Field Of Drug Discovery Research Articles

Thiadiazole - A promising structure in design and development of anti-Alzheimer agents.

The design and development of effective multitargeted agents in treating Alzheimer disease (AD) has always been a hot topic in the field of drug discovery. Since AD is a multifactorial disorder, various key hidden players such as deficit of acetylcholine (ACh), tau-protein aggregation, and oxidative stress have been associated with the incidence and progress of AD. In pursuit of improving efficacy and expanding the range of pharmacological activities of current AD drugs, the molecular hybridization method is also used intensively. Five-membered heterocyclic systems such as thiadiazole scaffolds have previously been shown to have therapeutic activity. Thiadiazole analogs as an anti-oxidant compound have been known to include a wide range of biological activity from anti-cancer to anti-Alzheimer properties. The suitable pharmacokinetic and physicochemical properties of the thiadiazole scaffold have introduced it as a therapeutic target in medicinal chemistry. The current review portrays the critical role of the thiadiazole scaffold in the design of various compounds with potential effects in the treatment of Alzheimer's disease. Furthermore, the rationale used behind hybrid-based design strategies and the outcomes achieved through the hybridization of Thiadiazole analogs with various core structures have been discussed. In addition, the data in the present review may help researchers in the design of new multidrug combinations that may provide new options for the treatment of AD.

NETWORK PHARMACOLOGY AND MOLECULAR DOCKING-BASED PREDICTIONS OF PHARMACOLOGICAL EFFECTS OF FERULIC ACID

Objectives: The main objective of this study is to reveal new possible pharmacological effects of ferulic acid. This is achieved by network pharmacology by discovering potential target genes for ferulic acid, along with constructing a PPI network for those targets and performing gene enrichment analysis to understand possible diseases or disorders being affected due to the target genes. The study involves the molecular docking of target genes with ferulic acid to understand the interactions between them. Methods: ADMETlab 2.0 was used for the pharmacokinetics study of ferulic acid. Using SwissTargetPrediction and STITCH database 79 target genes were retrieved which were used to construct a PPI network using the STRING database and for gene enrichment analysis using the ShinyGo tool. Analyzing the clusters generated by k-means clustering in the STRING database, three target gene proteins were further used to perform molecular docking with ferulic acid using PyRx software, and 2D and 3D visualization was done using Biovia Discovery Studio Visualizer. Results: The ADMET analysis ferulic acid showed drug-likeliness. SwissTargetPrediction and STITCH database revealed 79 potential target genes. Three proteins (RELA, ALOX15, and STAT3) were selected from the PPI network analysis using the STRING database for molecular docking and visualization. ALOX15 showed the least binding energy among all three target proteins. Gene enrichment analysis suggests the target proteins are involved in cancer, neurological disorders, psychiatric disorders, Alzheimer’s disease, etc. Conclusion: The findings of this research suggest that ferulic acid may have a wide range of pharmacological effects and gives a new perspective on its application in the field of drug discovery.

Enantioseparation of new axially chiral carboxylic acids on polysaccharide-based chiral stationary phases under normal phase elution conditions

In the last decade, the availability of new and versatile synthetic strategies for the preparation of substituted 4,4’-bipyridyl derivatives based on chemo- and regioselective functionalization of the 4,4’-bipyridine core has encouraged studies for exploring the bioactivity of these compounds in the fields of drug discovery and medicinal chemistry. In substituted 4,4’-bipyridines, chirality may emerge from restricted rotation induced by sterically hindered atoms or functional groups located around the 4,4’-biaryl bond (chiral axis). The first atropisomeric substituted 4,4’-bipyridine was prepared in 2008, and no asymmetric synthesis to produce pure atropisomers of chiral 4,4’-bipyridine derivatives has been available so far. Thus, in the last few years, our groups developed methods to separate atropisomers of a wide series of 4,4’-derivatives by high-performance liquid chromatography (HPLC) using polysaccharide-based chiral stationary phases (CSPs). In the frame of our interest in this field, we reported herein the synthesis of two new chiral carboxylic acids containing an axially chiral 4,4’-bipyridyl unit as source of chirality, and their HPLC enantioseparation on polysaccharide-based CSPs. In particular, the impact of analyte and CSP structures on the enantioseparation outcomes as well as mechanisms and noncovalent interactions underlying the enantioseparation were explored by using electrostatic potential analysis and molecular dynamics (MD) simulations.

