Integrated Network Pharmacology Approach for Drug Combination Discovery: A Multi-Cancer Case Study

Harnessing Drug Repurposing to Combat Breast Cancer by Targeting Altered Metabolism and Epithelial-to-Mesenchymal Transition Pathways

Breast cancer remains one of the most prevalent and challenging cancers to treat due to its complexity and heterogenicity. Cellular processes such as metabolic reprogramming and epithelial-to-mesenchymal transition (EMT) contribute to the complexity of breast cancer by driving uncontrolled cell division, metastasis, and resistance to therapies. Strategically targeting these intricate pathways can effectively impede breast cancer progression, thereby revealing significant potential for therapeutic interventions. Among various emerging therapeutic approaches, drug repurposing offers a promising avenue for enhancing clinical outcomes. In recent years, high-throughput screening, QSAR, and network pharmacology have been widely employed to identify promising repurposed drugs. As an outcome, several drugs, such as Metformin, Itraconazole, Pimozide, and Disulfiram, were repurposed to regulate metabolic and EMT pathways. Moreover, strategies such as combination therapy, targeted delivery, and personalized medicine were utilized to enhance the efficacy and specificity of the repurposed drugs. This review focuses on the potential of targeting altered metabolism and EMT in breast cancer through drug repurposing. It also highlights recent advancements in drug screening techniques, associated limitations, and strategies to overcome these challenges.

Comprehensive investigation of niosomal red palm wax gel encapsulating ginger (Zingiber officinale Roscoe): Network pharmacology, molecular docking, In vitro studies and phase 1 clinical trials

Ginger, a Zingeberaceae family member, is notable for its anti-inflammatory properties. This study explores the pharmaceutical mechanisms of ginger and red palm wax co-extract, developing novel niosomal formulations for enhanced transdermal delivery. Evaluations included physical characteristics, drug loading, in vitro release, network pharmacology, molecular docking, and biocompatibility. The niosomal ginger with red palm wax gel (NGPW) exhibited non-Newtonian fluid properties. The optimized niosome formulation (cholesterol: Tween80: Span60 = 12.5: 20: 5 w/w) showed a high yield (93.23 %), high encapsulation efficiency (54.71 %), and small size (264.33 ± 5.84 nm), prolonging in vitro anti-inflammatory activity. Human skin irritation and biocompatibility tests on 1 % NGPW showed favorable cytotoxicity and hemocompatibility results (ISO10993). Network pharmacology identified potential targets, while molecular docking highlighted high affinities between gingerol and red palm wax compounds with TRPM8 and TRPV1 proteins, suggesting pain inhibition via serotonergic synapse pathways. NGPW presents a promising transdermal pain inhibitory drug delivery strategy.

PHGDH: a novel therapeutic target in cancer

Serine is a key contributor to the generation of one-carbon units for DNA synthesis during cellular proliferation. In addition, it plays a crucial role in the production of antioxidants that prevent abnormal proliferation and stress in cancer cells. In recent studies, the relationship between cancer metabolism and the serine biosynthesis pathway has been highlighted. In this context, 3-phosphoglycerate dehydrogenase (PHGDH) is notable as a key enzyme that functions as the primary rate-limiting enzyme in the serine biosynthesis pathway, facilitating the conversion of 3-phosphoglycerate to 3-phosphohydroxypyruvate. Elevated PHGDH activity in diverse cancer cells is mediated through genetic amplification, posttranslational modification, increased transcription, and allosteric regulation. Ultimately, these characteristics allow PHGDH to not only influence the growth and progression of cancer but also play an important role in metastasis and drug resistance. Consequently, PHGDH has emerged as a crucial focal point in cancer research. In this review, the structural aspects of PHGDH and its involvement in one-carbon metabolism are investigated, and PHGDH is proposed as a potential therapeutic target in diverse cancers. By elucidating how PHGDH expression promotes cancer growth, the goal of this review is to provide insight into innovative treatment strategies. This paper aims to reveal how PHGDH inhibitors can overcome resistance mechanisms, contributing to the development of effective cancer treatments.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Prevention of neointimal hyperplasia after coronary artery bypass graft via local delivery of sirolimus and rosuvastatin: network pharmacology and in vivo validation

