[Obesity, type 2 diabetes and depression : which metabolic impact of antidepressants ?]

Metabolic disorders (dysglycaemia, dyslipidaemia, metabolic syndrome) may occur or worsen with antidepressants. Even if the metabolic risk appears lower with antidepressants than with certain atypical antipsychotics, it should not be neglected in clinical practice, especially with antidepressants that are associated with weight gain. Metabolic disorders may occur with both selective serotonin reuptake inhibitors and tricyclics, but with some between-molecule differences within each pharmacological family. Besides potential deleterious effects of medications, the psychiatric population is also exposed to bad lifestyle habits (unhealthy diet and sedentary life) and poor medication compliance, which all together could also contribute to enhance the risk of metabolic disorders and cardiovascular complications. Facing such a complex situation, it is not easy to individualize the metabolic risk inherent to antidepressants, also because an effective management of the depression could improve both the quality of life and the adherence to a better lifestyle.

Relating molecular descriptors to frontier orbital energy levels, singlet and triplet excited states of fused tricyclics using machine learning

Fused tricyclic organic compounds are an important class of organic electronic materials. In designing molecules for organic electronics, knowing what chemical structure that be used to tune the molecular property is one of the keys that can help to improve the material performance. In this research, we applied machine learning and data analytic approaches in addressing this problem. The energy states (Lowest Unoccupied Molecular Orbital (HOMO), Highest Occupied Molecular Orbitals (LUMO), singlet (E_s) and triplet (E_T) energy) of more than 10 thousand fused tricyclics are calculated. Corresponding descriptors are also generated. We find that the Coulomb matrix is a poorer descriptor than high-level descriptors in a multilayer perceptron neural network. Correlations as high as 0.95 is obtained using a multilayer perceptron neural network with Mean Absolute Error as low as 0.08 eV. The descriptors that are important in tuning the energy levels are revealed using the Random Forest algorithm. Correlations of such descriptors are also plotted. We found that the higher the number of tertiary amines, the deeper are the HOMO and LUMO levels. The presence of NN in the aromatic rings can be used to tune the E_S. However, there is no single dominant descriptor that can be correlated with the E_T. A collection of descriptors is found to give a far better correlation with E_T. This research demonstrated that machine learning and data analytics in guiding how certain chemical substructures correlate with the molecule energy states.

Novel tricyclics (e.g., GSK945237) as potent inhibitors of bacterial type IIA topoisomerases

During the course of our research on the lead optimisation of the NBTI (Novel Bacterial Type II Topoisomerase Inhibitors) class of antibacterials, we discovered a series of tricyclic compounds that showed good Gram-positive and Gram-negative potency. Herein we will discuss the various subunits that were investigated in this series and report advanced studies on compound 1 (GSK945237) which demonstrates good PK and in vivo efficacy properties.

Carbamazepine derivatives with P2X4 receptor-blocking activity

Antagonists for the P2 receptor subtype P2X4, an ATP-activated cation channel receptor, have potential as novel drugs for the treatment of neuropathic pain and other inflammatory diseases. In the present study, a series of 47 carbamazepine derivatives including 32 novel compounds were designed, synthesized, and evaluated as P2X4 receptor antagonists. Their potency to inhibit ATP-induced calcium influx in 1321N1 astrocytoma cells stably transfected with the human P2X4 receptor was determined. Additionally, species selectivity (human, rat, mouse) and receptor subtype selectivity (P2X4 vs P2X1, 2, 3, 7) were investigated for selected derivatives. The most potent compound of the present series, which exhibited an allosteric mechanism of P2X4 inhibition, was N,N-diisopropyl-5H-dibenz[b,f]azepine-5-carboxamide (34, IC50 of 3.44μM). The present study extends the so far very limited knowledge on structure-activity relationships of P2X4 receptor antagonists.