Comparative study of degree-based molecular descriptors of cyclodextrins through M-polynomial and NM-polynomial

Cyclodextrins are natural oligosaccharides used to increase the solubility of drugs. It has numerous applications in drug discovery, food storage and other fields. Loftsson et al. explained about applications of Cyclodextrins(CDs) in administrating the drugs through various ways. Jansook et al. has given insights into the structure, physicochemical properties and pharmaceutical applications of CDs. In the present work, cyclodextrin and its derivatives such as α, β, γ CDs are studied for which various degree and neighborhood degree-based topological indices are computed through M-polynomial and NM-polynomial respectively and the comparison of the indices for all three types of cyclodextrins are presented which are of great importance in QSPR/QSAR studies.

Design, Synthesis and Cytotoxic Activity of Novel Salicylaldehyde Hydrazones against Leukemia and Breast Cancer.

Despite the significant advancements in complex anticancer therapy, the search for new and more efficient specific anticancer agents remains a top priority in the field of drug discovery and development. Here, based on the structure-activity relationships (SARs) of eleven salicylaldehyde hydrazones with anticancer activities, we designed three novel derivatives. The compounds were tested in silico for drug-likeness, synthesized, and evaluated in vitro for anticancer activity and selectivity on four leukemic cell lines (HL-60, KE-37, K-562, and BV-173), one osteosarcomic cell line (SaOS-2), two breast adenocarcinomic cell lines (MCF-7 and MDA-MB-231), and one healthy cell line (HEK-293). The designed compounds were found to have appropriate drug likeness and showed anticancer activities in all cell lines tested; particularly, two of them exhibited remarkable anticancer activity in nanomolar concentrations on the leukemic cell lines HL-60 and K-562 and the breast cancer MCF-7 cells and extraordinary selectivity for the same cancer lines ranging between 164- and 1254-fold. The study also examined the effects of different substituents on the hydrazone scaffold and found that the 4-methoxy salicylic moiety, phenyl, and pyridinyl rings are the most appropriate for anticancer activity and selectivity of this chemical class.

FRET Based Biosensor: Principle Applications Recent Advances and Challenges.

Förster resonance energy transfer (FRET)-based biosensors are being fabricated for specific detection of biomolecules or changes in the microenvironment. FRET is a non-radiative transfer of energy from an excited donor fluorophore molecule to a nearby acceptor fluorophore molecule. In a FRET-based biosensor, the donor and acceptor molecules are typically fluorescent proteins or fluorescent nanomaterials such as quantum dots (QDs) or small molecules that are engineered to be in close proximity to each other. When the biomolecule of interest is present, it can cause a change in the distance between the donor and acceptor, leading to a change in the efficiency of FRET and a corresponding change in the fluorescence intensity of the acceptor. This change in fluorescence can be used to detect and quantify the biomolecule of interest. FRET-based biosensors have a wide range of applications, including in the fields of biochemistry, cell biology, and drug discovery. This review article provides a substantial approach on the FRET-based biosensor, principle, applications such as point-of-need diagnosis, wearable, single molecular FRET (smFRET), hard water, ions, pH, tissue-based sensors, immunosensors, and aptasensor. Recent advances such as artificial intelligence (AI) and Internet of Things (IoT) are used for this type of sensor and challenges.

Tracking chromatin state changes using nanoscale photo-proximity labelling.

Interactions between biomolecules underlie all cellular processes and ultimately control cell fate. Perturbation of native interactions through mutation, changes in expression levels or external stimuli leads to altered cellular physiology and can result in either disease or therapeutic effects1,2. Mapping these interactions and determining how they respond to stimulus is the genesis of many drug development efforts, leading to new therapeutic targets and improvements in human health1. However, in the complex environment of the nucleus, it is challenging to determine protein-protein interactions owing to low abundance, transient or multivalent binding and a lack of technologies that are able to interrogate these interactions without disrupting the protein-binding surface under study3. Here, we describe a method for the traceless incorporation of iridium-photosensitizers into the nuclear micro-environment using engineered split inteins. These Ir-catalysts can activate diazirine warheads through Dexter energy transfer to form reactive carbenes within an approximately 10 nm radius, cross-linking with proteins in the immediate micro-environment (a process termed µMap) for analysis using quantitative chemoproteomics4. We show that this nanoscale proximity-labelling method can reveal the critical changes in interactomes in the presence of cancer-associated mutations, as well as treatment with small-molecule inhibitors. µMap improves our fundamental understanding of nuclear protein-protein interactions and, in doing so, is expected to have a significant effect on the field of epigenetic drug discovery in both academia and industry.

