Current Computer - Aided Drug Design - Volume 19, Issue 1, 2023

Background: Silymarin is a flavonolignan extracted from Silybum marianum with various therapeutic applications. Many studies have focused on improving the bioavailability of silymarin due to its wide range of efficacy and low bioavailability. Chitosan, a naturally occurring polymeric substance, has a strong reputation for increasing the solubility of poorly soluble compounds. Objective: This study used artificial neural networks (ANNs) to measure the effects of pH, chitosan to silymarin ratio, chitosan to tripolyphosphate ratio, and stirring time on the loading efficiency of silymarin into chitosan particles. Methods: A model was developed to investigate the interactions between input factors and silymarin loading efficiency. The DPPH method was utilized to determine the antioxidant activity of an optimized formula and pure raw materials. Results: According to the outcome of the ANN model, pH and the chitosan to silymarin ratio demonstrated significant effects on loading efficiency. In addition, increased stirring time decreased silymarin loading, whereas the chitosan-to-tripolyphosphate ratio showed a negligible effect on loading efficiency. Conclusion: Maximum loading efficiency occurred at a pH of approximately∼5. Moreover, silymarin- loaded chitosan particles with a lower IC50 value (36.17 ± 0.02 ppm) than pure silymarin (165.04 ± 0.07 ppm) demonstrated greater antioxidant activity.

Background: Rhizoma drynariae, a classic prescription in traditional Chinese medicine, has long been used for the treatment of osteonecrosis of the femoral head (ONFH), but its potential targets and molecular mechanisms remain to be further explored. Objective: This study aims to explore the mechanism of Rhizoma drynariae in ONFH treatment via network pharmacology and in vitro experiments. Methods: Targets of Rhizoma drynariae and ONFH were predicted using relevant databases, and intersection analysis was conducted to screen for shared targets. A PPI network of the shared targets was built using STRING to identify the key targets. Functional enrichment analyses of Gene Ontology and KEGG pathway data were carried out using R software. The compound-target-pathway network was constructed for Rhizoma Drynariae in the treatment with ONFH using Cytoscape 3.9.0. Cell proliferation was assessed using CCK8 and apoptosis was detected using (Propidium Iodide) PI staining and western blotting. Results: This study depicts the interrelationship of the bioactive compounds of Rhizoma drynariae with ONFH-associated signaling pathways and target receptors and is a potential reagent for ONFH treatment. Conclusion: Based on a network pharmacology analysis and in vitro experiment, we predicted and validated the active compounds and potential targets of Rhizoma drynariae, provide valuable evidence of Rhizoma Drynariae in future ONFH treatment.

Aim: With several experimental studies establishing the role of Bacopa monnieri as an effective neurological medication, less focus has been employed to explore how effectively Bacopa monnieri brings about this property. The current work focuses on understanding the molecular interaction of the phytochemicals of the plant against different neurotrophic factors to explore their role and potential as potent anti-neurodegenerative drugs. Background: Neurotrophins play a crucial role in the development and regulation of neurons. Alterations in the functioning of these Neurotrophins lead to several Neurodegenerative Disorders. Albeit engineered medications are accessible for the treatment of Neurodegenerative Disorders, due to their numerous side effects, it becomes imperative to formulate and synthesize novel drug candidates. Objective: This study aims to investigate the potential of Bacopa monnieri phytochemicals as potent antineurodegenerative drugs by inspecting the interactions between Neurotrophins and target proteins. Methods: The current study employs molecular docking and molecular dynamic simulation studies to examine the molecular interactions of phytochemicals with respective Neurotrophins. Further inspection of the screened phytochemicals was performed to analyze the ADME-Tox properties in order to classify the screened phytochemicals as potent drug candidates. Results: The phytochemicals of Bacopa monnieri were subjected to in-silico docking with the respective Neurotrophins. Vitamin E, Benzene propanoic acid, 3,5-bis (1,1- dimethylethyl)- 4hydroxy-, methyl ester (BPA), Stigmasterol, and Nonacosane showed an excellent binding affinity with their respective Neurotrophins (BDNF, NT3, NT4, NGF). Moreover, the molecular dynamic simulation studies revealed that BPA and Stigmasterol show a very stable interaction with NT3 and NT4, respectively, suggesting their potential role as a drug candidate. Nonacosane exhibited a fluctuating binding behavior with NGF which can be accounted for by its long linear structure. ADME-Tox studies further confirmed the potency of these phytochemicals as BPA violated no factors and Vitamin E, Stigmasterol and Nonacosane violated 1 factor for Lipinski’s rule. Moreover, their high human intestinal absorption and bioavailability score along with their classification as non-mutagen in the Ames test makes these compounds more reliable as potent antineurodegenerative drugs. Conclusion: Our study provides an in-silico approach toward understanding the anti-neurodegenerative property of Bacopa monnieri phytochemicals and establishes the role of four major phytochemicals which can be utilized as a replacement for synthetic drugs against several neurodegenerative disorders.