BACKGROUND

Coronary artery bypass graft (CABG) is generally used to treat complex coronary artery disease. Treatment success is affected by neointimal hyperplasia (NIH) of graft and anastomotic sites. Although sirolimus and rosuvastatin individually inhibit NIH progression, the efficacy of combination treatment remains unknown.

METHODS

We identified cross-targets associated with CABG, sirolimus, and rosuvastatin by using databases including DisGeNET and GeneCards. GO and KEGG pathway enrichment analyses were conducted using R studio, and target proteins were mapped in PPI networks using Metascape and Cytoscape. For in vivo validation, we established a balloon-injured rabbit model by inducing NIH and applied a localized perivascular drug delivery device containing sirolimus and rosuvastatin. The outcomes were evaluated at 1, 2, and 4 weeks post-surgery.

RESULTS

We identified 115 shared targets between sirolimus and CABG among databases, 23 between rosuvastatin and CABG, and 96 among all three. TNF, AKT1, and MMP9 were identified as shared targets. Network pharmacology predicted the stages of NIH progression and the corresponding signaling pathways linked to sirolimus (acute stage, IL6/STAT3 signaling) and rosuvastatin (chronic stage, Akt/MMP9 signaling). In vivo experiments demonstrated that the combination of sirolimus and rosuvastatin significantly suppressed NIH progression. This combination treatment also markedly decreased the expression of inflammation and Akt signaling pathway-related proteins, which was consistent with the predictions from network pharmacology analysis.

CONCLUSIONS

Sirolimus and rosuvastatin inhibited pro-inflammatory cytokine production during the acute stage and regulated Akt/mTOR/NF-κB/STAT3 signaling in the chronic stage of NIH progression. These potential synergistic mechanisms may optimize treatment strategies to improve long-term patency after CABG.

Revolutionizing Cancer Treatment: Unveiling New Frontiers by Targeting the (Un)Usual Suspects

This Special Issue includes original articles and reviews on both established and innovative approaches to cancer targeting, showcased at the 29th IGB Workshop titled "Targeting the (un)usual suspects in cancer" "https://29thigbworkshop [...].

Computational Advancements in Cancer Combination Therapy Prediction

Given the high attrition rate of de novo drug discovery and limited efficacy of single-agent therapies in cancer treatment, combination therapy prediction through in silico drug repurposing has risen as a time- and cost-effective alternative for identifying novel and potentially efficacious therapies for cancer. The purpose of this review is to provide an introduction to computational methods for cancer combination therapy prediction and to summarize recent studies that implement each of these methods. A systematic search of the PubMed database was performed, focusing on studies published within the past 10 years. Our search included reviews and articles of ongoing and retrospective studies. We prioritized articles with findings that suggest considerations for improving combination therapy prediction methods over providing a meta-analysis of all currently available cancer combination therapy prediction methods. Computational methods used for drug combination therapy prediction in cancer research include networks, regression-based machine learning, classifier machine learning models, and deep learning approaches. Each method class has its own advantages and disadvantages, so careful consideration is needed to determine the most suitable class when designing a combination therapy prediction method. Future directions to improve current combination therapy prediction technology include incorporation of disease pathobiology, drug characteristics, patient multiomics data, and drug-drug interactions to determine maximally efficacious and tolerable drug regimens for cancer. As computational methods improve in their capability to integrate patient, drug, and disease data, more comprehensive models can be developed to more accurately predict safe and efficacious combination drug therapies for cancer and other complex diseases.

Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches

JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, Protein-Protein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies.

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies: An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an integrated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and informativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model.