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Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology., (© The Author(s) 2024. Published by Oxford University Press.)

Over the past several decades, mathematical modeling has been applied to increasingly wider scopes of questions in drug development. Accordingly, the range of modeling tools has also been evolving, as showcased by contributions of Jusko and colleagues: from basic pharmacokinetics/pharmacodynamics (PK/PD) modeling to today's platform-based approach of quantitative systems pharmacology (QSP) modeling. Aimed at understanding the mechanism of action of investigational drugs, QSP models characterize systemic effects by incorporating information about cellular signaling networks, which is often represented by omics data. In this perspective, we share a few examples illustrating approaches for the integration of omics into mechanistic QSP modeling. We briefly overview how the evolution of PK/PD modeling into QSP has been accompanied by an increase in available data and the complexity of mathematical methods that integrate it. We discuss current gaps and challenges of integrating omics data into QSP models and propose several potential areas where integrated QSP and omics modeling may benefit drug development., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. All authors are employed by AstraZeneca, (Copyright © 2023 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Bispecific T cell engagers (bsTCEs) have emerged as a promising class of cancer immunotherapy. Several bsTCEs have achieved marketing approval; dozens more are under clinical investigation. However, the clinical development of bsTCEs remains rife with challenges, including nuanced pharmacology, limited translatability of preclinical findings, frequent on-target toxicity, and convoluted dosing regimens. In this opinion article we present a distinct perspective on how quantitative systems pharmacology (QSP) can serve as a powerful tool for overcoming these obstacles. Recent advances in QSP modeling have empowered developers of bsTCEs to gain a deeper understanding of their context-dependent pharmacology, bridge gaps in experimental data, guide first-in-human (FIH) dose selection, design dosing regimens with expanded therapeutic windows, and improve long-term treatment outcomes. We use recent case studies to exemplify the potential of QSP techniques to support future bsTCE development., Competing Interests: Declaration of interests The authors declare no competing financial interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Though hundreds of drugs have been approved by the US Food and Drug Administration (FDA) for treating various rare diseases, most rare diseases still lack FDA-approved therapeutics. To identify the opportunities for developing therapies for these diseases, the challenges of demonstrating the efficacy and safety of a drug for treating a rare disease are highlighted herein. Quantitative systems pharmacology (QSP) has increasingly been used to inform drug development; our analysis of QSP submissions received by FDA showed that there were 121 submissions as of 2022, for informing rare disease drug development across development phases and therapeutic areas. Examples of published models for inborn errors of metabolism, non-malignant hematological disorders, and hematological malignancies were briefly reviewed to shed light on use of QSP in drug discovery and development for rare diseases. Advances in biomedical research and computational technologies can potentially enable QSP simulation of the natural history of a rare disease in the context of its clinical presentation and genetic heterogeneity. With this function, QSP may be used to conduct in-silico trials to overcome some of the challenges in rare disease drug development. QSP may play an increasingly important role in facilitating development of safe and effective drugs for treating rare diseases with unmet medical needs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Inc.)

Typical Quantitative Systems Pharmacology (QSP) workflows involve discussion of biology, supported by graphical diagrams, followed by construction of large Ordinary Differential Equation models. QSP Designer facilitates this process by providing enhanced graphical notation, which enables hierarchical presentation with modules and handling of combinatorial complexity with diagram node arrays. Whereas the software includes a simulation engine, a major feature is full model code generation in MATLAB, R, C, and Julia to support multiple modeling communities., (© 2023 Certara. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Immuno-oncology (IO) is a fast-expanding field due to recent success using IO therapies in treating cancer. As IO therapies do not directly kill tumor cells but rather act upon the patients' own immune cells either systemically or in the tumor microenvironment, new and innovative approaches are required to inform IO therapy research and development. Quantitative systems pharmacology (QSP) modeling describes the biological mechanisms of disease and the mode of action of drugs with mathematical equations, which has significant potential to address the big challenges in the IO field, from identifying patient populations that respond to different therapies to guiding the selection, dosing, and scheduling of combination therapy. To assess the perspectives of the community on the impact of QSP modeling in IO drug development and to understand current applications and challenges, the IO QSP working group-under the QSP Special Interest Group (SIG) of the International Society of Pharmacometrics (ISoP)-conducted a survey among QSP modelers, non-QSP modelers, and non-modeling IO program stakeholders. The survey results are presented here with discussions on how to address some of the findings. One of the findings is the differences in perception among these groups. To help bridge this perception gap, we present several case studies demonstrating the impact of QSP modeling in IO and suggest actions that can be taken in the future to increase the real and perceived impact of QSP modeling in IO drug research and development., (© 2022 The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

An increasing number of commonly prescribed drugs are known to interfere with mitochondrial function, which is associated with almost half of all Food and Drug Administration black box warnings, a variety of drug withdrawals, and attrition of drug candidates. This can mainly be attributed to a historic lack of sensitive and specific assays to identify the mechanisms underlying mitochondrial toxicity during drug development. In the last decade, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based systems pharmacological approaches. Here, we propose the implementation of a tiered systems pharmacology approach to detect adverse mitochondrial drug effects during preclinical drug development, which is based on a toolset developed to study inherited mitochondrial disease. This includes phenotypic characterization, profiling of key metabolic alterations, mechanistic studies, and functional in vitro and in vivo studies. Combined with binding pocket similarity comparisons and bottom-up as well as top-down metabolic network modeling, this tiered approach enables identification of mechanisms underlying drug-induced mitochondrial dysfunction. After validation of these off-target mechanisms, drug candidates can be adjusted to minimize mitochondrial activity. Implementing such a tiered systems pharmacology approach could lead to a more efficient drug development trajectory due to lower drug attrition rates and ultimately contribute to the development of safer drugs. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs adversely affect mitochondrial function, which can be detected using phenotypic assays. However, these methods provide only limited insight into the underlying mechanisms. In recent years, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based system pharmacological approaches. Their implementation in preclinical drug development could reduce the number of drug failures, contributing to safer drug design., (Copyright © 2023 by The Author(s).)

Quantitative systems pharmacology (QSP) models integrate comprehensive qualitative and quantitative knowledge about pharmacologically relevant processes. We previously proposed a first approach to leverage the knowledge in QSP models to derive simpler, mechanism-based pharmacodynamic (PD) models. Their complexity, however, is typically still too large to be used in the population analysis of clinical data. Here, we extend the approach beyond state reduction to also include the simplification of reaction rates, elimination of reactions, and analytic solutions. We additionally ensure that the reduced model maintains a prespecified approximation quality not only for a reference individual but also for a diverse virtual population. We illustrate the extended approach for the warfarin effect on blood coagulation. Using the model-reduction approach, we derive a novel small-scale warfarin/international normalized ratio model and demonstrate its suitability for biomarker identification. Due to the systematic nature of the approach in comparison with empirical model building, the proposed model-reduction algorithm provides an improved rationale to build PD models also from QSP models in other applications., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Good eyesight belongs to the most-valued attributes of health, and diseases of the eye are a significant healthcare burden. Case numbers are expected to further increase in the next decades due to an aging society. The development of drugs in ophthalmology, however, is difficult due to limited accessibility of the eye, in terms of drug administration and in terms of sampling of tissues for drug pharmacokinetics (PKs) and pharmacodynamics (PDs). Ocular quantitative systems pharmacology models provide the opportunity to describe the distribution of drugs in the eye as well as the resulting drug-response in specific segments of the eye. In particular, ocular physiologically-based PK (PBPK) models are necessary to describe drug concentration levels in different regions of the eye. Further, ocular effect models using molecular data from specific cellular systems are needed to develop dose-response correlations. We here describe the current status of PK/PBPK as well as PD models for the eyes and discuss cellular systems, data repositories, as well as animal models in ophthalmology. The application of the various concepts is highlighted for the development of new treatments for postoperative fibrosis after glaucoma surgery., (© 2023 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases., (© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Antibody-drug conjugates (ADCs) have gained traction in the oncology space in the past few decades, with significant progress being made in recent years. Although the use of pharmacometric modeling is well-established in the drug development process, there is an increasing need for a better quantitative biological understanding of the pharmacokinetic and pharmacodynamic relationships of these complex molecules. Quantitative systems pharmacology (QSP) approaches can assist in this endeavor; recent computational QSP models incorporate ADC-specific mechanisms and use data-driven simulations to predict experimental outcomes. Various modeling approaches and platforms have been developed at the in vitro, in vivo, and clinical scales, and can be further integrated to facilitate preclinical to clinical translation. These new tools can help researchers better understand the nature and mechanisms of these targeted therapies to help achieve a more favorable therapeutic window. This review delves into the world of systems pharmacology modeling of ADCs, discussing various modeling efforts in the field thus far., (© 2022 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: HK is AstraZeneca employee, and she participated in preparation of the manuscript.

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model-informed drug discovery and development approach can leverage adult-pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage-gate decisions informed by such an approach., (© 2022 Axcella Therapeutics and Critical Path Institute. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Quantitative systems pharmacology (QSP) is a quantitative and mechanistic platform describing the phenotypic interaction between drugs, biological networks, and disease conditions to predict optimal therapeutic response. In this meta-analysis study, we review the utility of the QSP platform in drug development and therapeutic strategies based on recent publications (2019-2021). We gathered recent original QSP models and described the diversity of their applications based on therapeutic areas, methodologies, software platforms, and functionalities. The collection and investigation of these publications can assist in providing a repository of recent QSP studies to facilitate the discovery and further reusability of QSP models. Our review shows that the largest number of QSP efforts in recent years is in Immuno-Oncology. We also addressed the benefits of integrative approaches in this field by presenting the applications of Machine Learning methods for drug discovery and QSP models. Based on this meta-analysis, we discuss the advantages and limitations of QSP models and propose fields where the QSP approach constitutes a valuable interface for more investigations to tackle complex diseases and improve drug development., (© 2021. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

For complex diseases, most drugs are highly ineffective, and the success rate of drug discovery is in constant decline. While low quality, reproducibility issues, and translational irrelevance of most basic and preclinical research have contributed to this, the current organ-centricity of medicine and the 'one disease-one target-one drug' dogma obstruct innovation in the most profound manner. Systems and network medicine and their therapeutic arm, network pharmacology, revolutionize how we define, diagnose, treat, and, ideally, cure diseases. Descriptive disease phenotypes are replaced by endotypes defined by causal, multitarget signaling modules that also explain respective comorbidities. Precise and effective therapeutic intervention is achieved by synergistic multicompound network pharmacology and drug repurposing, obviating the need for drug discovery and speeding up clinical translation., Competing Interests: Declaration of interests No interests are declared., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Quantitative systems pharmacology (QSP) is a relatively new discipline within modelling and simulation that has gained wide attention over the past few years. The application of QSP models spans drug-target identification and validation, through all drug development phases as well as clinical applications. Due to their detailed mechanistic nature, QSP models are capable of extrapolating knowledge to predict outcomes in scenarios that have not been tested experimentally, making them an important resource in experimental and clinical pharmacology. However, these models are complicated to work with due to their size and inherent complexity. This makes many applications of QSP models for simulation, parameter estimation and trial design computationally intractable. A number of techniques have been developed to simplify QSP models into smaller models that are more amenable to further analyses while retaining their accurate predictive capabilities. Different simplification techniques have different strengths and weaknesses and hence different utilities. Understanding the utilities of different methods is essential for selection of the best method for a particular situation. In this paper, we have created an overall framework for model simplification techniques that allows a natural categorisation of methods based on their utility. We provide a brief description of the concept underpinning the different methods and example applications. A summary of the utilities of methods is intended to provide a guide to modellers in their model endeavours to simplify these complicated models., (© 2020 British Pharmacological Society.)

Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field., (© 2021. The Author(s).)

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics., (© 2022. The Author(s).)

Significant advances in analytical technologies have dramatically improved our ability to deconvolute disease biology at molecular, cellular, and tissue levels. Quantitative system pharmacology (QSP) modeling is a computational framework to systematically integrate pharmaceutical properties of a drug candidate with scientific understanding of that deeper disease etiology, target expression, genetic variability, and human physiological processes, thus enabling more insightful drug development decisions related to efficacy and safety. In this chapter, we discuss the key attributes of QSP models in comparison to traditional models. We discuss a recommended four-step process to construct a QSP model to support drug development decisions. A number of illustrative QSP examples related to high-value drug development questions and decisions impacting target identification, lead generation and optimization, first in human studies, and clinical dose and schedule optimization are covered in the chapter. The future perspectives of QSP in the context of potential regulatory acceptance are also discussed., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

There is a demand for scientists trained in quantitative systems pharmacology (QSP) that has yet to be met by changes in graduate education. The multidisciplinary nature of QSP is not unlike its predecessor, pharmacokinetics (PKs) and pharmacodynamics (PDs) that have now become firmly established in many educational programs. A hindrance to the evolution of educational programs for QSP is explored and suggestions to move QSP into its proper position as a unique discipline are presented., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Ethnopharmacological Relevance: Ranunculus japonicus Thunb. (short for R. japonicus) is a topically applied herb with the activities of removing jaundice, nebula and edema, preventing malaria, stopping asthma, promoting diuresis and relieving pain. It was firstly recorded in Zhouhou Beiji Fang and has been used for the treatment of malaria, ulcers, carbuncle, jaundice, migraine, stomachache, toothache and arthritis for over 1800 years., Aim of the Study: This study aimed to uncover the potentially effective components of R. japonicus and the pharmacological mechanisms against rheumatoid arthritis (RA) by combing LC-MS and network pharmacology., Materials and Methods: Firstly, the chemical constituents of R. japonicus were qualitatively identified by UPLC-ESI-LTQ-Orbitrap MS. Then we performed target prediction by PharmMapper, protein-protein interaction (PPI) analysis via String, GO and KEGG pathway enrichment analysis by DAVID and constructed the compound-target-pathway network using Cytoscape. Thirdly, crucial compounds in the network were quantitatively analyzed to achieve quality control of R. japonicus. Finally, the pharmacological activities of R. japonicus and two potentially bioactive ingredients were validated in RA-FLSs (Rheumatoid Arthritis Fibroblast-like Synoviocytes) in vitro., Results: Overall fifty-four ingredients of R. japonicus were identified and forty-five components were firstly discovered in R. japonicus. Among them, twenty-seven validated compounds were predicted to act on twenty-five RA-related targets and they might exhibit therapeutic effects against RA via positive regulation of cell migration, etc. Nine potentially bioactive components of R. japonicus which played important roles in the compound-target-pathway network were simultaneously quantified by an optimized UPLC-ESI-Triple Quad method. In vitro, compared to control group, R. japonicus extract, berberine and yangonin significantly inhibited the migration capacity of RA-FLSs after 24 h treatment., Conclusion: This study clarified that R. japonicus and the bioactive ingredients berberine and yangonin might exert therapeutic actions for RA via suppressing the aggressive phenotypes of RA-FLSs through combined LC-MS technology and network pharmacology tools for the first time. The present research provided deeper understanding into the chemical profiling, pharmacological activities and quality control of R. japonicus and offered reference for further scientific research and clinical use of R. japonicus in treating RA., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

The deficiency of Vitamin D (V D ) is a common symptom of systemic scleroderma (SSc), but the correlation of V D deficiency and SSc is not completely clear. Therefore, a strategy based on network pharmacology was designed to explore the correlation of V D deficiency and SSc. After a series of network construction and analysis, 5 integrins were predicated as the kernel targets in the correlation of V D deficiency and SSc, including ITGA5, ITGA4, ITGB3, ITGB1 and ITGAV. The crucial pathways in which the kernel targets participated were mainly involved in the function of immune, vascular and internal organ. The regulation modules of crucial pathways were closely related to the biological processes in the pathological of SSc. Taken together, the analysis predicted that the deficiency of V D might affect the pathological of SSc through the mediation of these integrins. Therefore, targeted regulation of these integrins might be an effective therapy against SSc., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Network pharmacology uses bioinformatics to broaden our understanding of drug actions and thereby to advance drug discovery. Here we apply network pharmacology to generate testable hypotheses about the multi-target mechanism of celastrol against rheumatoid arthritis. We reconstructed drug-target pathways and networks to predict the likely protein targets of celastrol and the main interactions between those targets and the drug. Then we validated our predictions of four candidate targets (IKK-β, JNK, COX-2, MEK1) by performing docking studies with celastrol. The results suggest that celastrol acts against rheumatoid arthritis by regulating the function of several signaling proteins, including MMP-9, COX-2, c-Myc, TGF-β, c-JUN, JAK-1, JAK-3, IKK-β, SYK, MMP-3, JNK and MEK1, which regulate the functions of Th1 and Th2 cells, macrophages, fibroblasts and endothelial cells in rheumatoid arthritis. Celastrol is predicted to affect networks involved mainly in cancer, connective tissue disorders, organismal injury and abnormalities, tissue development, cell death and survival. This network pharmacology strategy may be useful for discovery of multi-target drugs against complex diseases., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Artemisia argyi (AA) is one of the renowned herbs in China often used in the treatment of gastric ulcer (GU). Aiming to predict the active compounds and systematically investigate the mechanisms of Artemisia argyi for GU treatment, the approach of network pharmacology, molecular docking, gene ontology (GO) analysis, and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were adopted, respectively, in present study. A total of 13 predicted targets of the 103 compounds in Artemisia argyi were obtained. Sorted by pathogenic mechanisms of targets and structure types of compounds, it was revealed that flavonoids and sesquiterpenes had better performance than monoterpenes. The network analysis showed that Phospholipase a2 (PA21B), Sulfotransferase family cytosolic 2b member 1 (ST2B1), Nitric-oxide synthase, endothelial (NOS3), Gastrin (GAST), neutrophil collagenase (MMP-8), Leukotriene A-4 hydrolase (LKHA4), Urease maturation factor HypB (HYPB), and Periplasmic serine endoprotease DegP (HtrA) were the key targets with intensely interaction. The functional enrichment analysis indicated that AA probably produced the gastric mucosa protection effects by synergistically regulating many biological pathways, such as NF-κB signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, VEGF signaling pathway, and Toll-like receptor signaling pathway, etc. In addition, C73 and C15 might be promising leading compounds with good molecular docking score. As a consequence, this study holistically illuminates the active constituents and mechanisms based on data analysis, which contributes to searching for leading compounds and the development of new drugs for gastric ulcer., (Copyright © 2019 Elsevier Inc. All rights reserved.)