Recent Advances in Quantum Computing for Drug Discovery and Development

Drug discovery and development is a time-consuming and cost-intensive process. Computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

Multicomponent Reaction-Assisted Drug Discovery: A Time- and Cost-Effective Green Approach Speeding Up Identification and Optimization of Anticancer Drugs.

Multicomponent reactions (MCRs) have emerged as a powerful strategy in synthetic organic chemistry due to their widespread applications in drug discovery and development. MCRs are flexible transformations in which three or more substrates react to form structurally complex products with high atomic efficiency. They are being increasingly appreciated as a highly exploratory and evolutionary tool by the medicinal chemistry community, opening the door to more sustainable, cost-effective and rapid synthesis of biologically active molecules. In recent years, MCR-based synthetic strategies have found extensive application in the field of drug discovery, and several anticancer drugs have been synthesized through MCRs. In this review, we present an overview of representative and recent literature examples documenting different approaches and applications of MCRs in the development of new anticancer drugs.

Drug-delivery research and much more: interview with Dan Peer.

Drug-delivery research and much more: interview with Dan Peer.

Application of Computing as a High-Practicability and -Efficiency Auxiliary Tool in Nanodrugs Discovery.

Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.

Potentialities of Antimicrobial Peptide Mastoparan C Isolated the European Hornet Vespa Crabro in Drug Discovery

Abstract: Exploring the potential of natural bioactive peptides is becoming more and more important for new drug discovery. First isolated from the European wasp Vespa crabro, the antimicrobial peptide Mastoparan C was found in higher content than many other molecules in the Mastoparan family. In addition to the mast cell degranulation that is similar to some components in insect venom, this compound possesses remarkable biological activities such as broad-spectrum antibacterial and antifungal, inhibiting the proliferation of many human cancer cell lines. However, unfortunately, Mastoparan C has the notable side effect of causing mammalian hemolysis. By investigating its structure-activity relationships, this review pointed out some suggestions to overcome the disadvantages of this potential peptide and thereby support further in-depth study in the drug discovery field.
 Keywords: Mastoparan C, antimicrobial peptide, biological activity, structure-activity relationships.

Nickel‐Photoredox‐Catalyzed Synthesis of sp2−sp3 Fragments and the Impact of Bicycloalkane Isosteric Replacement on Their Physical‐Chemical Properties

AbstractBicycloalkanes are well‐known isosteres of alkynes and aromatic rings and their use in the field of medicinal chemistry and drug discovery has been on the rise in the past ten years. Herein, we report the modular and practical synthesis of bicycloalkane‐containing fragments using nickel‐photoredox catalysis. The comparison of their relevant physical‐chemical properties with their aromatic counterparts shows a substantial improvement in LogP and aqueous solubility for bicyclo[1.1.1]pentane derivatives, but not for bicyclo[2.2.2]octane derivatives.

Sugars in Multicomponent Reactions: A Toolbox for Diversity-Oriented Synthesis

AbstractMulticomponent reactions (MCRs) cover strategically employed chemical transformations that incorporate three or more reactants in one pot leading to a functionalized final product. Thus, it is an ideal tool to achieve high levels of complexity, diversity, yields of desired products, atom economy, and reduced reaction times. Sugars belong to the class of naturally occurring compounds with fascinating applications in the field of drug discovery due to the presence of various hydroxy groups and well-defined stereochemistry. However, their potential in MCRs has been realized only recently. This account describes recent advances in the synthesis of sugar-derived heterocycles synthesized by MCRs. We hope to encourage the synthetic and medicinal chemistry community to apply this powerful MCR chemistry to generate novel glycoconjugate challenges.1 Introduction2 Synthesis of Various Functionalized Sugar Compounds2.1 Passerini and Ugi Multicomponent Reactions2.2 Petasis Reaction2.3 Hantzsch Reaction2.4 Domino Ferrier–Povarov Reaction2.5 Marckwald Reaction2.6 Groebke–Blackburn–Bienaymé (GBB) Reaction2.7 Prins–Ritter Reaction2.8 Debus–Radziszewski Imidazole Synthesis Reaction2.9 Mannich Reaction2.10 A3-Coupling Reaction2.11 [3+2]-Cycloaddition Reactions2.12 Miscellaneous Reactions3 Conclusion

ESA-FedGNN: Efficient secure aggregation for federated graph neural networks.

