journal article·2022-10

Polymeric nanoparticles-siRNA as an emerging nano-polyplexes against ovarian cancer

Urushi Rehman, Neha Parveen, Afsana Sheikh, Mohammed A.S. Abourehab, Amirhossein Sahebkar, Prashant Kesharwani

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Abstract

Ovarian cancer (OC) is considered fifth-deadliest cancer globally responsible for high mortality in women. As the conventional therapeutic and diagnostic approaches are ineffective in increasing the survival rates of advanced staged patients by more than 5 years, OC has resulted in high morbidity and mortality rates over the last two decades. As a result, there is a dire need for innovative treatment approaches to address the issues. RNAi and nanotechnology can be considered the most appropriate strategies that can be used to improve OC therapy and help circumvent the chemo-resistance. siRNA is considered highly successful in facilitating the knockdown of specific genes on entering the cytosol when administered in-vivo via inhibiting the mRNA expression responsible for translation of those specific genes through the mechanism called RNA interference (RNAi). However, the primary barrier of utmost importance in the clinical efficacy of employed siRNA for the treatment of OC is the systemic distribution to the targeted site from the administration site. As a result, nanoparticles are constructed to carry the siRNA molecules inside them to the targeted site by preventing serum degradation and enhancing the serum stability of administered siRNA. The present review assesses the developments made in the polymeric-based nanoparticle siRNA delivery for targeting particular genes involved in the prognosis of ovarian cancers and surpassing the chemo-resistance and thus improving the therapeutic potentials of administered agents.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: RNA Interference and Gene Delivery · Advanced biosensing and bioanalysis techniques · MicroRNA in disease regulation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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