Levomequitazine

The anticholinergic properties of the mequitazine enantiomer V0162 make it a drug candidate for the treatment of chronic obstructive airway diseases. Here, we compared V0162's in vitro pharmacological activity at recombinant human M3 muscarinic acetylcholine receptors (hM3Rs) with that of other anticholinergics, using (i) a radioligand binding assay, (ii) a functional reporter gene assay and (iii) a bronchoconstriction inhibition assay on human bronchial preparations. V0162 had high affinity for hM3Rs, with a pKi varying from 9.01 after a 2 h incubation to 9.21 after 23 h. The other mequitazine enantiomer (V0114) was less potent. V0162 displayed rapid off-kinetics and a biphasic time course of binding. V0162 was found to be an antagonist behaving as an inverse agonist for hM3R-mediated reporter gene activation, with much the same efficacy as atropine, ipratropium and tiotropium. However, in contrast to ipratropium and atropine, V0162's inhibitory potency was only slightly affected by compound washout. V0162 antagonized acetylcholine-mediated contractions in a human bronchial preparation; the pA2 values increased with the incubation time (up to 2 h). Moreover, there was a progressive increase in V0162's ability to inhibit electrically-induced contractions, which persisted after compound washout. In conclusion, V0162 is the most active mequitazine enantiomer at hM3Rs and shows a complex pattern of binding to the membrane compartment. These particular features may be of therapeutic value when persistent antagonism at hM3Rs is required.

The concept of molecular similarity is one of the most central in the fields of predictive toxicology and quantitative structure-activity relationship (QSAR) research. Many toxicological responses result from a multimechanistic process and, consequently, structural diversity among the active compounds is likely. Combining this knowledge, we introduce similarity boosted QSAR modeling, where we calculate molecular descriptors using similarities with respect to representative reference compounds to aid a statistical learning algorithm in distinguishing between different structural classes. We present three approaches for the selection of reference compounds, one by literature search and two by clustering. Our experimental evaluation on seven publicly available data sets shows that the similarity descriptors used on their own perform quite well compared to structural descriptors. We show that the combination of similarity and structural descriptors enhances the performance and that a simple stacking approach is able to use the complementary information encoded by the different descriptor sets to further improve predictive results. All software necessary for our experiments is available within the cheminformatics software framework AZOrange.