Combinatorial Chemistry & High Throughput Screening - Volume 25, Issue 4, 2022

Background: Rheumatoid arthritis (RA) is a chronic autoimmune disease, which affects the joints and causes significant pain, impairing patient's quality of life. Strychni semen showed promising results to treat RA. However, there are increasing safety concerns in using strychni semen due to its severe toxicity. Aim and Objective: The purpose of this review is to provide insight into using Strychni semen as an alternative medicine to treat RA, as well as to offer a method for the safe application of Strychni semen through processing and compatibility studies. Methods: Publications were retrieved and surveyed from CNKI and PubMed relevant to Strychni semen for a literature review. Results: This article summarized the mechanism of function of strychni semen in treating RA with its anti-inflammatory, analgesic, and immunomodulatory effect. Commonly used methods to attenuate the toxicity of Strychni semen were also discussed in this article. Conclusion: Strychni semen has a good therapeutic effect on RA, mainly by the modulation of immunity with anti-inflammatory and analgesic effects. Also, the reported toxicity of strychni semen can be effectively reduced by processing and compatibility methods. Hence, as an alternative medicine for RA treatment, strychni semen has a broad prospect.

Background: Curcumin, a hydrophobic polyphenolic compound present in Curcuma longa Linn. (Turmeric), has been used to improve various neurodegenerative conditions, including Amyotrophic lateral sclerosis, multiple sclerosis, Parkinson's disease, Prion disease, stroke, anxiety, depression, and ageing. However, the Blood-Brain Barrier (BBB) impedes the delivery of curcumin to the brain, limiting its therapeutic potential. Objective/Aim: This review summarises the recent advances towards the therapeutic efficacy of curcumin along with various novel strategies to overcome its poor bioavailability across the bloodbrain barrier. Methods: The data for the compilation of this review work were searched in PubMed Scopus, Google Scholar, and Science Direct. Results: Various approaches have been opted to expedite the delivery of curcumin across the blood-brain barrier, including liposomes, micelles, polymeric nanoparticles, exosomes, dualtargeting nanoparticles, etc. Conclusion: The review also summarises the numerous toxicological studies and the role of curcumin in CNS disorders.

Background: Berberine (BBR), an alkaloidal compound found in many plants, is widely used for hundreds of years in the traditional system of Chinese medicine. Objective/Aim: The present review is aimed to summarize the potential of Berberine in the amelioration of various neurological disorders. Methods: The collection of data for the compilation of this review work was searched in PubMed Scopus, Google Scholar, and Science Direct. Of late, researchers are more focused on its beneficial role in neurodegenerative diseases. Results: BBR has proven its protective role in numerous neurotoxicity models including, oxygen-glucose deprivation, mercury-induced, neurodegenerative model by ibotenic acid, and hypoxia caused by COCl2. BBR treatment averts the generation of reactive oxygen species in the oxygen-glucose deprivation model. Further, it subdues cytochrome c along with the divulge of apoptosis-inducing factors that indicate its beneficial action in the management of stroke. BBR diminished hydrogen peroxide-induced neuronal damage by enhancing the PI3k / Akt / Nrf-2 based pathway and showed a preventive impact on neurites of SH-SY5Y cells by averting the formation of ROS and inhibiting apoptosis. The impact of BBR on neurological disorder using a transgenic AD type mouse strain (TgCRND8) showed a reduction in the piling up of amyloid-β plaque. In mice, administration of BBR in the dose range of 5-10m/kg has been reported to raise the levels of serotonin (47%), dopamine (31%), and norepinephrine (29%) in CNS to allay depression. Conclusion: The present review is aimed to summarize the potential of Berberine in the amelioration of various neurological disorders.