In our previous studies, vitamin C (VC) exerts potent pharmacological activities against liver injury (LI). Therefore, this report was designed to use network pharmacology-based strategy to predict therapeutic targets of VC against LI, and further to investigate the pharmacological molecular mechanisms. Pathological targets of LI were identified, followed by acquisition of verified targets of VC. After constructing target-functional protein interaction network of VC against LI, the core therapeutic targets of VC against LI were obtained. Further, biological function and pathway enrichment analyses were performed on core therapeutic targets to evaluate the biological processes and key signaling pathways of VC against LI. As revealed in network pharmacology assays, 6 key therapeutic targets for VC against LI were identified, showing tumor necrosis factor (TNF), nuclear factor-kappa-B p65 (RELA), nuclear factor-kappa-B p105 (NFKB1), TNF receptor-associated factor 2 (TRAF2), interleukin 6 (IL-6) and interleukin 1 beta (IL1B). On the basis of data analyses from DAVID database and omicshare cloud platform, bio-functional enrichment assays showed that the therapeutic effects of VC against LI were closely associated with regulating inflammatory reaction and apoptosis. Further, pathway enrichment analysis indicated the anti-LI benefits of VC were principally implicated in regulating the top 20 signaling pathways, such as inflammation-associated TNF signaling pathway, NF-κB signaling pathway. Taken together, the bioinformatics data elucidate that anti-LI pharmacological activities of VC may be predominantly related to inhibition of inflammatory stress, contributing to suppression of LI development. These resultant findings highlight the predicted therapeutic targets may be potential biomarkers for anti-LI., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Objectives: Triphala is a herbal medicine that has been widely used for treating a variety of ailments. This study aims to systematically analyze the antitumor effects of Triphala on gynecological cancers., Methods: The antineoplastic activities of Triphala on gynecological cancers were analyzed using network pharmacology-based strategies. Afterward, the human ovarian cancer cell line SK-OV-3, cervical cancer cell line HeLa, and endometrial cancer cell line HEC-1-B were selected for experimetal valification., Results: Network pharmacology analysis suggested that Triphala could comprehensively intervene in proliferation and apoptosis through diverse signaling pathways, mainly including MAPK/ERK, PI3K/Akt/mTOR, and NF-κB/p53. The Cell Counting Kit 8 (CCK-8) assay illustrated that Triphala was able to inhibit cell proliferation with half inhibition concentration (IC 50 ) values of 98.28 ± 13.71, 95.56 ± 8.94, and 101.23 ± 7.76 µg/mL against SK-OV-3, HeLa, and HEC-1-B cells, respectively. The ELISA experiment demonstrated that Triphala was capable of promoting programmed cell death, with dosage correlations. The antiproliferative and proapoptotic activities were confirmed by flow cytometric analysis using Ki67 antibody and Annexin V/propidium iodide (PI) dual staining. Western blotting revealed a decrease in expression levels of phospho-Akt, phospho-p44/42, and phospho-NF-κB p56 in cells administered Triphala, which indicated that the possible mechanism could involve downregulation of MAPK/ERK, PI3K/Akt/mTOR, and NF-κB/p53 signaling pathways, as was predicted., Conclusion: Triphala holds great promise for treating gynecological cancers. Although the favorable pharmacological properties have been preliminarily investigated in this study, further studies are still needed to uncover the sophisticated mechanism of Triphala in cancer therapy.

Panax notoginseng (Burk) F. H. Chen , as traditional Chinese medicine, has a long history of high clinical value, such as anti-inflammatory, anti-oxidation, inhibition of platelet aggregation, regulation of blood glucose and blood pressure, inhibition of neuronal apoptosis, and neuronal protection, and its main ingredients are Panax notoginseng saponins (PNS). Currently, Panax notoginseng (Burk) F. H. Chen may improve mental function, have anti-insomnia and anti-depression effects, alleviate anxiety, and decrease neural network excitation. However, the underlying effects and the mechanisms of Panax notoginseng (Burk) F. H. Chen and its containing chemical constituents (PNS) on these depression-related or anxiety-related diseases has not been completely established. This review summarized the antidepressant or anxiolytic effects and mechanisms of PNS and analyzed network targets of antidepressant or anxiolytic actions with network pharmacology tools to provide directions and references for further pharmacological studies and new ideas for clinical treatment of nervous system diseases and drug studies and development. The review showed PNS and its components may exert these effects through regulating neurotransmitter mechanism (5-HT, DA, NE), modulation of the gamma-amino butyric acid (GABA) neurotransmission, glutamatergic system, hypo-thalamus-pituitary-adrenal (HPA) axis, brain-derived neurotrophic factor (BDNF), and its intracellular signaling pathways in the central nervous system; and produce neuronal protection by anti-inflammatory, anti-oxidation, or inhibition of neuronal apoptosis, or platelet aggregation and its intracellular signaling pathways. Network target analysis indicated PNS and its components also may have anti-inflammatory and anti-apoptotic effects, which leads to the preservation of brain nerves, and regulate the activity and secretion of nerve cells, exerting anti-depression and anxiolytic effects, which may provide new directions for further in-depth researches of related mechanisms.

