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Introduction: LIG is a biopolymer found in vascular plant cell walls that is created by networks of hydroxylated and methoxylated phenylpropane that are randomly crosslinked. Plant cell walls contain LIG, a biopolymer with significant potential for usage in modern industrial and pharmaceutical applications. It is a renewable raw resource. The plant is mechanically protected by this substance, which may increase its durability. Because it has antibacterial and antioxidant qualities, LIG also shields plants from biological and chemical challenges from the outside world. Researchers have done a great deal of work to create new materials and substances based on LIG. Numerous applications, including those involving antibacterial agents, antioxidant additives, UV protection agents, hydrogel-forming molecules, nanoparticles, and solid dosage forms, have been made with this biopolymer. Methods: For this review, a consistent literature screening using the Pubmed database from 2019–2024 has been performed. Results: The results showed that there is an increase in interest in lignin as an adaptable biomolecule. The most recent studies are focused on the biosynthesis and antimicrobial properties of lignin-derived molecules. Also, the use of lignin in conjunction with nanostructures is actively explored. Conclusions: Overall, lignin is a versatile molecule with multiple uses in industry and medical science [ABSTRACT FROM AUTHOR]

Based on diffusion kinetics and cell signal transduction kinetics,we studied the cell cycle arrest characteristics of small drug molecules. The theoretical model takes into account the dynamic characteristics of the transport of small drug molecules across the cell membrane and the blocking effect of drug molecules entering the cell on the cell cycle. We found that the drug molecules crossing the inner layer of the cell membrane and entering the cells will largely determine the blocking effect of drug molecules on related targeted genes and proteins.The transport characteristics of cell membrane to drug molecules are the key factors that affect drug molecules to block cell cycle. In addition,the degree of degradation of drug molecules will change the interaction time between drug molecules and targeted genes and proteins,thereby changing the inhibitory effect on the growth and proliferation of related cells. By the sensitivity analysis of the parameters in the model,we confirmed the inhibitory effect of many factors in the process of drug molecules passing through the cell membrane and entering the cell on the cell cycle. The theoretical results in this paper are consistent with the simulation and experimental observation,which further reveals the physical mechanism of drug small molecules blocking cell cycle,and can provide necessary references and new solutions for designing precise therapeutic drugs. [ABSTRACT FROM AUTHOR]

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

Introduction: LIG is a biopolymer found in vascular plant cell walls that is created by networks of hydroxylated and methoxylated phenylpropane that are randomly crosslinked. Plant cell walls contain LIG, a biopolymer with significant potential for usage in modern industrial and pharmaceutical applications. It is a renewable raw resource. The plant is mechanically protected by this substance, which may increase its durability. Because it has antibacterial and antioxidant qualities, LIG also shields plants from biological and chemical challenges from the outside world. Researchers have done a great deal of work to create new materials and substances based on LIG. Numerous applications, including those involving antibacterial agents, antioxidant additives, UV protection agents, hydrogel-forming molecules, nanoparticles, and solid dosage forms, have been made with this biopolymer. Methods: For this review, a consistent literature screening using the Pubmed database from 2019–2024 has been performed. Results: The results showed that there is an increase in interest in lignin as an adaptable biomolecule. The most recent studies are focused on the biosynthesis and antimicrobial properties of lignin-derived molecules. Also, the use of lignin in conjunction with nanostructures is actively explored. Conclusions: Overall, lignin is a versatile molecule with multiple uses in industry and medical science

