Nilanjan Adhikari

Exploring pyrazolo[3,4-d]pyrimidine phosphodiesterase 1 (PDE1) inhibitors: a predictive approach combining comparative validated multiple molecular modelling techniques

Phosphodiesterase 1 (PDE1) is a potential target for a number of neurodegenerative disorders such as Schizophrenia, Parkinson’s and Alzheimer’s diseases. A number of pyrazolo[3,4-d]pyrimidine PDE1 inhibitors were subjected to different molecular modelling techniques [such as regression-based quantitative structure-activity relationship (QSAR): multiple linear regression, support vector machine and artificial neural network; classification-based QSAR: Bayesian modelling and Recursive partitioning; Monte Carlo based QSAR; Open3DQSAR; pharmacophore mapping and molecular docking analyses] to get a detailed knowledge about the physicochemical and structural requirements for higher inhibitory activity. The planarity of the pyrimidinone ring plays an important role for PDE1 inhibition. The N-methylated function at the 5th position of the pyrazolo[3,4-d]pyrimidine core is required for interacting with the PDE1 enzyme. The cyclopentyl ring fused with the parent scaffold is necessary for PDE1 binding potency. The phenylamino substitution at 3rd position is crucial for PDE1 inhibition. The N2-substitution at the pyrazole moiety is important for PDE1 inhibition compared to the N1-substituted analogues. Moreover, the p-substituted benzyl side chain at N2-position helps to enhance the PDE1 inhibitory profile. Depending on these observations, some new molecules are predicted that may possess better PDE1 inhibition.

First molecular modeling report on novel arylpyrimidine kynurenine monooxygenase inhibitors through multi-QSAR analysis against Huntington’s disease: A proposal to chemists!

Huntingtons disease (HD) is caused by mutation of huntingtin protein (mHtt) leading to neuronal cell death. The mHtt induced toxicity can be rescued by inhibiting the kynurenine monooxygenase (KMO) enzyme. Therefore, KMO is a promising drug target to address the neurodegenerative disorders such as Huntingtons diseases. Fiftysix arylpyrimidine KMO inhibitors are structurally explored through regression and classification based multi-QSAR modeling, pharmacophore mapping and molecular docking approaches. Moreover, ten new compounds are proposed and validated through the modeling that may be effective in accelerating Huntingtons disease drug discovery efforts.

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.

Combating breast cancer with non-steroidal aromatase inhibitors (NSAIs): Understanding the chemico-biological interactions through comparative SAR/QSAR study

It is a challenging task to design target-specific and less toxic non-steroidal aromatase inhibitors (NSAIs) though the modeling studies for designing anti-aromatase molecules have been continuing for more than two decades to fight the dreaded estrogen-dependent breast cancer. In this article, different validated QSAR models are developed and analyzed to understand important physicochemical and structural parameters modulating the aromatase inhibitory activity of NSAIs. Physicochemical properties such as molar refractivity and dipole moment are found to be the most important parameters for controlling aromatase inhibition. This indicates the characteristic of bulky, complex and steric properties as well as, the flexibility of molecules is playing pivotal roles for aromatase inhibition. In many cases, hydrophobicity also plays important contribution. Regarding the structural point of view, some important indicator parameters are also found to be important for aromatase inhibitory activity. Though azole function is playing a crucial role by coordinating the heme moiety of the aromatase enzyme, the imidazole or the imidazolylmethyl ring systems may be better NSAIs than triazole, tetrazole or other azoles. The 4-pyridylmethyl group containing compounds are also found to be better NSAIs. The QSAR study, in a nutshell, provides a detailed understanding of the effectivity of NSAIs which is dependent mainly on the shape and size as well as the steric features of molecules and the heme-coordinator azole functions. These findings may open up a new horizon for designing new potential NSAIs that can be effective to reduce the mortality rate of breast cancer in future.

Exploring structural requirements of 1-N-substituted thiocarbamoyl-3-phenyl-2-pyrazolines as antiamoebic agents using comparative QSAR modelling

Amoebiasis is a potentially lethal disease and causes 70,000 deaths per year. To find structural requirements for more active antiamoebic agents than metronidazole, comparative QSAR modelling was done on thirty 1-N-substituted thiocarbamoyl-3-phenyl-2-pyrazolines. The best model was obtained by using PLS technique with R(A)(2) and R(CV)(2) value of 88.50% and 82.90%, respectively. Amoebicidal activity may increase when Wang-Ford charges at atom numbers 6 and 12 have large positive values. Number of six-membered ring and sum of Kier-Hall electrotopological states may also increase amoebicidal activity when these have large positive values. Increasing value of rotatable bond fraction, approximate surface area and mean atomic polarizability scaled on carbon atom may be detrimental for antiamoebic activity. Decrease in values of electrostatic potential charges at atom numbers 1 and 12 may be conducive for activity. Electrophilic attacks may be favourable at these positions.

Predictive comparative QSAR modelling of (phenylpiperazinyl-alkyl) oxindoles as selective 5-HT1A antagonists by stepwise regression, PCRA, FA-MLR and PLS techniques

5-Hydroxytryptamine, a neurotransmitter released by 5-HT neurons in raphe nuclei and 5-HT(1A) receptors are involved in the pain mechanism of migraine, prevention of postpartum haemorrhage, CNS effects like sleep, anxiety and thermoregulation. Comparative QSAR study was done on thirtytwo (phenylpiperazinyl-alkyl) oxindoles using stepwise regression, PCRA, FA-MLR and PLS techniques to find structurally significant models. ETSA indices at atom numbers 19, 20 and 22, RTSA indices at atom numbers 6, 10 and 20, charge at atom number 19 and presence of chlorine at the atom number 6 may be conducive for the receptor inhibition. Electrophilic attack at atom number 21 may be unfavourable but nucleophilic attack at atom numbers 8 and 14 may be beneficial for % 5-HT(1A) inhibition.

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.

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.