Discovery of Novel EGFR Inhibitor Targeting Wild-Type and Mutant Forms of EGFR: In Silico and In Vitro Study.

Targeting L858R/T790M and L858R/T790M/C797S mutant EGFR is a critical challenge in developing EGFR tyrosine kinase inhibitors to overcome drug resistance in non-small cell lung cancer (NSCLC). The discovery of next-generation EGFR tyrosine kinase inhibitors (TKIs) is therefore necessary. To this end, a series of furopyridine derivatives were evaluated for their EGFR-based inhibition and antiproliferative activities using computational and biological approaches. We found that several compounds derived from virtual screening based on a molecular docking and solvated interaction energy (SIE) method showed the potential to suppress wild-type and mutant EGFR. The most promising PD13 displayed strong inhibitory activity against wild-type (IC50 of 11.64 ± 1.30 nM), L858R/T790M (IC50 of 10.51 ± 0.71 nM), which are more significant than known drugs. In addition, PD13 revealed a potent cytotoxic effect on A549 and H1975 cell lines with IC50 values of 18.09 ± 1.57 and 33.87 ± 0.86 µM, respectively. The 500-ns MD simulations indicated that PD13 formed a hydrogen bond with Met793 at the hinge region, thus creating excellent EGFR inhibitory activity. Moreover, the binding of PD13 in the hinge region of EGFR was the major determining factor in stabilizing the interactions via hydrogen bonds and van der Waals (vdW). Altogether, PD13 is a promising novel EGFR inhibitor that could be further clinically developed as fourth-generation EGFR-TKIs.

Biological activity and ADME/Tox prediction of some 2-substituted benzoxazole derivatives.

In this study, we mainly focused on some in vitro biological activities of a series of (5 or 6)-amino-2- (substituted phenyl and benzyl) benzoxazole derivatives. For this purpose, we tested cytotoxic and genotoxic activities of them on cancer cell lines and their topoisomerase inhibitory activities. We also tested their cytotoxic and genotoxic activities on non-cancerous cells (L929) and their mutagenic activities by Ames test to evaluate their effects on healthy cells. Only TD5 was found cytotoxic on all the tested cancer cell lines and did not exhibit either cytotoxic or genotoxic activities against healthy cells, whereas TD1, TD2, TD3 and TD7 were more cytotoxic against only HeLa cells. Only TD4 was found as mutagenic derivative. None of the compounds had any topoisomerase inhibitory activities nevertheless some of them caused inhibition of topoisomerase II activity. Additionally, we used an in silico model to predict the drug-like properties of them to evaluate their bioavailability to the QikProp Properties Predictions. All the calculated properties were found in a permissible range. According to the data obtained from biological activity studies, it can be concluded that the methylene bridge at the position 2 of benzoxazole ring decreases cytotoxic activity on cancer cells and inhibitory activity on DNA topoisomerases.

Discovery of 5-(or 6)-benzoxazoles and oxazolo[4,5-b]pyridines as novel candidate antitumor agents targeting hTopo IIα.

Discovery of novel anticancer drugs which have low toxicity and high activity is very significant area in anticancer drug research and development. One of the important targets for cancer treatment research is topoisomerase enzymes. In order to make a contribution to this field, we have designed and synthesized some 5(or 6)-nitro-2-(substitutedphenyl)benzoxazole (1a-1r) and 2-(substitutedphenyl)oxazolo[4,5-b]pyridine (2a-2i) derivatives as novel candidate antitumor agents targeting human DNA topoisomerase enzymes (hTopo I and hTopo IIα). Biological activity results were found very promising for the future due to two compounds, 5-nitro-2-(4-butylphenyl)benzoxazole (1i) and 2-(4-butylphenyl)oxazolo[4,5-b]pyridine (2i), that inhibited hTopo IIα with 2 µM IC50 value. These two compounds were also found to be more active than reference drug etoposide. However, 1i and 2i did not show any satisfactory cyctotoxic activity on the HeLa, WiDR, A549, and MCF7 cancer cell lines. Moreover, molecular docking and molecular dynamic simulations studies for the most active compounds were applied in order to understand the mechanism of inhibition activity of hTopo IIα. In addition, in silico ADME/Tox studies were performed to predict drug-likeness and pharmacokinetic properties of all the tested compounds.