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

Tanshinone IIA is a pharmacologically active compound isolated from Danshen (Salvia miltiorrhiza), a traditional Chinese herbal medicine for the management of cardiac diseases and other disorders. But its underlying molecular mechanisms of action are still unclear. The present investigation utilized a data mining approach based on network pharmacology to uncover the potential protein targets of Tanshinone IIA. Network pharmacology, an integrated multidisciplinary study, incorporates systems biology, network analysis, connectivity, redundancy, and pleiotropy, providing powerful new tools and insights into elucidating the fine details of drug-target interactions. In the present study, two separate drug-target networks for Tanshinone IIA were constructed using the Agilent Literature Search (ALS) and STITCH (search tool for interactions of chemicals) methods. Analysis of the ALS-constructed network revealed a target network with a scale-free topology and five top nodes (protein targets) corresponding to Fos, Jun, Src, phosphatidylinositol-4, 5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA), and mitogen-activated protein kinase kinase 1 (MAP2K1), whereas analysis of the STITCH-constructed network revealed three top nodes corresponding to cytochrome P450 3A4 (CYP3A4), cytochrome P450 A1 (CYP1A1), and nuclear factor kappa B1 (NFκB1). The discrepancies were probably due to the differences in the divergent computer mining tools and databases employed by the two methods. However, it is conceivable that all eight proteins mediate important biological functions of Tanshinone IIA, contributing to its overall drug-target network. In conclusion, the current results may assist in developing a comprehensive understanding of the molecular mechanisms and signaling pathways of in a simple, compact, and visual manner., (Copyright © 2015 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.)

Systems pharmacology is an emerging field that integrates systems biology and pharmacology to advance the process of drug discovery, development and the understanding of therapeutic mechanisms. The aim of the present work is to highlight the role that the systems pharmacology plays across the traditional herbal medicines discipline, which is exemplified by a case study of botanical drugs applied in the treatment of depression. First, based on critically examined pharmacology and clinical knowledge, we propose a large-scale statistical analysis to evaluate the efficiency of herbs used in traditional medicines. Second, we focus on the exploration of the active ingredients and targets by carrying out complex structure-, omics- and network-based systematic investigations. Third, specific informatics methods are developed to infer drug-disease connections, with purpose to understand how drugs work on the specific targets and pathways. Finally, we propose a new systems pharmacology method, which is further applied to an integrated platform (Herbal medicine Systems Pharmacology) of blended herbal medicine and omics data sets, allowing for the systematization of current and traditional knowledge of herbal medicines and, importantly, for the application of this emerging body of knowledge to the development of new drugs for complex human diseases., (© The Author 2013. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

Network pharmacology, based on the theory of systems biology, is a new discipline that analyzes the biological network and screens out the nodes of particular interest, with the aim of designing poly-target drug molecule. It emphasizes maximizing drug efficacy and minimizing adverse effect via the multiple regulation of the signaling pathway. Coincidentally, almost all traditional Chinese medicine (TCM) and worldwide ethnomedicine exert therapeutic effect by targeting multiple molecules of the human body. In this overview, we offer a critique on the present perception of TCM and network pharmacology; illustrate the utility of network pharmacology in the study of single herb, medicine pair, and TCM formula; and summarize the recent progress of TCM-based drug discovery inspired by network pharmacology. Network pharmacology could be of great help in decreasing drug attrition rate and thus is essential in rational and cost-effective drug development. We also pinpoint the current TCM issues that could be tackled by the flexible combined use of network pharmacology and relevant disciplines., (© 2014 Wiley Periodicals, Inc.)

Platycladi cacumen (PC) is derived from the dry twigs and leaves of Platycladi orientalis (L.) Franco and exerts anti-epileptic effects. However, its mechanism of action remains unknown. The present study explored the potential anti-epileptic components and mechanisms of PC. The primary active components and targets of PC were analyzed using network pharmacology and a lipopolysaccharide (LPS)-induced murine microglial cell line (BV2) was used to confirm anti-epileptic effects by detecting reactive oxygen species (ROS), apoptosis, inflammatory markers, cell migration and signaling pathways. A total of 13 core active components showed druggable properties, of which deoxypicrop odophyllotoxin, hinokinin and isopimaric acid (IPA) were predicted to cross the blood-brain barrier. In total, 255 potential targets of these three compounds were predicted using SwissTargetPrediction and Similarity Ensemble Approach websites and 150 were associated with epilepsy. In vitro experiments confirmed that IPA significantly inhibited LPS-induced microglial oxidative stress and inflammation by decreasing the migration area, cellular ROS content, lactate dehydrogenase release and early phase of apoptosis. IPA also increased the mRNA expression of anti-oxidative enzymes (superoxide dismutase-1 and -2) and suppressed inflammatory cytokines (interleukin-1β and tumor necrosis factor-α). Furthermore, IPA phosphorylated AKT and mTOR proteins. Taken together, the present findings suggested that IPA is a potential anti-epileptic compound derived from PC. [ABSTRACT FROM AUTHOR]

