Current Computer - Aided Drug Design - Volume 18, Issue 7, 2022

Introduction: This report proposes the application of a new Machine Learning algorithm called Fuzzy Unordered Rules Induction Algorithm (FURIA)-C in the classification of druglike compounds with antidiabetic inhibitory ability toward the main two pharmacological targets: α-amylase and α-glucosidase. Methods: The two obtained QSAR models were tested for classification capability, achieving satisfactory accuracy scores of 94.5% and 96.5%, respectively. Another important outcome was to achieve various α-amylase and α-glucosidase fuzzy rules with high Certainty Factor values. Fuzzyrules derived from the training series and active classification rules were interpreted. An important external validation step, comparing our method with those previously reported, was also included. Results: The Holm’s test comparison showed significant differences (p-value<0.05) between FURIA-C, Linear Discriminating Analysis (LDA), and Bayesian Networks, the former beating the two latter according to the relative ranking score of the Holm’s test. Conclusion: From these results, the FURIA-C algorithm could be used as a cutting-edge technique to predict (classify or screen) the α-amylase and α-glucosidase inhibitory activity of new compounds and hence speed up the discovery of new potent multi-target antidiabetic agents.

Background: Post-stroke spasticity (PSS) is a major cause of disability, leading to severely impaired upper-limb flexibility and ability to walk and move, significantly affecting the quality of life of cerebral infarction patients. There is currently no recognized effective therapy. Alternatively, Chinese traditional medicine has shown promise for PSS treatment. In this regard, the BSLSF has been reported to be effective; however, its underlying mechanism remains unclear. Objective: The objective of this study is to clarify the main targets and pathways of Baishao Luoshi Formula (BSLSF) during PSS treatment, laying the foundation for further research on its pharmacological effects. Methods: In this study, network pharmacology and experimental verification were conducted to explore the potential mechanism of BSLSF systematically. After obtaining active ingredients of BSLSF from the TCMSP database, SwissTarget-Prediction and PharMapper were used to uncover BSLSF targets. PSS-related targets were gathered with GeneCards and Online Mendelian Inheritance in Man. The differentially expressed genes between BSLSF and PSS were identified by a Venn plot. The drugactive ingredient-target interaction network and Protein-protein interaction (PPI) were constructed using Cytoscape and further analyzed using the MCC algorithm of CytoHubba plugin. Then, Pathway enrichment and GO biological process enrichment analyses were performed. Subsequently, a mice model of middle cerebral artery occlusion (MCAO) was established for the in vivo experiments. Results: We found that AKT1, TNF, CASP3, VEGFA, and CREB1 were potential targets during PSS treatment. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that the mechanism of PSS was closely related to synaptic plasticity. And the immunohistochemical staining showed that BSLSF protected against ischemic stroke via the CCR5/CREB signaling pathway and probably affected synaptic plasticity. Conclusion: our study validated that treatment with BSLSF protected against ischemic stroke via the CCR5/CREB signaling pathway and could affect synaptic plasticity. In a sense, this study provides the basis for further extensive and in-depth analysis of BSLSF, enabling the quest for new drug targets at the same time.

Objective: To examine the effect and mechanism of volatile components of Rabdosia rubescens on gastric cancer. Methods: Gas chromatography-mass spectrometry was used to detect and identify the volatile components of R. rubescens. The network pharmacology method was used to analyze the targets of volatile components of R. rubescens in gastric cancer and to reveal their molecular mechanisms. The effects of volatile components of R. rubescens on gastric cancer cells were verified by biological experiments. Results: Thirteen volatile components of R. rubescens were selected as pharmacologically active components. The 13 active components had 83 targets in gastric cancer, and a Traditional Chinese Medicine-component-targets gastric cancer network was successfully constructed. Five core targets were obtained: TNF, IL1B, MMP9, PTGS2 and CECL8. The volatile components inhibited the proliferation of gastric cancer cells in a concentration-dependent manner and promoted the apoptosis of gastric cancer cells. The volatile components reduced the levels of TNF, IL1B, MPP9, and PTGS2 in a concentration-dependent manner. Conclusion: Our study demonstrates the effects of volatile components in R. rubescens on gastric cancer and provides preliminary findings on their mechanisms of action.

