Are Terminal Alkynes Necessary for MAO-A/MAO-B Inhibition? A New Scaffold Is Revealed.

A versatile family of quaternary propargylamines was synthesized employing the KA2 multicomponent reaction, through the single-step coupling of a number of amines, ketones, and terminal alkynes. Sustainable synthetic procedures using transition metal catalysts were employed in all cases. The inhibitory activity of these molecules was evaluated against human monoaminoxidase (hMAO)-A and hMAO-B enzymes and was found to be significant. The IC50 values for hMAO-B range from 152.1 to 164.7 nM while the IC50 values for hMAO-A range from 765.6 to 861.6 nM. Furthermore, these compounds comply with Lipinski's rule of five and exhibit no predicted toxicity. To understand their binding properties with the two target enzymes, key interactions were studied using molecular docking, all-atom molecular dynamics (MD) simulations, and MM/GBSA binding free energy calculations. Overall, herein, the reported family of propargylamines exhibits promise as potential treatments for neurodegenerative disorders, such as Parkinson's disease. Interestingly, this is the first time a propargylamine scaffold bearing an internal alkyne has been reported to show activity against monoaminoxidases.

Synthesis of Novel Hydrazide-Hydrazone Compounds and In Vitro and In Silico Investigation of Their Biological Activities against AChE, BChE, and hCA I and II.

The abnormal levels of the human carbonic anhydrase isoenzymes I and II (hCA I and II) and cholinesterase enzymes, namely, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), are linked with various disorders including Alzheimer's disease. In this study, six new nicotinic hydrazide derivatives (7-12) were designed and synthesized for the first time, and their inhibitory profiles against hCA I, hCA II, AChE, and BChE were investigated by in vitro assays and in silico studies. The structures of novel molecules were elucidated by using spectroscopic techniques and elemental analysis. These molecules showed inhibitory activities against hCA I and II with IC50 values ranging from 7.12 to 45.12 nM. Compared to reference drug acetazolamide (AZA), compound 8 was the most active inhibitor against hCA I and II. On the other hand, it was determined that IC50 values of the tested molecules ranged between 21.45 and 61.37 nM for AChE and between 18.42 and 54.74 nM for BChE. Among them, compound 12 was the most potent inhibitor of AChE and BChE, with IC50 values of 21.45 and 18.42 nM, respectively. In order to better understand the mode of action of these new compounds, state-of-the-art molecular modeling techniques were also conducted.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers.

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Atherogenic index of plasma is an independent predictor of mitral annular calcification.

BACKGROUND: In the latest reports, atherogenic indices have been related to acute coronary syndromes, stable coronary artery disease, heart failure and future cardiac events. Conventional atherosclerosis risk factors have been associated with mitral annular calcification (MAC), but data on the relationship between atherogenic indices and MAC are lacking. We aimed to investigate a possible relationship between MAC and atherogenic indices. METHODS: In total 741 patients (n = 427 with MAC and n = 314 without MAC) who were examined in our cardiology clinic from February 2016 to October 2021 were recruited in the study. Mitral annular calcification was diagnosed by transthoracic 2-dimensional echocardiography. The atherogenic coefficient (AC), Castelli risk index 1 (CRI-1), Castelli risk index 2 (CRI-2) and atherogenic index of plasma (AIP) were calculated by utilizing standard lipid test values. RESULTS: There was no statistically significant difference in sex, age, diabetes and hypertension status between the patient and the control groups. Serum triglyceride level, AIP, Hs-CRP, smoking and BMI were independently significantly associated with MAC in multiple regression analysis (p < 0.001). CONCLUSION: Higher AIP was related to the existence of MAC and also predict the presence of MAC independently. Studies evaluating the modification of these indices are needed.

Evaluation of ECG Repolarization Parameters in a Worker Cohort Working the Night Shift.

Objectives: Night Shift work is an increasingly common working order that affects human well-being and it is little known about its arrhythmic role in the cardiovascular system. We aimed to investigate the effect of working at night shifts on P-wave dispersion (Pd), QT and QTc dispersions (QTd, QTcd resp) on surface electrocardiography. Methods: We included 286 foundry workers who work at night shift and 100 foundry workers who work on day time only. The night shift workers were divided into three subgroups according to the length of time they worked at night shift. Surface electrocardiography and blood tests were applied for all participants. Results: Pd, QTd and QTcd values increased in the night shift workers compared to the day time workers (p < 0.05). In subgroup analysis; the night shift workers for more than 15 years had a significantly higher Pd, QTd and QTcd compared to others (p < 0.001). Correlation analysis revealed significant positive correlations with working duration and Pd (r = 0.578, p < 0.001) and QTcd (r = 0.417, p < 0.001). In the linear regression analysis, working duration at night shift was significantly associated with Pd and QTcd values, independent from other associated clinical risk parameters. Conclusions: This study makes one of the first attempts to assess changes in ECG parameters reflecting tendency to rhythm disturbances, in night shift workers. Our results further underline the importance of covering a comprehensive evaluation of ECG in periodical health check-ups in night shift workers to evaluate the risk of both atrial and ventricular arrhythmias.

