance is similar to that of ANN s, but that the predicted values have a smoother distribution and a slightly less-compressed range. 

ADMET Properties of Drugs Cytochrome P450 Monooxygenases, Chemistry of Drug Metabolizing Enzymes, Chemistry of Enzyme Inhibition, Chemistry and Mechanisms of Polymorphisms, Detection of... 

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