0% Complete
صفحه اصلی
/
4th international edition and 13th Iranian Conference on Bioinformatics
Development of Novel Cellulose Crystal-Hyaluronic Acid Anti-Cancer Carriers for Targeting
نویسندگان :
Yeganeh Abbasian Bajgiran
1
Maryam Azimzadeh Irani
2
1- Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
2- Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
کلمات کلیدی :
Drug delivery،Breast cancer،Hyaluronic acid،Cellulose Nano Crystals
چکیده :
Breast cancer is responsible for a higher death rate than any other kind of cancer. A significant marker of tumor-initiating cells is the cell surface glycoprotein CD44 receptor, which is noticeably overexpressed in breast cancer. Because of its critical involvement in cancer development, this receptor is an attractive target for drug delivery (Xu, Wu et al. 2016). The first step in creating a targeted medication delivery system that targets the CD44 receptor is choosing an identifier for this protein. Hyaluronic Acid (HA), a well-known ligand for this receptor, is the best option in this instance. Following that, a nano-carrier should be chosen to transport breast cancer medications; in this case, Cellulose NanoCrystals (CNCs) were used. CNCs are rod-shaped nanoparticles derived from cellulose. Their unique physicochemical features make them a suitable platform for drug delivery and therapeutic uses. Furthermore, the vast surface area improves their capacity to load medications and gives them better control over the release of therapeutic drugs (Seo, Lee et al. 2020) (Cirillo 2023). It is crucial to understand how this system interacts with other overexpressed membrane proteins. In particular, the current investigation looked at how HA-CNC interacts with CD44, its main target, as well as other membrane proteins, including CD133 and CD47, which are also overexpressed in breast cancer (Chen, Wang et al. 2022) (Brugnoli, Grassilli et al. 2019). The structures of CNC, HA, CD44, CD47, and CD133 were obtained from Polysac3DB (Nishiyama, Langan et al. 2002), PubChem (CID: 155618327) (Kim, Chen et al. 2023), the Protein Data Bank (PDB IDs: 4PZ4 and 2VSC) (Liu and Finzel 2014) (Hatherley, Graham et al. 2008), and UniProt (ID: O43490) (Varadi, Bertoni et al. 2024), respectively. Docking simulations were conducted using AutoDock 4.2 to investigate the interactions between membrane proteins and HA, and additionally between HA and CNC (Morris, Huey et al. 2009). Following docking, PyMOL 3.0 was employed to superimpose the HA-CNC complex with each membrane protein (Schrodinger 2015). Among the selected proteins, CD44 showed the highest binding affinity with HA (-5.6 kcal/mol), followed by CD47 (-4.2 kcal/mol) and CD133 (-2.9 kcal/mol). According to the docking studies, CD133 and HA have a binding energy of -2.9 kcal/mol, which is lower than that of CD44 but still suggests a significant interaction that may be useful for drug delivery applications. Stronger interactions with CD44 (-5.6 kcal/mol) and CD47 (-4.2 kcal/mol) are further evidence that HA-CNC complexes have the potential to be a multipurpose platform for combination or multi-target anti-cancer approaches. This potential is expected to attract the interest of researchers working on specific therapies for illnesses like breast cancer.
لیست مقالات
لیست مقالات بایگانی شده
Integrated bioinformatic analysis for the screening of hub genes & therapeutic drugs in high-grade serous ovarian cancer
Maryam Khalili - Behnaz Saffar
Novel Anti-ageing Strategy Via Targeting CST With Vitamin B1
SeyedMobin Mousavi Ghomi - Maryam Azimzadeh Irani - Aida Arezoumandchafi
Graphene Oxide Nanosheets as Drug Carriers for Erdafitinib in a Targeted Drug Delivery System: A Cutting-Edge Approach to Cancer Therapy
Hooriye Yahyaei - Nahid Shajari - Amirreza Saeni - Ahmadreza Saeni
Evaluation of Penetration Efficiency of BR2 Peptide in Breast Cancer cell Lines Using computational Methods
Fatemeh Davari - Mahboobeh Zarrabi
In Silico Design and Evaluation of a Multi-Epitope Vaccine Candidate Against Escherichia coli and Staphylococcus aureus Involved in Bovine Clinical Mastitis
Aryan Ghorbani - Negin Khalili-samani - Maryam Amirinia - Faezeh Jazayeri-soreshjani - Faranak Ravanan - Mohammad Oveysi-rastabi - Abbas Doosti
In-silico Drug Generation using Masked Language Modeling
Seyed Hassan Alavi - Zahra Ghorbanali - Fatemeh Zare-Mirakabad
Combination therapy synergism prediction for virus treatment using machine learning models
Shayan Majidifar - Arash Zabihian - Mohsen Hooshmand
ATIC as a Theranostic Biomarker in Gastrointestinal Cancers: Insights from Autophagy Pathway Analysis
Pooya Jalali - Maryam Eghbali - Zahra Eghabli - Yasaman Gholinezhad - Zahra Salehi
Leveraging Machine Learning Models for Virtual Screening of ZINC Database to Identify JAK1 Inhibitors
Negar Abdolmaleki - Hamid Mahdiuni
Investigating the Role of EMT genes in Multiple Myeloma: A Bioinformatic Approach
Seyedeh Zahra Mousavi - Hamid Mahdizadeh - Mehdi Totonchi
ثمین همایش، سامانه مدیریت کنفرانس ها و جشنواره ها - نگارش 40.4.1