Discovery, optimization, and target identification of novel coumarin derivatives as HIV-1 reverse transcriptase-associated ribonuclease H inhibitors

Despite significant advances in antiretroviral therapy, acquired immunodeficiency syndrome remains as one of the leading causes of death worldwide. New antiretroviral drugs combined with updated treatment strategies are needed to improve convenience, tolerability, safety, and antiviral efficacy of available therapies. In this work, a focused library of coumarin derivatives was exploited by cell phenotypic screening to discover novel inhibitors of HIV-1 replication. Five compounds (DW-3, DW-4, DW-11, DW-25 and DW-31) showed moderate activity against wild-type and drug-resistant strains of HIV-1 (IIIB and RES056). Four of those molecules were identified as inhibitors of the viral RT-associated RNase H. Structural modification of the most potent DW-3 and DW-4 led to the discovery of compound 8a. This molecule showed increased potency against wild-type HIV-1 strain (EC = 3.94 ± 0.22 μM) and retained activity against a panel of mutant strains, showing EC values ranging from 5.62 μM to 202 μM. In enzymatic assays, 8a was found to inhibit the viral RNase H with an IC of 12.3 μM. Molecular docking studies revealed that 8a could adopt a binding mode similar to that previously reported for other active site HIV-1 RNase H inhibitors.Natural Science Foundation of China (NSFC Nos. 81973181, 81903453), Shandong Provincial Key research and development project (Nos. 2019JZZY021011), Shandong Provincial Natural Science Foundation (ZR2019BH011, ZR2020YQ61, ZR2020JQ31), Foreign cultural and educational experts Project (GXL20200015001), Qilu Young Scholars Program of Shandong University, the Taishan Scholar Program at Shandong Province, and KU Leuven (GOA 10/014). Work in Madrid was supported by the Spanish Ministry of Science and Innovation (grant PID2019-104176RB-I00/AEI/10.13039/501100011033), and an institutional grant of Fundación Ramón Areces (Madrid, Spain)

Novel indolylarylsulfone derivatives as covalent HIV-1 reverse transcriptase inhibitors specifically targeting the drug-resistant mutant Y181C

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) are widely used in combination therapies against HIV-1. However, emergent and transmitted drug resistance compromise their efficacy in the clinical setting. Y181C is selected in patients receiving nevirapine, etravirine and rilpivirine, and together with K103N is the most prevalent NNRTI-associated mutation in HIV-infected patients. Herein, we report on the design, synthesis and biological evaluation of a novel series of indolylarylsulfones bearing acrylamide or ethylene sulfonamide reactive groups as warheads to inactivate Cys181-containing HIV-1 RT via a Michael addition reaction. Compounds I-7 and I-9 demonstrated higher selectivity towards the Y181C mutant than against the wild-type RT, in nucleotide incorporation inhibition assays. The larger size of the NNRTI binding pocket in the mutant enzyme facilitates a better fit for the active compounds, while stacking interactions with Phe227 and Pro236 contribute to inhibitor binding. Mass spectrometry data were consistent with the covalent modification of the RT, although off-target reactivity constitutes a major limitation for further development of the described inhibitors.by grants PID2019-104176RB-I00/AEI/10.13039/501100011033) (Spanish Ministry of Science and Innovation) and 2019AEP001 (CSIC), as well as an institutional grant of Fundación Ramón Areces (awarded to the CBMSO)

Design and development of 3D hierarchical ultra-microporous CO2-sieving carbon architectures for potential flow-through CO2 capture at typical practical flue gas temperatures