Lipid bilayers -- the main matrix of cell membranes -- are a paradigm of soft molecular assemblies whose properties have been evolutionarily optimized to satisfy the functional requirements of cells. For instance, lipid bilayers must be rigid enough to serve as the protective barrier between cells and their environment, yet fluid enough to enable the diffusion of proteins and molecular clusters necessary for biological functions. Inspired by their biological multifunctionality, lipid membranes have also been used as a central design element in many practical applications including artificial cells, drug nanocarriers, and biosensors. Whether biological or synthetic, lipid membranes often involve molecular or nanoscopic additives that modulate the membrane properties through various mechanisms. Hence, how lipid membranes respond to additives has justifiably drawn much attention in recent years. This review summarizes findings and observations on different classes of additives and their effects on structural, thermodynamic, elastic, and dynamical membrane properties that are central to biological function or synthetic membrane performance. The review primarily focuses on phospholipids as a major component of cell membranes and a widely used lipid type in synthetic membrane designs. [ABSTRACT FROM AUTHOR]

The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness. [ABSTRACT FROM AUTHOR]

Background: Monkeypox virus (mpox) disease is caused by a double-stranded DNA virus from the Poxviridae family. The mpox virus showed structural similarity with smallpox virus disease. The recent outbreak of mpox infection in the rest of African countries causes public health issues of increased pandemic potential. Mpox virus is involved in the viral replication cycle through the biocatalytic reaction of precursor polyproteins cleavage. Objectives: The main objective of the study was to analyze the molecular interactions between mpox and FDA-approved drugs. Methods: The primary and secondary structure of the protein was retrieved and FDA approved drug was screened using AutoDock. The best hit was analyzed and the molecular interactions were studied. Model validation analyzes the peptide, energy of hydrogen bonds, steric conflicts and bond planarity. Z-score was calculated using ProSA-web tool and the score tested the native fold from other alternative folds. Results: The confidence level of the submitted amino acids was> 80 % and the maximum confidence score for a single template was 98.2 %. The generated proteinase model was subjected to analyze the distribution of atoms and the using ERRAT server. The overall quality score was 88.535 and this value represents the amino acid percentage with anticipated error value and the value falling below the rejection limit. The Z-score of this study result was within the Z-score range (−4.17) validated for native enzymes. The binding pockets of the enzyme were determined in this study and two binding pockets were predicted using the automatic online tool using the web server. The selected FDA-approved drugs were ordered based on their minimum binding energy to the proteinase. Conclusions: Molecular docking studies revealed the involvement of various hydrophobic interactions between FDA-approved drugs and amino acid residues of monkeypox virus proteinase.

Lipid bilayers—the main matrix of cell membranes—are a paradigm of soft molecular assemblies whose properties have been evolutionarily optimized to satisfy the functional requirements of cells. For instance, lipid bilayers must be rigid enough to serve as the protective barrier between cells and their environment, yet fluid enough to enable the diffusion of proteins and molecular clusters necessary for biological functions. Inspired by their biological multifunctionality, lipid membranes have also been used as a central design element in many practical applications including artificial cells, drug nanocarriers, and biosensors. Whether biological or synthetic, lipid membranes often involve molecular or nanoscopic additives that modulate the membrane properties through various mechanisms. Hence, how lipid membranes respond to additives has justifiably drawn much attention in recent years. This review summarizes findings and observations on different classes of additives and their effects on structural, thermodynamic, elastic, and dynamical membrane properties that are central to biological function or synthetic membrane performance. The review primarily focuses on phospholipids as a major component of cell membranes and a widely used lipid type in synthetic membrane designs.