Synthesis and molecular docking studies of some novel antimicrobial benzamides.

Common use of classical antibiotics has caused to the growing emergence of many resistant strains of pathogenic bacteria. Therefore, we aimed to synthesize a number of N-(2-hydroxy-(4 or 5)-nitrophenyl)benzamide derivatives as a new class of antimicrobial compounds. Moreover, our second goal is to predict the interaction between active structures and enzymes (DNA -gyrase and FtsA) in the binding mode. In this study, thirteen N-(2-hydroxy-(4 or 5-nitrophenyl)-substituted-benzamides were synthesized and determined for their antimicrobial activity using the microdilution method. According to this work, none of the compounds showed any activity against Candida albicans and its clinical isolate. Some of the benzamides (4N1, 5N1, 5N2) displayed very significant activity against Staphylococcus aureus and MSSA with <4 µg/ml MIC value, even they were found to be more potent than ceftazidime. 4N1 was also found to be more effective than gentamicin against Enterococcus faecalis clinical isolate. Molecular docking studies revealed that 4N1, 5N1, and 5N2 showed a good interactions with DNA-gyrase. Moreover, 5N1 has interacted with FtsA enzyme in the binding mode, as well. Only compound 5N4 displayed very good activity against Escherichia coli ATCC 25922. These findings showed us that 4N1, 5N1, 5N2, and 5N4 could be lead compounds to discover new antibacterial candidates against multidrug-resistant strains.

Prediction of retention characteristics of heterocyclic compounds.

The CORAL software ( http://www.insilico.eu/coral ) was used to build up quantitative structure-property relationships (QSPRs) for the retention characteristics of 93 derivatives of three groups of heterocyclic compounds: 2-phenyl-1,3-benzoxazoles, 4-benzylsulfanylpyridines, and benzoxazines. The QSPRs are one-variable models based on the optimal descriptors calculated from the molecular structure represented by simplified molecular input-line entry systems (SMILES). Each symbol (or two undivided symbols) of SMILES is characterized by correlation weight. The optimal descriptor is the sum of the correlation weights. The numerical data on the correlation weights were calculated with the Monte Carlo method by the manner which provides best correlation between endpoint and optimal descriptor for the calibration set. The predictive ability of the model is checked with the validation set (compounds invisible during building up of the model). The approach has been checked with three random splits into the training, calibration, and validation sets: all models have apparent predictive potential. The mechanistic interpretation of the molecular features extracted from SMILES as the promoters of increase or decrease of examined endpoints is suggested.

Generated 3D-common feature hypotheses using the HipHop method for developing new topoisomerase I inhibitors.

The continued interest in designing novel topoisomerase I (Topo I) inhibitors and the lack of adequate ligand-based computer-aided drug discovery efforts combined with the drawbacks of structure-based design prompted us to explore the possibility of developing ligand-based three-dimensional (3D) pharmacophore(s). This approach avoids the pitfalls of structure-based techniques because it only focuses on common features among known ligands; furthermore, the pharmacophore model can be used as 3D search queries to discover new Topo I inhibitory scaffolds. In this article, we employed the HipHop module using Discovery Studio to construct plausible binding hypotheses for clinically used Topo I inhibitors, such as camptothecin, topotecan, belotecan, and SN-38, which is an active metabolite of irinotecan. The docked pose of topotecan was selected as a reference compound. The first hypothesis (Hypo 01) among the obtained 10 hypotheses was chosen for further analysis. Hypo 01 had six features, which were two hydrogen-bond acceptors, one hydrogen-bond donor, one hydrophob aromatic and one hydrophob aliphatic, and one ring aromatic. Our obtained hypothesis was checked by using some of the aromathecin derivatives which were published for their Topo I inhibitory potency. Moreover, five structures were found to be possible anti-Topo I compounds from the DruglikeDiverse database. From this research, it can be suggested that our model could be useful for further studies in order to design new potent Topo I-targeting antitumor drugs.

