Combinatorial Chemistry & High Throughput Screening - Volume 25, Issue 12, 2022

The modern pharmaceutical industry is transitioning from traditional methods to advanced technologies like artificial intelligence. In the current scenario, continuous efforts are being made to incorporate computational modeling and simulation in drug discovery, development, design, and optimization. With the advancement in technology and modernization, many pharmaceutical companies are approaching in silico trials to develop safe and efficacious medicinal products. To obtain marketing authorization for a medicinal product from the concerned National Regulatory Authority, manufacturers must provide evidence for the safety, efficacy, and quality of medical products in the form of in vitro or in vivo methods. However, more recently, this evidence was provided to regulatory agencies in the form of modeling and simulation, i.e., in silico evidence. Such evidence (computational or experimental) will only be accepted by the regulatory authorities if it considered as qualified by them, and this will require the assessment of the overall credibility of the method. One must consider the scrutiny provided by the regulatory authority to develop or use the new in silico evidence. The United States Food and Drug Administration and European Medicines Agency are the two regulatory agencies in the world that accept and encourage the use of modeling and simulation within the regulatory process. More efforts must be made by other regulatory agencies worldwide to incorporate such new evidence, i.e., modeling and simulation (in silico) within the regulatory process. This review article focuses on the approaches of in silico trials, the verification, validation, and uncertainty quantification involved in the regulatory evaluation of biomedical products that utilize predictive models.

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a dismal prognosis, according to updated statistics. The solute carrier family 17 member 2 (SLC17A2) has not been studied in liver cancer. Therefore, we evaluated the role of SLC17A2 in HCC by bioinformatics analysis. Objective: The objective of the study was to explore the value of SLC17A2 in the prognosis and diagnosis of hepatocellular carcinoma. Methods: The expression level of SLC17A2 in HCC and the clinicopathological data were analyzed based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and validated by immunohistochemical staining. In addition, the Kaplan–Meier plotter database and receiver operating characteristic (ROC) curve analysis were used to explore the prognostic and diagnostic significance. Some online databases were used to analyze the relationship between immune cell infiltration and analyze the relationship between immune cell infiltration and SLC17A2 in HCC. Results: Multivariate Cox regression analysis showed that SLC17A2 expression was low in HCC (P < 0.05) and closely related to the clinical stage of HCC. In addition, SLC17A2 had a certain diagnostic value in HCC according to ROC curve analysis. Further biological analyses showed that SLC17A2 can regulate fatty acid metabolism, amino acid metabolism and cytochrome P450- related metabolism. Notably, we found that SLC17A2 expression was closely correlated with the infiltration of most immune cells in HCC. Conclusion: SLC17A2 expression is low in HCC and correlates with immune infiltration, so it may serve as an independent prognostic factor for HCC.

Background: Ag-TiO2 nanoparticles catalyzed synthesis of 12-aryl-8,9,10,12- tetrahydrobenzo[a]-xanthen-11-ones have been enhanced via a three-component one-pot reaction betweenβ-naphthol, several aldehydes and dimedone in H2O at room temperature. Xanthenes are essential intermediates in chemistry owing to their vast difference in biological activity. Methods: This process offered significant advantages containing appropriate cost efficiency, low amount of the catalyst, application of low-cost available Ag-TiO2 nanoparticles catalyst, purification of the product by non-chromatographic method, easy process, good atom economy, simple isolation and reusability of nanocatalyst. Results: Ag-TiO2 nanoparticles catalyst shows easy access to Xanthenes with appropriate yields in short reaction time and purity. This nanoparticles catalyst was recycled and recovered by easy filtration and was reused up to five times with only an unimportant loss in its catalytic efficacy. Conclusion: This method achieves to have a numerous scope relating to the difference in the aldehydes. Correspondingly, the attractiveness of this research was that H2O was the only by-product.

