Synergistic combination therapy delivered via layer-by-layer nanoparticles induces solid tumor regression of ovarian cancer

通过逐层纳米粒子递送的协同联合疗法诱导卵巢癌实体肿瘤消退

阅读:4
作者:Stephanie Kong, Pearl Moharil, Abram Handly-Santana, Natalie Boehnke, Richard Panayiotou, Victoria Gomerdinger, Gil Covarrubias, Ivan S Pires, Ioannis Zervantonakis, Joan Brugge, Paula T Hammond

Abstract

The majority of patients with high grade serous ovarian cancer (HGSOC) develop recurrent disease and chemotherapy resistance. To identify drug combinations that would be effective in treatment of chemotherapy resistant disease, we examined the efficacy of drug combinations that target the three antiapoptotic proteins most commonly expressed in HGSOC-BCL2, BCL-XL, and MCL1. Co-inhibition of BCL2 and BCL-XL (ABT-263) with inhibition of MCL1 (S63845) induces potent synergistic cytotoxicity in multiple HGSOC models. Since this drug combination is predicted to be toxic to patients due to the known clinical morbidities of each drug, we developed layer-by-layer nanoparticles (LbL NPs) that co-encapsulate these inhibitors in order to target HGSOC tumor cells and reduce systemic toxicities. We show that the LbL NPs can be designed to have high association with specific ovarian tumor cell types targeted in these studies, thus enabling a more selective uptake when delivered via intraperitoneal injection. Treatment with these LbL NPs displayed better potency than free drugs in vitro and resulted in near-complete elimination of solid tumor metastases of ovarian cancer xenografts. Thus, these results support the exploration of LbL NPs as a strategy to deliver potent drug combinations to recurrent HGSOC. While these findings are described for co-encapsulation of a BCL2/XL and a MCL1 inhibitor, the modular nature of LbL assembly provides flexibility in the range of therapies that can be incorporated, making LbL NPs an adaptable vehicle for delivery of additional combinations of pathway inhibitors and other oncology drugs.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。