Background: Drug development requires a lot of money and time, and the outcome of the challenge is unknown. So, there is an urgent need for researchers to find a new approach that can reduce costs. Therefore, the identification of drug-target interactions (DTIs) has been a critical step in the early stages of drug discovery. These computational methods aim to narrow the search space for novel DTIs and to elucidate the functional background of drugs. Most of the methods developed so far use binary classification to predict the presence or absence of interactions between the drug and the target. However, it is more informative but also more challenging to predict the strength of the binding between a drug and its target. If the strength is not strong enough, such a DTI may not be useful. Hence, the development of methods to predict drug-target affinity (DTA) is of significant importance Method: We have improved the GraphDTA model from a dual-channel model to a triple-channel model. We interpreted the target/protein sequences as time series and extracted their features using the LSTM network. For the drug, we considered both the molecular structure and the local chemical background, retaining the four variant networks used in GraphDTA to extract the topological features of the drug and capturing the local chemical background of the atoms in the drug by using BiGRU. Thus, we obtained the latent features of the target and two latent features of the drug. The connection of these three feature vectors is then inputted into a 2 layer FC network, and a valuable binding affinity is the output. Result: We used the Davis and Kiba datasets, using 80% of the data for training and 20% of the data for validation. Our model showed better performance when compared with the experimental results of GraphDTA Conclusion: In this paper, we altered the GraphDTA model to predict drug-target affinity. It represents the drug as a graph and extracts the two-dimensional drug information using a graph convolutional neural network. Simultaneously, the drug and protein targets are represented as a word vector, and the convolutional neural network is used to extract the time-series information of the drug and the target. We demonstrate that our improved method has better performance than the original method. In particular, our model has better performance in the evaluation of benchmark databases.

Introduction: Drug repositioning aims to screen drugs and therapeutic goals from approved drugs and abandoned compounds that have been identified as safe. This trend is changing the landscape of drug development and creating a model of drug repositioning for new drug development. In the recent decade, machine learning methods have been applied to predict the binding affinity of compound proteins, while deep learning is recently becoming prominent and achieving significant performances. Among the models, the way of representing the compounds is usually simple, which is the molecular fingerprints, i.e., a single SMILES string. Methods: In this study, we improve previous work by proposing a novel representing manner, named SMILES#, to recode the SMILES string. This approach takes into account the properties of compounds and achieves superior performance. After that, we propose a deep learning model that combines recurrent neural networks with a convolutional neural network with an attention mechanism, using unlabeled data and labeled data to jointly encode molecules and predict binding affinity. Results: Experimental results show that SMILES# with compound properties can effectively improve the accuracy of the model and reduce the RMS error on most data sets. Conclusion: We used the method to verify the related and unrelated compounds with the same target, and the experimental results show the effectiveness of the method.

Background: Molecular logic gate always makes use of fluorescent dyes to realize fluorescence signals. The labeling of the fluorophore is relatively expensive, resulting in low yield, and singly labeled impurities affect the affinity between the target and the aptamer. Label-free fluorescent aptamer biosensor strategy has attracted widespread interest due to lower cost and simplicity. Objective: Herein, we have designed an AND logic gate fluorescent aptasensor for detecting carbohydrate antigen 15-3(CA15-3) based on label-free fluorescence signal output. Materials and Methods: A hairpin DNA probe consists of CA15-3 aptamer and partly anti-CA15- 3 aptamer sequences as a long stem and G-rich sequences of the middle ring as a quadruplexforming oligomer. G-rich sequences can fold into a quadruplex by K+, and then G-quadruplex interacts specifically with N-methylmesoporphyrin IX(NMM), leading to a dramatic increase in fluorescence of NMM. With CA15-3 and NMM as the two inputs, the fluorescence intensity of the NMM is the output signal. Lacking CA15-3 or NMM, there is no significant fluorescence enhancement, and the output of the signal is “0”. The fluorescence signal dramatically increases and the output of the signal is “1” only when CA15-3 protein and NMM are added at the same time. Results: This biosensor strategy was observed to possess selectivity and high sensitivity for detecting CA15-3 protein from 10 to 500 U mL-1 and the detection limit was found to be 10 U mL-1, which also showed good reproducibility in spiked human serum. Conclusion: In summary, the proposed AND logic gate fluorescent aptasensor could specifically detect CA15-3.

