Enzyme/pH dual stimuli-responsive nanoplatform co-deliver disulfiram and doxorubicin for effective treatment of breast cancer lung metastasis

ABSTRACT

Objectives

Metastasis is still one of the main obstacles in the treatment of breast cancer. This study aimed to develop disulfiram (DSF) and doxorubicin (DOX) co-loaded nanoparticles (DSF-DOX NPs) with enzyme/pH dual stimuli-responsive characteristics to inhibit breast cancer metastasis.

Methods

DSF-DOX NPs were prepared using the amphiphilic poly(ε-caprolactone)-b-poly(L-glutamic acid)-g-methoxy poly(ethylene glycol) (PCL-b-PGlu-g-mPEG) copolymer by a classical dialysis method. In vitro release tests, in vitro cytotoxicity assay, and anti-metastasis studies were conducted to evaluate pH/enzyme sensitivity and therapeutic effect of DSF-DOX NPs.

Results

The specific pH and enzyme stimuli-responsiveness of DSF-DO NPs can be attributed to the transformation of secondary structure and the degradation of amide bonds in the PGlu segment, respectively. This accelerated drug release significantly increased the cytotoxicity to 4T1 cells. Compared with the control group, the DSF-DOX NPs showed a strong inhibition of in vitro metastasis with a wound healing rate of 36.50% and a migration rate of 18.39%. Impressively, in vivo anti-metastasis results indicated that the metastasis of 4T1 cells was almost completely suppressed by DSF-DOX NPs.

Conclusion

DSF-DOX NPs with controllable tumor site delivery of DOX and DSF were a prospectively potential strategy for metastatic breast cancer treatment.

KEYWORDS:

• Breast cancer metastasis
• disulfiram
• doxorubicin
• enzyme/pH dual-responsive
• polymeric nanoparticles
• PCL-b-PGlu-g-mPEG

Author contributions

Peifu Xiao contributed to conceptualization, investigation, methodology, writing – original draft, and writing – review and editing. Xiaoguang Tao contributed to investigation, methodology, and writing – original draft. Hanxun Wang contributed to software and methodology. Hongbing Liu contributed to methodology and software. Yupeng Feng contributed to investigation and formal analysis. Yueqi Zhu contributed to investigation. Zhengzhen Jiang contributed to investigation. Tian Yin contributed to visualization and supervision. Yu Zhang contributed to visualization and supervision. Haibing He contributed to formal analysis and supervision. Jingxin Gou contributed to formal analysis, supervision, and funding acquisition. Xing Tang contributed to conceptualization, project administration, and funding acquisition. All authors agreed to be accountable for all aspects of the work, and they approved the final version of the paper.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers of this manuscript have no relevant financial or other relationships to disclose.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/17425247.2023.2237888

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (82204298) and the National Key R&D Program of China (2020YFE0201700).