Amit Kumar Halder

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. Since both class 1 HDACs and MMP-2/9 are involved in the migration and invasion of cancer, an attempt has been taken to design dual MMP-2/HDAC-8 inhibitors by pharmacophore mapping and molecular docking approaches. The designed molecules were synthesized and showed a range of inhibitory activity against different MMP subtypes. Most of these designed compounds were selective towards MMP-2 but less potent against anti-targets like MMP-8, -12, etc. The highly active MMP-2 inhibitors were also found to be active towards HDAC-8 but less potent against other class 1 HDACs (HDAC-1 and -2). Molecular dynamics simulations revealed that the designed compounds may be acting through a distinct mechanism of action in the ‘acetate ion channel’ of HDAC-8. Some potent dual MMP-2/HDAC-8 inhibitors were further explored for in vitro cellular assays against human lung carcinoma cell line A549. These analyses revealed that some of these dual inhibitors have considerable anti-migratory and anti-invasive properties. The work may help to obtain some useful dual inhibitors.

Comparative validated molecular modeling of p53-HDM2 inhibitors as antiproliferative agents.

Tumor suppressor protein p53 regulates the cell cycle and inhibits tumor growth. It is inactivated by mutation or binding with human double minute 2 (HDM2) protein. The HDM2 is a promising target for treatment of p53 protein related cancers. Molecular modeling techniques such as 2D-QSAR, pharmacophore mapping and 3D-QSAR analyses were performed on 155 structurally diverse HDM2 inhibitors to understand structural and physicochemical requirements for higher activity. The linear and spline 2D-QSAR models were developed through multiple linear regression and genetic functional algorithm methods. The 2D-QSAR models suggested that number of fluorine, chlorine, tertiary nitrogen atoms as well as donor feature, stereogenic centers and higher value of solvent accessible surface area are important features in defining activity. Monte Carlo method was applied to generate QSAR models that determined structural indicators (alerts) for increase or decrease of the biological activity. Ligand-based pharmacophore mapping showed importance of two hydrophobic, one hydrophobic aromatic, one ring aromatic and one donor features. The structure-based pharmacophore model demonstrated significance of two hydrophobic, one ring aromatic and two acceptor features. The pharmacophore (ligand) aligned structures were subjected to 3D-QSAR analyses. The structure-based pharmacophore was also used for pharmacophore restraint molecular docking to analyze ligand-receptor interactions and for adjudging predictability as well as validation of different modeling techniques. These comparative molecular modeling techniques may help to design novel HDM2 inhibitors.

Comparative QSAR modelling of 2-phenylindole-3-carbaldehyde derivatives as potential antimitotic agents

QSAR modelling was done on some 2-phenylindole-3-carbaldehyde derivatives to find out structural requirements for more active antimitotic agents. Four statistical methods were used to develop models. The results show the importance of ETSA indices, RTSA indices, IC1, SIC4, Jhetv and MSD on the activity. Electrostatic potential charges of atoms, increased surface area, and presence of bulky group along Y-axis and chlorine substitution were also found to be important.

Validated predictive QSAR modeling of N-aryl-oxazolidinone-5-carboxamides for anti-HIV protease activity

Validated predictive QSAR modeling was done on some N-aryl-oxazolidinone-5-carboxamides for higher anti-HIV protease activities. Stepwise regression developed significant models showing importance of atom based descriptors like RTSA indices, Wang-Ford charges and different whole molecular descriptors. The true predictabilities of QSAR models were justified by challenging these against an external dataset. A representative high active compound was predicted by this modeling. It showed that internal and external validations may lead to the same conclusion.

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Abstract Schizophrenia is a complex disorder of thinking and behaviour (0.3−0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington’s disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.

