VIRTUAL SCREENING AND DISCOVERY OF LEAD COMPOUNDS AS POTENTIAL DNA METHYLTRANSFERASE 1 INHIBITORS AND ANTICANCER AGENTS

Epigenetic changes consist of DNA methylation, histone modification, micro RNA and genome imprinting. DNA methylation of the CpG islands is one of the main methods of epigenetic inactivation of genes and aberrant methylations at promoter regions of tumor suppressor genes can alter gene expression and play an important role in cancer development. DNA methyltransferase I (Dnmt1) is the enzyme responsible for maintaining methylation patterns during cell division and it is overexpressed in many cancers. Thus, Dnmt1 is a promising therapeutic target for development of novel anticancer agents and epigenetic modulators. We have developed two promising class of lead candidates, compounds 5-hydroxy-2-(4-hydroxyphenethyl)-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline-1-carboxamide 47, 2-(2-(1H-indol-3-yl)ethyl)-5-hydroxy-3-oxo-N-pentyl-4-(4-(trifluoromethyl)phenyl)isoindoline- 1-carboxamide 51 and 1-(4-isopropylphenyl)-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole 96, as potential leads compounds that can be optimized for pharmaceutical applications.

De-mystifying the epigenetic free for all : pharmacophore modeling for epigenetic cancer therapy

Epigenetic regulators have quickly become one of the most widely studied therapeutic agents for a vast array of diseases, making histone deacetylase inhibitors (HDIs) and DNA methyl-transferase (DNMT) inhibitors commonly used molecules in pre-clinical and clinical anti-cancer studies. Their ability to regulate gene expression and to potentiate the effects of other chemotherapeutic drugs has put HDIs and DNMT inhibitors in the spotlight not only as single agents, but also as combined therapy. The plethora of HDIs and DNMT inhibitors available nowadays has led to promising results in Phase I, II and III clinical oncology studies. While it was first believed that these molecules would all have an additive or synergistic effect when combined with the classical chemotherapeutic drugs available, our group and others have shown that epigenetic regulators potentiate the effects of some, but not all, anti-cancer molecules. Pharmacophore modeling may therefore serve the purpose to optimize pre-clinical research and to develop more efficient and targeted therapies incorporating epigenetic regulators

Rational drug design of antineoplastic agents using 3D-QSAR, cheminformatic, and virtual screening approaches

Support was kindly provided by the EU COST Action CM1406 and CA15135. KN and JV kindly acknowledge national project number 172033 supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia.Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.PostprintPeer reviewe

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

HIGHLIGHTS Many CNS targets are being explored for multi-target drug designNew databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compoundsQSAR, 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-HT2A -R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs