Oral administered drugs are mainly absorbed in the small intestine. Here, depending on drug composition and size, absorption can happen through a variety of processes . Through the epithelial cells and the lamina pro- pria the drug passes from the lumen into the blood stream in the capillaries. On its way it might be metabolised, transported away from the tract where absorption is possible or accumulate in organs other than those of treatment. Besides a fundamental interest in understanding the basic mechanisms by which a drug is assimilated by the human body, the kinetics of drug absorption is also a topic of much practical interest. A detailed knowledge of this process, resulting in the prediction of the drug absorption profile, can be of much help in the drug development stage .
To this end, several kinetic models for drug absorption within the body have been introduced (see e.g. ). They necessarily introduce some simplifications belonging to the category of the so-called three-compartment models where the substances (such as drugs or nutrients) move between three volumes (e.g. the human organs). In fact the models require two kinds of molecular properties. First are purely physical characteristics, such as solubility, differential solubility, LogP etc. These quantities are easy to measure or to calcualte, have direct physical meaning and sufficient to predict absorbtion profile of passively absorbed drugs. Actively transported molecules interact with protein transporters and therefore prediction for actively transporting compounds require a lot of separate knowledge of binding to and kinetics of the transporting proteins.
The major objective of this investigation was to develop a drug absorbtion prediction approach based on entirely different paradigm, thus avoiding difficulties of both knowledge-based and QSAR-based models, and therefore capable of better predictions. Recently it was observed that experimental values of molecular activities against a large proteins set can be used for predicting broad biological effects . In this investigation we take advantage of this concept and develop a novel quntitative method for identification of actively transported drugs. To do that we performed a docking study of a few hundreds small molecules (mostly drugs) against a diversified 510 proteins set representing human proteom. Using available absorbtion data for each of the molecules we obtained a support vector classifier capable to identify proteins which affinity for drugs correlates well with the active absorption of these drugs in 81% cases. The observation helped us improve our passive absorbtion model by adding non-liner fluxes associated with the transporting protein to obtain also a quantitative model of the passively absorbed drugs.
Ref: arXiv:0810.2617 [ps, pdf, other]
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