Novel aminopeptidase N inhibitors derived from antineoplaston AS2–5 (Part I)

Paradigm shift of "classical" HDAC inhibitors to "hybrid" HDAC inhibitors in therapeutic interventions

'Epigenetic' regulation of genes via post-translational modulation of proteins is the current mainstay approach for the disease therapies, particularly explored in the Histone Deacetylase (HDAC) class of enzymes. Mainly sight saw in cancer chemotherapeutics, HDAC inhibitors have also found a promising role in other diseases (neurodegenerative disorders, cardiovascular diseases, and viral infections) and successfully entered in various combination therapies (pre-clinical/clinical stages). The prevalent flexibility in the structural design of HDAC inhibitors makes them easily tuneable to merge with other pharmacophore modules for generating multi-targeted single hybrids as a novel tactic to overcome drawbacks of polypharmacy. Herein, we reviewed the putative role of prevalent HDAC hybrids inhibitors in the current and prospective stage as a translational approach to overcome the limitations of the existing conventional drug candidates (parent molecule) when used either alone (drug resistance, solubility issues, adverse side effects, selectivity profile) or in combination (pharmacokinetic interactions, patient compliance) for treating various diseases.

Design of dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping, molecular docking, synthesis and biological activity

Recent analyses have highlighted the promotion of cancer migration and invasion, mediated through HDAC via MMP-2 and MMP-9.

Exploring QSAR and pharmacophore mapping of structurally diverse selective matrix metalloproteinase-2 inhibitors

Objectives and Methods

Matrix metalloproteinase-2 (MMP-2) is a potential target in metastases. Regression (conventional 2D QSAR) and classification (recursive partitioning (RP), Bayesian modelling) QSAR, pharmacophore mapping and 3D QSAR (comparative molecular field analysis and comparative molecular similarity analysis) were performed on 202 MMP-2 inhibitors.

Key findings

Quality of the regression models was justified by internal (Q2) and external (R2Pred) cross-validation parameters. Stepwise regression was used to develop linear model (Q2 = 0.822, R2Pred = 0.667). Genetic algorithm developed linear (Q2 = 0.845, R2Pred = 0.638) and spline model (Q2  = 0.882, R2Pred = 0.644). The RP and Bayesian models showed cross-validated area under receiver operating characteristic curve (AUCROC_CV) of 0.805 and 0.979 respectively. QSAR models depicted importance of descriptors like five-membered rings, fractional positively charged surface area, lipophilocity and so on. Higher molecular volume was found to be detrimental. Pharmacophore mapping was performed with two tools – Hypogen and PHASE. Both models indicated that one hydrophobic and three hydrogen bond acceptor features are essential. The Pharmacophore-aligned structures were used for CoMFA (Q2 of 0.586 and R2Pred of 0.689) and CoMSIA (Q2 of 0.673 and R2Pred of 0.758), results of which complied with the other analyses.

Conclusions

All modelling techniques were compared to each other. The current study may help in designing novel MMP-2 inhibitors.

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of 'multiple pharmacophore screening' for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.