Background: Developing new, efficient, and environment-friendly small molecule fungicides is the key to effectively prevent and control plant pathogenic oomycetes. α/β-Naphthol is an important raw material for drug synthesis. Due to its special structure, α/β-naphthol and its analogs possess significant biological activity. The preparation and anti-oomycete activity of novel sulfonate derivatives based on α/β-naphthol against Phytophthora capsici have not been reported yet. Methods: Thirty-two sulfonate derivatives of α/β-naphthol (4a-p and 5a-p) were prepared. The structure of all title compounds was identified by ¹H NMR, MS, and m.p. The anti-oomycete activity of 4a-p and 5a-p against P. capsici was determined using the mycelial growth rate method. Results: With our ongoing research aimed at the discovery and development of fungicidal agents, 4a-p and 5a-p were designed and synthesized, and their anti-oomycete activity against P. capsici was evaluated in vitro. Two compounds 4a and 5a were found to have good anti-oomycete activity against P. capsici, and their corresponding EC50 values were 63.41 and 65.21 mg/L, respectively. Conclusion: It has been observed that the substituent R in these derivatives is aliphatic, which is very important for maintaining their anti-oomycete activity. The results lay a foundation for further design and development of sulfonate derivatives of α/β-naphthol as fungicidal agents. The structure- fungicidal activity relationship of α/β-naphthol derivatives is under investigation in our laboratory.

Background and Objective: Inflammation is a common disease which can induce many diseases. There are unique advantages of Traditional Chinese Medicine (TCM) to anti-inflammation. Tieguanyin (TGY) is a well-known beverage; the quality is determined by aroma, taste, liquor color, and shape. The volatile compounds produce the flavor of tea, which can be lost with the increase of storage time. TGY has an excellent antiinflammatory effect; its volatile compounds also have an anti-inflammatory impact that is unclear. This study aimed to identify volatile compounds and anti-inflammatory mechanisms within the validity period (TGY1) and the out-of-date (TGY2). Methods: The volatile compounds of TGY1 and TGY2 were analyzed with headspace solid-phase microextraction (HS-PME) and identified by Gas chromatography-mass spectrometry (GC-MS). The percentage of volatile compounds was calculated by the peak area normalization method. The compounds of the targets were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP), PubChem Database, and Swiss Target Prediction database. Next, the disease potential targets were screened by the GeneCards database, Online Mendelian Inheritance in Man (OMM) database, and Therapeutic Target Database (TTD). Furthermore, core targets were screened by the Search Tool for the Retrieval of Inter-acting Genes/Proteins (STRING) database. Then, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of core targets was performed by the ClueGo plugin Cytoscape 3.7.1 software. At last, Autodock vina software performed molecular docking between the main compounds and core targets. Results: Significant differences in volatile compounds and percentage contents in TGY were observed; the 61 volatile compounds in TGY1 and 57 volatile compounds in TGY2 were identified. After excluding the unidentified compounds, a total of 47 volatile compounds were obtained from TGY1 and TGY2. With the use of network pharmacology, 34 core targets and 23 signaling pathways from TGY1, 28 core targets, and 19 signaling pathways from TGY2 were screened. The main common core targets of TGY1 and TGY2 contained MAPK3, TNF, MAPK1, SRC, etc., while the main different core targets included PTGS2, CAT, etc. A total of 12 biological processes are shared by TGY1 and TGY2, among which the cellular response to oxidative stress is the primary biological process. The different biological processes of TGY1 and TGY2 include cellular response to lipopolysaccharide, androgen receptor signaling pathway, etc. There were 14 common signaling pathways in TGY1 and TGY2, among which the thyroid hormone signaling pathway is the main common signaling pathway. The differential signaling pathways in TGY1 and TGY2 included the erbB signaling pathway, Chagas disease, etc. Molecular docking results showed that the ordinand and differential volatile compounds of TGY1 and TGY2 had different binding forces with the core targets. Conclusion: The GC-MS experiment showed significant differences in volatile compounds and percentage contents in TGY1 and TGY2. Network pharmacology indicated that they have anti-inflammatory effects. Besides, they were different in core targets, biological processes, and signaling pathways but shared similar anti-inflammatory mechanisms. Molecular docking results showed that the binding force of the TGY1 compounds to the core target is greater than that of the TGY2. Therefore, expired TGY affects volatile compounds, resulting in differences in the anti-inflammatory mechanism. The study provided a theoretical framework for further development and application of used medicinal and edible species. In addition, the application of expired TGY under safe conditions can also have anti-inflammatory effects. These results shed new light on the rational use of resources.