Herbal medicines are frequently blended in the form of multi-drug combinations primarily based on the precept of medicinal compatibility, to achieve the purpose of treating diseases. However, due to the lack of appropriate techniques and the multi-component and multi-target nature of Chinese medicine compounding, it is tough to explain how the drugs interact with each other. As a rising discipline, cyber pharmacology has formed a new approach characterized by using holistic and systematic "network targets" via the cross-fertilization of computer technology, bioinformatics, and different multidisciplinary disciplines. It can broadly screen the active ingredients of traditional Chinese medicine, enhance the effective utilization of drugs, and elucidate the mechanism of drug action. We will overview the principles of Chinese medicine compounding and dispensing, the research methods of network pharmacology, and the software of network pharmacology in the lookup of compounded Chinese medicines, aiming to supply thoughts for the better application of network pharmacology in the research of Chinese medicines. [ABSTRACT FROM AUTHOR]

Background: Oxidative stress (OS) is one of the major causes of ovarian aging and dysfunction. Indole-3-propionic acid (IPA) is an indole compound derived from tryptophan with free radical scavenging and antioxidant properties, and thus may have potential applications in protecting ovarian function, although the exact mechanisms are unknown. This study aims to preliminarily elucidate the potential mechanisms of IPA that benefit ovarian reserve function through network pharmacology, molecular docking, and experimental verification. Methods: The related protein targets of IPA were searched on SwissTargetPrediction, TargetNet, BATMAN-TCM, and PharmMapper databases. The potential targets of diminished ovarian reserve (DOR) were identified from OMIM, GeneCards, DrugBank, and DisGeNET databases. The common targets were uploaded directly to the STRING database to construct PPI networks. We then performed GO and KEGG enrichment analysis on the targets. Subsequently, molecular docking and molecular dynamics simulation were used to validate the binding conformation of IPA to candidate targets. Furthermore, we carried out in vitro experiments to validate the prediction results of network pharmacology. Results: We identified a total of 61 potential targets for the interaction of IPA with DOR. The PPI network topological parameter analysis yielded 13 hub genes for DOR treatment. The GO biological process enrichment analysis identified 293 entries, mainly enriched in aging, signal transduction, response to hypoxia, negative regulation of apoptotic process, and positive regulation of cell proliferation. The KEGG enrichment analysis mainly included lipid and atherosclerosis, progesterone-mediated oocyte maturation, AGE-RAGE, relaxin, estrogen, and other signaling pathways. The molecular docking further revealed the direct binding of IPA with six hub proteins including NOS3, AKT1, EGFR, PPARA, SRC, and TNF. In vitro experiments showed that IPA pretreatment attenuated H2O2-induced cellular oxidative stress damage, while IPA exerted cytoprotective and antioxidant damage effects by regulating the six hub genes and antioxidant proteins. Conclusion: We systematically illustrated the potential protective effects of IPA against DOR through multiple targets and pathways using network pharmacology, and further verified the cytoprotective effect and antioxidant properties of IPA through in vitro experiments. These findings provide new insights into the targets and molecular mechanisms whereby IPA improves DOR. [ABSTRACT FROM AUTHOR]

Porcine Epidemic Diarrhea Virus (PEDV) poses a significant threat to neonatal piglets, particularly due to the limited efficacy of existing vaccines and the scarcity of efficacious therapeutic drugs. Gegen Qinlian Decoction (GQD) has been employed for over two millennia in treating infectious diarrhea. Nonetheless, further scrutiny is required to improve the drug's efficacy and elucidate its underlying mechanisms of action. In this study, a modified GQD (MGQD) was developed and demonstrated its capacity to inhibit the replication of PEDV. Animal trials indicated that MGQD effectively alleviated pathological damage in immune tissues and modulated T-lymphocyte subsets. The integration of network analysis with UHPLC-MS/MS facilitated the identification of active ingredients within MGQD and elucidated the molecular mechanisms underlying its therapeutic effects against PEDV infections. In vitro studies revealed that MGQD significantly impeded PEDV proliferation in IPEC-J2 cells, promoting cellular growth via virucidal activity, inhibition of viral attachment, and disruption of viral biosynthesis. Furthermore, MGQD treatment led to increased expression levels of IFN-α, IFN-β, and IFN-λ3, while concurrently decreasing the expression of TNF-α, thereby enhancing resistance to PEDV infection in IPEC-J2 cells. In conclusion, our findings suggest that MGQD holds promise as a novel antiviral agent for the treatment of PEDV infections. [ABSTRACT FROM AUTHOR]

Radix ginseng and Schisandra chinensis have been extensively documented in traditional Chinese medicine (TCM) for their potential efficacy in treating dementia. However, the precise mechanism of their therapeutic effects remains to be fully elucidated. In this study, air flow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) and network pharmacology are used to investigate the pharmacodynamics and mechanism underlying the herbal combination consisting of Radix ginseng-Schisandra chinensis (RS) in a rodent model for Alzheimer's disease (AD). Brain histopathological findings suggested that RS attenuates hippocampal damage in AD mice, making this combination a potential AD treatment. Twenty-eight biomarkers were identified by spatial metabolomics analysis, which are intricately linked to neuroinflammation, neurotransmitter imbalance, energy deficiency, oxidative stress, and aberrant fatty acid metabolism in AD. The total extract of RS (TE) affected 22 of these biomarkers, with the small molecule components of RS (SN) significantly influencing 19 and the large molecule components of RS (PR) impacting 14. Nine small molecule components are likely to dominate the pharmacodynamics of RS. We constructed a target interaction network based on the corresponding bioactivities that revealed relationships amongst 11 key biomarkers, 8 active ingredients and 12 critical targets. This research illustrates the immense potential of spatial metabolomics and network pharmacology in the study of TCM, revealing the targets and mechanisms underlying herbal formulas. [ABSTRACT FROM AUTHOR]

