Omics Data Integration and Analysis for Systems Pharmacology

Network-based multi-omics integrative analysis methods in drug discovery: a systematic review

The integration of multi-omics data from diverse high-throughput technologies has revolutionized drug discovery. While various network-based methods have been developed to integrate multi-omics data, systematic evaluation and comparison of these methods remain challenging. This review aims to analyze network-based approaches for multi-omics integration and evaluate their applications in drug discovery. We conducted a comprehensive review of literature (2015-2024) on network-based multi-omics integration methods in drug discovery, and categorized methods into four primary types: network propagation/diffusion, similarity-based approaches, graph neural networks, and network inference models. We also discussed the applications of the methods in three scenario of drug discovery, including drug target identification, drug response prediction, and drug repurposing, and finally evaluated the performance of the methods by highlighting their advantages and limitations in specific applications. While network-based multi-omics integration has shown promise in drug discovery, challenges remain in computational scalability, data integration, and biological interpretation. Future developments should focus on incorporating temporal and spatial dynamics, improving model interpretability, and establishing standardized evaluation frameworks.

Drug Mechanism: A bioinformatic update

A drug Mechanism of Action (MoA) is a complex biological phenomenon that describes how a bioactive compound produces a pharmacological effect. The complete knowledge of MoA is fundamental to fully understanding the drug activity. Over the years, many experimental methods have been developed and a huge quantity of data has been produced. Nowadays, considering the increasing omics data availability and the improvement of the accessible computational resources, the study of a drug MoA is conducted by integrating experimental and bioinformatics approaches. The development of new in silico solutions for this type of analysis is continuously ongoing; herein, an updating review on such bioinformatic methods is presented. The methodologies cited are based on multi-omics data integration in biochemical networks and Machine Learning (ML). The multiple types of usable input data and the advantages and disadvantages of each method have been analyzed, with a focus on their applications. Three specific research areas (i.e. cancer drug development, antibiotics discovery, and drug repurposing) have been chosen for their importance in the drug discovery fields in which the study of drug MoA, through novel bioinformatics approaches, is particularly productive.

Design of a multi-category drug information integration platform for intelligent pharmacy management: A needs analysis study

A drug store was never just an area to fill personal solution. Patients considered drug specialists to be counsels, somebody who could help them pick an over-the-counter treatment or understanding the portion and directions for a solution. Drug stores, similar to the remainder of the medical services business, are going through changes. Nowadays, one of the main highlights of any structure is the board. The executives give the refinement needed to wrap up any responsibility in a particular way. The executive framework of a drug store can be utilized to deal with most drug store related errands. This report has provided data on the best way to fabricate and execute a Pharmacy Management System. The primary objective of this system is to expand exactness, just as security and proficiency, in the drug shop. This undertaking is focused on the drug store area, determined to offer engaging and reasonable programming answers to assist them with modernizing to rival shops (helping out other equal modules in a similar examination program). This study will clarify the system's thoughts concerning the board issues and arrangements of a drug store. Likewise, this study covers the main parts of the Pharmacy application's investigation, execution, and look.

Network Pharmacology and Molecular Docking Analysis Reveal Insights into the Molecular Mechanism of Shengma-Gegen Decoction on Monkeypox

Background: A new viral outbreak caused by monkeypox has appeared after COVID-19. As of yet, no specific drug has been found for its treatment. Shengma-Gegen decoction (SMGGD), a pathogen-eliminating and detoxifying agent composed of four kinds of Chinese herbs, has been demonstrated to be effective against several viruses in China, suggesting that it may be effective in treating monkeypox, however, the precise role and mechanisms are still unknown. Methods: Network pharmacology was used to investigate the monkeypox-specific SMGGD targets. These targets were analyzed via String for protein-to-protein interaction (PPI), followed by identification of hub genes with Cytoscape software. Function enrichment analysis of the hub targets was performed. The interactions between hub targets and corresponding ligands were validated via molecular docking. Results: Through screening and analysis, a total of 94 active components and 8 hub targets were identified in the TCM-bioactive compound-hub gene network. Molecular docking results showed that the active components of SMGGD have strong binding affinity for their corresponding targets. According to functional analysis, these hub genes are mainly involved in the TNF, AGE-RAGE, IL-17, and MAPK pathways, which are linked to the host inflammatory response to infection and viral replication. Therefore, SMGGD might suppress the replication of monkeypox virus through the MAPK signaling pathway while also reducing inflammatory damage caused by viral infection. Conclusion: SMGGD may have positive therapeutic effects on monkeypox by reducing inflammatory damage and limiting virus replication.

Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU- based disease: The Multi-Targeting Drug DREAM Challenge

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets ('polypharmacology'). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Identifying the active compounds and mechanism of action of Banxia Xiexin decoction for treating ethanol-induced chronic gastritis using network pharmacology combined with UPLC-LTQ-Orbitrap MS

BACKGROUND

Banxia Xiexin decoction (BXD), a traditionally prescribed Chinese medicine, has been used to treat chronic gastritis for many years. However, the underlying mechanism and targets for its effects remain unknown. In the present study, we predicted the targets and active compounds of BXD in the treatment of chronic gastritis through network pharmacology and ultra-performance liquid chromatography coupled with linear trap quadrupole-Orbitrap mass spectrometry (UPLC-LTQ-Orbitrap MS).

METHOD

A chronic gastritis model was established in rats by oral administration of 56 % ethanol. BXD was orally administered for 7 days. Stomach tissues were collected for histopathological analysis, and tumour necrosis factor (TNF)-α, interleukin (IL)-2, IL-8, and lactate dehydrogenase (LDH) levels were measured by enzyme-linked immunosorbent assay. UPLC-LTQ-Orbitrap MS was established to analyse compounds in rat plasma following oral BXD administration. The absorbed ingredients were selected as candidate active compounds. The chronic gastritis-related targets were screened using multiple databases. The potential targets for the treatment of chronic gastritis were used to construct a protein-protein interaction (PPI) network and were also analysed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Finally, molecular docking was used to uncover the interaction between multi-components and putative targets, and the results were verified by surface plasmon resonance (SPR).

RESULTS

Intragastric administration of BXD ameliorated stomach injury resulting from chronic gastritis in rats and decreased the levels of TNF-α, IL-2, IL-8, and LDH. A comprehensive systematic strategy was used to successfully identify 38 candidate targets and 14 active compounds in BXD. Based on the network of compounds-targets and PPI, three hub genes that were associated with BXD therapy for chronic gastritis were selected and included intercellular adhesion molecule-1, peroxisome proliferator-activated receptor gamma and mitogen-activated protein kinase 14. The results of molecular docking and SPR demonstrated that the active compounds in BXD demonstrate affinity for these targets. Additionally, an enrichment analysis revealed that treatment of chronic gastritis with BXD primarily involves cytokine activation, the inflammatory response and nuclear factor-kappa B, hypoxia-inducible factor-1, phosphatidylinositol-3-kinase-protein-serine-threonine kinase and Janus kinase-signal transducer and activator of transcription signalling pathways, which may mediate the effects of BXD in the treatment of chronic gastritis.

CONCLUSION

BXD exhibits a therapeutic effect in ethanol-induced gastritis through multi-compound, multi-target and multi-pathway mechanisms. A strategy of network pharmacology combined with SPR may provide a feasible approach to explore the targets of herbal medicine and uncover novel bioactive components.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

High-dimensionality Data Analysis of Pharmacological Systems Associated with Complex Diseases

It is widely accepted that molecular reductionist views of highly complex human physiologic activity, e.g., the aging process, as well as therapeutic drug efficacy are largely oversimplifications. Currently some of the most effective appreciation of biologic disease and drug response complexity is achieved using high-dimensionality (H-D) data streams from transcriptomic, proteomic, metabolomics, or epigenomic pipelines. Multiple H-D data sets are now common and freely accessible for complex diseases such as metabolic syndrome, cardiovascular disease, and neurodegenerative conditions such as Alzheimer's disease. Over the last decade our ability to interrogate these high-dimensionality data streams has been profoundly enhanced through the development and implementation of highly effective bioinformatic platforms. Employing these computational approaches to understand the complexity of age-related diseases provides a facile mechanism to then synergize this pathologic appreciation with a similar level of understanding of therapeutic-mediated signaling. For informative pathology and drug-based analytics that are able to generate meaningful therapeutic insight across diverse data streams, novel informatics processes such as latent semantic indexing and topological data analyses will likely be important. Elucidation of H-D molecular disease signatures from diverse data streams will likely generate and refine new therapeutic strategies that will be designed with a cognizance of a realistic appreciation of the complexity of human age-related disease and drug effects. We contend that informatic platforms should be synergistic with more advanced chemical/drug and phenotypic cellular/tissue-based analytical predictive models to assist in either de novo drug prioritization or effective repurposing for the intervention of aging-related diseases. SIGNIFICANCE STATEMENT: All diseases, as well as pharmacological mechanisms, are far more complex than previously thought a decade ago. With the advent of commonplace access to technologies that produce large volumes of high-dimensionality data (e.g., transcriptomics, proteomics, metabolomics), it is now imperative that effective tools to appreciate this highly nuanced data are developed. Being able to appreciate the subtleties of high-dimensionality data will allow molecular pharmacologists to develop the most effective multidimensional therapeutics with effectively engineered efficacy profiles.