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Baláž, Š.; Lukacova, V. Subcellular pharmacokinetics and its potential for library focusing. J. Mol. Graph. Model. 2002, 20, 6, 479–490.

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Baláž, Š.; Lukacova, V. Subcellular pharmacokinetics and its potential for library focusing. J. Mol. Graph. Model. 2002, 20, 6, 479–490.

QDB archive DOI: 10.15152/QDB.36   DOWNLOAD

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Property pIGC50: 96-h Tetrahymena toxicity as log(1/IGC50) [log(L/mmol)]

Compounds: 129 | Models: 0 | Predictions: 0

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Title: Baláž, Š.; Lukacova, V. Subcellular pharmacokinetics and its potential for library focusing. J. Mol. Graph. Model. 2002, 20, 6, 479–490.
Abstract: Subcellular pharmacokinetics (SP) optimizes biology-related factors in the design of libraries for high throughput screening by defining comparatively narrow ranges of properties (lipophilicity, amphiphilicity, acidity, reactivity, 3D-structural features) of the included compounds. The focusing ensures appropriate absorption, distribution, metabolism, excretion, and toxicity (ADMET) in those test biosystems, which are more complex than isolated receptors, and in humans. The SP deploys conceptual models that include transport and accumulation in a series of membranes, protein binding, hydrolysis, and other reactions with cell constituents. The kinetics of drug disposition is described as a non-linear disposition function of drug structure and properties. The SP capabilities are illustrated here using a model-based quantitative structure-activity relationship of toxicity of phenolic compounds against Tetrahymena pyriformis as dependent on lipophilicity and acidity. The resulting SP models clearly outperform empirical models in predictive ability outside the parameter space, as revealed by the leave-extremes-out cross-validation technique with omission of compounds beyond pre-defined lipophilicity and acidity ranges. The SP models do not change substantially if the parameters space is shrunk within some limits. In contrast, the shapes of empirical models vary widely depending upon the fraction of the data set used for their optimization. Once calibrated for a given biosystem, the SP models provide a detailed recipe for tailoring the drug properties to ensure optimum ADMET. The focusing is more accurate than with traditional empirical QSAR studies, assessment of drug-likeness, or the rules for identification of compounds with permeability problems.
URI: http://hdl.handle.net/10967/36
http://dx.doi.org/10.15152/QDB.36
Date: 2012-05-23


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