Background: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterations can impact biological activity via in-silico. Aim: Our present study aimed to work on a trending machine learning approach with a new opensource data analysis python script for the discovery of anticancer lead via building the QSAR model by using 53 compounds of thiazole derivatives. Methods: A python script has been executed with 53 small thiazole chemicals using Google collaboratory interface. A total of 82 CDK molecular descriptors were downloaded from “chemdes” web server and used for our study. After training the model, we checked the model performance via cross-validation of the external test set. Results: The generated QSAR model afforded the ordinary least squares (OLS) regression as R² = 0.542, F=8.773, and adjusted R² (Q2) =0.481, std. error = 0.061, reg.coef_developed were of, - 0.00064 (PC1), -0.07753 (PC2), -0.09078 (PC3), -0.08986 (PC4), 0.05044 (PC5), and reg.intercept_ of 4.79279 developed through stats models, formula module. The performance of test set prediction was done by multiple linear regression, support vector machine, and partial least square regression classifiers of sklearn module, which generated the model score of 0.5424, 0.6422 and 0.6422 respectively. Conclusion: Hence, we conclude that the R2values (i.e. the model score) obtained using this script via three diverse algorithms were correlated well and there is not much difference between them and may be useful in the design of a similar group of thiazole derivatives as anticancer agents.

Background: To date, very few small drug molecules are used for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has been discovered since the epidemic commenced in November 2019. SARS-CoV-2 RdRp and spike protein are essential targets for drug development amidst whole variants of coronaviruses. Objective: This study aims to discover and recognize the most effective and promising small molecules against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and spike protein targets through molecular docking screening of 39 phytochemicals from five different Ayurveda medicinal plants. Methods: The phytochemicals were downloaded from PubChem, and SARS-CoV-2 RdRp and spike protein were taken from the protein data bank. The molecular interactions, binding energy, and ADMET properties were analyzed. Results: Molecular docking analysis identified some phytochemicals, oleanolic acid, friedelin, serratagenic acid, uncinatone, clemaphenol A, sennosides B, trilobine and isotrilobine from ayurvedic medicinal plants possessing greater affinity against SARS-CoV-2-RdRp and spike protein targets. Two molecules, namely oleanolic acid and sennosides B, with low binding energies, were the most promising. Furthermore, based on the docking score, we carried out MD simulations for the oleanolic acid and sennosides B-protein complexes. Conclusion: Molecular ADMET profile estimation showed that the docked phytochemicals were safe. The present study suggested that active phytochemicals from medicinal plants could inhibit RdRp and spike protein of SARS-CoV-2.

Background: A network pharmacology study on the biological action of Tripterygium wilfordii on myocardial fibrosis (MF). Methods: The effective components and potential targets of tripterygium wilfordii were screened from the TCMSP database to develop a combination target network. A protein-protein interaction network was constructed by analyzing the interaction between tripterygium wilfordii and MF; then, the Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed. Furthermore, molecular docking was utilized to verify the network analysis results. Results: It was predicted that MF has 29 components contributing to its effectiveness and 87 potential targets. It is predicted that Tripterygium wilfordii has 29 active components and 87 potential targets for the treatment of MF. The principal active components of tripterygium wilfordii include kaempferol, β-sitosterol, triptolide, and Nobiletin. Signaling pathways: AGE-RAGE, PI3K-Akt, and MAPK may be involved in the mechanism of its action.7 Seven key targets (TNF, STAT3, AKT1, TP53, VEGFA, CASP3, STAT1) are possibly involved in treating MF by tripterygium wilfordii. Conclusion: This study shows the complex network relationship between multiple components, targets, and pathways of Tripterygium wilfordii in treating MF.