Another possible determinant for ischemic stroke with nonvalvular atrial fibrillation other than conventional oral anticoagulant treatment: The relationship between whole blood viscosity and stroke☆.

OBJECTIVES: Patients with nonvalvular atrial fibrillation (NVAF) still experience ischemic stroke despite recommended medications and this could be the consequence of increased whole blood viscosity (WBV). We evaluated the predictive value of WBV for stroke in patients with NVFA despite receiving oral anticoagulant (OAC) therapy. METHODS: One thousand and forty-three NVAF patients on OAC medication were followed up for median 36.13 ± 18.31 months. WBV was calculated according to the validated de Simone's formula. RESULTS: WBV was significantly higher in stroke group when compared to non-stroke group at both low shear rate (LSR) and high shear rate (HSR). Multiple regression analysis demonstrated an independent association between WBV and stroke when adjusted for other risk factors. CONCLUSIONS: WBV appears to be a profitable predictor of ischemic stroke in patients with NVAF receiving OAC.

Identifying highly effective coumarin-based novel cholinesterase inhibitors by in silico and in vitro studies.

Inhibition of high cholinesterase levels including acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), is one of the most important strategies for the treatment of Alzheimer's disease (AD). Clinically limited drugs are used in the treatment of AD, so there is a need to find new effective inhibitors today. Therefore, in this study, synthesized six coumarin carboxamides (A1, A2, B1-B4) were evaluated against AChE and BChE by combined in silico and in vitro studies. The in vitro assessment of studied compounds revealed that A1, A2, B3, and B4 showed highest inhibition potential against AChE and BChE. As demonstrated with our structure activity relationship (SAR) study, the promising inhibition result of AChE at nanomolar concentrations was obtained with heterocyclic amines including pyrrolidine and N-methyl piperazine moieties for tertiary amide substituted coumarin compounds B3 and B4, displaying KI values of 9.78 nM and 8.07 nM, respectively. Thus, compounds B3 and B4 had around 5.7- and 6.9-fold more potency compared to the reference molecule, neostigmine. Moreover, coumarin-3-carboxamide derivative A1 bearing benzylmorpholine moiety on coumarin scaffold at position 3 displayed stronger inhibition potential against BChE. Furthermore, in order to better understand their molecular mechanisms in these targets, we conducted molecular docking and MD simulations. Our promising preclinical results show that the lead compounds A1, A2, B3 and B4 have high potential as effective inhibitors for the treatment of AD.

In silico identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening.

Cardiovascular diseases are the world's leading cause of death. Hypertension is an important risk factor for cardiovascular and renal diseases. Angiotensin-converting enzyme (ACE) can be a possible therapeutic target for managing angiotensin I conversion to angiotensin II and ultimately controlling hypertension. Indole is an significant fragment used in many medicines approved by FDA. For this reason, the molecules in their fragments containing" indol" keywords were taken from the Specs-SC (small compound) database. The predicted therapeutc activity values (TAV) of these compounds against hypertension were evaluated using binary models of QSAR by MetaCore/MetaDrug. For the 26 separate QSAR models of toxicity, molecules with measured TAV greater than 0.5 were used. 3792 non-toxic compounds were investigated by molecular docking study and molecular dynamics simulations for their ACE inhibitory activity. Glide standard precision (SP) of Maestro Molecular Modeling pocket was used to perform molecular docking. Short molecular dynamics (MD) simulations (5-ns) were carried out by initiating the top docking poses of selected 40 molecules. To quantitatively evaluate the predicted binding affinity of a screened compound, average MM/GBSA scores of screened ligands were calculated and based on their binding free energy values, hit compounds were identified for the long (100-ns) MD simulations. Root mean square deviation and root mean square fluctuations were also calculated to assess the structural characteristics and observe fluctuations of the 100-ns time scale. Thus, with the application of text mining and integrated molecular modeling we reported novel indole-based hit inhibitors for ACE-1.Communicated by Ramaswamy H. Sarma.

