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

Opportunities and perspectives of small molecular phosphodiesterase inhibitors in neurodegenerative diseases

Phosphodiesterase (PDE) is a superfamily of enzymes that are responsible for the hydrolysis of two second messengers: cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). PDE inhibition promotes the gene transcription by activating cAMP-response element binding protein (CREB), initiating gene transcription of brain-derived neurotrophic factor (BDNF). The procedure exerts neuroprotective profile, and motor and cognitive improving efficacy. From this point of view, PDE inhibition will provide a promising therapeutic strategy for treating neurodegenerative disorders. Herein, we summarized the PDE inhibitors that have entered the clinical trials or been discovered in recent five years. Well-designed clinical or preclinical investigations have confirmed the effectiveness of PDE inhibitors, such as decreasing Aβ oligomerization and tau phosphorylation, alleviating neuro-inflammation and oxidative stress, modulating neuronal plasticity and improving long-term cognitive impairment.

Advances in targeting Phosphodiesterase 1: From mechanisms to potential therapeutics

Phosphodiesterase 1 (PDE1) is an enzyme entrusted with the hydrolysis of the second messengers cAMP and cGMP, thereby governing a plethora of metabolic processes, encompassing ion channel modulation and cellular apoptosis. Recent advancements in the realm of small molecule structural variations have greatly facilitated the exploration of innovative applications for PDE1. Remarkably, a recent series of PDE1 inhibitors (PDE1i) have been meticulously formulated and devised, showcasing enhanced selectivity and potency. Among them, ITI-214 has entered Phase II clinical trials, holding promise for the treatment of Parkinson's disease and heart failure. Nevertheless, the majority of current PDE1 inhibitors have encountered substantial side effects in clinical trials attributable to their limited selectivity, this predicament presents a formidable obstacle in the development of specific small molecule inhibitors targeting PDE1. This Perspective endeavors to illuminate the potential design approaches, structure-activity relationships, and biological activities of current PDE1i, aiming to offer support and insights for clinical practice and the development of novel PDE1i.

Investigating the antibacterial mechanism of Ampelopsis cantoniensis extracts against methicillin-resistant Staphylococcus aureus via in vitro and in silico analysis

Methicillin-resistant Staphylococcus aureus (MRSA) is a critical pathogen responsible for a wide variety of serious infectious diseases in humans. The accelerated phenomena of drug tolerance, drug resistance, and dysbacteriosis provoked by antibiotic misuse are impeding the effectiveness of contemporary antibiotic therapies primarily used to treat this common worldwide pathogen. In this study, the antibacterial activity of 70% ethanol extract and multiple polar solvents of Ampelopsis cantoniensis were measured against the clinical MRSA isolate. The agar diffusion technique was employed to determine the zone of inhibition (ZOI), accompanied by the use of a microdilution series to identify the minimal inhibitory concentration (MIC) and minimal bactericidal concentration (MBC). Our results revealed that the ethyl acetate fraction exhibited the most significant antibacterial activity, which was determined to be bacteriostatic based on the MBC/MIC ratio 8. A list of compounds isolated from A. cantoniensis was computationally studied to further investigate the mechanism of action with the bacterial membrane protein PBP2a. The combination of molecular docking and molecular dynamics methods showed that the main compound, dihydromyricetin (DHM), is expected to bind to PBP2a at allosteric site. In addition, DHM was identified as the major compound of ethyl acetate fraction, which accounts for 77.03 ± 2.44% by high performance liquid chromatography (HPLC) analysis. As a concluding remark, our study addressed the antibacterial mechanism and suggested the prioritization of natural products derived from A. cantoniensis as a potential therapy for MRSA.Communicated by Ramaswamy H. Sarma.

