Current Computer - Aided Drug Design - Volume 19, Issue 6, 2023

Aim: This article aims to quantitatively analyze the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm. Background: In the last two decades, the global pharmaceutical industry has faced the dilemma of low research & development (R&D) success rate. The US is the world's largest pharmaceutical market, while China is the largest emerging market. Objective: To collect data from the database and apply machine learning to build the model. Methods: LightGBM algorithm was used to build the model and identify the factor important to the performance of pharmaceutical companies. Results: The prediction accuracy for US companies was 80.3%, while it was 64.9% for Chinese companies. The feature importance shows that the net profit growth rate and debt liability ratio are significant in financial indicators. The results indicated that the US may continue to dominate the global pharmaceutical industry, while several Chinese pharmaceutical companies rose sharply after 2015 with the narrowing gap between the Chinese and US pharmaceutical industries. Conclusion: In summary, our research quantitatively analyzed the growth trend of listed pharmaceutical companies in the US and China by a machine learning algorithm, which provide a novel perspective for the global pharmaceutical industry. According to the R&D capability and profitability, 141 US-listed and 129 China-listed pharmaceutical companies were divided into four levels to evaluate the growth trend of pharmaceutical firms.

Background: Vibrio cholerae, the causative agent of cholera, has been responsible for global epidemics and many other problems over the centuries. It is one of the main public health issues in less-developed and developing countries and is considered one of the deadliest infectious agents. Therefore, precise and susceptible detection of V. cholerae from environmental and biological samples is critical. Aptamers provide a rapid, sensitive, highly specific, and inexpensive alternative to traditional methods. Objective: The present study develops a new protocol inspired by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) to implement an in silico aptamer selection against V. cholerae, which can also be employed in the case of other pathogenic microorganisms. Methods: First, we built an oligonucleotide pool and screened it based on the secondary structure. Following that, we modeled the tertiary structures of filtered sequences and performed RNAprotein dockings to assess binding affinities between RNA sequences and Outer Membrane Protein U (OmpU), an effective marker in distinguishing epidemic strains of V. cholerae, which constitute up to 60% of the total outer membrane protein. Finally, we used molecular dynamics simulation to validate the results. Results: Three sequences (ChOmpU^apta) were proposed as final aptameric candidates. Analysis of the top-ranked docking results revealed that these candidate aptamers bound to all subunits of OmpU at the extracellular side with high affinity. Moreover, ChOmpU^apta-3 and ChOmpU^apta-2 were fully stable and formed strong bonds under dynamic conditions. Conclusion: We propose incorporating these candidate sequences into aptasensors for V. cholerae detection.

Background: DosR is a transcriptional regulator of Mycobacterium tuberculosis (MTB), governing the expression of a set of nearly 50 genes that is often referred to as ‘dormancy regulon’. The inhibition of DosR expression by an appropriate inhibitor may be a crucial step against MTB. Objective: We targeted the DosR with natural metabolites, ursolic acid (UA) and carvacrol (CV), using in silico approaches. Methods: The molecular docking, molecular dynamics (MD) simulation for 200 ns, calculation of binding energies by MM-GBSA method, and ADMET calculation were performed to evaluate the inhibitory potential of natural metabolites ursolic acid (UA) and carvacrol (CV) against DosR of MTB. Results: Our study demonstrated that UA displayed significant compatibility with DosR during the 200 ns timeframe of MD simulation. The thermodynamic binding energies by MM-GBSA also suggested UA conformational stability within the binding pocket. The SwissADME, pkCSM, and OSIRIS DataWarrior showed a drug-likeness profile of UA, where Lipinski profile was satisfied with one violation (MogP > 4.15) with no toxicities, no mutagenicity, no reproductive effect, and no irritant nature. Conclusion: The present study suggests that UA has the potency to inhibit the DosR expression and warrants further investigation on harnessing its clinical potential.

Background: The compounds containing heterocyclic cores with O, N and/or S atoms are bioactive and valuable molecules in the field of drug discovery and development. There are several applications in different areas for the molecules having oxadiazole moiety in their structures viz. herbicides and corrosion inhibitors, electron-transport materials, polymers and luminescent materials. Hence, demand for new anticonvulsant, antibacterial and analgesic agents has turned into an imperative assignment in the area of medicinal chemistry to improve therapeutic efficacy as well as safety. Methods: In the journey of new anticonvulsive, antibacterial and analgesic molecules with better potency, some newer Oxadiazole analogues were attained by a sequence of synthetic steps with the substituted acrylic acids. IR and ¹H-NMR spectral data were used for the structure elucidation of obtained chemical compounds. In this perspective, the anticonvulsant, antibacterial and analgesic activities were evaluated for synthetically obtained newer chemical moieties. Furthermore, a molecular docking study was performed to elucidate the binding modes of synthesized ligands in the active pockets of Cox-1/2 enzymes, DNA Gyrase and GABA inhibitors. Results: It has been observed that all the synthetic molecules showed good analgesic activity while A1 molecule demonstrated better analgesic activity. In the case of anticonvulsant and antibacterial activity among other ligands, C1 molecule possessed profound anticonvulsant activity whereas B1 molecule showed maximum antibacterial activity and molecular docking study also endorsed the same consequences. Conclusion: It might be recognized from the present study that prepared compounds are distinctive in lieu of their structure and noticeable biological activity. In the quest for a newer group of anticonvulsant, antibacterial and analgesic molecules, these compounds might be useful for the society.