Graph Neural Network (GNN) architecture is a state-of-the-art model, which can obtain complete node embedding features and rich data information by aggregating the information of nodes and neighbors. Therefore, GNNs are widely used in electronic shopping, drug discovery (especially for the treatment of COVID-19) and other fields, promoting the explosive development of machine learning. However, user interaction, data sharing and circulation are highly sensitive to privacy, and centralized storage can lead to data isolation. Therefore, Federated Learning with high efficiency and strong security and privacy enhancement technology based on secure aggregation can improve the security dilemma faced by GNN. In this paper, we propose an Efficient Secure Aggregation for Federated Graph Neural Network(ESA-FedGNN), which can efficiently reduce the cost of communication and avoid computational redundancy while ensuring data privacy. Firstly, a novel secret sharing scheme based on numerical analysis is proposed, which employs Fast Fourier Transform to improve the computational power of the neural network in sharing phase, and leverages Newton Interpolation method to deal with the disconnection and loss of the client in reconstruction phase. Secondly, a regular graph embedding based on geometric distribution is proposed, which optimizes the aggregation speed by using data parallelism. Finally, a double mask is adopted to ensure privacy and prevent malicious adversaries from stealing model parameters. We achieve improvements compared to in state-of-the-art works. This research helps to provide security solutions related to the practical development and application of privacy-preserving graph neural network technology.

Label-Free Multiplexed Microfluidic Analysis of Protein Interactions Based on Photonic Crystal Surface Mode Imaging.

High-throughput protein assays are crucial for modern diagnostics, drug discovery, proteomics, and other fields of biology and medicine. It allows simultaneous detection of hundreds of analytes and miniaturization of both fabrication and analytical procedures. Photonic crystal surface mode (PC SM) imaging is an effective alternative to surface plasmon resonance (SPR) imaging used in conventional gold-coated, label-free biosensors. PC SM imaging is advantageous as a quick, label-free, and reproducible technique for multiplexed analysis of biomolecular interactions. PC SM sensors are characterized by a longer signal propagation at the cost of a lower spatial resolution, which makes them more sensitive than classical SPR imaging sensors. We describe an approach for designing label-free protein biosensing assays employing PC SM imaging in the microfluidic mode. Label-free, real-time detection of PC SM imaging biosensors using two-dimensional imaging of binding events has been designed to study arrays of model proteins (antibodies, immunoglobulin G-binding proteins, serum proteins, and DNA repair proteins) at 96 points prepared by automated spotting. The data prove feasibility of simultaneous PC SM imaging of multiple protein interactions. The results pave the way to further develop PC SM imaging as an advanced label-free microfluidic assay for the multiplexed detection of protein interactions.

Exploring the potential of Rhizopus oryzae AUMC14899 as a novel endophytic fungus for the production of l-tyrosine and its biomedical applications

BackgroundA significant threat to the public's health is the rise in antimicrobial resistance among numerous nosocomial bacterial infections. This may be a detriment to present initiatives to enhance the health of immune-compromised patients. Consequently, attention has been devoted to exploring new bioactive compounds in the field of drug discovery from endophytes. Therefore, this study is the first on the production of l-tyrosine (LT) as a promising bio-therapeutic agent from endophytic fungi.ResultsA new endophytic fungal isolate has been identified for the first time as Rhizopus oryzae AUMC14899 from Opuntia ficus-indica (L.) and submitted to GenBank under the accession number MZ025968. Separation of amino acids in the crude extract of this fungal isolate was carried out, giving a higher content of LT, which is then characterized and purified. LT exhibited strong antibacterial and anti-biofilm activities against multidrug-resistant Gram-negative and Gram-positive bacteria. The recorded minimum inhibitory concentration (MIC) values ranged from 6 to 20 µg/ml. In addition, LT caused a strong reduction in biofilm formation and disrupted the preformed biofilm. Moreover, results indicated that LT supported cell viability, evidencing hemocompatibility and no cytotoxicity.ConclusionOur findings suggest that LT has potential as a therapeutic agent due to its potential antibacterial, anti-biofilm, hemocompatibility, and lack of cytotoxic activities, which may also increase the range of therapy options for skin burn infections, leading to the development of a novel fungal-based drug.