In silico investigation of the interactions of certain drugs proposed for the treatment of Covid-19 with the paraoxonase-1.

Coronavirus disease 2019 (Covid-19) has caused one of the biggest pandemics of modern times, infected over 240 million people and killed over 4.9 million people, and continues to do so. Although many drugs are widely recommended in the treatment of this disease, the interactions of these drugs with an anti-atherosclerotic enzyme, paraoxonase-1 (PON1), are not well known. In our study, we investigated the interactions of 18 different drugs, which are claimed to be effective against covid-19, with the PON1 enzyme and its genetics variants L55M and Q192R with molecular docking, molecular dynamics simulation and free energy calculation method MM/PBSA. We found that ruxolitinib, dexamethasone, colchicine; dexamethasone, sitagliptin, baricitinib and galidesivir, ruxolitinib, hydroxychloroquine were the most effective compounds in binding PON1-w, PON1L55M and PON1Q192R respectively. Mainly, sitagliptin, galidesivir and hydroxychloroquine have attracted attention by showing very high affinity (<-300 kJ/mol) according to the MM/PBSA method. We concluded that the drug interactions should be considered and more attention should be paid in the use of these drugs.Communicated by Ramaswamy H. Sarma.

Antitumor activity against human promyelocytic leukemia and in silico studies of some benzoxazines.

Cancer is one of the deadliest diseases in the world today, and the incidence of cancer is increasing. Leukemia is a type of blood cancer defined as the uncontrolled proliferation of abnormal leukocytes in the blood and bone marrow. The HL-60 (human promyelocytic leukemia) cell line, derived from a single patient with acute promyelocytic leukemia, provides a unique in vitro model system for studying the cellular and molecular events involved in the proliferation and differentiation of leukemic cells. In this study, antitumor activities on the HL-60 of some of the resynthesized benzoxazine derivatives (BXN-01 and BXN-02) were investigated. The results of in vitro studies obtained were compared a standard drug, etoposide. In vitro results showed that BXN-01 and BXN-02 were found to be extremely effective compared to etoposide (IC50 value: 10 µM) with IC50 values of 5 nM and 25 nM, respectively. Furthermore, molecular docking studies were carried out for preliminary prediction of possible interaction modes between compounds and the active site of the target macromolecules, hTopo IIα, HDAC2, and RXRA. Then, in silico ADME/Tox studies were performed to predict drug-likeness and pharmacokinetic properties of BXN-01 and BXN-02.Communicated by Ramaswamy H. Sarma.

Design, synthesis, and computational studies of benzimidazole derivatives as new antitubercular agents.

The increase in the drug-resistant strains of Mycobacterium tuberculosis has led researchers to new drug targets. The development of new compounds that have effective inhibitory properties with the selective vital structure of Mycobacterium tuberculosis is required in new scientific approaches. The most important of these approaches is the development of inhibitor molecules for Mycobacterium cell wall targets. In this study, first of all, the antitubercular activity of 23 benzimidazole derivatives was experimentally determined. And then molecular docking studies were carried out with 4 different targets: Arabinosyltransferase C (EmbC), Filamentous Temperature Sensitive Mutant Z (FtsZ), Protein Tyrosine Phosphatase B (PtpB), and Decaprenylphosphoryl-ß-D-ribose-2'-oxidase (DprE1). It has been determined that benzimidazole derivatives show activity through the DprE1 enzyme. It is known that DprE1, which has an important role in the synthesis of the cell envelope from Arabinogalactan, is also effective in the formation of drug resistance. Due to this feature, the DprE1 enzyme has become an important target for drug development studies. Also, it was chosen as a target for this study. This study aims to identify molecules that inhibit DprE1 for the development of more potent and selective antitubercular drugs. For this purpose, molecular docking studies by AutoDock Vina, and CDOCKER and molecular dynamics (MD) simulations and in silico ADME/Tox analysis were implemented for 23 molecules. The molecules exhibited binding affinity values of less than -8.0 kcal/mol. After determining the compound's anti-TB activities by a screening test, the best-docked results were detected using compounds 20, 21, and 30. It was found that 21, was the best molecule with its binding affinity value, which was supported by MD simulations and in silico ADME modeling results.Communicated by Ramaswamy H. Sarma.