Pharmacological modulation of phosphodiesterase-7 as a novel strategy for neurodegenerative disorders

Neurodegenerative illness develops as a result of genetic defects that cause changes at numerous levels, including genomic products and biological processes. It entails the degradation of cyclic nucleotides, cyclic adenosine monophosphate (cAMP), and cyclic guanosine monophosphate (cGMP). PDE7 modulates intracellular cAMP signalling, which is involved in numerous essential physiological and pathological processes. For the therapy of neurodegenerative illnesses, the normalization of cyclic nucleotide signalling through PDE inhibition remains intriguing. In this article, we shall examine the role of PDEs in neurodegenerative diseases. Alzheimer's disease, Multiple sclerosis, Huntington's disease, Parkinson's disease, Stroke, and Epilepsy are related to alterations in PDE7 expression in the brain. Earlier, animal models of neurological illnesses including Alzheimer's disease, Parkinson's disease, and multiple sclerosis have had significant results to PDE7 inhibitors, i.e., VP3.15; VP1.14. In addition, modulation of CAMP/CREB/GSK/PKA signalling pathways involving PDE7 in neurodegenerative diseases has been addressed. To understand the etiology, treatment options of these disorders mediated by PDE7 and its subtypes can be the focus of future research.

Inhibition of phosphodiesterase: A novel therapeutic target for the treatment of mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) is the most common form of dementia and is ranked as the 6th leading cause of death in the US. The prevalence of AD and dementia is steadily increasing and expected cases in USA is 14.8 million by 2050. Neuroinflammation and gradual neurodegeneration occurs in Alzheimer's disease. However, existing medications has limitation to completely abolish, delay, or prevent disease progression. Phosphodiesterases (PDEs) are large family of enzymes to hydrolyze the 3'-phosphodiester links in cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) in signal-transduction pathways for generation of 5'-cyclic nucleotides. It plays vital role to orchestrate several pharmacological activities for proper cell functioning and regulating the levels of cAMP and cGMP. Several evidence has suggested that abnormal cAMP signaling is linked to cognitive problems in neurodegenerative disorders like AD. Therefore, the PDE family has become a widely accepted and multipotential therapeutic target for neurodegenerative diseases. Notably, modulation of cAMP/cGMP by phytonutrients has a huge potential for the management of AD. Natural compounds have been known to inhibit phosphodiesterase by targeting key enzymes of cGMP synthesis pathway, however, the mechanism of action and their therapeutic efficacy has not been explored extensively. Currently, few PDE inhibitors such as Vinpocetine and Nicergoline have been used for treatment of central nervous system (CNS) disorders. Considering the role of flavonoids to inhibit PDE, this review discussed the therapeutic potential of natural compounds with PDE inhibitory activity for the treatment of AD and related dementia.

Copper-Catalyzed Oxidative [3 + 2]-Annulation of Quinoxalin-2(1H)-one with Oxime Esters toward Functionalized Pyrazolo[1,5-a]quinoxalin-4(5H)-ones as Opioid Receptor Modulators

Pyrazolo[1,5-a]quinoxalin-4(5H)-one derivatives as novel opioid receptor modulators have been synthesized via copper-catalyzed oxidative [3 + 2]-annulation of quinoxalin-2(1H)-one and oxime-O-acetates. This hydrazine-free C-C and N-N bond formation strategy starts with the generation of C₂N₁ synthon using oxime acetate, which reacts in a [3 + 2] manner with quinoxalin-2(1H)-one, followed by oxidative aromatization. The synthesized compounds were tested against opioid receptors, of which eight compounds exhibited an antagonistic effect with EC₅₀ < 5 μM at various opioid receptors. Molecular docking studies were performed to identify the binding of active pyrazolo[1,5-a]quinoxalin-4(5H)-one ligands with hKOR protein. Docking results indicated that compounds 3d and 3g participate in hydrogen bonding with the hydroxyl group of T111 of the active site pocket residue.

Applying comparative molecular modelling techniques on diverse hydroxamate-based HDAC2 inhibitors: an attempt to identify promising structural features for potent HDAC2 inhibition

Histone deacetylase 2 (HDAC2) has been implicated in a variety of cardiovascular and neurodegenerative disorders as well as in cancers. Thus, HDAC2 has become an exclusive target for anticancer drug development. Therefore, the development of newer HDAC2 inhibitors in disease conditions is a prime goal to restrain such a scenario. Although a handful of HDAC inhibitors was accepted for the treatment of HDAC-related disease conditions, the non-selective nature of these entities is one of the major setbacks in the treatment of specific HDAC isoform-related pathophysiology. In this framework, the analyses of pre-existing molecules are essential to identify the important structural features that can fulfil the requirements for the cap and linker moieties to obtain potent and effective HDAC2 inhibition. Thus, in this study, the implementation of a combined comparative 2D and 3D molecular modelling techniques was done on a group of 92 diverse hydroxamate derivatives having a wide range of HDAC2 inhibitory potency. Besides other crucial features, this study upheld the importance of groups like triazole and benzyl moieties along with the molecular fields that are crucial for regulating HDAC2 inhibition. The outcomes of this study may be employed for the designing of HDAC2 inhibitors in future.

