Exploration of potential molecular mechanisms and genotoxicity of anti-cancer drugs using next generation knowledge discovery methods.

Background & Objectives: Accurate identification of molecular and toxicological functions of potential drug candidates is crucial for drug discovery and development. This may aid in the evaluation of the risks of genotoxicity and carcinogenesis. In addition, in silico characterization of existing and new drugs might offer clues for future investigations and aid in the development of anticancer treatments. Using next-generation knowledge discovery (NGKD) methodology, we endeavored to establish a risk assessment of anticancer drugs for their molecular mechanism(s) and genotoxicity. Methods: This study was performed at the Faculty of Applied Medical Sciences, King Abdulaziz University (KAU), Jeddah, Saudi Arabia, in November 2022. Using innovative in silico model systems, we assessed the molecular mechanism of action and toxicity of around 20 distinct substances such as Deguelin, Etoposide, Camptothecin, Cytarabine (Ara-C), Cisplatin, Hydroxyurea, Trichostain A, Antimycin, Colchicine, 2-deoxyglucose, Tunicamycin, Thapsigargin, Vinblastin, Docetaxel, Oxaliplatin, Methotrexate, 5-flurouracil, Bleomycin, Taxol (Paclitaxel), and Apicidin. Using the Ingenuity Pathway Analysis (IPA) knowledge base, the number of targets for each compound was determined in silico. Subsequently, they were examined using Fisher's exact test and Benjamini Hochberg Multiple Testing Correction (P<0.05) and submitted to core analysis with IPA to decode the biological and toxicological activities differently controlled by these drugs. In addition, a multiple comparison module in IPA was used to compare the core analyses of each molecule. In addition, we obtained the top 100 protein targets of Etoposide, Camptothecin, and Ara-C using SwissTargetPrediction, as well as the key pathways and gene ontologies affected by these drugs and disease associations using the WebGestalt tool. Results: We identified distinct toxicological signatures and canonical signaling pathways in tumor cell lines regulated by these 20 anticancer drugs. These signaling pathways included cell death and apoptosis in addition to molecular processes, p53 signaling, and aryl hydrocarbon receptor signaling. The TP53 signaling pathway is utilized by these agents to effectively trigger cell death and apoptosis, and p53 functions as a master regulator in a variety of cellular stress responses, including genotoxic stress. Conclusion: Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and effectiveness of treatment. Our mechanism based "NGKD" tools have more relevance for the identification of safer therapies and has the potential to lead to the rational screening of drug candidates targeting specific molecular networks and canonical pathways implicated in cancer and genotoxicity. In addition, the combination of protein, microRNA and metabolome profiles may be essential for the development of translatable biomarkers for the safety and efficacy of pharmacotherapeutic agents.Our research has laid the groundwork for the discovery of additional biomarkers that assess both the safety and the effectiveness of a treatment.

Comprehensive assessment of nine target prediction web services: which should we choose for target fishing?

Identification of potential targets for known bioactive compounds and novel synthetic analogs is of considerable significance. In silico target fishing (TF) has become an alternative strategy because of the expensive and laborious wet-lab experiments, explosive growth of bioactivity data and rapid development of high-throughput technologies. However, these TF methods are based on different algorithms, molecular representations and training datasets, which may lead to different results when predicting the same query molecules. This can be confusing for practitioners in practical applications. Therefore, this study systematically evaluated nine popular ligand-based TF methods based on target and ligand-target pair statistical strategies, which will help practitioners make choices among multiple TF methods. The evaluation results showed that SwissTargetPrediction was the best method to produce the most reliable predictions while enriching more targets. High-recall similarity ensemble approach (SEA) was able to find real targets for more compounds compared with other TF methods. Therefore, SwissTargetPrediction and SEA can be considered as primary selection methods in future studies. In addition, the results showed that k = 5 was the optimal number of experimental candidate targets. Finally, a novel ensemble TF method based on consensus voting is proposed to improve the prediction performance. The precision of the ensemble TF method outperforms the individual TF method, indicating that the ensemble TF method can more effectively identify real targets within a given top-k threshold. The results of this study can be used as a reference to guide practitioners in selecting the most effective methods in computational drug discovery.

Antiatherogenic Effects of Quercetin in the THP-1 Macrophage Model In Vitro, With Insights Into Its Signaling Mechanisms Using In Silico Analysis.

Background: Atherosclerosis (AS), a major risk factor for stroke and brain tissue destruction, is an inflammatory disease of the blood vessels, and the underlying pathology is inflammation mediated by various chemokines and cytokines. Quercetin, a natural flavonol, is reported to have both anti-inflammatory and antioxidant properties. As such, in the present study, we evaluated the antiatherogenic effects of quercetin in a human THP-1 cell line in vitro and also the signaling mechanisms using in silico analysis. Materials and Methods: THP-1 macrophages exposed to different concentrations of quercetin (5-100 µM for 24 h) were tested for cytotoxicity. Real-time gene expression assay for intercellular adhesion molecule-1 (ICAM-1) and monocyte chemoattractant protein-1 (MCP-1) was carried out following treatment with quercetin at 15 and 30 µM for 24 h either in the absence or presence of interferon (IFN-γ) for 3 h to induce inflammation. Monocyte migration and cholesterol efflux were also assessed. Results: Quercetin did not exert any cytotoxic effects on THP-1 cells at the various concentrations tested. The gene expression assay showed a significant decrease in ICAM-1 (by 3.05 and 2.70) and MCP-1 (by 22.71 and 27.03), respectively. Quercetin at 15 µM decreased THP-1 monocyte migration by 33% compared to the MCP-1-treated cells. It also increased cholesterol efflux significantly by1.64-fold and 1.60-fold either alone or in combination with IFN-γ, respectively. Ingenuity Pathway Analysis of the molecular interactions of quercetin identified canonical pathways directly related to lipid uptake and cholesterol efflux. Furthermore, CD36, SR-A, and LXR-α also demonstrated significant increases by 72.16-, 149.10-, and 29.68-fold, respectively. Conclusion: Our results from both in vitro and in silico studies identified that quercetin inhibited the THP-1 monocyte migration, MCP-1, and ICAM-1 and increased cholesterol efflux probably mediated via the LXR/RXR signaling pathway. Therefore, quercetin will help prevent cell infiltration in atherosclerotic plaques and reduce the risk of stroke or brain destruction.

