Functional personalized complex combination nano therapy for osteosarcoma.

Area of Science:

• Oncology
• Pharmacology
• Biotechnology

Background:

• Osteosarcoma (OS) presents a significant challenge due to genetic heterogeneity and lack of targeted therapies.
• Personalized medicine approaches using patient-derived cells are emerging but not yet established for drug cocktail identification.

Purpose of the Study:

• To investigate a sequential drug screening workflow for identifying patient-specific drug cocktails for osteosarcoma.
• To evaluate the efficacy of novel drug combinations and their nanoparticle formulations in preclinical models.

Main Methods:

• Systematic screening of 17 drugs across four osteosarcoma cell lines.
• Identification and formulation of synergistic drug pairs (e.g., trametinib-ponatinib) in polydopamine nanoparticles.
• In vivo studies using K7M2 murine models to assess tumor accumulation, efficacy, and toxicity of nanoparticle drug pairs.

Main Results:

• Differential drug responses observed across osteosarcoma cell lines; LLMs failed to predict OS cell efficacy.
• Identified synergistic non-chemotherapeutic drug pairs, including trametinib-ponatinib and rapamycin-ganetespib.
• Nanoparticle drug pairs demonstrated superior tumor accumulation and efficacy over standard chemotherapy, with alternating administration outperforming concomitant regimens.

Conclusions:

• A functional, sequential drug screening approach can identify effective personalized drug cocktails for osteosarcoma.
• Nanoparticle formulation and alternating administration enhance therapeutic efficacy and reduce toxicity.
• This strategy offers a promising avenue to overcome limitations in current osteosarcoma treatment.