Monkeypox virus (mpox) disease is caused by a double-stranded DNA virus from the Poxviridae family. The mpox virus showed structural similarity with smallpox virus disease. The recent outbreak of mpox infection in the rest of African countries causes public health issues of increased pandemic potential. Mpox virus is involved in the viral replication cycle through the biocatalytic reaction of precursor polyproteins cleavage. The main objective of the study was to analyze the molecular interactions between mpox and FDA-approved drugs. The primary and secondary structure of the protein was retrieved and FDA approved drug was screened using AutoDock. The best hit was analyzed and the molecular interactions were studied. Model validation analyzes the peptide, energy of hydrogen bonds, steric conflicts and bond planarity. Z-score was calculated using ProSA-web tool and the score tested the native fold from other alternative folds. The confidence level of the submitted amino acids was> 80 % and the maximum confidence score for a single template was 98.2 %. The generated proteinase model was subjected to analyze the distribution of atoms and the using ERRAT server. The overall quality score was 88.535 and this value represents the amino acid percentage with anticipated error value and the value falling below the rejection limit. The Z-score of this study result was within the Z-score range (−4.17) validated for native enzymes. The binding pockets of the enzyme were determined in this study and two binding pockets were predicted using the automatic online tool using the web server. The selected FDA-approved drugs were ordered based on their minimum binding energy to the proteinase. Molecular docking studies revealed the involvement of various hydrophobic interactions between FDA-approved drugs and amino acid residues of monkeypox virus proteinase. [ABSTRACT FROM AUTHOR]

Abstract Chiral piperidine scaffolds are prevalent as the common cores of a large number of active pharmaceuticals in medical chemistry. This review outlined the diversity of chiral piperidine scaffolds in recently approved drugs, and also covers the scaffolds in leads and drug candidates. The significance of chiral piperidine scaffolds in drug design is also discussed in this article. With the introduction of chiral piperidine scaffolds into small molecules, the exploration of drug-like molecules can be benefitted from the following aspect: (1) modulating the physicochemical properties; (2) enhancing the biological activities and selectivity; (3) improving pharmacokinetic properties; and (4) reducing the cardiac hERG toxicity. Given above, chiral piperidine-based discovery of small molecules will be a promising strategy to enrich our molecules' library to fight against diseases.

Prostate cancer (PoC) is currently one of the most frequent cancers in males. Though a lot of work has been done on it, the biomarkers in the progression of prostate cancer have not been fully identified. In this research, we used the GSE70466 and GSE104131 for RNA-Seq gene expression datasets to reveal the important biomarkers and related pathways of prostate cancer. Furthermore, Differentially Expressed Genes (DEGs) were extracted from the dataset with the R statistical language tools, and to investigate the functional enrichment of the DEGs, an online repository Enrichr was used. Then, another database, STRING was used in this work to generate the protein-protein interaction (PPI) network and represented the network using the well-known Cytoscape software. After that, a Cystoscape plug-in MCODE identified clusters from the PPI network. In the end, some drug compounds that may be useful in the therapy of PoC have been revealed. A total of 83 common DEGs (43 upregulated and 40 downregulated) were exposed using some statistical criteria. Overexpressed DEGs were engaged in the metabolism of glutamine family amino acids and alpha-amino acids, while underexpressed DEGs were mostly involved in the regulation of the transmembrane receptor protein serine/threonine kinase signaling pathway and positive regulation of potassium ion transport. In addition, the overexpressed DEGs were highly involved with Arginine and Proline metabolism, whereas the underexpressed DEGs were connected with Proteoglycans in cancer, according to the KEGG pathway analysis. A total of 154 nodes and 2068 connections were used to construct the PPI. Using the connectivity method, we conclude 20 genes (INS; VEGFA; CDH1; IGF1; ITGB1; IGF1R; CAV1; ESR1; KDR; TGFB1; CD44; MMP9; PIK3R1; SHC1; IGF2; SMAD4; TGFBR1; ENG; IRS1; VWF) as hub genes. Finally, we identified and validated two important biomarkers as well as certain pharmacological compounds that could be effective in the therapy of PoC.