Spermidine-Eugenol Supplement Preserved Inflammation-Challenged Intestinal Cells by Stimulating Autophagy

Increases in non-communicable and auto-immune diseases, with a shared etiology of defective autophagy and chronic inflammation, have motivated research both on natural products in drug discovery fields and on the interrelationship between autophagy and inflammation. Within this framework, the tolerability and protective effects of a wheat-germ spermidine (SPD) and clove eugenol (EUG) combination supplement (SUPPL) were investigated on inflammation status (after the administration of lipopolysaccharide (LPS)) and on autophagy using human Caco-2 and NCM460 cell lines. In comparison to the LPS treatment alone, the SUPPL + LPS significantly attenuated ROS levels and midkine expression in monocultures, as well as occludin expression and mucus production in reconstituted intestinal equivalents. Over a timeline of 2-4 h, the SUPPL and SUPPL + LPS treatments stimulated autophagy LC3-11 steady state expression and turnover, as well as P62 turnover. After completely blocking autophagy with dorsomorphin, inflammatory midkine was significantly reduced in the SUPPL + LPS treatment in a non-autophagy-dependent manner. After a 24 h timeline, preliminary results showed that mitophagy receptor BNIP3L expression was significantly downregulated in the SUPPL + LPS treatment compared to the LPS alone, whereas conventional autophagy protein expression was significantly higher. The SUPPL shows promise in reducing inflammation and increasing autophagy to improve intestinal health.

Progress of Artificial Intelligence in Drug Synthesis and Prospect of Its Application in Nitrification of Energetic Materials.

Artificial intelligence technology shows the advantages of improving efficiency, reducing costs, shortening time, reducing the number of staff on site and achieving precise operations, making impressive research progress in the fields of drug discovery and development, but there are few reports on application in energetic materials. This paper addresses the high safety risks in the current nitrification process of energetic materials, comprehensively analyses and summarizes the main safety risks and their control elements in the nitrification process, proposes possibilities and suggestions for using artificial intelligence technology to enhance the "essential safety" of the nitrification process in energetic materials, reviews the research progress of artificial intelligence in the field of drug synthesis, looks forward to the application prospects of artificial intelligence technology in the nitrification of energetic materials and provides support and guidance for the safe processing of nitrification in the propellants and explosives industry.

The Role of "Physiologically Based Pharmacokinetic Model (PBPK)" New Approach Methodology (NAM) in Pharmaceuticals and Environmental Chemical Risk Assessment.

Physiologically Based Pharmacokinetic (PBPK) models are mechanistic tools generally employed in the pharmaceutical industry and environmental health risk assessment. These models are recognized by regulatory authorities for predicting organ concentration-time profiles, pharmacokinetics and daily intake dose of xenobiotics. The extension of PBPK models to capture sensitive populations such as pediatric, geriatric, pregnant females, fetus, etc., and diseased populations such as those with renal impairment, liver cirrhosis, etc., is a must. However, the current modelling practices and existing models are not mature enough to confidently predict the risk in these populations. A multidisciplinary collaboration between clinicians, experimental and modeler scientist is vital to improve the physiology and calculation of biochemical parameters for integrating knowledge and refining existing PBPK models. Specific PBPK covering compartments such as cerebrospinal fluid and the hippocampus are required to gain mechanistic understanding about xenobiotic disposition in these sub-parts. The PBPK model assists in building quantitative adverse outcome pathways (qAOPs) for several endpoints such as developmental neurotoxicity (DNT), hepatotoxicity and cardiotoxicity. Machine learning algorithms can predict physicochemical parameters required to develop in silico models where experimental data are unavailable. Integrating machine learning with PBPK carries the potential to revolutionize the field of drug discovery and development and environmental risk. Overall, this review tried to summarize the recent developments in the in-silico models, building of qAOPs and use of machine learning for improving existing models, along with a regulatory perspective. This review can act as a guide for toxicologists who wish to build their careers in kinetic modeling.