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction.

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Nature's treasures, however, could be used more effectively. Yet, reliable pipelines for the large-scale target prediction of natural products are still rare. We developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs)-a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17ß-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target predictions and guidance on using the respective tools.

In vitro Evaluation of the Anti-inflammatory Effects of Thymoquinone in Osteoarthritis and in silico Analysis of Inter-Related Pathways in Age-Related Degenerative Diseases.

Chronic inflammation is a common underlying factor in osteoarthritis (OA) and most age-related degenerative diseases. As conventional therapies help only in partial alleviation of symptoms in OA, stem cell-based therapies and herbal supplements are being widely explored. Thymoquinone (TQ), an active ingredient of Nigella sativa is reported to have immunomodulatory, anti-inflammatory and antioxidant properties. We evaluated the effects of TQ on bone marrow MSCs (BM-MSCs) derived from OA patients and its interrelated pathways in inflammation and age-related degenerative diseases using Ingenuity Pathway Analysis (IPA) as well as possible molecular targets using SwissTargetPrediction. BM-MSCs were derived from OA patients and their stemness properties were characterized by studying the MSCs related CD surface marker expression and differentiation into adipocytes, osteoblasts, and chondrocytes. Treatment with TQ (100 nM-5 µM) demonstrated cell death, especially at higher concentrations. MTT assay demonstrated a significant concentration-dependent decrease in cell viability which ranged from 20.04% to 69.76% with higher doses (300 nM, 1 µM, and 5 µM), especially at 48h and 72h. Additional cell viability testing with CellTiter-Blue also demonstrated a significant concentration-dependent decrease in cell viability which ranged from 27.80 to 73.67% with higher doses (300 nM, 1 µM, 3 µM, and 5 µM). Gene expression analysis following treatment of BM-MSCs with TQ (1 and 3 µM) for 48h showed upregulation of the anti-inflammatory genes IL-4 and IL-10. In contrast, the pro-inflammatory genes namely IFN-γ, TNF-α, COX-2, IL-6, IL-8, IL-16, and IL-12A although were upregulated, compared to the lower concentration of TQ (1 µM) they were all decreased at 3 µM. The pro-apoptotic BAX gene was downregulated while the SURVIVIN gene was upregulated. IPA of the molecular interaction of TQ in inflammation and age-related degenerative diseases identified canonical pathways directly related to synaptogenesis, neuroinflammation, TGF-ß, and interleukin signaling. Further screening led to the identification of 36 molecules that are involved in apoptosis, cell cycle regulation, cytokines, chemokines, and growth factors. SwissTargetPrediction of TQ identified potential molecular targets with high probability. TQ exerted anti-inflammatory effects and therefore can be a useful adjuvant along with conventional therapies against inflammation in OA and other age-related degenerative diseases.

Decoding the Role of Sphingosine-1-Phosphate in Asthma and Other Respiratory System Diseases Using Next Generation Knowledge Discovery Platforms Coupled With Luminex Multiple Analyte Profiling Technology.

Sphingosine-1-phosphate (S1P) is a pleiotropic sphingolipid derived by the phosphorylation of sphingosine either by sphingosine kinase 1 (SPHK1) or SPHK2. Importantly, S1P acts through five different types of G-protein coupled S1P receptors (S1PRs) in immune cells to elicit inflammation and other immunological processes by enhancing the production of various cytokines, chemokines, and growth factors. The airway inflammation in asthma and other respiratory diseases is augmented by the activation of immune cells and the induction of T-helper cell type 2 (Th2)-associated cytokines and chemokines. Therefore, studying the S1P mediated signaling in airway inflammation is crucial to formulate effective treatment and management strategies for asthma and other respiratory diseases. The central aim of this study is to characterize the molecular targets induced through the S1P/S1PR axis and dissect the therapeutic importance of this key axis in asthma, airway inflammation, and other related respiratory diseases. To achieve this, we have adopted both high throughput next-generation knowledge discovery platforms such as SwissTargetPrediction, WebGestalt, Open Targets Platform, and Ingenuity Pathway Analysis (Qiagen, United States) to delineate the molecular targets of S1P and further validated the upstream regulators of S1P signaling using cutting edge multiple analyte profiling (xMAP) technology (Luminex Corporation, United States) to define the importance of S1P signaling in asthma and other respiratory diseases in humans.

Application of the SwissDrugDesign Online Resources in Virtual Screening.

SwissDrugDesign is an important initiative led by the Molecular Modeling Group of the SIB Swiss Institute of Bioinformatics. This project provides a collection of freely available online tools for computer-aided drug design. Some of these web-based methods, i.e., SwissSimilarity and SwissTargetPrediction, were especially developed to perform virtual screening, while others such as SwissADME, SwissDock, SwissParam and SwissBioisostere can find applications in related activities. The present review aims at providing a short description of these methods together with examples of their application in virtual screening, where SwissDrugDesign tools successfully supported the discovery of bioactive small molecules.