QSAR based on hybrid optimal descriptors as a tool to predict antibacterial activity against Staphylococcus aureus.

BACKGROUND: Staphylococcus aureus bacterial infections are still a serious health care problem. Therefore, the development of new drugs for these infections is a constant requirement. Quantitative structure-activity relationship (QSAR) methods can assist this development. METHODS: The study included 151 structurally diverse compounds with antibacterial activity against S. aureus ATCC 25923 (Endpoint 1) or the drug-resistant clinical isolate of S. aureus (Endpoint 2). QSARs based on hybrid optimal descriptors were used. RESULTS: The predictive potential of developed models has been checked with three random splits into training, passive training, calibration, and validation sets. The proposed models give satisfactory predictive models for both endpoints examined. CONCLUSIONS: The results of the study show the possibility of SMILES-based QSAR in the evaluation of the antibacterial activity of structurally diverse compounds for both endpoints. Although the developed models give satisfactory predictive models for both endpoints examined, splitting has an apparent influence on the statistical quality of the models.

Unsupervised seizure identification on EEG.

BACKGROUND AND OBJECTIVE: Epilepsy is one of the most common neurological disorders, whose development is typically detected via early seizures. Electroencephalogram (EEG) is prevalently employed for seizure identification due to its routine and low expense collection. The stochastic nature of EEG makes manual seizure inspections laborsome, motivating automated seizure identification. The relevant literature focuses mostly on supervised machine learning. Despite their success, supervised methods require expert labels indicating seizure segments, which are difficult to obtain on clinically-acquired EEG. Thus, we aim to devise an unsupervised method for seizure identification on EEG. METHODS: We propose the first fully-unsupervised deep learning method for seizure identification on raw EEG, using a variational autoencoder (VAE). In doing so, we train the VAE on recordings without seizures. As training captures non-seizure activity, we identify seizures with respect to the reconstruction errors at inference time. Moreover, we extend the traditional VAE training loss to suppress EEG artifacts. Our method does not require ground-truth expert labels indicating seizure segments or manual feature extraction. RESULTS: We implement our method using the PyTorch library and execute experiments on an NVIDIA V100 GPU. We evaluate our method on three benchmark EEG datasets: (i) intracranial recordings from the University of Pennsylvania and the Mayo Clinic, (ii) scalp recordings from the Temple University Hospital of Philadelphia, and (iii) scalp recordings from the Massachusetts Institute of Technology and the Boston Children's Hospital. To assess performance, we report accuracy, precision, recall, Area under the Receiver Operating Characteristics Curve (AUC), and p-value under the Welch t-test for distinguishing seizure vs. non-seizure EEG windows. Our approach can successfully distinguish seizures from non-seizure activity, with up to 0.83 AUC on intracranial recordings. Moreover, our algorithm has the potential for real-time inference, by processing at least 10 s of EEG in a second. CONCLUSION: We take the first successful steps in deep learning-based unsupervised seizure identification on raw EEG. Our approach has the potential of alleviating the burden on clinical experts regarding laborsome EEG inspections for seizures. Furthermore, aiding the identification of early seizures via our method could facilitate successful detection of epilepsy development and initiate antiepileptogenic therapies.

Improved Training Efficiency for Retinopathy of Prematurity Deep Learning Models Using Comparison versus Class Labels.

