journal article·2020-03Open access

Saikosaponin D loaded macrophage membrane-biomimetic nanoparticles target angiogenic signaling for breast cancer therapy

Kaiju Sun, Wenjing Yu, Bai Ji, Changbao Chen, Hongmei Yang, Yangyang Du, Meiyu Song, Hongqiao Cai, Fei Yan, Rui Su

Abstract

Breast cancer remains the most frequently diagnosed malignancy among women worldwide. Although effective, numerous treatment strategies have been developed, which suffer from severe side effects due to high toxicity and low specificity. Thus, alternative drugs with promising anti-tumor activity, less toxicity, and high therapeutic index are highly desirable. Saikosaponin D (SsD), a triterpene saponin derived from Bupleurum, exhibit potential therapeutic properties for cancer therapy. However, severe side effects and systemic toxicities due to poor pharmacokinetics and non-specific distribution of SsD in vivo limited its applications in clinical trials. Therefore, to overcome these limitations and enhance the therapeutic efficiency of SsD, herein, we developed a macrophage-biomimetic drug delivery system by coating macrophage membrane hybridized with T7 peptide on the surface of poly (latic-co-glycolic acid) nanoparticles (SCMNPs). Remarkably, SCMNPs demonstrated targeted specificity to cancer cells with the characteristics of immune escaping, selective accumulation, and enhanced cell endocytosis. Moreover, SCMNPs effectively inhibited tumor growth and metastasis of breast cancer in vitro and in vivo through VEGFR, AKT, and ERK related to the angiogenic pathway. In conclusion, a rationally developed novel SsD-based biomimetic strategy via the biological properties of macrophages presents a complementary therapeutic paradigm for precise and effective breast cancer therapy.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Natural product bioactivities and synthesis · Natural Compounds in Disease Treatment · Galectins and Cancer Biology

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

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