Superparamagnetic iron oxide nanoparticles (SPIONs) are the most commonly used negative MRI contrast agent which affect the transverse (T2) relaxation time

The purpose of this study was to prepare nanoparticles made of tri-block poly(lactide)–poly(ethylene glycol)–poly (lactide) (PLA–PEG–PLA) with controlled size as drug carrier. Artificial neural networks (ANNs) were used to identify factors which influence particle size. In this way, PLA–PEG–PLA was synthesized and was made into nanoparticles by nanoprecipitation under different conditions. The copolymer and the resulting nanoparticles were characterized by various techniques such as proton nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, gel permeation chromatography, photon correlation spectroscopy and scanning electron microscopy. The developed model was assessed and found to be of high quality. The model was then used to survey the effects of processing factors including polymer concentration, amount of drug, solvent ratio and mixing rate on particle size of polymeric nanoparticles. It was observed that polymer concentration is the most affecting parameter on nanoparticle size distribution. The results demonstrate the potential of ANNs in modelling and identification of critical parameters effective on final particle size.

Multiple factors are involved in the development of cancers and their effects on survival rate. Many are related to chemo-resistance of tumor cells. Thus, treatment with a single therapeutic agent is often inadequate for successful cancer therapy. Ideally, combination therapy inhibits tumor growth through multiple pathways by enhancing the performance of each individual therapy, often resulting in a synergistic effect. Polymeric nanoparticles prepared from block co-polymers have been a popular platform for co-delivery of combinations of drugs associated with the multiple functional compartments within such nanoparticles. Various polymeric nanoparticles have been applied to achieve enhanced therapeutic efficacy in cancer therapy. However, reported drug ratios used in such systems often vary widely. Thus, the same combination of drugs may result in very different therapeutic outcomes. In this review, we investigated polymeric co-delivery systems used in cancer treatment and the drug combinations used in these systems for synergistic anti-cancer effect. Development of polymeric co-delivery systems for a maximized therapeutic effect requires a deeper understanding of the optimal ratio among therapeutic agents and the natural heterogenicity of tumors.