Purpose: To compare the efficacy and efficiency of training neural networks for medical image classification using comparison labels indicating relative disease severity versus diagnostic class labels from a retinopathy of prematurity (ROP) image dataset. Design: Evaluation of diagnostic test or technology. Participants: Deep learning neural networks trained on expert-labeled wide-angle retinal images obtained from patients undergoing diagnostic ROP examinations obtained as part of the Imaging and Informatics in ROP (i-ROP) cohort study. Methods: Neural networks were trained with either class or comparison labels indicating plus disease severity in ROP retinal fundus images from 2 datasets. After training and validation, all networks underwent evaluation using a separate test dataset in 1 of 2 binary classification tasks: normal versus abnormal or plus versus nonplus. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) values were measured to assess network performance. Results: Given the same number of labels, neural networks learned more efficiently by comparison, generating significantly higher AUCs in both classification tasks across both datasets. Similarly, given the same number of images, comparison learning developed networks with significantly higher AUCs across both classification tasks in 1 of 2 datasets. The difference in efficiency and accuracy between models trained on either label type decreased as the size of the training set increased. Conclusions: Comparison labels individually are more informative and more abundant per sample than class labels. These findings indicate a potential means of overcoming the common obstacle of data variability and scarcity when training neural networks for medical image classification tasks.

Benzoxazines as new human topoisomerase I inhibitors and potential poisons.

BACKGROUND: The numbers of topoisomerase I targeted drugs on the market are very limited although they are used clinically for treatment of solid tumors. Hence, studies about finding new chemical structures which specifically target topoisomerase I are still remarkable. OBJECTIVES: In this present study, we tested previously synthesized 3,4-dihydro-2H-1,4-benzoxazin-3-one derivatives to reveal their human DNA topoisomerase I inhibitory potentials. METHODS: We investigated inhibitory activities of 3,4-dihydro-2H-1,4-benzoxazin-3-one derivatives on human topoisomerase I by relaxation assay to clarify inhibition mechanisms of effective derivatives with EMSA and T4 DNA ligase based intercalation assay. With SAR study, it was tried to find out effective groups in the ring system. RESULTS: While 10 compounds showed catalytic inhibitory activity, 8 compounds were found to be potential topoisomerase poisons. 4 of them also exhibited both activities. 2-hydroxy-3,4-dihydro-2H-1,4-benzoxazin-3-one (BONC-001) was the most effective catalytic inhibitor (IC50:8.34 mM) and ethyl 6-chloro-4-methyl-3-oxo-3,4-dihydro-2H-1,4-benzoxazin-2-acetate (BONC-013) was the strongest potential poison (IC50:0.0006 mM). BONC-013 was much more poisonous than camptothecin (IC50:0.034 mM). Intercalation assay showed that BONC-013 was not an intercalator and BONC-001 most probably prevented enzyme-substrate binding in an unknown way. Another important result of this study was that OH group instead of ethoxycarbonylmethyl group at R position of benzoxazine ring was important for hTopo I catalytic inhibition while the attachment of a methyl group of R1 position at R2 position were play a role for increasing of its poisonous effect. CONCLUSION: As a result, we presented new DNA topoisomerase I inhibitors which might serve novel constructs for future anticancer agent designs. Graphical abstract.

Plus Disease in Retinopathy of Prematurity: Convolutional Neural Network Performance Using a Combined Neural Network and Feature Extraction Approach.

Purpose: Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods: We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results: For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions: Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance: The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Robust Fusion of c-VEP and Gaze.

Brain computer interfaces (BCIs) are one of the developing technologies, serving as a communication interface for people with neuromuscular disorders. Electroencephalography (EEG) and gaze signals are among the commonly used inputs for the user intent classification problem arising in BCIs. Fusing different types of input modalities, i.e. EEG and gaze, is an obvious but effective solution for achieving high performance on this problem. Even though there are some simplistic approaches for fusing these two evidences, a more effective method is required for classification performances and speeds suitable for real-life scenarios. One of the main problems that is left unrecognized is highly noisy real-life data. In the context of the BCI framework utilized in this work, noisy data stem from user error in the form of tracking a nontarget stimuli, which in turn results in misleading EEG and gaze signals. We propose a method for fusing aforementioned evidences in a probabilistic manner that is highly robust against noisy data. We show the performance of the proposed method on real EEG and gaze data for different configurations of noise control variables. Compared to the regular fusion method, robust method achieves up to 15% higher classification accuracy.