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.

In silico development of potential therapeutic for the pain treatment by inhibiting voltage-gated sodium channel 1.7

The voltage-gated sodium channel Nav1.7 can be considered as a promising target for the treatment of pain. This research presents conformational-independent and 3D field-based QSAR modeling for a series of aryl sulfonamide acting as Nav1.7 inhibitors. As descriptors used for building conformation-independent QSAR models, SMILES notation and local invariants of the molecular graph were used with the Monte Carlo optimization method as a model developer. Different statistical methods, including the index of ideality of correlation, were used to test the quality of the developed models, robustness and predictability and obtained results were good. Obtained results indicate that there is a very good correlation between 3D QSAR and conformation-independent models. Molecular fragments that account for the increase/decrease of a studied activity were defined and used for the computer-aided design of new compounds as potential analgesics. The final evaluation of the developed QSAR models and designed inhibitors were carried out using molecular docking studies, bringing to light an excellent correlation with the QSAR modeling results.

Development of potential therapeutics for pain treatment by inducing Sigma 1 receptor antagonism – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for pain treatment.

SARS-CoV Mpro inhibitory activity of aromatic disulfide compounds: QSAR model

The main protease (M^pro) of SARS-associated coronavirus (SARS-CoV) had caused a high rate of mortality in 2003. Current events (2019-2020) substantiate important challenges for society due to coronaviruses. Consequently, advancing models for the antiviral activity of therapeutic agents is a necessary component of the fast development of treatment for the virus. An analogy between anti-SARS agents suggested in 2017 and anti-coronavirus COVID-19 agents are quite probable. Quantitative structure-activity relationships for SARS-CoV are developed and proposed in this study. The statistical quality of these models is quite good. Mechanistic interpretation of developed models is based on the statistical and probability quality of molecular alerts extracted from SMILES. The novel, designed structures of molecules able to possess anti-SARS activities are suggested. For the final assessment of the designed molecules inhibitory potential, developed from the obtained QSAR model, molecular docking studies were applied. Results obtained from molecular docking studies were in a good correlation with the results obtained from QSAR modeling.

In silico exploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer's disease using SMILES based attributes in Monte Carlo optimization

Alzheimer's disease is the most common neurodegenerative disorder and being a social burden Alzheimer's has become an economic liability on developing countries. With limited understanding regarding the cause of disease, it is commonly identified by extracellular deposit of amyloid β (Aβ) peptides as senile plaques. Pyroglutamated Aβ is identified from the brain of AD patients and constituted the majority of total Aβ present. The formation of Pyroglutamated Aβ could be hindered by the use of Glutaminyl cyclase inhibitors and could efficiently improve the symptoms of Alzheimer's. The literature revealed the competence of quantitative structure activity/property relationship studies in drug discovery. The present work explores the efficiency of Monte Carlo based QSAR modelling studies on a dataset of 125 Glutaminyl cyclase inhibitors with pKi taken as the endpoint for QSAR analysis. The dataset is divided into training, subtraining, calibration and validation sets resulting in the generation of five random splits. The validation is performed in accordance with the Organization of Economic Corporation and Development principles. The values of R², Q², index of ideality of correlation, concordance correlation coefficient, av. rm2 and delta rm2 of calibration set of the best split are found to be 0.9012, 0.8775, 0.9479, 0.9435, 0.8347 and 0.0847, respectively. The structural features responsible for increasing the inhibitory activity are identified. These structural features are added to a base compound from the dataset to design six novel molecules. These new molecules possess improved inhibitory activity as compare to the base compound. The results are further supported by docking studies.Communicated by Vsevolod Makeev.

