AdenoCure 3X: Establishing a Sensitized Cancer Cell Model to Accelerate Novel Drug Discovery
Cancer is a major cause mortality globally, and chemotherapy is frequently employed as a treatment for cancer. Regrettably, cancers possess the ability for developing resistance to conventional treatments, and the rising recurrence of these drug-resistant cancers requires further research and the advancement of treatment approaches. To address the growing challenge of chemoresistance, this study presents a novel approach for developing a sensitized cancer cell model using AdenoCure 3X to accelerate the discovery of novel drugs. We constructed a tetracistronic adenoviral vector, Ad-MBRG for targeted delivery of tumour suppressor genes MOAP-1, Bax, and RASSF1A, known to be epigenetically silenced in many cancers and contribute to apoptotic pathway dysfunction. This approach aims to restore apoptotic signaling and enhance cancer cell sensitivity to treatment. The study outlines a multi-step methodology for establishing the Ad-MBRG-mediated sensitized cell model, including amplification and quantification of Ad-MBRG particles, optimization of infection efficiency in the MCF7-CR cancer cell line, and evaluation of their sensitivity compared to non-infected controls. Furthermore, we explored the potential synergistic effects between Ad-MBRG infection and anti-cancer compounds derived from natural products. This model holds promise for screening anti-cancer compounds, particularly those from natural sources, and evaluating their potential to overcome chemoresistance. This paves the way for further exploration of adenovirus-mediated gene therapy as a platform for novel drug discovery and improved cancer treatment strategies.
Authors
- Ee Wern Tan (ORCID: https://orcid.org/0000-0002-2106-1449)
- Kuan Onn Tan (ORCID: https://orcid.org/0000-0002-0947-7337)
- Bey Hing Goh (ORCID: https://orcid.org/0000-0003-1006-3649)
Publication Details
- Journal
- Progress in Drug Discovery & Biomedical Science
- Published
- 2024-08-30
- DOI
- https://doi.org/10.36877/pddbs.a0000530
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.36
Funders
- Sunway University