Background: Tumorigenesis, metastasis, and treatment response of hepatocellular carcinoma (HCC) are regulated by unfolded protein responses (UPR) signaling pathways, including IRE1a, PERK, and ATF6, but little is known about UPR related genes with HCC prognosis and therapeutic indicators. Objective: We aimed to identify a UPR related prognostic signature (UPRRPS) for HCC and explore the potential effect of the current signature on the existing molecular targeted agents and immune checkpoint inhibitors (ICIs). Methods: We used The Cancer Genome Atlas (TCGA) database to screen candidate UPR genes (UPRGs), which are expressed differentially between hepatocellular carcinoma and normal liver tissue and associated with prognosis. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, which was validated using data from the International Cancer Genome Consortium (ICGC) database and evaluated by the C-index. Then immune and molecular characteristics stratified by the current UPRRPS were analyzed, and the corresponding drug sensitivity was conducted. Results: Initially, 42 UPRGs from the TCGA database were screened as differentially expressed genes, which were also associated with HCC prognosis. Using the LASSO regression analysis, nine UPRGs (EXTL3, PPP2R5B, ZBTB17, EIF2S2, EIF2S3, HDGF, SRPRB, EXTL2, and TPP1) were used to develop a UPRRPS to predict the OS of HCC patients in the TCGA set with the Cindex of 0.763. The current UPRRPS was also well-validated in the ICGC set with the C-index of 0.700. Multivariate Cox regression analyses also confirmed that the risk score was an independent risk factor for HCC in both the TCGA and ICGC sets (both P<0.05). Functional analyses showed that low-risk score was associated with increased natural killer cells, T helpers, tumor immune dysfunction and exclusion score, microsatellite instability expression, and more benefit from ICIs; the high-risk score was associated with increased active dendritic cells, Tregs, T-cell exclusion score, and less benefit from ICIs. Gene set enrichment analyses showed that the signaling pathways of VEGF, MAPK, and mTOR were enriched in high UPRRPS, and the drug sensitivities of the corresponding inhibitors were all significantly higher in the high UPRRPS subgroup (all P<0.001). Conclusion: With the current findings, UPRRPS was a promising biomarker for predicting the prognosis of HCC patients. UPRRPS might also be taken as a potential indicator to guide the management of immune checkpoint inhibitors and molecular targeted agents.

Background: The monotropic membrane protein monoamine oxidase B (MAO-B) has been shown to be a crucial drug target for the treatment of neurodegenerative diseases. The design of recent inhibitor therapeutic agents of MAO-B involves conjugation and modification of a chalcone scaffold comprising two aryl or heteroaryl rings connected via a short spacer unit with rotatable bonds. Supported by experimental data, these modifications often result in high potent inhibitor compounds. Methods: In this study, we employ molecular dynamics simulations to unravel the impact of extended double bond conjugation in two novel compounds, F1 and MO10, toward the inhibition of the MAO-B protein. It was revealed that extended double bond conjugation induced a unidirectional orientation and motion of F1 and MO10, suggesting a stable binding pocket anchorage favouring high-affinity pocket interactions. Results: Conformational analyses also revealed that the incorporated double bond extension impeded the motion of individual binding pocket residues, which subsequently disrupted the functionality of MAO-B. Discussion: Real-time structural dynamics also revealed that the extended double bond conjugation mediated peculiar interactions with MAO-B binding pocket residues characterized by π-alkyl, π-π stacking, and π-sulphur interactions which buried both compounds into the hydrophobic core of MAO-B and ultimately induced higher binding affinities of both F1 and MO10. Conclusion: These insights present useful structural perspectives of the extended double bond conjugation associated with the experimentally reported enhanced inhibitory activity of F1 and MO10 against MAO-B.