Purine/purine isoster based scaffolds as new derivatives of benzamide class of HDAC inhibitors

This study reports the design, synthesis and evaluation of a series of histone deacetylase (HDAC) inhibitors containing purine/purine isoster as a capping group and an N-(2-aminophenyl)-benzamide unit. In vitro cytotoxicity studies reveal that benzamide 14 suppressed the growth of triple-negative breast cancer cells MDA-MB-231 (IC₅₀ = 1.48 μM), MDA-MB-468 (IC₅₀ = 0.65 μM), and liver cancer cells HepG2 (IC₅₀ = 2.44 μM), better than MS-275 (5) and Chidamide (6). Compared to the well-known HDAC inhibitor SAHA, 14 showed a higher toxicity (IC₅₀ = 0.33 μM) in three leukemic cell lines, K-562, KG-1 and THP-1. Moreover, 14 was found to be equally virulent in the HDAC-sensitive and -resistant gastric cell lines, YCC11 and YCC3/7, respectively, indicating the potential of 14 to overcome HDACi resistance. Furthermore, substantial inhibitory effects more pronounced than MS-275 (5) and Chidamide (6) were displayed by 14 towards HDAC1, 2 and 3 isoforms with IC₅₀ values of 0.108, 0.585 and 0.563 μM respectively. Compound 14 also exhibited a potent antitumor efficacy in human MDA-MB-231 breast cancer xenograft mouse model, providing a potential lead for the development of anticancer agents.

Development of novel therapeutics for the treatment of glaucoma based on actin-binding kinase inhibition – in silico approach

QSAR modeling with computer-aided drug design were used for the in silico development of novel therapeutics for glaucoma treatment.

Good and bad molecular fingerprints for human rhinovirus 3C protease inhibition: identification, validation, and application in designing of new inhibitors through Monte Carlo-based QSAR study

HRV 3 C protease (HRV 3Cpro) is an important target for common cold and upper respiratory tract infection. Keeping in view of the non-availability of drug for the treatment, newer computer-based modelling strategies should be applied to rationalize the process of antiviral drug discovery in order to decrease the valuable time and huge expenditure of the process. The present work demonstrates a structure wise optimization using Monte Carlo-based QSAR method that decomposes ligand compounds (in SMILES format) into several molecular fingerprints/descriptors. The current state-of-the-art in QSAR study involves the balance of correlation approach using four different sets: training, invisible training, calibration, and validation. The final models were also validated through mean absolute error, index of ideality of correlation, Y-randomization and applicability domain analysis. R2 and Q2 values for the best model were 0.8602, 0.8507 (training); 0.8435, 0.8331 (invisible training); 0.7424, 0.7020 (calibration); 0.5993, 0.5216 (validation), respectively. The process identified some molecular substructures as good and bad fingerprints depending on their effect to increase or decrease the HRV 3Cpro inhibition. Finally, new inhibitors were designed based on the fundamental concept to replace the bad fragments with the good fragments as well as including more good fragments into the structure. The study points out the importance of the fingerprint based drug design strategy through Monte Carlo optimization method in the modelling of HRV 3Cpro inhibitors.

Design and development of novel therapeutics for coronary heart disease treatment based on cholesteryl ester transfer protein inhibition - in silico approach

Cholesteryl ester transfer protein (CETP) belongs to the group of enzymes which inhibition have the application in the treatment of cardiovascular diseases. This study presents QSAR modeling for a set of compounds acting as CETP inhibitors based on the Monte Carlo optimization with SMILES notation and molecular graph-based descriptors, and field-based 3D modeling. A 3D QSAR model was developed for one random split into the training and test sets, whereas conformation independent QSAR models were developed for three random splits, with the results suggesting there is an excellent correlation between them. Various statistical approaches were used to assess the statistical quality of the developed models, including robustness and predictability, and the obtained results were very good. This study used a novel statistical metric known as the index of ideality of correlation for the final assessment of the model, and the results that were obtained suggested that the model was good. Also, molecular fragments which account for the increases and/or decreases of a studied activity were defined and then used for the computer-aided design of new compounds as potential CETP inhibitors. The final assessment of the developed QSAR model and designed inhibitors was done using molecular docking, which revealed an excellent correlation with the results from QSAR modeling.Communicated by Ramaswamy H. Sarma.