Multiple myeloma (MM), second most common hematological malignancy, still remains beyond cure because of acquirement of drug resistance. Proteasome inhibitor such as carfilzomib (Cfz) therapy which has been used as one of the key therapies against MM recently, is obstructed by the incidence of Cfz resistance. The underlying mechanism of this acquired Cfz resistance in MM is very little understood. Therefore, the current study was aimed to investigate the differentially expressed genes (DEGs), associated micro RNAs (miRNAs), and transcription factors (TFs) from the microarray datasets of Cfz resistant and Cfz sensitive MM cell lines, obtained from Gene Expression Omnibus (GEO) database. DEGs were detected using GEO2R tool from two datasets and common DEGs were identified through Venn diagram. Gene ontology (GO) and pathway enrichment analysis were performed on DAVID database. Through STRING database and Cytoscape tool, protein–protein interaction (PPI) network of DEGs was constructed. Genetic alterations in DEGs were investigated using COSMIC database. Interaction network between DEGs and miRNAs as well as TFs were obtained and constructed by using mirDIP, TRRUST, and miRNet tools. Drug gene interaction analysis was performed to identify potential drug molecules on DGIdb tool. Several common DEGs were identified in Cfz resistant MM. PDGF, VEGF, and Wnt signaling pathways were significantly enriched and might be involved in MM progression. miRNA analysis identified hsa-mir-124-3p, hsa-mir-26a-5p that can target DEGs. Various drug molecules such as dabrafenib, vemurafenib, and venetoclax that could potentially attenuate the MM pathophysiology, were detected. The entire study might provide a new understanding about the Cfz resistance in MM. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflammatory response and an immunosuppressive microenvironment. A seven-gene inflammation-related risk signature was designed employing the LASSO and Cox regression analyses for the inflammation-related genes. The ccRCC specimens were classified into two groups with high and low risk by calculating the risk scores. The specimens in the group with high risk showed a poor prognosis and were positively correlated with immune inhibitory factors. Moreover, a nomogram was created by incorporating inflammation-related risk signatures and clinical characteristics. The ROC and DCA curves indicated a satisfactory efficiency of the nomogram for predicting the survival outcomes. Furthermore, we identified the potential therapeutic drug molecules through CMap analysis. The findings of our study may act as a guide for further research on new prognostic biomarkers and therapies. [ABSTRACT FROM AUTHOR]

The insertion of small drug molecules into DNA can change its electrical properties, thereby controlling the probability of its electrical transmission. This characteristic has enabled its widespread application in molecular electronics. However, the current understanding of the intercalation properties and electronic transmission mechanisms is still not deep enough, which severely restricts its practical application. In this paper, the density functional theory and the non-equilibrium Green's function formula are combined to bind three different small drug molecules to the same sequence of DNA through intercalation, in order to discuss the impact of intercalation and molecular structure on the electrical properties of DNA. After inserting two MAR70 molecules, the conductivity decreased from 2.38×10 -5 G 0 to 3.37×10 -7 G 0 . Upon the insertion of Nogalamycin, the conductivity dropped to 2.01×10 -5 G 0 , only slightly lower than that of bare B-DNA. However, when cyanomorpholinodoxorubicin was inserted, the conductivity was 2.65×10 -6 G 0 . In our study, we observed some common characteristics. After intercalating with drug molecules, new energy levels were induced, altering the positions of the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energy levels, resulting in a narrowed bandgap and consequently reduced conductivity of the complex. Furthermore, the conductivity was also related to the number of inserted drug molecules, fewer inserted molecules led to a decrease in conductivity. The results of this study indicate that embedding drug molecules can reduce or regulate the conductivity of DNA, providing new insights for its application in the field of nanoelectronics., (Copyright © 2024. Published by Elsevier Inc.)

