Search results for "Cell permeability"
showing 3 items of 3 documents
Provisional Classification and in Silico Study of Biopharmaceutical System Based on Caco-2 Cell Permeability and Dose Number
2013
Today, early characterization of drug properties by the Biopharmaceutics Classification System (BCS) has attracted significant attention in pharmaceutical discovery and development. In this direction, the present report provides a systematic study of the development of a BCS-based provisional classification (PBC) for a set of 322 oral drugs. This classification, based on the revised aqueous solubility and the apparent permeability across Caco-2 cell monolayers, displays a high correlation (overall 76%) with the provisional BCS classification published by World Health Organization (WHO). Current database contains 91 (28.3%) PBC class I drugs, 76 (23.6%) class II drugs, 97 (31.1%) class III d…
A guide to the use of pore-forming toxins for controlled permeabilization of cell membranes
1993
Depending on the size of the pores one wishes to produce in plasma membranes, the choice will probably fall on one of the three toxins discussed above. S. aureus alpha-toxin should be tried first when pores of 1-1.5 nm diameter are required. This is generally the case when Ca2+ and nucleotide dependence of a given process is being studied. If alpha-toxin does not work, this is probably due to the fact that the toxin either does not produce pores, or that the pores are too small. In this case, high concentrations of alpha-toxin should be tried. If this still does not work, we recommend the use of HlyA. When very large pores are to be created, e.g. for introduction of antibodies into the cell…
In Silico Prediction of Caco-2 Cell Permeability by a Classification QSAR Approach
2011
In the present study, 21 validated QSAR models that discriminate compounds with high Caco-2 permeability (Papp ≥8×10(-6) cm/s) from those with moderate-poor permeability (Papp <8×10(-6) cm/s) were developed on a novel large dataset of 674 compounds. 20 DRAGON descriptor families were used. The global accuracies of obtained models were ranking between 78-82 %. A general model combining all types of molecular descriptors was developed and it classified correctly 81.56 % and 83.94 % for training and test sets, respectively. An external set of 10 compounds was predicted and 80 % was correctly assessed by in vitro Caco-2 assays. The potential use of the final model was evaluated by a virtual s…