Computational Simulations Identified Two Candidate Inhibitors of Cdk5/p25 to Abrogate Tau-associated Neurological Disorders

Deregulation of Cdk5 is a hallmark in neurodegenerative diseases and its complex with p25 forms Cdk5/p25, thereby causes severe neuropathological insults. Cdk5/p25 abnormally phosphorylates tau protein, and induces tau-associated neurofibrillary tangles in neurological disorders. Therefore, the pharmacological inhibition of Cdk5/p25 alleviates tau-associated neurological disorders. Herein, computational simulations probed two candidate inhibitors of Cdk5/p25. Structure-based pharmacophore investigated the essential complementary chemical features of ATP-binding site of Cdk5 in complex with roscovitine. Resultant pharmacophore harbored polar interactions with Cys83 and Asp86 residues and non-polar interactions with Ile10, Phe80, and Lys133 residues of Cdk5. The chemical space of selected pharmacophore was comprised of two hydrogen bond donors, one hydrogen bond acceptor, and three hydrophobic features. Decoy test validation of pharmacophore obtained highest Guner-Henry score (0.88) and enrichment factor score (7.23). The screening of natural product drug-like databases by validated pharmacophore retrieved 1126 compounds as candidate inhibitors of Cdk5/p25. The docking of candidate inhibitors filtered 10 molecules with docking score >80.00 and established polar and non-polar interactions with the ATP-binding site residues of Cdk5/p25. Finally, molecular dynamics simulation and binding free energy analyses identified two candidate inhibitors of Cdk5/p25. During 30 ns simulation, the candidate inhibitors established <3.0 Å root mean square deviation and stable hydrogen bond interactions with the ATP-binding site residues of Cdk5/p25. The final candidate inhibitors obtained lowest binding free energies of -122.18 kJ/mol and - 117.26 kJ/mol with Cdk5/p25. Overall, we recommend two natural product candidate inhibitors to target the pharmacological inhibition of Cdk5/p25 in tau-associated neurological disorders.

2D- and 3D-QSAR modelling, molecular docking and in vitro evaluation studies on 18β-glycyrrhetinic acid derivatives against triple-negative breast cancer cell line

Triple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure-activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r²), LOO-based internal regression (q²) and external test set regression (pred_r²) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitro activities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.Communicated by Ramaswamy H. Sarma.

Development of novel therapeutics for glaucoma filtration surgery based on transforming growth factor-β receptor 1 inhibition

QSAR modeling with computer-aided drug design was used for the in silico development of novel therapeutics for glaucoma filtration surgery.

Exploring the structural determinants of novel xanthine derivatives as A2B adenosine receptor antagonists: a computational study

Adenosine is a ubiquitous endogenous nucleoside that controls numerous physiological functions via interacting with its specific G-coupled receptors. Activation of adenosine receptors (AdoRs), particularly A_2B AdoRs promotes the release of inflammatory cytokines; reduces vascular permeabilization and induces angiogenesis, thereby making A_2B AdoR becomes a potentially pharmacological target for drug development. Presently, for investigating the structural determinants of 164 xanthine derivatives as A_2B AdoR antagonists, we performed an in silico study integrating with 3D-QSAR, docking and molecular dynamics (MD) simulation. The obtained optimal model shows strong predictability (Q² = 0.647, R²_ncv = 0.955, and R²_pred = 0.848). Additionally, to explore the binding mode of the ligand with A_2B AdoR and to understand their binding mechanism, docking analysis, MD simulations (20 ns), and the calculation of binding free energy were also carried out. Finally, the structural determinants of these xanthine derivatives were identified and a total of 20 novel A_2B AdoR antagonists with improved potency were computationally designed, and their synthetic feasibility and selectivity were also evaluated. The information derived from the present study offers a better appreciation for exploring the interaction mechanism of the ligand with A_2B AdoR, which could be helpful for designing novel potent A_2B AdoR antagonists. Communicated by Ramaswamy H. Sarma.