The most frequent endocrine cancer of the head and neck is thyroid carcinoma (THCA). Although there is increasing evidence linking THCA to genetic alterations, the exact molecular mechanism behind this relationship is not yet completely known to the researchers. There is still much to learn about THCA's molecular roots and genetic biomarkers. Though drug therapies are the best choice after metastasis, unfortunately, the majority of the patients progressively develop resistance against the therapeutic drugs after receiving them for a few years. Therefore, multi-targeted different variants of therapeutic drugs may be essential for effective treatment against THCA. To understand molecular mechanisms of THCA development and progression and explore multi-targeted different variants of therapeutic drugs, we detected 80 common differentially expressed genes (cDEGs) between THCA and non-THCA samples from six microarray gene expression datasets using the statistical LIMMA approach. Through protein-protein interaction (PPI) network analysis, we identified the top-ranked eight differentially expressed genes (TIMP1, FN1, THBS1, RUNX2, SHANK2, TOP2A, LRP2, and ACTN1) as the THCA-causing key genes (KGs), where 6 KGs (TIMP1, TOP2A, FN1, ACTN1, RUNX2, THBS1) are upregulated and 2 KGs (LRP2, SHANK2) are downregulated. The expression pattern analysis of KGs with the independent TCGA database by Box plots also confirmed their upregulated and downregulated patterns. The expression analysis of KGs in different stages of THCA development indicated that these KGs might be utilized as early diagnostic and prognostic biomarkers. The pan-cancer analysis of KGs indicated a substantial correlation of KGs with multiple cancers, including THCA. Some transcription factors (TFs) and microRNAs were detected as the key transcriptional and post-transcriptional regulators of KGs using gene regulatory network (GRN) analysis. The enrichment analysis of the cDEGs revealed several key molecular functions, biological processes, cellular components, and pathways significantly associated with THCA. These findings highlight critical mechanisms influenced by the identified key genes (KGs), providing deeper insight into their roles in THCA development. Then we detected 6 repurposable drug molecules (Entrectinib, Imatinib, Ponatinib, Sorafenib, Retevmo, and Pazopanib) by molecular docking with KGs-mediated receptor proteins, ADME/T analysis, and cross-validation with the independent receptors. Therefore, these findings might be useful resources for wet lab researchers and clinicians to consider an effective treatment strategy against THCA., Competing Interests: Declaration of Competing Interest We can confirm that the work entitled “Gene-Expression Profile Analysis to Disclose Diagnostics and Therapeutics Biomarkers for Thyroid Carcinoma” is an original work and neither this nor any similar manuscript, in whole or in part, is under consideration, in a press, published, or reported elsewhere. All co-authors have seen and agreed with the contents of the manuscript. We would like to declare that we have no any conflict of interest for this work. Anyone can use any information from our manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

A new series of [1,2,4]triazolo[4,3-b][1,2,4,6]thiatriazine-1,1-dioxides were synthesized, charac-terized, and evaluated for their acaricidal activity against the larvae and adults of H. scupense. The title compounds were obtained via the reaction of N-alkyl(aryl)-N′-(4H-1,2,4-triazol-3-yl)amidines and sulfuryl chloride in the presence of pyridine. The structure of the synthesized compounds was confirmed by FTIR, 1H and 13C NMR, and mass spectra and elemental analyses. Adult immersion test (AIT) was performed to determine the mortality of ticks and inhibition of oviposition and hatching after application of these compounds. Larval packet test (LPT) was performed on the larvae produced from the collected ticks. All tested compounds displayed a potent acaricidal activity. Among them, 2-benzyl-3-phenyl-2H-[1,2,4]triazolo[4,3-b][1,2,4,6]thia-triazine 1,1-dioxide showed the most potent ixodicide activity with an LC50 value of 0.19 mg/mL against 0.06 mg/mL for the standard drug amitraz. Furthermore, after 24 h of exposure, this compound caused a high mortality on H. scupense larvae, as it induced 100% mortality at the highest tested concentration (1 mg/mL). [ABSTRACT FROM AUTHOR]

