Integrating machine-learning and nanotechnology to quantify pH-modulated oxaliplatin release.

Area of Science:

• Nanomedicine
• Materials Science
• Computational Biology

Background:

• Targeted drug delivery systems are crucial for improving cancer therapy efficacy and reducing side effects.
• Oxaliplatin is a platinum-based chemotherapy drug with significant activity against various cancers, but its clinical use is limited by toxicity and poor bioavailability.
• pH-sensitive nanocarriers offer a promising strategy for targeted drug release in the acidic tumor microenvironment.

Purpose of the Study:

• To formulate and characterize pH-sensitive, surfactant-based nanomicelles for targeted delivery of Oxaliplatin to breast cancer cells.
• To employ machine learning models for analyzing and predicting pH-dependent drug release kinetics.
• To evaluate the in vitro cytotoxicity of Oxaliplatin-loaded nanomicelles against breast cancer cells and normal fibroblasts.

Main Methods:

• Nanomicelles were prepared using Pluronic F-127 via thin-film hydration and characterized for size, morphology, and encapsulation efficiency.
• Drug release studies were conducted at physiological (pH 7.4) and acidic (pH 5.4) conditions.
• Machine learning models (Random Forest, Gradient Boosting, SVR) with SHAP analysis were used to model drug release kinetics.
• Cytotoxicity was assessed using the MTT assay on MCF-7 breast cancer cells and L929 normal fibroblasts.

Main Results:

• Monodisperse, spherical nanomicelles with a hydrodynamic diameter of 290.3 nm and 40.2% encapsulation efficiency were successfully prepared.
• Significant pH-responsive drug release was observed, with 77.5% cumulative release at pH 5.4 compared to 43.5% at pH 7.4 within 96 hours.
• Kinetic modeling indicated a shift from Fickian diffusion to anomalous transport at acidic pH.
• Machine learning models demonstrated high interpolation accuracy (R² > 0.97), with SHAP analysis revealing key release transition points.
• Oxaliplatin-loaded nanomicelles showed enhanced cytotoxicity against MCF-7 cells and reduced toxicity against L929 cells compared to free Oxaliplatin.

Conclusions:

• Surfactant-based nanomicelles serve as an effective platform for pH-directed Oxaliplatin delivery, enhancing its therapeutic index for breast cancer treatment.
• Machine learning integration provides a powerful tool for understanding complex drug release mechanisms and optimizing nanomedicine formulation.
• This approach holds significant potential for developing advanced, targeted nanomedicine delivery systems for cancer therapy.