Iron deficiency anemia is a global health concern, affecting around 2 billion people. Oral iron therapy often causes severe gastro‐intestinal issues. Eruca sativa, member of the Brassicaceae family, is valued in traditional medicine and renowned for its rich iron and vitamin C content. This study aims to evaluate the anti‐anemic properties of E. sativa extract in vivo and identify its compounds targeting anemia mechanisms using network pharmacology. Thirty‐two Sprague–Dawley rats (200 ± 250 g) were split into two distinct groups, iron‐deficient and iron‐sufficient. Three different doses (200, 400, and 800 mg/kg) of aqueous extract of E. sativa were checked against anemia by studying hematological, oxidative stress, and histopathological parameters. GC–MS analysis of E. sativa revealed its phytochemical profile, followed by ADME screening. Network pharmacology explored targets related to iron deficiency anemia, with oral bioavailability and drug likeness assessment for compounds. The administration of extracts significantly improved various blood parameters, including osmotic fragility, Hb, RBCs, MCV, PCV, and alkaline phosphatase; catalase activity; and histopathological parameters such as liver in both iron‐deficient and iron‐sufficient rats (p < .001). Seventy‐nine compounds were identified in E. sativa aqueous extract, with only six of them found to be bioavailable and drug‐like against multiple targets. Gene ontology and pathway analysis revealed their diverse molecular, biological, and cellular functions. One gene EGFR was found to have functional association with ID anemia, suggesting potential for using E. sativa extracts. The study concludes that E. sativa extract has potential for iron deficiency anemia treatment, offering hope for future pharmaceutical interventions. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULT CONCLUSION Sulforaphene is a derivative of glucosinolate and a potential bioactive substance used for treating colon cancer. This study aimed to evaluate the potential inhibitory effect and mechanisms of sulforaphene in human colon cancer Caco‐2 cells. Network pharmacology, molecular docking, and experimental verification were performed to elucidate potential sulforaphene mechanisms in the treatment of this condition.Network pharmacology predicted 27 intersection target genes between sulforaphene and colon cancer cell inhibition. Key sulforaphene targets associated with colon cancer cell inhibition were identified as EGFR, MAPK14, MCL1, GSK3B, PARP1, PTPRC, NOS2, CTSS, TLR9, and CTSK. Gene ontology functional enrichment analysis revealed that the above genes were primarily related to the positive regulation of peptidase activity, cytokine production in the inflammatory response, and the cell receptor signaling pathway. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that sulforaphene mainly inhibited the proliferation of cancer cells by affecting apoptosis as well as the signaling pathways of PD‐1, Toll‐like receptor, T cell receptor, and P13k–Akt. Molecular docking results further confirmed that CTSS, GSK3B, and NOS2 were significantly up‐regulated and had good binding affinity with sulforaphene. In vitro experiments also indicated that sulforaphene had a significant inhibitory effect on human colon cancer Caco‐2 cells.This paper revealed the pharmacodynamic mechanism of sulforaphene in the treatment of colon cancer for the first time. It provides scientific insight into the development of sulforaphene as a medicinal resource. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The application of network pharmacology (NP) has advanced our understanding of the complex molecular mechanisms underlying diseases, including neck, head, and oral cancers, as well as thyroid carcinoma. This review aimed to explore the therapeutic potential of natural network pharmacology using compounds and traditional Chinese medicines for combating these malignancies. NP serves as a pivotal tool that provides a comprehensive view of the interactions among compounds, genes, and diseases, thereby contributing to the advancement of disease treatment and management. In parallel, this review discusses the significance of publicly accessible databases in the identification of oral, head, and neck cancer-specific genes. These databases, including those for head and neck oral cancer, head and neck cancer, oral cancer, and genomic variants of oral cancer, offer valuable insights into the genes, miRNAs, drugs, and genetic variations associated with these cancers. They serve as indispensable resources for researchers, clinicians, and drug developers, contributing to the pursuit of precision medicine and improved treatment of these challenging malignancies. In summary, advancements in NP could improve the globalization and modernization of traditional medicines and prognostic targets as well as aid in the development of innovative drugs. Furthermore, this review will be an eye-opener for researchers working on drug development from traditional medicines by applying NP approaches. [ABSTRACT FROM AUTHOR]

Hypertension has become a major contributor to the morbidity and mortality of cardiovascular diseases worldwide. Despite the evidence of the anti‐hypertensive effect of gastrodia‐uncaria granules (GUG) in hypertensive patients, little is known about its potential therapeutic targets as well as the underlying mechanism. GUG components were sourced from TCMSP and HERB, with bioactive ingredients screened. Hypertension‐related targets were gathered from DisGeNET, OMIM, GeneCards, CTD, and GEO. The STRING database constructed a protein–protein interaction network, visualized by Cytoscape 3.7.1. Core targets were analyzed via GO and KEGG using R package ClusterProfiler. Molecular docking with AutodockVina 1.2.2 revealed favorable binding affinities. In vivo studies on hypertensive mice and rats validated network pharmacology findings. GUG yielded 228 active ingredients and 1190 targets, intersecting with 373 hypertension‐related genes. PPI network analysis identified five core genes: AKT1, TNF‐α, GAPDH, IL‐6, and ALB. Top enriched GO terms and KEGG pathways associated with the anti‐hypertensive properties of GUG were documented. Molecular docking indicated stable binding of core components to targets. In vivo study showed that GUG could improve vascular relaxation, alleviate vascular remodeling, and lower blood pressure in hypertensive animal models possibly through inhibiting inflammatory factors such as AKT1, mTOR, and CCND1. Integrated network pharmacology and in vivo experiment showed that GUG may exert anti‐hypertensive effects by inhibiting inflammation response, which provides some clues for understanding the effect and mechanisms of GUG in the treatment of hypertension. [ABSTRACT FROM AUTHOR]