BackgroundSARS-CoV-2 causes coronavirus disease 2019 (COVID-19), a new coronavirus pneumonia, and containing such an international pandemic catastrophe remains exceedingly difficult. Asthma is a severe chronic inflammatory airway disease that is becoming more common around the world. However, the link between asthma and COVID-19 remains unknown. Through bioinformatics analysis, this study attempted to understand the molecular pathways and discover potential medicines for treating COVID-19 and asthma.MethodsTo investigate the relationship between SARS-CoV-2 and asthma patients, a transcriptome analysis was used to discover shared pathways and molecular signatures in asthma and COVID-19. Here, two RNA-seq data (GSE147507 and GSE74986) from the Gene Expression Omnibus were used to detect differentially expressed genes (DEGs) in asthma and COVID-19 patients to find the shared pathways and the potential drug candidates.ResultsThere were 66 DEGs in all that were classified as common DEGs. Using a protein-protein interaction (PPI) network created using various bioinformatics techniques, five hub genes were found. We found that asthma has some shared links with the progression of COVID-19. Additionally, protein-drug interactions with common DEGs were also identified in the datasets.ConclusionWe investigated possible links between COVID-19 and asthma using bioinformatics databases, which might be useful in treating COVID-19 patients. More studies on populations affected by these diseases are needed to elucidate the molecular mechanism behind their association.

Development of effective adsorbents for the removal of contaminants from wastewater is indispensable due to increasing water scarcity and a lack of pure drinking water, which are prevailing as a result of rapid industrialization and population growth. Recently, the development of new adsorbents and their effective use without generating secondary waste is receiving huge consideration. In order to protect the environment from primary and secondary pollution, the development of adsorbents from wastes and their recycling have become conventional practices aimed at waste management. As a result, significant progress has been made in the synthesis of new porous carbon and metal-organic frameworks as adsorbents, with the objective of using them for the removal of pollutants. While many different kinds of pollutants are produced in the environment, drug pollutants are the most vicious because of their tendency to undergo significant structural changes, producing metabolites and residues with entirely different properties compared to their parent compounds. Chemical reactions involving oxidation, hydrolysis, and photolysis transform drugs. The resulting compounds can have detrimental effects on living beings that are present in soil and water. This review stresses the development of adsorbents with adjustable porosities for the broad removal of primary drug pollutants and their metabolites, which are formed as a result of drug transformations in environmental matrices. This keeps adsorbents from building up in the environment and prevents them from becoming significant pollutants in the future. Additionally, it stops secondary pollution caused by the deterioration of the used adsorbents. Focus on the development of effective adsorbents with flexible porosities allows for the complete removal of coexisting contaminants and makes a substantial contribution to wastewater management. In order to concentrate more on the development of flexible pore adsorbents, it is crucial to comprehend the milestones reached in the research and applications of porous magnetic adsorbents based on metal and carbon, which are discussed here. An overview of magnetic porous adsorbents based on carbon and metal organic framework used for the removal of drug pollutants is presented. Drug transformations in environmental matrices leading to the formation of transformed entities necessitates the development of flexible pore adsorbents for the complete removal of the drug pollutants. This is essential for waste management and sustainable development in addition to reducing secondary pollution. [Display omitted] • Water scarcity has increased as a result of water contamination in recent years. • The adsorption process has gained much importance because of its high potential in the removal of pollutants. • High-efficiency magnetic porous adsorbents based on metal and biomass that remove specific pollutants are addressed. • Ideal adsorbents that are competent for the removal of pharmaceutical pollutants, which cause adverse effects due to structural transformations in different environmental matrices, are enumerated. • The development of flexible pore adsorbents for the removal of drug and their transformed entities is recommended. [ABSTRACT FROM AUTHOR]