Background: Buyang Huanwu Decoction (BHD) is used to regulate blood circulation and clear collaterals and is widely used in coronary heart disease. However, the active compounds and the mechanism of BHD used to treat restenosis are less understood. Objective: The study aimed to explore the potential mechanism of Buyang Huanwu decoction BHD for the treatment of restenosis using network pharmacology and molecular docking experiments. Methods: The bioactive components of BHD and their corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) and Encyclopaedia of Traditional Chinese Medicine (ETCM) databases as well as literature. Restenosisassociated therapeutic genes were identified from the OMIM, Drugbank, GEO, and Dis- GeNET databases. Genes related to the vascular smooth muscle cell (VSMC) phenotype were obtained from the gene ontology (GO) database and literature. The core target genes for the drug-disease-VSMC phenotype were identified using the Venn tool and Cytoscape software. Moreover, the “drug-component-target-pathway” network was constructed and analyzed, and pathway enrichment analysis was performed. The connection between the main active components and core targets was analyzed using the AutoDock tool, and PyMOL was used to visualize the results. Results: The “compound-target-disease” network included 80 active ingredients and 599 overlapping targets. Among the bioactive components, quercetin, ligustrazine, ligustilide, hydroxysafflor yellow A, and dihydrocapsaicin had high degree values, and the core targets included TP53, MYC, APP, UBC, JUN, EP300, TGFB1, UBB, SP1, MAPK1, SMAD2, CTNNB1, FOXO3, PIN1, EGR1, TCF4, FOS, SMAD3, and CREBBP. A total of 365 items were obtained from the GO functional enrichment analysis (p < 0.05), whereas the enrichment analysis of the KEGG pathway identified 30 signaling pathways (p < 0.05), which involved the TGF-β signaling pathway, Wnt signaling pathway, TRAF6-mediated induction of NF-ΚB and MAPK pathway, TLR7/8 cascade, and others. The molecular docking results revealed quercetin, luteolin, and ligustilide to have good affinity with the core targets MYC and TP53. Conclusion: The active ingredients in BHD might act on TP53, MYC, APP, UBC, JUN, and other targets through its active components (such as quercetin, ligustrazine, ligustilide, hydroxysafflor yellow A, and dihydrocapsaicin). This action of BHD may be transmitted via the involvement of multiple signaling pathways, including the TGF-β signaling pathway, Wnt signaling pathway, TRAF6-mediated induction of NF-ΚB and MAPK pathway, and TLR7/8 cascade, to treat restenosis by inhibiting the phenotype switching and proliferation of VSMC.

Introduction: Mental disorders are very serious complicated disorders. Schizophrenia is one of the most baffling mental disorders. The new series 7-(2-(benzo[d]thiazol-2- ylamino)ethoxy)-4-methyl-2H-chromen-2- synthesized in search of newer compounds for Schizophrenia. Methods: Synthesis is done by refluxing in dry pyridine with various substituted 2-amino benzothiazoles derivatives (3a-3k) and 7-(2-Chloroethoxy)-4-methyl-2H-chromen-2-one (2). The molecular docking approach was used to screen these generated derivatives. Chem Bio Draw Ultra 12 was used to draw the compounds, which were then exposed to all potential conformations of compounds interacting with receptors. The Glide 7.6, Schrodinger 2017 Maestro 11.3 was used to achieve molecular docking. The Dopamine receptor 6CM4 serotonin 5TUD PDBs were acquired from the database of Brookhaven Protein. Using the OPLS 2005 force field, the ligand-protein hydrogen-bond network was acquired, along with the overall energy reduced. A glide score was used to rate the docking poses. Results: The produced compounds have been identified with the use of analytical and spectral data. All of the produced substances were tested and analyzed for serotonin 5HT2 antagonistic and dopamine D2 activity, which can be considered as a measure of typical antipsychotic properties. Conclusion: Compounds 4b, 4c, 4e, 4g & 4i have demonstrated promising pharmacological action in preliminary studies. According to the preceding findings, compounds with electronwithdrawing substitutions, such as 4e & 4b, have a good atypical profile of antipsychotics.;

Background: Artemisiae capillariae (Yinchen, YC) is a well-known herbal medicine used to treat drug-induced liver diseases, while the bioactive phytochemicals and pharmacological targets of YC remain unclear. Objective: The study aimed to probe the key active components in YC and determine the potential molecular mechanisms of YC protect against DILI. Methods: In this study, we first delved into the active chemicals and targets of YC, identified potential anti-AILI targets for YC, mapped the components-targets network, performed proteinprotein interaction (PPI) analysis, gene ontology (GO) enrichment, and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway analyses of the action targets. This led to figure out the liver protective mechanism of YC against AILI. Analyzing the molecular docking of key targets, binding domain of ingredients and targets reveals the effective interaction, and the binding energy explains the efficiency and stability of the interactions. Results: Network analysis identified 53 components in YC; by systematic screening 13 compounds were selected, which were associated with 123 AILI-related genes. The core ingredients were quercetin, capillarisin and Skrofulein, and the identified crucial genes were AKT1, TNF, and IL6. The GO and KEGG pathway enrichment analysis results indicated that the anti-AILI targets of YC mainly take a part in the regulation of oxidative stress and immune, with related signaling pathways including PI3K/AKT and IL17. Furthermore, the binding pockets of YC bioactive ingredients and key targets were revealed, and the binding ability was proved by molecular docking analysis. Conclusion: This study has revealed the potential bioactive molecules and mechanism of YC in AILI and provided a possible strategy for the identification of active phytochemicals against druginduced liver injury.