Developing effective carbon materials for post-combustion CO2 capture (PCC) has received great attentions over many recent years, owing to their desirable adsorption?desorption performance and exceptional thermo-oxidative stability compared to virtually any other capture materials typically the wide array of amine-based sorbent materials. However, due to the nature of physical adsorption, virtually none of the carbon materials reported so far can be practically used for PCC applications without deep flue gas cooling to ambient or even lower temperatures in order to achieve appreciable levels of CO2 uptake capacities at low CO2 partial pressures. Here, we present a category of 3D hierarchical molecular sieving carbon architectures that are able to operate at realistic flue gas temperatures with exceedingly high reversible CO2 capacities. The breakthrough CO2-sieving carbon materials are prepared from using a cost-effective and commercially widely available precursor of polymeric polyisocyanurates with a facile one-step compaction-activation methodology. Tested at sensible flue gas temperatures of 40?70 o C and a low CO2 partial pressure of 0.15 bar, the best performing materials are found to have exceedingly high reversible CO2 capacities of up to 2.30mmol/g at 40 o C and 1.90mmol/g at 70 o C. Advanced characterisations suggest that the unique geometry and chemistry of the easily available precursor material coupled with the characteristics of the compaction-activation protocol used are responsible for the CO2-sieving structures and capacities of the 3D carbon architectures. The findings essentially change the general perception that carbon-based materials can hardly find applications in post-combustion capture due to their low CO2 uptake capacity at low CO2 partial pressures and realistic flue gas temperatures

Design, synthesis and biological evaluation of 3-hydroxyquinazoline-2,4(1H,3H)-diones as dual inhibitors of HIV-1 reverse transcriptase-associated RNase H and integrase

A novel series of 3-hydroxyquinazoline-2,4(1H,3H)-diones derivatives has been designed and synthesized. Their biochemical characterization revealed that most of the compounds were effective inhibitors of HIV-1 RNase H activity at sub to low micromolar concentrations. Among them, II-4 was the most potent in enzymatic assays, showing an IC50 value of 0.41 ± 0.13 μM, almost five times lower than the IC50 obtained with β-thujaplicinol. In addition, II-4 was also effective in inhibiting HIV-1 IN strand transfer activity (IC50 = 0.85 ± 0.18 μM) but less potent than raltegravir (IC50 = 71 ± 14 nM). Despite its relatively low cytotoxicity, the efficiency of II-4 in cell culture was limited by its poor membrane permeability. Nevertheless, structure-activity relationships and molecular modeling studies confirmed the importance of tested 3-hydroxyquinazoline-2,4(1H,3H)-diones as useful leads for further optimization.Financial support from the National Natural Science Foundation of China (NSFC No. 81273354), the Key Project of NSFC for International Cooperation (No. 81420108027), the Key Research and Development Project of Shandong Province (No. 2017CXGC1401), the Young Scholars Program of Shandong University (YSPSDU No. 2016WLJH32, to P. Z.), the Major Project of Science and Technology of Shandong Province (No. 2015ZDJS04001) is gratefully acknowledged. Work in Madrid was supported by grant BIO2016-76716-R (AEI/FEDER, UE) (Spanish Ministry of Economy, Industry and Competitiveness) and an institutional grant of Fundación Ramón Areces. The technical assistance of Mr. Kris Uyttersprot, and Mrs. Kristien Erven, for the HIV experiments is gratefully acknowledged.Peer reviewe

Medicinal chemistry strategies towards the development of non-covalent SARS-CoV-2 Mpro inhibitors

The main protease (Mpro) of SARS-CoV-2 is an attractive target in anti-COVID-19 therapy for its high conservation and major role in the virus life cycle. The covalent Mpro inhibitor nirmatrelvir (in combination with ritonavir, a pharmacokinetic enhancer) and the non-covalent inhibitor ensitrelvir have shown efficacy in clinical trials and have been approved for therapeutic use. Effective antiviral drugs are needed to fight the pandemic, while non-covalent Mpro inhibitors could be promising alternatives due to their high selectivity and favorable druggability. Numerous non-covalent Mpro inhibitors with desirable properties have been developed based on available crystal structures of Mpro. In this article, we describe medicinal chemistry strategies applied for the discovery and optimization of non-covalent Mpro inhibitors, followed by a general overview and critical analysis of the available information. Prospective viewpoints and insights into current strategies for the development of non-covalent Mpro inhibitors are also discussed.We gratefully acknowledge financial support from Major Basic Research Project of Shandong Provincial Natural Science Foundation (ZR2021ZD17, China), Science Foundation for Outstanding Young Scholars of Shandong Province (ZR2020JQ31, China), Foreign Cultural and Educational Experts Project (GXL20200015001, China), Guangdong Basic and Applied Basic Research Foundation (2021A1515110740, China), China Postdoctoral Science Foundation (2021M702003). This work was supported in part by the Ministry of Science and Innovation of Spain through grant PID2019-104176RB-I00/AEI/10.13039/501100011033 awarded to Luis Menéndez-Arias; An institutional grant of the Fundación Ramón Areces (Madrid, Spain) to the CBMSO is also acknowledged.Peer reviewe