Geometrically unsaturated binuclear double-stranded metallohelicates constructed with pyridyl hydrazine ligands and lanthanide metal ions via metal-templated self-assembly. The single-crystal X-ray analysis suggests the formation of helical assembly through its coordination with pyridyl, azomethine nitrogen's and ketonic oxygen with metal ions. Lanthanide ions are known to possess multiple coordination sites, other labile sites are occupied with solvents and anions. The lanthanide helicates are exhaustively characterized and adopted as a catalyst to synthesize benzothiazole scaffolds starting from 2-fluoroaniline and phenyl isothiocyanate substrates via tandem thiourea intermediate/cyclization pathway. The mechanistic investigation suggests the reaction proceeds via the nucleophilic aromatic substitution pathway. The protocols are extended for the synthesis of drug molecules such as Riluzole and Dimazole. [Display omitted] • Construction of binuclear double-stranded metallohelicates with pyridyl hydrazine ligands and lanthanide ions. • Terbium helicates are adopted as efficient catalysts for tandem benzothiazole synthesis starting from amine and isothiocyanates. • The catalyst is specific towards the formation of thiourea at rt and high temperature as (100 °C). • The protocol extended towards the synthesis of Riluzole and Dimazole. [ABSTRACT FROM AUTHOR]

Multiple myeloma (MM), second most common hematological malignancy, still remains irremediable because of acquisition of drug resistance. Glucocorticoid (GC) therapy, which is used as one of the key therapies against MM, is hindered by the incidence of GC resistance. The underlying mechanism of this acquired GC resistance in MM is not fully elucidated. Therefore, the present study was aimed to identify the differentially expressed genes (DEGs), associated micro RNAs (miRNAs), and transcription factors (TFs) from the microarray datasets of GC-resistant and GC-sensitive MM cell lines, obtained from Gene Expression Omnibus (GEO) database. DEGs were identified using GEO2R tool from two datasets and common DEGs were obtained by constructing Venn diagram. Then the Gene ontology (GO) and pathway enrichment analysis were performed using DAVID database. Genetic alterations in DEGs were examined using COSMIC database. Protein–protein interaction (PPI) network of DEGs was constructed using STRING database and Cytoscape tool. Network of interaction of DEGs and miRNAs as well as TFs were obtained and constructed using mirDIP, TRRUST, and miRNet tools. Drug gene interaction was studied to identify potential drug molecules by DGIdb tool. Six common DEGs, CDKN1A, CDKN2A, NLRP11, BTK, CD52, and RELN, were found to be significantly upregulated in GC-resistant MM and selected for further analysis. miRNA analysis detected hsa-mir-34a-5p that could interact with maximum target DEGs. Two TFs, Sp1 and Sp3, were found to regulate the expression of selected DEGs. The entire study may provide a new understanding about the GC resistance in MM. [ABSTRACT FROM AUTHOR]

A tris(pentafluorophenyl)borane catalysed method for the synthesis of boronic acid esters from aromatic amines in yields of up to 93% was devised. Mild conditions, benign reagents, short reaction times, low temperatures and a wide substrate scope characterize the method. The reaction was found applicable to the synthesis of boronic acid ester derivatives of complex drug molecules in up to 86% isolated yield and high purity suitable for labelling. These boronates were subsequently labelled with [18F]fluoride ion in radiochemical yields of up to 55% with and even without isolation of the boronate-intermediate.

Coronavirus disease 2019 (COVID-19)-driven global pandemic triggered innumerable health complications, imposing great challenges in managing other respiratory diseases like asthma. Furthermore, increases in the underlying inflammation involved in the fatality of COVID-19 have been linked with lack of vitamin D. In this research work, we intend to investigate the possible genetic linkage of asthma and vitamin D deficiency with the severity and fatality of COVID-19 using a network-based approach. We identified and analysed 41 and 14 differentially expressed genes (DEGs) of COVID-19 being common with asthma and vitamin D deficiency, respectively, through the comparative differential gene expression analysis and their footprints on signalling pathways. Gene set enrichment analysis for GO terms and signalling pathways reveals key biological activities, including inflammatory response-related pathways (e.g., cytokine- and chemokine-mediated signalling pathways, IL-17, and TNF signalling pathways). Besides, the Protein–Protein Interaction network analysis of those DEGs reveals hub proteins, some of which are reported as inflammatory antiviral interferon-stimulated biomarkers that potentially drive the cytokine storm leading to COVID-19 severity and fatality, and contributes in the early stage of viral replication, respectively. Moreover, the regulatory network analysis found these DEGs associated with antiviral and tumour inhibitory transcription factors and micro-RNAs. Finally, drug–target enrichment analysis yields tetradioxin, estradiol, arsenenous acid, and zinc, which have been reported to be effective in suppressing the pro-inflammatory cytokines production, and other respiratory tract infections. Our results yield shared biomarker-driven key hypotheses followed by network-based analytics, demystifying the mechanistic details of COVID-19 comorbidity of asthma and vitamin D deficiency with their potential therapeutic implications.

