Molecular design of proneurogenic and neuroprotective compounds-allosteric NMDA receptor modulators

5β-reduced neuroactive steroids as modulators of growth and viability of postnatal neurons and glia

Endogenous neurosteroids (NS) and their synthetic analogs, neuroactive steroids (NAS), are potentially useful drug-like compounds affecting the pathophysiology of miscellaneous central nervous system disorders (e.g. Alzheimer´s disease, epilepsy, depression, etc.). Additionally, NS have been shown to promote neuron viability and neurite outgrowth upon injury. The molecular, structural and physicochemical basis of the NS effect on neurons is so far not fully understood, and the development of new, biologically relevant assays is essential for their comparative analysis and for assessment of their mechanism of action. Here, we report the development of a novel, plate-based, high-content in vitro assay for screening of NS and newly synthesized, 5β-reduced NAS for the promotion of postnatal neuron survival and neurite growth using fluorescent, postnatal mixed cortical neuron cultures isolated from thy1-YFP transgenic mice. The screen allows a detailed time course analysis of different parameters, such as the number of neurons or neurite lengths of 7-day, in vitro neuron cultures. Using the screen, we identify a new NAS, compound 42, that promotes the survival and growth of postnatal neurons significantly better than several endogenous NS (dehydroepiandrosterone, progesterone, and allopregnanolone). Interestingly, we demonstrate that compound 42 also promotes the proliferation of glia (in particular oligodendrocytes) and that the glial function is critical for its neuron growth support. Computational analysis of the biological data and calculated physicochemical properties of tested NS and NAS demonstrated that their biological activity is proportional to their lipophilicity. Together, the screen proves useful for the selection of neuron-active NAS and the comparative evaluation of their biologically relevant structural and physicochemical features.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.