Aim: This study aims to develop and establish a computational model that can identify potent molecules for p21-activating kinase 1 (PAK1) Background: PAK1 is a well-established drug target that has been explored for various therapeutic interventions. Control of this protein requires an indispensable inhibitor to curb the structural changes and subsequent activation of signalling effectors responsible for the progression of diseases, such as cancer, inflammatory, viral, and neurological disorders. Objective: The study aims to establish a computational model that could identify active molecules which will further provide a platform for developing potential PAK1 inhibitors. Methods: A congeneric series of 27 compounds were considered for this study, with Ki (nm) covering a minimum of 3 log range. The compounds were developed based on a previously reported Group-I PAK inhibitor, namely G-5555. The 27 compounds were subjected to the SP and XP mode of docking to understand the binding mode, its conformation and interaction patterns. To understand the relevance of biological activity from computational approaches, the compounds were scored against generated water maps to obtain WM/MM ΔG binding energy. Moreover, molecular dynamics analysis was performed for the highly active compound to understand the conformational variability and stability of the complex. We then evaluated the predictable binding pose obtained from the docking studies. Results: From the SP and XP modes of docking, the common interaction pattern with the amino acid residues Arg299 (cation-π), Glu345 (Aromatic hydrogen bond), hinge region Leu347, salt bridges Asp393 and Asp407 was observed, among the congeneric compounds. The interaction pattern was compared with the co-crystal inhibitor FRAX597 of the PAK1 crystal structure (PDB id: 4EQC). The correlation with different docking parameters in the SP and XP modes was insignificant and thereby revealed that the SP and XP’s scoring functions could not predict the active compounds. This was due to the limitations in the docking methodology that neglected the receptor flexibility and desolvation parameters. Hence, to recognise the desolvation and explicit solvent effects, as well as to study the Structure-Activity Relationships (SARs) extensively, WaterMap (WM) calculations were performed on the congeneric compounds. Based on displaceable unfavourable hydration sites (HS) and their associated thermodynamic properties, the WM calculations facilitated in understanding the significance of correlation in the folds of activity of highly active (19 and 17), moderately active (16 and 21) and less active (26 and 25) compounds. Furthermore, the scoring function from WaterMap, namely WM/MM, led to a significant R2 value of 0.72 due to a coupled conjunction with MM treatment and displaced unfavourable waters at the binding site. To check the “optimal binding conformation”, molecular dynamics simulation was carried out with the highly active compound 19 to explain the binding mode, stability, interactions, solvent-accessible area, etc., which could support the predicted conformation with bioactive conformation. Conclusion: This study determined the best scoring function, established SARs and predicted active molecules through a computational model. This will contribute to the development of the most potent PAK1 inhibitors.

Background: Interleukin-12 receptor (IL12R) is a type I cytokine receptor that can promote hematopoiesis and regulate innate and adaptive immunity. It binds with the IL12 ligand, which activates the IL-12 signaling pathway that triggers hematopoietic progenitor cell proliferation and differentiation process. The structure of IL12:IL12R complex is not known. Objective: The present work describes a de novo computational method for rational protein designing to elucidate the structure of IL12:IL12R complex. Methods: Homology modeling, docking, and MD simulation methods were used to design mimics of the interaction of IL12 and IL12R. RResults: 3D structure prediction and validation confirm the accurate structure of IL12R protein that contains immunoglobin domain, fibronectin type three domain, cytokine-binding domain, and WSXWS motif. Molecular docking and MD simulation revealed that IL12R bound tightly with IL12 ligand at their interface. The estimated binding energy of the docked complex was -26.7 kcal/mol, and the interface area was 281.4 Å2. Hotspot prediction suggested that ARG34, SER58, GLU61, CYS62, LEU63, SER73, ASP142, GLN146, LYS168, THR169 ARG181, ARG183, ARG189, and TYR193 residues in IL12 ligand interacted with SER175, ALA176, CYS177, PRO178, ALA179, ALA180, GLU181, GLU182, ALA192, VAL193, HIS194, ARG208, TYR246, GLN289, ASP290, ARG291, TYR292, TYR293 and SER294 residues in IL12 receptor. Conclusion: The results of the study provides a simulated molecular structure of IL12:IL12R complex that could offer a promising target complex to substantiate IL12 based drug-designing approaches.

Background: Hepatitis C virus (HCV) is an enveloped and positive-stranded RNA virus that is a major causative agent of chronic liver diseases worldwide. HCV has become the main cause of liver transplantations and there is no effective drug for all hepatitis genotypes. Elucidation of the life cycle and non-structural proteins of HCV, involved in viral replication, are attractive targets for the development of antiviral drugs.. Methods: In this work, pharmacoinformatics approaches coupled with docking analyses were applied on HCV non-structural proteins to identify the novel potential hits and HCV drugs. Molecular docking analyses were carried out on HCV-approved drugs, followed by the ligandbased pharmacophore generation to screen the antiviral libraries for novel potential hits. Results: Virtual screening technique has top-ranked five novel compounds (ZINC00607900, ZINC03635748, ZINC03875543, ZINC04097464, and ZINC12503102) along with their least binding energies (-8.0 kcal/mol, -6.1 kcal/mol, -7.5 kcal/mol, -7.4 kcal/mol, and -7.3 kcal/mol, respectively) and stability with the non-structural proteins target. Conclusion: These promising hits exhibited better absorption and ADMET properties as compared to the selected drug molecules. These potential compounds extracted from in silico approach may be significant in drug design and development against Hepatitis and other liver diseases.