Background: Cancer stem cells (CSCs) are now being considered as the initial component in the development of pancreatic adenocarcinoma (PAAD). Our aim was to develop a CSCrelated signature to assess the prognosis of PAAD patients for the optimization of treatment. Methods: Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue in the Cancer Genome Atlas (TCGA) were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the CSC-related gene sets. Then, univariate, Lasso Cox regression analyses and multivariate Cox regression were applied to construct a prognostic signature using the CSC-related genes. Its prognostic performance was validated in TCGA and ICGC cohorts. Furthermore, Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors in PAAD, and a prognostic nomogram was established. Results: The Kaplan-Meier analysis, ROC curve and C-index indicated the good performance of the CSC-related signature at predicting overall survival (OS). Univariate Cox regression and multivariate Cox regression revealed that the CSC-related signature was an independent prognostic factor in PAAD. The nomogram was superior to the risk model and AJCC stage in predicting OS. In terms of mutation and tumor immunity, patients in the high-risk group had higher tumor mutation burden (TMB) scores than patients in the low-risk group, and the immune score and the ESTIMATE score were significantly lower in the high-risk group. Moreover, according to the results of principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA), the low-risk and high-risk groups displayed different stemness statuses based on the risk model. Conclusion: Our study identified four CSC-related gene signatures and established a prognostic nomogram that reliably predicts OS in PAAD. The findings may support new ideas for screening therapeutic targets to inhibit stem characteristics and the development of PAAD.

Background: As a vital part of the tumor environment, immune cells affect the progression of tumors, and their composition and role vary in different types of tumors and influence prognosis. These immune cells have the potential to be beneficially targeted for immunotherapy, or, conversely, they may react negatively, even leading to drug resistance. For these reasons, probing into the composition and possible effects of immune cells in lung cancer is conducive to discovering valuable therapeutic targets. Materials and Methods: The lung adenocarcinoma gene expression data were downloaded from the TCGA database (https://cancergenome.nih.gov/; https://portal.gdc.cancer.gov/), and the lung adenocarcinoma gene expression matrix was converted into an immune cell-matrix using CIBERSORT software (https://cibersort.stanford.edu/), followed by an analysis of immune cells in lung adenocarcinoma tissues. Results: The results showed that among all immune cells in lung adenocarcinoma tissues, macrophages (Mφ) had the highest number, followed by T cells. The number of plasma cells in lung adenocarcinoma tissues was higher than in adjacent normal tissues. Compared with those in adjacent normal tissues, the number of resting memory clusters of differentiation 4 (CD4)⁺ T cells was lower, whereas active memory CD4⁺ T cells were higher in lung adenocarcinoma tissues. In addition, the number of CD8⁺ T cells was negatively related to that of resting memory CD4⁺ T cells, with a correlation coefficient of -0.44, whereas it showed a positive association with the number of active memory CD4⁺ T cells, with a correlation coefficient of 0.47. It was found that among various immune cells infiltrating lung adenocarcinoma tissues, unstimulated Mφ (M0), alternatively activated Mφ (M2), and resting memory CD4⁺ T cells accounted for the largest proportions. However, these three types of immune cells were found to be lower in lung adenocarcinoma tissues than in adjacent normal tissues. Conclusion: Immune cells infiltrating lung adenocarcinoma tissues are complex, which affect the development and progression of the tumor and may also be a significant cause of drug resistance. Studying the changes in immune cell infiltration during the development of specific types of tumors contributes to disease progression interpretation, prognosis assessment, and potential solutions to the existing drug resistance issue. In this paper, the status of immune cells in lung adenocarcinoma tissues was preliminarily discussed based on the database mining, but more experimental studies and in-depth discussions are needed in the future.