Screening of Clinically Approved and Investigation Drugs as Potential Inhibitors of SARS-CoV-2: A Combined in silico and in†vitro Study.

In the current study, we used 7922 FDA approved small molecule drugs as well as compounds in clinical investigation from NIH's NPC database in our drug repurposing study. SARS-CoV-2 main protease as well as Spike protein/ACE2 targets were used in virtual screening and top-100 compounds from each docking simulations were considered initially in short molecular dynamics (MD) simulations and their average binding energies were calculated by MM/GBSA method. Promising hit compounds selected based on average MM/GBSA scores were then used in long MD simulations. Based on these numerical calculations following compounds were found as hit inhibitors for the SARS-CoV-2 main protease: Pinokalant, terlakiren, ritonavir, cefotiam, telinavir, rotigaptide, and cefpiramide. In addition, following 3 compounds were identified as inhibitors for Spike/ACE2: Denopamine, bometolol, and rotigaptide. In order to verify the predictions of in silico analyses, 4 compounds (ritonavir, rotigaptide, cefotiam, and cefpiramide) for the main protease and 2 compounds (rotigaptide and denopamine) for the Spike/ACE2 interactions were tested by in vitro experiments. While the concentration-dependent inhibition of the ritonavir, rotigaptide, and cefotiam was observed for the main protease; denopamine was effective at the inhibition of Spike/ACE2 binding.

Near-physiological-temperature serial crystallography reveals conformations of SARS-CoV-2 main protease active site for improved drug repurposing.

The COVID-19 pandemic has resulted in 198 million reported infections and more than 4 million deaths as of July 2021 (covid19.who.int). Research to identify effective therapies for COVID-19 includes: (1) designing a vaccine as future protection; (2) de novo drug discovery; and (3) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determine two apo structures of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease at ambient temperature by serial femtosecond X-ray crystallography. We employ detailed molecular simulations of selected known main protease inhibitors with the structures and compare binding modes and energies. The combined structural and molecular modeling studies not only reveal the dynamics of small molecules targeting the main protease but also provide invaluable opportunities for drug repurposing and structure-based drug design strategies against SARS-CoV-2.

Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors.

B-Cell lymphoma 2 (BCL-2) regulates cell death in humans. In this study, combined multiscale in silico approaches and in vitro studies were employed. A small-molecule library that includes more than 210 000 compounds was used. The predicted therapeutic activity value (TAV) of the compounds in this library was computed with the binary cancer quantitative structure-activity relationships (QSAR) model. The molecules with a high calculated TAV were used in 26 individual toxicity QSAR models. As a result of this screening protocol, 288 nontoxic molecules with high predicted TAV were identified. These selected hits were then screened against the BCL-2 target protein using hybrid docking and molecular dynamics (MD) simulations. The interaction energies of identified compounds were compared with two known BCL-2 inhibitors. Then, the short MD simulations were carried out by initiating the best docking poses of 288 molecules. Average MM/GBSA energies were computed, and long MD simulations were employed to selected hits. The same calculations were also applied for two known BCL-2 inhibitors. Moreover, a five-site (AHRRR) structure-based pharmacophore model was constructed, and this model was used in the screening of the same database. On the basis of hybrid data-driven ligand identification study, final hits were selected and used in in vitro studies. Based on results of the time-resolved fluorescence resonance energy transfer (TR-FRET) analysis, further filtration was carried out for the U87-MG cell line tests. MTT cell proliferation assay analysis results showed that selected three potent compounds were significantly effective on glioma cells.

Identifying new piperazine-based PARP1 inhibitors using text mining and integrated molecular modeling approaches.

One of the important molecular targets for antitumor drug discovery is the polyadenosine diphosphate-ribose polymerase-1 (PARP1) enzyme. It is linked with various biological functions including DNA repair and apoptosis. It is primarily a nuclear enzyme linked to chromatin, which is activated by DNA damage. Improved expression of PARP1 in melanomas, breast cancer, lung cancer and other neoplastic diseases is often observed. A tremendous PARP research concerning cancer and ischemia is progressing very rapidly. There are currently four PARP1 inhibitors approved by the FDA on the market, namely Olaparib, Rucaparib, Niraparib and Talazoparib. All of these molecules are non-selective inhibitors of PARP1. Currently there is an urgent need for novel and selective PARP1 inhibitors. In this work, asmall molecule database (Specs SC) were used to identify the new selective lead inhibitors of PARP1. Piperazine scaffold is an important fragment that is used in many currently used FDA approved drugs in different diseases including PARP1 inhibitor Olaparib. Thus, based on text mining studies, 4674 compounds thatinclude piperazine fragments were identified and virtually screened at the binding pocket of target protein PARP1. Compounds that have high docking scores were used in molecular dynamics (MD) simulations. Free energy calculations were also performed to compare the predicted binding energies with known PARP1 inhibitors. The critical amino acid interactions of these newly identified hits in the binding pocket were also investigated in detail for better understanding of the structural features required for next generation PARP1 inhibitors. Thus, here together with combination of text-mining and integrated molecular modeling approaches, we identified novel piperazine-based hits against PARP1 enzyme.Communicated by Ramaswamy H. Sarma.