Development of non-peptide ACE inhibitors as novel and potent cardiovascular therapeutics: An in silico modelling approach

Angiotensin-converting enzyme (ACE) inhibitors have been acknowledged as first-line agents for the treatment of hypertension and a variety of cardiovascular disorders. In this context, quantitative structure-activity relationship (QSAR) models for a series of non-peptide compounds as ACE inhibitors are developed based on Simplified Molecular Input-Line Entry System (SMILES) notation and local graph invariants. Three random splits into the training and test sets are used. The Monte Carlo method is applied for model development. Molecular docking studies are used for the final assessment of the developed QSAR model and the design of novel inhibitors. The statistical quality of the developed model is good. Molecular fragments responsible for the increase/decrease of the studied activity are calculated. The computer-aided design of new compounds, as potential ACE inhibitors, is presented. The predictive potential of the applied approach is tested, and the robustness of the model is proven using different methods. The results obtained from molecular docking studies are in excellent correlation with the results from QSAR studies. The presented study may be useful in the search for novel cardiovascular therapeutics based on ACE inhibition.

First Report on the Validated Classification-Based Chemometric Modeling of Human Rhinovirus 3C Protease (HRV 3Cpro) Inhibitors

Human rhinoviruses (HRVs), a major cause of common cold and upper respiratory infections, may trigger severe respiratory complications like asthma and COPD. To date, no drugs are available in the market which are designed as novel HRV inhibitors despite the involvement of some pharmaceutical companies' due to economical and clinical constraints. HRV 3C protease may be a potential target for drug design as it plays crucial role in viral RNA replication and virion assembly process. Therefore, designing novel HRV 3Cpro inhibitors is necessary and demanding in the field of antiviral drug design. In this article, statistically significant and validated classification-based QSARs of a series of HRV 3Cpro inhibitors were performed for the first time as per the authors' knowledge. Results suggest that oxopyrrolidine and piperidinone rings are favored whereas carboxybenzyl and unsubstituted benzyl functions may be unfavorable. Moreover, this group, along with cyclic alkyl or aryl ring structures may favor HRV 3Cpro inhibition. These observations may be utilized for the design of a higher active anti-HRV agent in future.

Diverse classes of HDAC8 inhibitors: in search of molecular fingerprints that regulate activity

AIM

HDAC8 is one of the crucial enzymes involved in malignancy. Structural explorations of HDAC8 inhibitory activity and selectivity are required.

MATERIALS & METHODS

A mathematical framework was constructed to explore important molecular fragments responsible for HDAC8 inhibition. Bayesian classification models were developed on a large set of structurally diverse HDAC8 inhibitors.

RESULTS

This study helps to understand the structural importance of HDAC8 inhibitors. The hydrophobic aryl cap function is important for HDAC8 inhibition whereas benzamide moiety shows a negative impact on HDAC8 inhibition.

CONCLUSION

This work validates our previously proposed structural features for better HDAC8 inhibition. The comparative learning between the statistical and intelligent methods will surely enrich future drug design aspects of HDAC8 inhibitors.

Structural exploration of hydroxyethylamines as HIV-1 protease inhibitors: new features identified

The current study deals with chemometric modelling strategies (Naïve Bayes classification, hologram-based quantitative structure-activity relationship (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA)) to explore the important features of hydroxylamine derivatives for exerting potent human immunodeficiency virus-1 (HIV-1) protease inhibition. Depending on the statistically validated reliable and robust quantitative structure-activity relationship (QSAR) models, important and crucial structural features have been identified that may be responsible for enhancing the activity profile of these hydroxylamine compounds. Arylsulfonamide function along with methoxy or fluoro substitution is important for enhancing activity. Bulky steric substitution at the sulfonamide nitrogen disfavours activity whereas smaller hydrophobic substitution at the same position is found to be favourable. Apart from the crucial oxazolidinone moiety, pyrrolidine, cyclic urea and methyl ester functions are also responsible for increasing the HIV-1 protease inhibitory profile. Observations derived from these modelling studies may be utilized further in designing promising HIV-1 protease inhibitors of this class.

Structural refinement and prediction of potential CCR2 antagonists through validated multi-QSAR modeling studies

Chemokines trigger numerous inflammatory responses and modulate the immune system. The interaction between monocyte chemoattractant protein-1 and chemokine receptor 2 (CCR2) may be the cause of atherosclerosis, obesity, and insulin resistance. However, CCR2 is also implicated in other inflammatory diseases such as rheumatoid arthritis, multiple sclerosis, asthma, and neuropathic pain. Therefore, there is a paramount importance of designing potent and selective CCR2 antagonists despite a number of drug candidates failed in clinical trials. In this article, 83 CCR2 antagonists by Jhonson and Jhonson Pharmaceuticals have been considered for robust validated multi-QSAR modeling studies to get an idea about the structural and pharmacophoric requirements for designing more potent CCR2 antagonists. All these QSAR models were validated and statistically reliable. Observations resulted from different modeling studies correlated and validated results of other ones. Finally, depending on these QSAR observations, some new molecules were proposed that may exhibit higher activity against CCR2.

