Design and In Vitro Evaluation of Novel Cationic Lipids for siRNA Delivery in Breast Cancer Cell Lines

Abstract

Breast cancer is the most common cause of cancer mortality in Western nations, with a terrible prognosis. Many studies show that siRNA plays a role in the development of tumors by acting as a tumor suppressor and apoptosis inhibitor or both. siRNAs may be used as diagnostic and prognostic biomarkers in breast cancer. Antisurvivin siRNA was chosen as a therapeutic target in breast cancer treatment because it directly targets survivin, an inhibitor of apoptosis protein, that causes cell death. However, siRNA-based treatment has significant limitations, including a lack of tissue selectivity, a lack of effective delivery mechanisms, low cellular absorption, and the possibility of systemic toxicity. To address some of these issues, we provide a siRNA delivery method based on cationic lipids. In the recent past, cationic liposomes have displayed that they offer a remarkable perspective in proficient siRNA delivery. The presence of a positive charge plays a vital role in firm extracellular siRNA binding along with active intracellular siRNA separation and low biological adversities. Consequently, the methods for developing innovative cationic lipids through rendering and utilization of appropriate positive charges would certainly be helpful for benign and effective siRNA delivery. In the current study, an effort was made to synthesize a 3,4-dimethoxyaniline lipid (DMA) to improve the effectiveness and protection of successful siRNA delivery. DMA cationic lipid successfully delivered survivin siRNA that reduced the survivin mRNA expression, indicating the possibility of utilizing siRNA therapeutics for breast cancer. It is expected that this innovative quaternary amine-based liposome can open up new avenues in the process of developing an easy and extensively used platform for siRNA delivery. Cationic lipoplexes, a potential carrier system for siRNA-based therapies in the treatment of breast cancer, were proven by our data.