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design

Bayesian Regularization Algorithm Based Recurrent Neural Network Method and NSGA-II for the Optimal Design of the Reflector

The optical-mechanical system of a space camera is composed of several complex components, and the effects of several factors (weight, gravity, modal frequency, temperature, etc.) on its system performance need to be considered during ground tests, launch, and in-orbit operation. In order to meet the system specifications of the optical camera system, the dimensional parameters of the optical camera structure need to be optimized. There is a highly nonlinear functional relationship between the dimensional parameters of the optical machine structure and the design indexes. The traditional method takes a significant amount of time for finite element calculation and is less efficient. In order to improve the optimization efficiency, a recurrent neural network prediction model based on the Bayesian regularization algorithm is proposed in this paper, and the NSGA-II is used to globally optimize multiple prediction objectives of the prediction model. The reflector of the space camera is used as an example to predict the weight, first-order modal frequency, and gravitational mirror deformation root mean square of the reflector, and to complete the lightweight design. The results show that the prediction model established by BR-RNN-NSGA-II offers high prediction accuracy for the design indexes of the reflector, which all reach over 99.6%, and BR-RNN-NSGA-II can complete the multi-objective optimization search efficiently and accurately. This paper provides a new idea of optimization of optical machine structure, which enriches the theory of complex structure design

Characterization of nanocrystalline ZnFe₂O₄ prepared by using polyvinyl alcohol gel method

1207-1209Nanometer-sized ZnFe2O4 particles have been prepared by using polyvinyl alcohol gel method without any intermediate phase formation. Particle sizes could be controlled in the range of 6.3-13.9 nm by a suitable heat treatment from 723 to 923 K. The crystal structure and particle morphology have been examined with XRD and TEM. EPR technique has also been used to investigate the obtained ZnFe2O4 samples. All samples have a broad EPR signal with a g value of about 2.006. The quantitative EPR measurement shows that the line width and the intensity of the Fe3+ signals depend on the calcination temperature and the particle size

Applying a Locally Linear Embedding Algorithm for Feature Extraction and Visualization of MI-EEG

Robotic-assisted rehabilitation system based on Brain-Computer Interface (BCI) is an applicable solution for stroke survivors with a poorly functioning hemiparetic arm. The key technique for rehabilitation system is the feature extraction of Motor Imagery Electroencephalography (MI-EEG), which is a nonlinear time-varying and nonstationary signal with remarkable time-frequency characteristic. Though a few people have made efforts to explore the nonlinear nature from the perspective of manifold learning, they hardly take into full account both time-frequency feature and nonlinear nature. In this paper, a novel feature extraction method is proposed based on the Locally Linear Embedding (LLE) algorithm and DWT. The multiscale multiresolution analysis is implemented for MI-EEG by DWT. LLE is applied to the approximation components to extract the nonlinear features, and the statistics of the detail components are calculated to obtain the time-frequency features. Then, the two features are combined serially. A backpropagation neural network is optimized by genetic algorithm and employed as a classifier to evaluate the effectiveness of the proposed method. The experiment results of 10-fold cross validation on a public BCI Competition dataset show that the nonlinear features visually display obvious clustering distribution and the fused features improve the classification accuracy and stability. This paper successfully achieves application of manifold learning in BCI