Multiple molecular modelling studies on some derivatives and analogues of glutamic acid as matrix metalloproteinase-2 inhibitors

Matrix metalloproteinase-2 (MMP-2) is a potential target in anticancer drug discovery due to its association with angiogenesis, metastasis and tumour progression. In this study, 67 glutamic acid derivatives, synthesized and evaluated as MMP-2 inhibitors, were taken into account for multi-QSAR modelling study (regression-based 2D-QSAR, classification-based LDA-QSAR, Bayesian classification QSAR, HQSAR, 3D-QSAR CoMFA and CoMSIA as well as Open3DQSAR). All these QSAR studies were statistically validated individually. Regarding the 3D-QSAR analysis, the Open3DQSAR results were better than CoMFA and CoMSIA, although all these 3D-QSAR models supported each other. The importance of biphenylsulphonyl moiety over phenylacetyl/naphthylacetyl moieties was established due to its association with favourable steric and hydrophobic characters. HQSAR, LDA-QSAR and Bayesian classification QSAR studies also suggested that the biphenylsulphonamido group was better than the phenylacetylcarboxamido function. Additionally, glutamines were proven to be far better inhibitors than isoglutamines. Observations obtained from the current study were revalidated and supported by the earlier reported molecular modelling studies. Depending on these observations, newer glutamic acid-based compounds may be designed further in future for potent MMP-2 inhibitory activity.

Identification of structural requirements and prediction of inhibitory activity of natural flavonoids against Zika virus through molecular docking and Monte Carlo based QSAR Simulation

There has been growing interest in the research of flavonoids due to their potential antiviral activities. Recently, some natural flavonoids have shown potential inhibitory activity against zika virus NS3-NS2B protease. In order to accelerate the drug discovery efforts using flavonoids, a Monte Carlo simulation-based QSAR method has been applied to find out the structural requirements for the inhibitory activity. The best QSAR model was obtained using SMILES descriptors and HSG descriptors with ¹EC connectivity with the following statistical parameters: R ² = 0.9569 and Q ² = 0.9050 for the test set. The best model was further utilised for the prediction of inhibitory activity of some other natural flavonoids. Four flavonoids (amentoflavone, fisetin, isorhamnetin and theaflavin-3-gallate) have shown higher predicted inhibitory activity and further validated by performing docking analysis. This study may help in understanding and performing natural flavonoids-based drug discovery against zika virus.

Hydroxyethylamine derivatives as HIV-1 protease inhibitors: a predictive QSAR modelling study based on Monte Carlo optimization

Application of HIV-1 protease inhibitors (as an anti-HIV regimen) may serve as an attractive strategy for anti-HIV drug development. Several investigations suggest that there is a crucial need to develop a novel protease inhibitor with higher potency and reduced toxicity. Monte Carlo optimized QSAR study was performed on 200 hydroxyethylamine derivatives with antiprotease activity. Twenty-one QSAR models with good statistical qualities were developed from three different splits with various combinations of SMILES and GRAPH based descriptors. The best models from different splits were selected on the basis of statistically validated characteristics of the test set and have the following statistical parameters: r² = 0.806, Q² = 0.788 (split 1); r² = 0.842, Q² = 0.826 (split 2); r² = 0.774, Q² = 0.755 (split 3). The structural attributes obtained from the best models were analysed to understand the structural requirements of the selected series for HIV-1 protease inhibitory activity. On the basis of obtained structural attributes, 11 new compounds were designed, out of which five compounds were found to have better activity than the best active compound in the series.