Classification and comparison via neural networks.

We consider learning from comparison labels generated as follows: given two samples in a dataset, a labeler produces a label indicating their relative order. Such comparison labels scale quadratically with the dataset size; most importantly, in practice, they often exhibit lower variance compared to class labels. We propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels in the same training pipeline, using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that, by incorporating comparisons, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons.

Synthesis, antimicrobial activity, pharmacophore analysis of some new 2-(substitutedphenyl/benzyl)-5-[(2-benzofuryl)carboxamido]benzoxazoles.

The synthesis and antimicrobial activity of a new series of 2-(substitutedphenyl/benzyl)-5-[(2-benzofuryl)carboxamido]benzoxazole derivatives 3-12 were described. The in vitro antimicrobial activity of the compounds was determined against some Gram-positive, Gram-negative bacteria and fungi and their drug-resistant isolates in comparison with standard drugs. Antimicrobial results indicated that the synthesized compounds possessed a broad spectrum of activity with MIC values 500-15.625 microg/ml. In the series, the most active compound against Candida krusei and Candida albicans isolate is 8 with MIC value 31.25 microg/ml. However, it is one dilution less potent than the compared fluconazole. Some of the screened compounds exhibit significant activity, having MIC value as 31.25 microg/ml in Pseudomonas aeruginosa having same activity as Rifampicin. Furthermore, considering the worth of developing new antibacterial agents against drug-resistant P. aeruginosa the present study explores the structure-activity relationship analysis of 2-(substitutedphenyl/benzyl)-5-[(2-benzofuryl)carboxamido]benzoxazoles using 3D-common features pharmacophore hypotheses approach.

Synthesis and biological activity of some new benzoxazoles.

The synthesis and antimicrobial activity of a new series of 5-ethylsulphonyl-2-(substituted-phenyl/substituted-benzyl and/or phenylethyl)benzoxazole derivatives (3a-3t) except 3a, 3g, 3h, 3k [R.S. Pottorf, N.K. Chadha, M. Katkevies, V. Ozola, E. Suna, H. Ghane, T. Regberg, M.R. Player, Tetrahedron Lett. 44 (1) (2003) 175] were described. The in vitro antimicrobial activity of the compounds was determined against some Gram-positive, Gram-negative bacteria, a fungi Candida albicans and their drug-resistant isolates in comparison with standard drugs. Antimicrobial results indicated that the synthesized compounds possessed a broad spectrum of activity with MIC values 250-7.81 microg/ml. While all compounds are less potent than fluconazole against C. albicans, most of them are more potent than fluconazole against C. albicans isolate.

Vibrational spectroscopic studies and ab initio calculations of 5-nitro-2-(p-fluorophenyl)benzoxazole.

5-Nitro-2-(p-fluorophenyl)benzoxazole was prepared by heating 2-hydroxy-5-nitro aniline with p-fluorobenzoic acid in polyphosphoric acid. The FT-IR spectrum is recorded and analysed. The vibrational frequencies and corresponding vibrational assignments are examined theoretically using the Gaussian03 set of quantum chemistry codes. Predicted infrared and Raman intensities are reported.

Vibrational spectroscopic studies and ab initio calculations of 5-methyl-2-(p-methylaminophenyl)benzoxazole.

FT-IR spectra of 5-methyl-2-(p-methylaminophenyl)benzoxazole was recorded and analysed. The vibrational frequencies of the compound have been computed using the Hartree-Fock/6-31G* basis and compared with the experimental values.