Learning how to use IAM chromatography for predicting permeability

The interest for IAM (Immobilized Artificial Membranes) chromatography in the prediction of drug permeability is increasing. Here we firstly set-up a dataset of 253 molecules including neutral and ionized drugs and few organic compounds for which we either measured or retrieved from the literature IAM.PC.DD2 log K_w^IAM data. Then we applied block relevance (BR) analysis to extract from PLS models the relative contribution of intermolecular forces governing log K_w^IAM and Δlog K_w^IAM (a combined descriptor calculated from log K_w^IAM). Finally, the relationship between log K_w^IAM, Δlog K_w^IAM and passive permeability determined in both PAMPA and MDCK-LE systems was looked for. Models provided the basis for a rational application of IAM chromatography in permeability prediction.

Antitumor properties of substituted (αE)-α-(1H-indol-3-ylmethylene)benzeneacetic acids or amides

A novel class of indole derivatives characterized by a (αE)-α-(1H-indol-3-ylmethylene)benzeneacetic acid or amide scaffold was synthesized. These derivatives, assayed for cell-growth inhibition activity against a panel of six different tumor cell lines, showed strong antiproliferative activity and selectivity mainly towards DU145 cell line. In particular, compounds 2d-m and 5 stand out for their cell growth inhibitory activity and, among them, compound 2d emerged for its selectivity towards DU145 with respect to other tested tumor cell lines. DU145 treated with 1μM of 2d for 72h showed p21(Cip1) induction and suppression of Akt signaling together with induction of Rb. From a computational point of view, two different approaches were used in order to study topology and electronic properties of the novel compounds and to shed light on their drug-likeness properties. Firstly, topological and electronic features of the compounds endowed with the most relevant biological activity were deepened; in parallel, some ADME properties like solubility and permeability were predicted.