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Jingxin Mao1,2*- 1Chongqing Medical and Pharmaceutical College, Chongqing, China
- 2College of Pharmaceutical Sciences, Southwest University, Chongqing, China
Editorial on the Research Topic
The role of genes and network pharmacology in new drug discovery
In the ever-evolving field of new drug development, genetics and network pharmacology have emerged as two pivotal elements that are revolutionizing the traditional paradigm of drug research and development (Hopkins, 2008). Historically, new drug development has relied heavily on broader and more empirical approaches. However, with advances in genetic science and network pharmacology, this field is undergoing a profound transformation.
Genes, as the basic units of inheritance, are critical to understanding the underlying molecular mechanisms of disease. In the past, the understanding of disease was limited to superficial symptoms. The advent of high-throughput sequencing technology has enabled the decoding of the entire human genome, opening the door for researchers to the microscopic world and providing a vast amount of information on various disease-related gene variations (Nizamani et al., 2023). This invaluable knowledge allows researchers to identify potential drug targets with unprecedented precision. In cancer research, for instance, the discovery of specific oncogenes such as breast cancer susceptibility gene (BRCA) 1 and BRCA2 has directly led to the development of targeted therapies such as poly (ADP-ribose) polymerase protein (PARP) inhibitors (Hobbs et al., 2021). These drugs can precisely target cancer cells carrying BRCA mutations, leading to more significant therapeutic effects and drastically reduced side effects compared to traditional chemotherapy, thereby greatly improving the treatment experience and quality of life for cancer patients.
On the other hand, network pharmacology takes a holistic view of complex biological systems. It recognizes that diseases are not caused by a single gene or protein but rather by perturbations in intricate molecular networks. By thoroughly analyzing the interactions between genes, proteins, and small molecules, network pharmacology aims to identify multitarget drugs that can regulate multiple nodes in disease-related networks (Mao et al., 2024). This approach is particularly important for complex diseases such as neurodegenerative disorders and metabolic syndromes, which are often caused by multiple deregulated signaling pathways. For example, in Alzheimer’s disease research, network-based drug discovery strategies are exploring drugs that can simultaneously target amyloid-beta (β) aggregation, tau phosphorylation, and neuroinflammatory pathways, potentially bringing new therapeutic hope to Alzheimer’s patients (Mayo et al., 2024).
However, the integration of genetics and network pharmacology in the development of new drugs presents numerous challenges. One major obstacle is the complexity of data analysis. Large-scale genomic data generated by high-throughput experiments, along with complex network-related data, require advanced bioinformatics and computational tools for accurate interpretation (Lin, 2024). Furthermore, new drug targets identified through these methods still require time-consuming and resource-intensive preclinical and clinical validation. From laboratory research to animal experiments and human clinical trials, each step demands substantial time, manpower, and funding, and issues at any stage can hinder the entire research and development process (Hodos et al., 2016).
Therefore, the title of this Research Topic “The role of genes and network pharmacology in new drug discovery” was carried out which aims to better provide the academic forefront of computational methods for biomedical research in pharmacology and medicine in healthcare big data. A total of four original research articles were collected from well-known authors in the relevant field. Lu et al. found that through various molecular and computer simulation experiments the pituitary tumor transforming gene (PTTG) gene family (PTTG1, PTTG2, and PTTG3P) is continuously upregulated in osteosarcoma (OS) cell lines and has the potential to serve as a diagnostic biomarker. The related cytosine-phosphate-guanine (CpG) islands exhibit significant hypomethylation, gene mutations are rare, and functional assays confirm their carcinogenic effects. The authors also determined that calcitriol is the most suitable drug for PTTG gene therapy of OS, which opens up new avenues for understanding the pathogenesis of OS and developing targeted therapies. Wang and Mao investigated the pharmacological mechanism of β-sitosterol in the treatment of rheumatoid arthritis. Through network pharmacology and molecular docking experiments, they demonstrated that β-sitosterol can effectively bind to six core targets, significantly inhibit the excessive proliferation of MH7A cells, and regulate the expression of related genes. It may be possible to inhibit rheumatoid arthritis by regulating the forkhead box (Fox)O and phosphatidylinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways. Zhang et al. identified prognostic genes related to lung adenocarcinoma (LUAD) through univariate analysis and used machine learning to construct a pre-screened key gene small nuclear ribonucleoprotein polypeptide A (SNRPA) for in vitro experiments on LUAD cell lines. This study demonstrates the prognostic value and clinical application of nucleotide metabolism in LUAD. Prognostic features constructed based on nucleotide metabolism-related genes can accurately predict patient prognosis and guide immunotherapy for LUAD. Hossain et al. conducted a comprehensive bioinformatics analysis of the factors involved in cyclin-dependent kinases regulatory subunit (CKS) 1B in LUAD and lung squamous cell carcinoma (LUSC) and explored its potential role as a biomarker for early detection and treatment of lung cancer. The study demonstrated the immunotherapeutic characteristics and prognostic significance of CKS1B in cancer progression.