Aim: This study aimed to identify promising allosteric inhibitors with the potential to inhibit EGFR1, PI3K, and BRAF kinases as a single agent or in a combination of existing drugs, thus acting as a therapeutic option when traditional drugs fail to give a beneficial response in disease pathology. Background: Upregulation of EGFR1 activates several downstream signaling pathways, resulting in pathophysiological alterations that contribute to cancer. The RAS/RAF/MEK/ERK (MAPK) and PI3K/Akt/mTOR (PI3K/Akt/mTOR) pathways are major downstream signalling partners induced by EGFR1 activation. Despite their vast importance, allosteric FDA-approved drugs targeting EGFR1 and these pathways are not available. Objective: The objective of the study is to identify novel multi-kinase small molecules with the potential to inhibit major sites of amplification of cancer signalling pathways, i.e., EGFR1, PI3K/Akt/mTOR, and RAS/RAF/MEK/ERK (MAPK) signalling pathways targeting allosteric sites. Methods: In silico methods were used to identify the potential inhibitors using EGFR1, PI3, and BRAF crystal structures complexed with allosteric inhibitors. The potential novel molecules were confirmed for their drug-likeness. Their stability of binding was also confirmed using molecular dynamics simulation studies. To eliminate false negatives, this study used a pharmacophore and structure-based targeting method. Results: The current study was effective in identifying drug-like small molecules, such as ZINC38783966, ZINC01456629, ZINC01456628, and 124173751, 137352549, 137353176, 137352399, 132020316 from ZINC and PubChem database, respectively, with a potential to bind EGFR1 (6DUK), PI3 (4A55) and BRAF (6P3D) at allosteric sites. A 50 ns molecular dynamics investigation also revealed that these potential novel multitarget kinase allosteric inhibitors exhibited stable binding. Conclusion: Alterations in EGFR1, PI3K/Akt/mTOR, and RAS/RAF/MEK/ERK (MAPK) signalling pathways are observed in cancers in high frequency and are also used by viral and environmental toxicants for pathologic purposes. These multi-kinase allosteric inhibitors will provide insight into allosteric drug discovery and deepen our understanding of targeting these pathways, either individually or in combination with orthosteric inhibitors.

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) variants with novel spike protein mutations has been shown to be influencing the epidemiological and clinical aspects of the COVID-19 pandemic. Objective: Due to studies showing various dietary benefits of zinc as a viral replication inhibitor as well as an immunity enhancer, organometallic complexes incorporating zinc ions can be ideal antiviral candidates due to their structural variation and diverse stereochemistry. Methods: In silico studies were conducted for the virtual screening of zinc complexes with SARSCoV- 2 and host proteins to explore their effect on viral entry and replication activity. Molegro Virtual Docker along with AutoDock was used for the identification of potential SARS-CoV-2 inhibitor complexes from the Cambridge Structural Database (CSD). Molecular dynamics (MD), density functional theory (DFT), chemical absorption, distribution, metabolism, excretion, and toxicity properties (ADMET) were used to support the findings from virtual screening. Results: In correlation with SARS-CoV-2 RNA-dependent RNA polymerase and spike receptorbinding domain bound with ACE2 docking results, the compound (bis(3,5-dimethyl-1H-pyrazole)- bis(2-furoato)-zinc(ii)) (CSD code ECOZAA) occurs to be a potential metal complex SARS-CoV-2 receptor inhibitor. The compound ECOZAA was observed (in silico binding affinity = - 179.29kcal/mol) to behave better than the clinically approved drug Remdesivir (in silico binding affinity = -62.69kcal/mol) against SARS-CoV-2 RNA-dependent RNA polymerase. The large HOMO- LUMO gap for the ECOZAA compound is an indication of the low chemical reactivity as well as the great kinetic stability of the compound. Conclusion: Thus, this study highlights the potential use of zinc metal complexes as SARS-CoV-2 viral entry and replication inhibitors.