N-Methyl-3-(tetrahydro-2H-pyran-4-yl)-2,3,4,9-tetrahydro-1H-carbazole-6-carboxamides as a novel class of cannabinoid receptors agonists with low CNS penetration.

Cannabinoid CB(1) receptor agonists exhibit potent analgesic effects in rodents and humans, but their clinical utility as analgesic drugs is often limited by centrally mediated side effects. We report herein the preparation of N-methyl-3-(tetrahydro-2H-pyran-4-yl)-2,3,4,9-tetrahydro-1H-carbazole-6-carboxamides as a novel class of hCB(1)/hCB(2) dual agonists with attractive physicochemical properties. More specifically, (R)-N,9-dimethyl-N-(4-(methylamino)-4-oxobutyl)-3-(tetrahydro-2H-pyran-4-yl)-2,3,4,9-tetrahydro-1H-carbazole-6-carboxamide, displayed an extremely low level of CNS penetration (Rat Cbr/Cplasma=0.005 or 0.5%) and was devoid of CNS side effects during pharmaco-dynamic testing.

Collaborative filtering on a family of biological targets.

Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.