Objective: Hepatitis C Virus (HCV) is very dreadful as it can attack an estimated 71 million people around the world. The World Health Organization (WHO) reported that every year about 399000 people die due to HCV caused by chronic cirrhosis and liver cancer globally. There are many drugs available for the treatment of HCV. But drug resistance and toxicity are major issues. The quest for potential drugs utilizing repositioning would be a very useful and economical method to combat HCV. Methods: One of the most common HCV targets is RNA-dependent RNA polymerase (RdRp). The RdRp is common in HCV, Dengue virus (DENV), Zika virus (ZIKV), and Yellow fever virus (YFV) belonging to the same family of Flaviviridae. An attempt has been made in the present study to reposition different DENV, ZIKV, and YFV RdRp inhibitors against HCV NS5B polymerase utilizing structure-based molecular docking which explores the affinity and mode of binding of these RdRp inhibitors. Results: Several 87 compounds having dengue, yellow fever and zika RdRp inhibitory activities have been taken into consideration for the screening of potential RdRp leads utilizing docking simulation, which focuses on the affinity and mode of binding of sofosbuvir diphosphate, a standard HCV, RdRp inhibitor. Conclusion: The compounds 6 (N-sulfonylanthranilic acid derivative), 17 (R1479), 20 (DMB220), 23 (FD-83-KI26), 40 (CCG-7648), 50 (T-1106), 65 (mycophenolic acid), and 69 (DMB213) exhibited docking score within the range of -7.602 to -8.971 Kcal/Mol having almost same mode of interaction as compared to the reference drug molecule. The drugs mentioned above possess satisfactory affinity to bind the hepatitis C viral RdRp and thus may be used to treat the disease. Therefore, these predicted compounds may be potential leads for further testing of anti HCV activity and can be repurposed to combat HCV. The high throughput shotgun of drug repurposing utilizing structure-based docking simulation freeware would be a cost-effective way to screen the potential anti-HCV leads.

Background: In this study, Near-field electrospinning (NFES) technique is used with a cylindrical collector to fabricate a large area permanent piezoelectric micro and nanofibers by a prepared solution. NFES requires a small electric field to fabricate fibers Objective: The objective of this paper to investigate silver nanoparticle (Ag-NP)/ Polyvinylidene fluoride (PVDF) composite as the best piezoelectric material with improved properties to produced tremendously flexible and sensitive piezoelectric material with pertinent conductance Methods: In this paper, we used controllable electrospinning technique based on Near-field electrospinning (NFES). The process parameter for Ag-NP/PVDF composite electrospun fiber based on pure PVDF fiber. A PVDF solution concentration of 18 wt.% and 6 wt.% silver nitrate, which is relative to the weight of PVDF wt.% with 1058 μS conductivity fibers, have been directly written on a rotating cylindrical collector for aligned fiber PVDF/Ag-NP fibers are patterned on fabricated copper (Cu) interdigitated electrodes were implemented on a thin flexible polyethylene terephthalate (PET) substrate and Polydimethylsiloxane (PDMS) used as a package to enhance the durability of the PVDF/ Ag-NP device. Results: A notable effect on the piezoelectric response has been observed after Ag-NP addition, confirmed by XRD characterization and tapping test of Ag-NP/PVDF composite fiber. The morphology of the PVDF/Ag-NP fibers and measure diameter by scanning electron microscopy (SEM) and Optical micrograph (OM), of fiber. Finally, a diameter of PVDF/Ag-NP fibers up to ∼7 μm. The high diffraction peak at 2θ = 20.5˚ was investigated by X-ray diffraction (XRD) in the piezoelectric crystal β-phase structure. Further addition of silver nanoparticles (Ag- NPs) in the PVDF solution resulted in enhancing the electromechanical conversion of the fibers from ∼0.1 V to ∼1 V. Conclusion: In conclusion, we can say that confirmed and validated the addition of Ag-NP in PVDF could enhance the piezoelectric property by using NFES technique with improved crystalline phase content can be useful for a wide range of power and sensing applications like biomedical devices and energy harvesting, among others.

