Current Computer - Aided Drug Design - Current Issue

Astrocytoma is the most common glioma, accounting for about 65% of glioblastoma. Its malignant transformation is also one of the important causes of patient mortality, making it the most prevalent and difficult to treat in primary brain tumours. However, little is known about the underlying mechanisms of this transformation.

In this study, we established a ceRNA network to screen out the potential regulatory pathways involved in the malignant transformation of IDH-mutant astrocytomas. Firstly, the Chinese Glioma Genome Atlas (CGGA) was employed to compare the expression levels of the differential expressed genes (DEGs) in astrocytomas. Then, the ceRNA-regulated network was constructed based on the interaction of lncRNA-miRNA-mRNA. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to explore the main functions of the differentially expressed genes. COX regression analysis and log-rank test were combined to screen the ceRNA network further. In addition, quantitative real-time PCR (qRT-PCR) was conducted to identify the potential regulatory mechanisms of malignant transformation in IDH-mutant astrocytoma.

A ceRNA network with 34 lncRNAs, 29 miRNAs, and 71 mRNAs. GO and KEGG analyses results suggested that DEGs were associated with tumor-associated molecular functions and pathways. In addition, we screened two ceRNA regulatory networks using Cox regression analysis and log-rank test. QRT-PCR assay identified the NAA11/hsa-miR-142-3p/GS1-39E22.2 regulatory axis of the ceRNA network to be associated with the malignant transformation of IDH-mutant astrocytoma.

The discovery of this mechanism deepens our understanding of the molecular mechanisms of malignant transformation in astrocytomas and provides new perspectives for exploring glioma progression and targeted therapies.

Valproic acid (VPA)-induced hepatotoxicity is among the most common and severe adverse drug reactions, limiting its clinical application. Recent studies have suggested that activating the farnesoid X receptor (FXR) could be a promising therapeutic approach to alleviate VPA-induced hepatotoxicity; however, related research remains limited.

This study aims to comprehensively investigate the mechanisms underlying FXR activation by obeticholic acid (OCA) for the treatment of VPA-induced hepatotoxicity.

Network pharmacology was performed to identify potential targets and pathways underlying the amelioration of VPA-induced hepatotoxicity by OCA. The identified pathways were validated through GEO data analysis, and the affinities between OCA and potential key targets were predicted using molecular docking as well as molecular dynamics simulations.

A total of 462 targets associated with VPA-induced hepatotoxicity and 288 targets of OCA were identified, with 81 shared targets. KEGG pathway and GO enrichment analysis indicated that the effect of OCA on VPA-induced hepatotoxicity primarily involved lipid metabolism, as well as oxidative stress and inflammation. The results from GEO data analysis, molecular docking, and molecular dynamics simulations revealed a close association between bile secretion, the PPAR signaling pathway, and the treatment of VPA-induced hepatotoxicity by OCA.

Our findings suggest that OCA exhibits potential therapeutic efficacy against VPA-induced hepatotoxicity through multiple targets and pathways, thereby highlighting the therapeutic potential of FXR as a target for treating VPA-induced hepatotoxicity.

Berberine (BBR), also known as berberine hydrochloride, was isolated from the rhizomes of the Coptis chinensis. Studies have reported that BBR plays an important role in glycolipid metabolism, including insulin resistance (IR). The targets, and molecular mechanisms of BBR against hyperlipid-induced IR is worthy to be further studied.

The related targets of BBR were identified via Pharmmapper database and relevant targets of diabetes were obtained through GeneCards and Online Mendelian Inheritance in Man (OMIM) database. The common targets were employed with the STRING database and visualized with the protein-protein interactions (PPI) network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to explore the biological progress and pathways. In vitro, human hepatocellular carcinomas (HepG2) cell was used as experimental cell line, and an insulin resistant HepG2 cell model (IR-HepG2) was constructed using free fatty acid induction. After intervention with BBR, glucose consumption and uptake in HepG2 cells were observed. Molecular docking was used to test the interaction between BBR and key targets, and real-time fluorescence quantitative PCR was used to detect the regulatory effect of BBR on related targets.