Background: As COVID-19 pandemic continues to affect people’s lives, the government of India gave emergency use approval to the ayurvedic antimalarial drug Ayush-64 in April 2021 to treat asymptomatic COVID-19 positive and mild COVID-19 positive patients. Objective: This study aims to explore the therapeutic potential of Ayush-64 to treat COVID-19 and provide a new approach for repurposing Ayurvedic drugs. Methods: The bioactives present in Ayush-64 were found along with their targets, and a plantbioactive- target network was created. A protein-protein interaction network of the common targets of Ayush-64 and COVID-19 was constructed and analyzed to find the key targets of Ayush-64 associated with the disease. Gene ontology and pathway enrichment analysis were performed to find COVID-19 related biological processes and pathways involved by the key targets. The key bioactives were docked with SARS-CoV-2 main protease 3CL, native Human Angiotensin-converting Enzyme ACE2, Spike protein S1, and RNA-dependent RNA polymerase RdRp. Results: From the 336 targets for Ayush-64, we found 38 key targets. Functional enrichment analysis of the key targets resulted in 121 gene ontology terms and 38 pathways. When molecular docking was performed with four receptors, thirteen bioactives showed good binding affinity comparable to that of the eight drugs presently used to treat COVID-19. Conclusion: Network pharmacological analysis and molecular docking study of Ayush-64 revealed that it can be recommended to treat COVID-19. Further in vitro and in vivo studies are needed to confirm the results. The study demonstrated a new approach for repurposing Ayurvedic drugs.

Background: Patients with mild cognitive impairment (MCI) suffer from a high risk of developing Alzheimer’s disease (AD). Cumulative evidence has demonstrated that the development of AD is a complex process that could be modulated by miRNAs. Here, we aimed to identify miRNAs involved in the pathway, and interrogate their ability to predict prognosis in patients with MCI. Methods: We obtained the miRNA-seq profiles and the clinical characteristics of patients with MCI from the Gene Expression Omnibus (GEO). Cox regression analysis was used to construct a risk level model. The receiver operating characteristic (ROC) curve was used to assess the performance of the model for predicting prognosis. Combined with clinical characteristics, factors associated with prognosis were identified and a predictive prognosis nomogram was developed and validated. Through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we evaluated molecular signatures for the candidate miRNAs. Results: Our analysis identified 120 DEmiRNAs. The Cox regression analysis showed that two miRNAs could serve as risk factors for disease development. A risk level model was constructed. Age, apoe4, and risk level were associated with the prognosis. We developed a nomogram to predict disease progression. The calibration curve and concordance index (C-index) demonstrated the reliability of the nomogram. Functional enrichment analysis showed that these miRNAs were involved in regulating both cGMP-PKG and Sphingolipid signaling pathways. Conclusion: We have identified miRNAs associated with the development of MCI. These miRNAs could be used for early diagnosis and surveillance in patients with MCI, enabling prediction of the development of AD.

Background and Objective: HuanglianShangqing pill (HLSQ), a well-known traditional Chinese medicine (TCM), has been used to treat fever in China for a long time. Our previous study had demonstrated that a total of 45 prototype components of HLSQ could be absorbed into the plasma of rats after intragastric administration. However, the detailed mechanisms related to the antipyretic effects of HLSQ were still unclear. Methods: In the present work, urinary metabolomics coupled with network pharmacology were employed to evaluate the mechanisms of HLSQ in the treatment of fever compared with ibuprofen (IBU) and paracetamol (APAP). Results: In pyrexia rats, a total of 11 potential metabolites and a disturbed TCA cycle were found. The metabolic regulation effects of HLSQ on fever rats were similar to APAP and could make the TCA cycle disorder return to normal by reducing citrate, β-hydroxybutyrate, succinate. In addition, HLSQ could adjust the intestinal microbial disorder and inhibit inflammatory factors, including IL6, TNF, VEGFA, TP53, STAT3, etc. There were 40 components acting on fever targets in HLSQ; among them, luteolin, apigenin, ursolic acid, kaempferol, wogonin, daidzein, baicalein, emodin, berberine, and oroxylin A were the main active compounds of HLSQ in the treatment of fever. Conclusion: The antipyretic mechanisms of HLSQ are inhibition of inflammatory factors, action on the TCA cycle, and regulation of gut microbiota.