Investigation of novel indole-based HIV-1 protease inhibitors using virtual screening and text mining.

Human immunodeficiency virus type 1 protease (HIV-1 PR) inhibitors have been used as possible therapeutic agents for HIV-1 infection in clinical study. Most of the HIV therapy-related problems usually stem from long-term opioid usage. The rapid development of drug-resistant variants limits the long-term effectiveness of current inhibitors as therapeutic agents. In addition, different side effects were reported. Further drug development is required to design new compounds which have similar efficacy as the drugs currently used in HIV infection but without having undesirable side effects. Indole derivatives were considered as one of the effective HIV inhibitors. Indole is an important fragment used in many FDAapproved medicines and used in various diseases. For this purpose, in this study the molecules containing" indole" keywords in their fragments are taken from the Specs-SC database which includes 212520 small molecules. 5194 molecules that include indole keywords are selected. These selected molecules are then screened against HIV-1 PR target protein using molecular docking simulations. Then the molecules are ranked according to the their docking scores. Top docking poses of ten ligands and FDA approved drug Amprenavir are subjected to 100 ns Molecular Dynamics (MD) simulations. Thus, by using combination of text mining and integrated molecular modeling approaches, we identified novel indole-based hits against HIV-1 PR.Communicated by Ramaswamy H. Sarma.

Design, Synthesis, and Molecular Modeling Studies of Novel Coumarin Carboxamide Derivatives as eEF-2K Inhibitors.

Eukaryotic elongation factor-2 kinase (eEF-2K) is an unusual alpha kinase commonly upregulated in various human cancers, including breast, pancreatic, lung, and brain tumors. We have demonstrated that eEF-2K is relevant to poor prognosis and shorter patient survival in breast and lung cancers and validated it as a molecular target using genetic methods in related in vivo tumor models. Although several eEF-2K inhibitors have been published, none of them have shown to be potent and specific enough for translation into clinical trials. Therefore, development of highly effective novel inhibitors targeting eEF-2K is needed for clinical applications. However, currently, the crystal structure of eEF-2K is not known, limiting the efforts for designing novel inhibitor compounds. Therefore, using homology modeling of eEF-2K, we designed and synthesized novel coumarin-3-carboxamides including compounds A1, A2, and B1-B4 and evaluated their activity by performing in silico analysis and in vitro biological assays in breast cancer cells. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) area results showed that A1 and A2 have interaction energies with eEF-2K better than those of B1-B4 compounds. Our in vitro results indicated that compounds A1 and A2 were highly effective in inhibiting eEF-2K at 1.0 and 2.5 µM concentrations compared to compounds B1-B4, supporting the in silico findings. In conclusion, the results of this study suggest that our homology modeling along with in silico analysis may be effectively used to design inhibitors for eEF-2K. Our newly synthesized compounds A1 and A2 may be used as novel eEF-2K inhibitors with potential therapeutic applications.

A novel hybrid method named electron conformational genetic algorithm as a 4D QSAR investigation to calculate the biological activity of the tetrahydrodibenzazosines.

To understand the structure-activity correlation of a group of tetrahydrodibenzazocines as inhibitors of 17ß-hydroxysteroid dehydrogenase type 3, we have performed a combined genetic algorithm (GA) and four-dimensional quantitative structure-activity relationship (4D-QSAR) modeling study. The computed electronic and geometry structure descriptors were regulated as a matrix and named as electron-conformational matrix of contiguity (ECMC). A chemical property-based pharmacophore model was developed for series of tetrahydrodibenzazocines by EMRE software package. GA was employed to choose an optimal combination of parameters. A model has been developed for estimating anticancer activity quantitatively. All QSAR models were established with 40 compounds (training set), then they were considered for selective capability with additional nine compounds (test set). A statistically valid 4D-QSAR ( Rtraining2=0.856 , Rtest2=0.851 and q2 = 0.650) with good external set prediction was obtained.