Integrating regression and classification-based QSARs with molecular docking analyses to explore the structure-antiaromatase activity relationships of letrozole-based analogs

Aromatase is a multienzyme complex responsible for the biosynthesis of estrogen and its level has been found higher in breast cancer. Although the designing strategy of aromatase inhibitors (AIs) has continued for more than four decades, it may still be in demand to design highly effective and less toxic AIs. In this study, several chemometric approaches have been used to explore the important structural features of a series of letrozole-based analogs for their promising aromatase inhibitory activity. All techniques are statistically validated individually and in turn, validated with each other along with the structure–activity relationship (SAR) observations. The imidazole ring has been found to interact with the heme iron, whereas the triazole ring system has not shown any interaction. Moreover, imidazole function is better than 1,2,3-triazole, whereas 1,2,3-triazole is better than the 1,2,5-triazole ring system. Additionally, a bulky aryl substitution in the azole ring along with the orientation of the azole nitrogens and the cyanophenyl function has an essential role in the inhibition of aromatase. Furthermore, a cyano group substituted at the phenyl moiety interacts with Arg115, Met374, and Ser478 at the enzyme active site to form hydrogen bonding interactions. These observations are useful for designing potential AIs in the future.

Structure–activity relationships of hydroxamate-based histone deacetylase-8 inhibitors: reality behind anticancer drug discovery

The pan-histone deacetylase (HDAC) inhibitors comprise a fish-like structural orientation where hydrophobic aryl- and zinc-binding groups act as head and tail, respectively of a fish. The linker moiety correlates the body of the fish linking head and tail groups. Despite these pan-HDAC inhibitors, selective HDAC-8 inhibitors are still in demand as a safe remedy. HDAC-8 is involved in invasion and metastasis in cancer. This review deals with the rationale behind HDAC-8 inhibitory activity and selectivity along with detailed structure–activity relationships of diverse hydroxamate-based HDAC-8 inhibitors. HDAC-8 inhibitory potency may be increased by modifying the fish-like pharmacophoric features of such type of pan-HDAC inhibitors. This review may provide a preliminary basis to design and optimize new lead molecules with higher HDAC-8 inhibitory activity. This work may surely enlighten in providing useful information in the field of target-specific anticancer therapy.

In silico analysis of binding interaction of conantokins with NMDA receptors for potential therapeutic use in Alzheimer's disease

Background

The N-methyl-D-aspartate (NMDA) receptors are glutamate receptors that play vital roles in central nervous system development and are involved in synaptic plasticity, which is an essential process for learning and memory. The subunit N-methyl D-aspartate receptor subtype 2B (NR2B) is the chief excitatory neurotransmitter receptor in the mammalian brain. Disturbances in the neurotransmission mediated by the NMDA receptor are caused by its overexposure to glutamate neurotransmitter and can be treated by its binding to an antagonist. Among several antagonists, conantokins from cone snails are reported to bind to NMDA receptors.

Methods

This study was designed to analyze the binding mode of conantokins with NMDA receptors in both humans and rats. To study interactions, dockings were performed using AutoDock 4.2 and their results were further analyzed using various computational tools.

Results

Detailed analyses revealed that these ligands can bind to active site residues of both receptors as reported in previous studies.

Conclusions

In light of the present results, we suggest that these conantokins can act as antagonists of those receptors and play an important role in understanding the importance of inhibition of NMDA receptors for treatment of Alzheimer’s disease.

The advancement of multidimensional QSAR for novel drug discovery - where are we headed?

INTRODUCTION

The Multidimensional quantitative structure-activity relationship (multidimensional-QSAR) method is one of the most popular computational methods employed to predict interesting biochemical properties of existing or hypothetical molecules. With continuous progress, the QSAR method has made remarkable success in various fields, such as medicinal chemistry, material science and predictive toxicology. Areas covered: In this review, the authors cover the basic elements of multidimensional -QSAR including model construction, validation and application. It includes and emphasizes the very recent developments of multidimensional -QSAR such as: HQSAR, G-QSAR, MIA-QSAR, multi-target QSAR. The advantages and disadvantages of each method are also discussed and typical examples of their application are detailed. Expert opinion: Although there are defects in multidimensional-QSAR modeling, it is still of enormous help to chemists, biologists and other researchers in various fields. In the authors' opinion, the latest more precise and feasible QSAR models should be further developed by integrating new descriptors, algorithms and other relevant computational techniques. Apart from being applied in traditional fields (e.g. lead optimization and predictive risk assessment), QSAR should be used more widely as a routine method in other emerging research fields including the modeling of nanoparticles(NPs), mixture toxicity and peptides.

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

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.