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry., Competing Interests: No potential conflict of interest was declared by authors., (© 2024 The Authors.)

Site‐specific incorporation of deuterium into drug molecules to study and improve their biological properties is crucial for drug discovery and development. Herein, we describe a palladium‐catalyzed room‐temperature deuterogenolysis of carbon–oxygen bonds in alcohols and ketones with D2 balloon for practical synthesis of deuterated pharmaceuticals and chemicals with benzyl‐site (sp3 C−H) D‐incorporation. The highlights of this deoxygenative deuteration strategy are mild conditions, broad scope, practicability and high chemoselectivity. To enable the direct use of D2O, electrocatalytic D2O‐splitting is adapted to in situ supply D2 on demand. With this system, the precise incorporation of deuterium in the metabolic position (benzyl‐site) of ibuprofen is demonstrated in a sustainable and practical way with D2O. [ABSTRACT FROM AUTHOR]

Poly(vinyldiaminotriazine) nanoparticles adsorbed 5-fluorouracil, thymine, theobromine, and xanthine in aqueous phase. Each drug molecule formed triple hydrogen bonds with a diaminotriazine moiety at the nanoparticle surface. Adsorption isotherm studies suggested monolayer adsorption while the adsorption capacity was strongly dependent on the drug compositions/structures (0.053 mmol/g 5-fluorouracil, 0.094 mmol/g thymine, 0.0078 mmol/g theobromine, and 0.0065 mmol/g xanthine). The drug compositional/structural effects were also reflected in the adsorption kinetics. In addition to neutral pH adsorption, studies were performed at pH below the polymer's pKa and above the drugs' pKa, separately. Reducing hydrophobicity of the hydrogen bonding sites by protonating the diaminotriazine moieties at pH 4.5 resulted in more than 3 times decrease in adsorption capacities whereas deprotonating the drug molecules at pH 9 increased the adsorption capacity due to electrostatic attraction between the negatively charged drugs and the positively charged nanoparticles, which was supported by strong dependency of the adsorption capacity on ionic strength. The adsorption was reversible for all the four drugs. While increasing temperature caused faster desorption, reducing hydrophobicity of the diaminotriazine moieties by protonation at pH 4.5 had a stronger effect on both cumulative desorption and burst release. The results of this study demonstrated an important role of synergistic interaction between hydrogen bonding and hydrophobic affinity in poly(vinyldiaminotriazine) nanoparticle adsorption of the drugs in the aqueous phase. [ABSTRACT FROM AUTHOR]

Antibiotic resistance is an alarming public health concern that affects millions of individuals across the globe each year. A major challenge in the development of effective antibiotics lies in their limited ability to permeate cells, noting that numerous susceptible antibiotic targets reside within the bacterial cytosol. Consequently, improving the cellular permeability is often a key consideration during antibiotic development, underscoring the need for reliable methods to assess the permeability of molecules across cellular membranes. Currently, methods used to measure permeability often fail to discriminate between the arrival within the cytoplasm and the overall association of molecules with the cell. Additionally, these techniques typically possess throughput limitations. In this work, we describe a luciferase-based assay designed for assessing the permeability of molecules in the cytosolic compartment of Gram-negative bacteria. Our findings demonstrate a robust system that can elucidate the kinetics of intracellular antibiotic accumulation in live bacterial cells in real time.