Exploring structural requirements of aurone derivatives as antimalarials by validated DFT-based QSAR, HQSAR, and COMFA–COMSIA approach

Chloroquine resistance is nowadays a great problem in malaria. Aurone derivatives were effective against chloroquine resistant parasite. Validated density functional theory (DFT)-based chemometric modeling, hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity analysis (CoMSIA) studies were conducted on 35 aurone derivatives having antimalarial activity. 2D-QSAR models were developed on the training sets by Y-based ranking method. This model was validated on 50 pairs of the test and the training sets by k-Means cluster analysis method. HQSAR, CoMFA, and CoMSIA models were validated by standard techniques and each method validates the DFT-based 2D-QSAR study and in turn validates the earlier observed structural activity relationship data as well as each other. DFT-based 2D-QSAR model suggests that the increase of Mulliken charge at C14 and HOMO density located on C11 may be conducive to antimalarial activity. Ethyl group attached to C14 and the increase of the value of chemical potential may be beneficial for antimalarial activity. Methoxy fragment is important for better antimalarial activity by HQSAR study. CoMFA analysis shows a favorable steric green region is located near C14 whereas the unfavorable yellow region is far away from C14. A large blue region located near C14 indicates the positively charged groups are favorable at this position. CoMSIA steric features correlates well with the CoMFA steric features. CoMSIA study suggests the bulky hydrophobic substitution at C14 is necessary for antimalarial activity. These results may be utilized to obtain potential antimalarial molecules.

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

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.

Robust design of some selective matrix metalloproteinase-2 inhibitors over matrix metalloproteinase-9 through in silico/fragment-based lead identification and de novo lead modification: Syntheses and biological assays.

Broad range of selectivity possesses serious limitation for the development of matrix metalloproteinase-2 (MMP-2) inhibitors for clinical purposes. To develop potent and selective MMP-2 inhibitors, initially multiple molecular modeling techniques were adopted for robust design. Predictive and validated regression models (2D and 3D QSAR and ligand-based pharmacophore mapping studies) were utilized for estimating the potency whereas classification models (Bayesian and recursive partitioning analyses) were used for determining the selectivity of MMP-2 inhibitors over MMP-9. Bayesian model fingerprints were used to design selective lead molecule which was modified using structure-based de novo technique. A series of designed molecules were prepared and screened initially for inhibitions of MMP-2 and MMP-9, respectively, as these are designed followed by other MMPs to observe the broader selectivity. The best active MMP-2 inhibitor had IC50 value of 24nM whereas the best selective inhibitor (IC50=51nM) showed at least 4 times selectivity to MMP-2 against all tested MMPs. Active derivatives were non-cytotoxic against human lung carcinoma cell line-A549. At non-cytotoxic concentrations, these inhibitors reduced intracellular MMP-2 expression up to 78% and also exhibited satisfactory anti-migration and anti-invasive properties against A549 cells. Some of these active compounds may be used as adjuvant therapeutic agents in lung cancer after detailed study.

Structural findings of phenylindoles as cytotoxic antimitotic agents in human breast cancer cell lines through multiple validated QSAR studies

Antimitotic agents are potential compounds for the treatment of breast cancer. Cytotoxicity is one of the parameters required for anticancer activity. A validated comparative molecular modeling study was performed on a set of phenylindole derivatives through R-group QSAR (RQSAR), regression-based and linear discriminant analysis (LDA)-based 2D QSAR studies and kernel-based partial least square (KPLS) analyses as well as CoMSIA 3D-QSAR study. Antiproliferative activities against two breast cancer cell lines (MDA-MB-231 and MCF7) were separately used as dependent variables. The RQSAR analysis highlighted different E-state indices and pharmacophoric requirements of important substitutions. The best 2D-QSAR model is established on the basis of three machine learning tools – MLR, SVM and ANN. The 2D-QSAR models depicted importance of different structural, physicochemical and topological descriptors. While RQSAR analyses demonstrated the fingerprint requirements of various substitutions, the KPLS analyses showed these requirements for the entire molecule. The CoMSIA model further refines these interpretations and reveals how subtle variations in these structures may influence biological activities. Observations of different modeling techniques complied with each other. The current QSAR study may be used to design potential antimitotic agents. It also demonstrates the utilities of different molecular modeling tools to elucidate the SAR.

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.