In vitro and in silico studies of nitrobenzamide derivatives as potential anti-neuroinflammatory agents.

Communicated by Ramaswamy H. Sarma.

Novel AChE and BChE inhibitors using combined virtual screening, text mining and in vitro binding assays.

In the current work, we developed a computational pipeline method for predicting the binding affinities of studied compounds at the specific target sites. Since many approved therapeutic compounds involve indole or indole-derivative rings, in the current study we focused compounds including these fingerprints. Initially, 212520 compounds were retrieved from Specs-SC library and after the conversion of IUPAC text file format, compounds that include 'indol' keyword (5194 compounds) were used in binary QSAR-based models to screen against a defined therapeutic activity "Alzheimer's disease" (AD). The molecules that have higher AD therapeutic activity values (>0.5) were then used in the 26 different toxicity-QSAR models. Binary QSAR models resulted 89 hits that have high AD therapeutic activity and no toxicity. Selected 89 molecules were then screened against acetylcholinesterase (AChE) targets using molecular docking and top-docking poses of compounds were used in initially short (10 ns) molecular dynamics (MD) simulations. Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) binding free energy calculations were performed for 89 ligands and tightly bound 17 ligands based on average MM/GBSA scores were selected for long (100 ns) MD simulations. The same protocol was also applied for the known 4 AChE inhibitors. Selected hits were also docked to the binding pocket of butyrylcholinesterase (BChE). Finally, based on MM/GBSA scores, as well as their corresponding docking scores and metabolite production profiles, 7 compounds were selected and their in vitro tests were performed. Out of 7 compounds, 6 of them showed µM-level inhibition for both AChE and BChE targets.Communicated by Ramaswamy H. Sarma.

Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors.

The excessive activity of acetylcholinesterase enzyme (AChE) causes different neuronal problems, especially dementia and neuronal cell deaths. Food and Drug Administration (FDA) approved drugs donepezil, rivastigmine, tacrine and galantamine are AChE inhibitors and in the treatment of Alzheimer's disease (AD) these drugs are currently prescribed. However, these inhibitors have various adverse side effects. Therefore, there is a great need for the novel selective AChE inhibitors with fewer adverse side effects for the effective treatment. In this study, combined ligand-based and structure-based virtual screening approaches were used to identify new hit compounds from small molecules library of National Cancer Institute (NCI) containing approximately 265,000 small molecules. In the present study, we developed a computational pipeline method to predict the binding affinities of the studied compounds at the specific target sites. For this purpose, a text mining study was carried out initially and compounds containing the keyword "indol" were considered. The therapeutic activity values against AD were screened using the binary quantitative structure activity relationship (QSAR) models. We then performed docking, molecular dynamics (MD) simulations and free energy analysis to clarify the interactions between selected ligands and enzyme. Thus, in this study we identified new promising hit compounds from a large database that may be used to inhibit the enzyme activity of AChE.

Application of electron conformational-genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction.

Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)-which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements-were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (R(2)(training)= 0.848, R(2)(test))= 0.904, with a cross-validated q(2) = 0.780).

4D-QSAR analysis and pharmacophore modeling: electron conformational-genetic algorithm approach for penicillins.

4D-QSAR studies were performed on a series of 87 penicillin analogues using the electron conformational-genetic algorithm (EC-GA) method. In this EC-based method, each conformation of the molecular system is described by a matrix (ECMC) with both electron structural parameters and interatomic distances as matrix elements. Multiple comparisons of these matrices within given tolerances for high active and low active penicillin compounds allow one to separate a smaller number of matrix elements (ECSA) which represent the pharmacophore groups. The effect of conformations was investigated building model 1 and 2 based on ensemble of conformers and single conformer, respectively. GA was used to select the most important descriptors and to predict the theoretical activity of the training (74 compounds) and test (13 compounds, commercial penicillins) sets. The model 1 for training and test sets obtained by optimum 12 parameters gave more satisfactory results (R(training)(2)=0.861, SE(training)=0.044, R(test)(2)=0.892, SE(test)=0.099, q(2)=0.702, q(ext1)(2)=0.777 and q(ext2)(2)=0.733) than model 2 (R(training)(2)=0.774, SE(training)=0.056, R(test)(2)=0.840, SE(test)=0.121, q(2)=0.514, q(ext1)(2)=0.641 and q(ext2)(2)=0.570). To estimate the individual influence of each of the molecular descriptors on biological activity, the E statistics technique was applied to the derived EC-GA model.