262 overlapped targets were extracted from BBR and diabetes. In the KEGG enrichment analysis, the peroxisome proliferator activated receptor (PPAR) signaling pathway was included. In vitro experiments, BBR can significantly increase sugar consumption and uptake in IR HepG2 cells, while PPAR inhibitors can weaken the effect of BBR on IR-HepG2.

The PPAR signaling pathway is one of the important pathways for BBR to improve high-fat-induced insulin resistance in HepG2 cells.

Blocking Kv 1.2 and Kv 1.3 potassium channels using scorpion venom-derived toxins holds potential therapeutic value. These channels are implicated in autoimmune diseases such as neurodegenerative diseases, multiple sclerosis, rheumatoid arthritis, and type 1 diabetes.

The present work aims at the discovery and in silico activity analysis of potassium channel blockers (KTxs) from the cDNA library derived from the venom gland of Iranian scorpion Hemiscorpius lepturus (H. lepturus).

The sequence regarding potassium channel blockers were extracted based on Gene Ontology for H. lepturus venom gland. Homology analyses, superfamily, family, and evolutionary signatures of H. lepturus KTxs (H.L KTxs) were determined by using BLASTP, COBALT, PROSITE, and InterPro servers. The predicted 3D structures of H.L KTxs were superimposed against their homologs to predict structure activity relationship. Molecular docking analysis was also performed to predict the binding affinity of H.L KTxs to Kv 1.2 and Kv 1.3 channels. Finally, the toxicity was predicted.

Seven H.L KTxs, designated as Leptukalin, were extracted from the cDNA library of H. lepturus venom gland. Homology analyses proved that they can act as potassium channel blockers and they belong to the superfamily and family of Scorpion Toxin-like and Short-chain scorpion toxins, respectively. Structural alignment results confirmed the activity of H.L KTxs. Binding affinity of all H.L KTxs to Kv 1.2 and Kv 1.3 channels ranged from -4.4 to -5.5 and -4 to -5.7 Kcal/mol, respectively. In silico toxicity assay showed that Leptukalin 3, Leptukalin 5, and Leptukalin 7 were non-toxic.

Three non-toxic KTxs, Leptukalin 3, 5, and 7, were successfully discovered from the cDNA library of H. lepturus venom gland. Gathering all data together, the discovered peptides are promising potassium channel blockers. Accordingly, Leptukalin 3, 5, and 7 could be suggested for complementary in vitro studies and mouse model of autoimmune diseases.

Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets.

The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance.

We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement.

Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.

The prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing globally, impacting individuals in Western nations and rapid growing in Asian countries due to sedentary lifestyles; thus, NAFLD has emerged as a significant worldwide health concern. Presently, lifestyle changes represent the primary approach to managing NAFLD.

This research aimed to identify the potential drug targets for treating NAFLD through comprehensive in silico computational analysis. These include the prediction of the three-dimensional structure of the protein, the prediction of inhibitors by PubChem and ZINC, molecular docking by Autodcok, pharmacophore modeling, molecular dynamics simulation by the OPLS_2005 force field, and the orthorhombic box solvent model Intermolecular Interaction Potential 3 Points Transferable to the selected compound. The toxicity of the lead compounds was analyzed through AdmetSAR software.

The protein associated with the PNPLA3 gene, whose overall three-dimensional structure was 95% accurate, were retrieved following inhibitor selection via PubChem and ZINC. Among the selected inhibitors and docked compounds with ID 10033935 (ellagitannin) showed a minimum E-Score of -17.266. In docking and pharmacophore modeling the compound ellagitannin shows promise as a potential drug candidate. Moreover, the molecular dynamics and structural stability of the protein-ligand complex were evaluated with several metrics such as as root mean square fluctuation and root mean square deviation and resulted in the stability not only of PNPLA3-10033935 (ellagitannin) but also of compound PNPLA3-71448940 and PNPLA3-5748394 complexed proteins at 400 ns with very slight variation.

Overall, ellagitannin was identified as the best druggable target with the best therapeutics profile. The findings of our study can pave the way for the development of a new drug against NALFD.