YAP/TAZ Inhibitor-Based Drug Delivery System for Selective Tumor Accumulation and Cancer Combination Therapy

Ziqian Zhang1, Zhangyi Luo1, Haozhe Huang1

  • 1Center for Pharmacogenetics, Department of Pharmaceutical Science, University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania 15261, United States.

Biomacromolecules
|December 7, 2024
PubMed

Insights

Researchers developed novel prodrug polymers to improve the delivery of niflumic acid (NA), a YAP/TAZ inhibitor. This nanocombination therapy effectively enhanced breast cancer treatment by targeting tumors and inhibiting growth.

Area of Science:

  • Oncology
  • Materials Science
  • Nanotechnology

Background:

  • Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) are key drivers in cancer development and therapy resistance.
  • Pharmacological inhibition of YAP/TAZ is a promising strategy for cancer treatment.
  • Niflumic acid (NA), a YAP/TAZ inhibitor, has limited clinical utility due to poor in vivo half-life.

Purpose of the Study:

  • To develop improved prodrug strategies for YAP/TAZ inhibition.
  • To create self-assembled nanoparticles for enhanced delivery of niflumic acid.
  • To investigate a nanocombination therapy for breast cancer treatment.

Main Methods:

  • Synthesized a series of niflumic acid-based prodrug polymers.
  • Investigated physicochemical properties of self-assembled nanoparticles.
  • Utilized optimal prodrug polymer as a micellar nanocarrier for receptor tyrosine kinase inhibitors (RTKIs).

Main Results:

  • Developed prodrug polymers that self-assemble into nanoparticles, improving niflumic acid bioavailability.
  • The optimal prodrug nanocarrier selectively accumulated in tumors.
  • Combination therapy with RTKIs (e.g., Dasatinib) synergistically inhibited tumor growth.

Conclusions:

  • Prodrug polymer nanoparticles offer a viable strategy to overcome niflumic acid's limitations.
  • Nanocombination therapy demonstrates enhanced efficacy in preclinical breast cancer models.
  • This approach holds potential for improved cancer treatment strategies.

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