AUTHOR=Nikolic Katarina , Mavridis Lazaros , Djikic Teodora , Vucicevic Jelica , Agbaba Danica , Yelekci Kemal , Mitchell John B. O. TITLE=Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies JOURNAL=Frontiers in Neuroscience VOLUME=10 YEAR=2016 URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00265 DOI=10.3389/fnins.2016.00265 ISSN=1662-453X ABSTRACT=
Many CNS targets are being explored for multi-target drug design New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds QSAR, virtual screening and docking methods increase the potential of rational drug design
The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2