Background: Alzheimer’s disease is a destructive nervous system disease which causes structural, biochemical and electrical abnormalities inside the human brain and results due to genetic and various environmental factors. Traditional therapeutic agents of Alzheimer’s disease such as tacrine and physostigmine have been found to cause adverse effects to the nervous system and gastrointestinal tract. Nanomaterials like graphene, metals, carbon-nanotubes and metal-oxides are gaining attention as potential drugs against Alzheimer’s disease due to their properties such as large surface area, which provide clinical efficiency, targeted drug designing and delivery. Objectives: Designing new drugs by using experimental approaches is a time-consuming, tedious and laborious process which also requires advanced technologies. This study aims to identify some novel drug candidates against Alzheimer’s disease with no or less associated side effects using molecular docking approaches Methods: In this study, we utilized nanoinformatics based approaches for evaluating the interaction properties of various nanomaterials and metal nanoparticles with the drug targets, including TRKB kinase domain, EphA4 and histone deacetylase. Furthermore, the drug-likeness of carbon nanotubes was confirmed through ADME analysis. Results: Carbon nanotubes, either single or double-walled in all the three-configurations, including zigzag, chiral, and armchair forms, are found to interact with the target receptors with varying affinities Conclusion: This study provides novel and clearer insights into the interaction properties and drug suitability of known putative nanoparticles as potential agents for the treatment of Alzheimer’s disease.

Background: The recent Zika Virus (ZIKV) outbreak provides a spur for new, efficient, and safe anti-Zika Virus agents. RNA-dependent RNA polymerase (RdRp) is critical amongst the seven non-structural proteins for viral replication and considered an attractive drug target. Methods: In this study, molecular docking approach was used to rationally screen the library of 5000 phytochemicals to find inhibitors against NS5 RdRp. LigX tool was used to analyze the 2D plots of receptor-ligand interactions. The top-ranked compounds were then subjected to in-silico pharmacokinetic study. Results: The compounds namely Polydatin, Dihydrogenistin, Liquiritin, Rhapontin and Cichoriin were successfully bound inside the pocket of NS5 RdRp. Polydatin was the leading phytochemical that showed high docking score -18.71 (kcal/mol) and bonding interaction at the active-site of NS5 RdRp. They were subjected to analyze drug-like properties that further reinforced their validation and showed that they have more capability to attach with the receptor as compared to SOFOSBUVIR control drug. MD simulation of the top two complexes was performed and the simulated complexes showed stability and ligands were kept within the bonding pocket. Conclusion: The study might facilitate the development of a natural and cost-effective drug against ZIKV. Further validation, however, is necessary to confirm its effectiveness and its biocompatibility.

Background: Metabolic syndrome is closely related to cardiovascular disease, and the prevalence of metabolic syndrome in postmenopausal women is increasing rapidly. Objective: The purpose of this study is to investigate the association between the number of breastfed children and the risk factors for metabolic syndrome in postmenopausal women and to evaluate the association between metabolic syndrome and bone mineral density and body composition variables in postmenopausal women depending on the number of breastfed children Methods: Data from KNHANES V-1 and 2 (2010-2011) were used, and a total of 939 postmenopausal women with 1 to 6 breastfed children aged 65-80 years participated in this study. We divided these women into three groups (group1 with 1-2, group2 with 3-4, group3 with 5-6) depending on the number of breastfed children Results: In the analysis of the associations between metabolic syndrome and its risk factors, highdensity lipoprotein cholesterol was the most negatively strongly associated with group1 (OR=0.103 [0.047-0.225]), triglyceride showed the highest association with group2 (OR=7.760 [3.770-15.97]) and group3 (OR=7.668 [4.102-14.33]). The risk factors of metabolic syndrome except for highdensity lipoprotein cholesterol and triglyceride was not associated with group1. In contrast, all risk factors of metabolic syndrome displayed a high association with group2 and group3. Conclusion: The findings of the present study suggest that the number of breastfed children is significantly associated with a more significant number of risk factors of metabolic syndrome in postmenopausal women, and the association between metabolic syndrome and body composition variables may differ depending on the number of breastfed children.