Objective: Baicalein, one of the most abundant flavonoids found in Chinese herb Scutellaria baicalensis Georgi, exhibits pharmacological activities against various cancers. However, the precise pharmacological mechanism of baicalein in treating castration-resistant prostate cancer (CRPC) remains elusive. This study aimed to elucidate the potential mechanism of baicalein against CRPC through a combination of network pharmacology and experimental approaches, thereby providing new avenues for research in CRPC treatment. Methods: The pharmacological and molecular properties of baicalein were obtained using the TCMSP database. Baicalein-related targets were collected from multiple sources including SwissTarget Prediction, PharmMapper and CTD. Targets related to CRPC were acquired from DisGeNET, GeneCards, and CTD. The protein-protein interaction (PPI) was analyzed using STRING 11.5, and Cytoscape 3.7.2 software was utilized to explore the core targets of baicalein on CRPC. GO and KEGG pathway enrichment analysis were performed using DAVID database. Cell experiments were carried out to confirm the validity of the targets. Results: A total of 131 potential targets of baicalein for the treatment of CRPC were obtained. Among them, TP53, AKT1, ALB, CASP3, and HSP90AA1, etc., were recognized as core targets by Cytoscape 3.7.2. GO function enrichment analysis yielded 926 entries, including 703 biological process (BP) terms, 84 cellular component (CC) terms and 139 molecular function (MF) terms. The KEGG pathway enrichment analysis unveiled 159 signaling pathways, mainly involved in Pathways in cancer, prostate cancer, AGE-RAGE signaling pathway in diabetic complications, TP53 signaling pathway, and PI3K-Akt signaling pathway, etc. Cell experiments confirmed that baicalein may inhibit the proliferation of CRPC cells and induce cell cycle arrest in the G1 phase. This effect could be associated with the TP53/CDK2/cyclin E1 pathway. In addition, the results of CETSA suggest that baicalein may directly bind to TP53. Conclusion: Based on network pharmacology analysis and cell experiments, we have predicted and validated the potential targets and related pathways of baicalein for CRPC treatment. This comprehensive approach provides a scientific basis for elucidating the molecular mechanism underlying the action of baicalein in CRPC treatment. Furthermore, these findings offer valuable insights and serve as a reference for the research and development of novel anti-CRPC drugs. [ABSTRACT FROM AUTHOR]

Introduction: Pycnogenol (PYC), a standardized extract from French maritime pine, has traditionally been used to treat inflammation. However, its primary active components and their mechanisms of action have not yet been determined. Methods: This study employed UPLC-MS/MS (Ultra-high performance liquid chromatography-tandem mass spectrometry) and network pharmacology to identify the potential active components of PYC and elucidate their anti inflammatory mechanisms by cell experiments. Results: 768 PYC compounds were identified and 19 anti-inflammatory compounds were screened with 85 target proteins directly involved in the inflammation. PPI (protein-protein interaction) analysis identified IL6, TNF, MMP9, IL1B, AKT1, IFNG, CXCL8, NFKB1, CCL2, IL10, and PTGS2 as core targets. KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis suggested that the compound in PYC might exert anti-inflammatory effects through the IL17 and TNF signal pathways. Cell experiments determined that PYC treatment can reduce the expression of IL6 and IL1β to relieve inflammation in LPS (lipopolysaccharide)-induced BV2 cells. Conclusion: PYC could affect inflammation via multi-components, -targets, and -mechanisms. [ABSTRACT FROM AUTHOR]

ABSTRACT Gastric cancer (GC) is the most common type of cancer of the digestive system with high morbidity and mortality. Chemotherapy and targeted therapy are used to treat patients with advanced GC. However, side effects and drug resistance to the two modalities remain the main challenges. The Buzhong Yiqi decoction (BZYQD), a classical traditional Chinese medicine formula, has been reported for the treatment of various types of cancers. However, the underlying pharmacological mechanism has not been fully elucidated. Therefore, this study integrated network pharmacology, molecular docking, cancer public databases, and cell experiments to explore the potential bioactive compounds and BZYQD's mechanism of action against GC. A total of 245 targets of BZYQD, 5291 GC‐related targets, and 186 were identified as their common targets through the database. Network analysis confirmed AKT1, TP53, TNF, and EGFR to be the core targets, while the main compounds observed were quercetin, kaempferol, and β‐Sitosterol. The core signaling pathways included PI3K‐AKT, MAPK, TNF, and IL‐17. Molecular docking revealed good binding activity for the main compounds and core targets. Based on the database's validation of core targets, a large number of core genes were verified to be consistent with this study. Quercetin, kaempferol, and β‐Sitosterol were found to significantly reduce the growth of GC cells in the MTT experiment. The current study revealed that BZYQD may inhibit GC progression by interfering with core targets such as AKT1, TP53, TNF, EGFR, and MAPK3, and by regulating the activity of PI3K‐AKT, MAPK, TNF, and IL‐17 signaling pathways. [ABSTRACT FROM AUTHOR]

AbstractThe SwissTargetPrediction was employed to predict the potential drug targets of the active component of Si-Miao-Yong-An decoction (SMYAD). The therapeutic targets for HF were searched in the Genecard database, and Cytoscape3.9.1 software was used to construct the "drug-component-target-disease network" diagram. In addition, the String platform was used to construct Protein-Protein Interaction (PPI) network, and the DAVID database was used for GO and KEGG analysis. AutoDockTools-1.5.6 software was used for molecular docking verification. Network pharmacology studies have shown that AKT 1, ALB, and CASP 3 are the key targets of action of SMYAD against heart failure. The active compounds are quercetin and kaempferol. [ABSTRACT FROM AUTHOR]