RO4938581, a GABAAα5 modulator, displays strong CYP1A2 autoinduction properties in rats

Basmisanil, a highly selective GABAA-α5 negative allosteric modulator: preclinical pharmacology and demonstration of functional target engagement in man

GABA_A-α5 subunit-containing receptors have been shown to play a key modulatory role in cognition and represent a promising drug target for cognitive dysfunction, as well as other disorders. Here we report on the preclinical and early clinical profile of a novel GABA_A-α5 selective negative allosteric modulator (NAM), basmisanil, which progressed into Phase II trials for intellectual disability in Down syndrome and cognitive impairment associated with schizophrenia. Preclinical pharmacology studies showed that basmisanil is the most selective GABA_A-α5 receptor NAM described so far. Basmisanil bound to recombinant human GABA_A-α5 receptors with 5 nM affinity and more than 90-fold selectivity versus α1, α2, and α3 subunit-containing receptors. Moreover, basmisanil inhibited GABA-induced currents at GABA_A-α5 yet had little or no effect at the other receptor subtypes. An in vivo occupancy study in rats showed dose-dependent target engagement and was utilized to establish the plasma exposure to receptor occupancy relationship. At estimated receptor occupancies between 30 and 65% basmisanil attenuated diazepam-induced spatial learning impairment in rats (Morris water maze), improved executive function in non-human primates (object retrieval), without showing anxiogenic or proconvulsant effects in rats. During the Phase I open-label studies, basmisanil showed good safety and tolerability in healthy volunteers at maximum GABA_A-α5 receptor occupancy as confirmed by PET analysis with the tracer [¹¹C]-Ro 15-4513. An exploratory EEG study provided evidence for functional activity of basmisanil in human brain. Therefore, these preclinical and early clinical studies show that basmisanil has an ideal profile to investigate potential clinical benefits of GABA_A-α5 receptor negative modulation.

Using molecular features of xenobiotics to predict hepatic gene expression response

Despite recent advances in molecular medicine and rational drug design, many drugs still fail because toxic effects arise at the cellular and tissue level. In order to better understand these effects, cellular assays can generate high-throughput measurements of gene expression changes induced by small molecules. However, our understanding of how the chemical features of small molecules influence gene expression is very limited. Therefore, we investigated the extent to which chemical features of small molecules can reliably be associated with significant changes in gene expression. Specifically, we analyzed the gene expression response of rat liver cells to 170 different drugs and searched for genes whose expression could be related to chemical features alone. Surprisingly, we can predict the up-regulation of 87 genes (increased expression of at least 1.5 times compared to controls). We show an average cross-validation predictive area under the receiver operating characteristic curve (AUROC) of 0.7 or greater for each of these 87 genes. We applied our method to an external data set of rat liver gene expression response to a novel drug and achieved an AUROC of 0.7. We also validated our approach by predicting up-regulation of Cytochrome P450 1A2 (CYP1A2) in three drugs known to induce CYP1A2 that were not in our data set. Finally, a detailed analysis of the CYP1A2 predictor allowed us to identify which fragments made significant contributions to the predictive scores.