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Purpose: Biodegradable polymeric nanoparticles of different architectures based on polyethylene glycol-co-poly(ε-caprolactone) block copolymers have been loaded with noscapine (NOS) to study their effect on its anticancer activity. It was intended to use solubility of NOS in an acidic environment and ability of the nanoparticles to passively target drugs into cancer tissue to modify the NOS pharmacokinetic properties and reduce the requirement for frequent injections., Methods: Linear and star-shaped copolymers were synthetized and used to formulate NOS loaded nanoparticles. Cytotoxicity was performed using a sulforhodamine B method on MCF-7 cells, while biocompatibility was determined on rats followed by hematological and histopathological investigations., Results: Formulae with the smallest particle sizes and adequate entrapment efficiency revealed that NOS loaded nanoparticles showed higher extent of release at pH 4.5. Colloidal stability suggested that nanoparticles would be stable in blood when injected into the systemic circulation. Loaded nanoparticles had IC50 values lower than free drug. Hematological and histopathological studies showed no difference between treated and control groups. Pharmacokinetic analysis revealed that formulation P1 had a prolonged half-life and better bioavailability compared to drug solution., Conclusions: Formulation of NOS into biodegradable polymeric nanoparticles has increased its efficacy and residence on cancer cells while passively avoiding normal body tissues. Graphical Abstract ᅟ.

In this study, di- and triblock copolymers based on polyethylene glycol and polylactide were synthesized by ring-opening polymerization and characterized by proton nuclear magnetic resonance and gel permeation chromatography. Nanoparticles containing noscapine were prepared from these biodegradable and biocompatible copolymers using the nanoprecipitation method. The prepared nanoparticles were characterized for size and drug entrapment efficiency, and their morphology and size were checked by transmission electron microscopy imaging. Artificial neural networks were constructed and tested for their ability to predict particle size and entrapment efficiency of noscapine within the formed nanoparticles using different factors utilized in the preparation step, namely polymer molecular weight, ratio of polymer to drug, and number of blocks that make up the polymer. Using these networks, it was found that the polymer molecular weight has the greatest effect on particle size. On the other hand, polymer to drug ratio was found to be the most influential factor on drug entrapment efficiency. This study demonstrated the ability of artificial neural networks to predict not only the particle size of the formed nanoparticles but also the drug entrapment efficiency. This may have a great impact on the design of polyethylene glycol and polylactide-based copolymers, and can be used to customize the required target formulations.