Taken together, genetics and network pharmacology have opened up new avenues for new drug development. By integrating genetic information and network-based analytical power, researchers can develop more effective and personalized drugs. Overcoming current challenges in data analysis and target validation is critical to realizing the full potential of these innovative methods in the pharmaceutical industry. Only by continuously overcoming technical bottlenecks and strengthening multidisciplinary collaboration can we accelerate the development of new drugs and bring more treatment options and hope for recovery to patients worldwide.
Author contributions
JM: Conceptualization, Funding acquisition, Writing–original draft, Writing–review and editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Chongqing Shapingba District Joint Key Project of Science and Health (2024SQKWLHZD008), the Scientific Research Project of Chongqing Medical and Pharmaceutical College (ygzrc2024104, ygz2022104 and ygzrc2024101), and the Teaching and Research Reform Project of Chongqing Medical and Pharmaceutical College (CQYGZJG2423) respectively.
Conflict of interest
The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
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References
Hobbs, E. A., Litton, J. K., and Yap, T. A. (2021). Development of the PARP inhibitor talazoparib for the treatment of advanced BRCA1 and BRCA2 mutated breast cancer. Expert Opin. Pharmacother. 22(14), 1825–1837. doi:10.1080/14656566.2021.1952181
Hodos, R. A., Kidd, B. A., Shameer, K., Readhead, B. P., and Dudley, J. T. (2016). In silico methods for drug repurposing and pharmacology. Wiley Interdiscip. Rev.: Syst. Biol. Med. 8(3), 186–210. doi:10.1002/wsbm.1337
Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nat. Chem. Biol. 4(11), 682–690. doi:10.1038/nchembio.118
Lin, H. (2024). Artificial intelligence with great potential in medical informatics: a brief review. Medinformatics 1(1), 2–9. doi:10.47852/bonviewMEDIN42022204
Mao, J., Zheng, K., and Weng, X. (2024). Medical big data in cancer research. Front. Mol. Biosci. 11, 1395607. doi:10.3389/fmolb.2024.1395607
Mayo, P., Pascual, J., Crisman, E., Domínguez, C., López, M. G., León, R., et al. (2024). Innovative pathological network-based multitarget approaches for Alzheimer’s disease treatment. Med. Res. Rev. 44 (6), 2367–2419. doi:10.1002/med.22045
Keywords: network pharmacology, gene, new drug discovery, molecular biology, genome sequencing
Citation: Mao J (2025) Editorial: The role of genes and network pharmacology in new drug discovery. Front. Genet. 16:1578094. doi: 10.3389/fgene.2025.1578094
Received: 17 February 2025; Accepted: 24 February 2025;
Published: 05 March 2025.
Edited and reviewed by:
Quan Zou, University of Electronic Science and Technology of China, ChinaCopyright © 2025 Mao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Jingxin Mao, bWFvbWFvMTk4NUBlbWFpbC5zd3UuZWR1LmNu, bW1tNTE4QDE2My5jb20=