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Manganese dioxide (MnO2) nanoparticles (NPs) were discovered in previous work to be effective in improving tumor oxygenation (hypoxia) and reducing H2O2 and acidity in the tumor microenvironment (TME) via local injection. To develop MnO2 formulations useful for clinical application, hybrid NPs are designed with tailored hydrophobicity and structure suitable for intravenous injection, with good blood circulation, biocompatibility, high tumor accumulation, and programmable oxygen generation rate. Two different hybrid NPs are constructed by embedding polyelectrolyte-MnO2 (PMD) in hydrophilic terpolymer/protein-MnO2 (TMD) or hydrophobic polymer/lipid-MnO2 (LMD) matrices. The in vitro reactivity of the MnO2 toward H2O2 is controlled by matrix material and NP structure and dependent on pH with up to two-fold higher O2 generation rate at acidic (tumor) pH than at systemic pH. The hybrid NPs are found to be safe to cells in vitro and organs in vivo and effectively decrease tumor hypoxia and hypoxia-inducible-factor-1alpha through local or systemic administration. Fast acting TMD reduces tumor hypoxia by 70% in 0.5 h by local injection. Slow acting LMD exhibits superior tumor accumulation and retention through the systemic administration and decreased hypoxia by 45%. These findings encourage a broader use of hybrid MD NPs to overcome TME factors for cancer treatment.

Although a great number of studies may be found in literature about the parameters affecting the size of chitosan nanoparticles, no systematic work so far has detailed the factors affecting the polydispersity of chitosan as an important factor determining the quality of many preparations. Herein, using artificial neural networks (ANNs), four independent variables, namely, pH and concentration of chitosan solution as well as time and amplitude of sonication of the solution were studied to determine their influence on the polydispersity of solution. We found that in an ultrasound prepared nanodispersion of chitosan, all the four input parameters have reverse but non-linear relation with the polydispersity of the nanoparticles.

The pharmacokinetic properties of chitosan nanoparticles have been shown to mainly depend on its particle size. The aim of this study was to concurrently evaluate and model the effective parameters, namely, chitosan concentration, buffer pH, amplitude and time of sonication, on the particle size of chitosan nanoparticles. Chitosan solutions were prepared and sonicated with different values for the above mentioned parameters. The data were then modeled using artificial neural networks (ANNs). The results illustrated that all four input parameters affect the size of prepared chitosan nanoparticles. While a reverse effect was observed between the size and the buffer pH as well as time and amplitude of sonication, the concentration was found to directly influence the particle size. The optimum condition to obtain the minimum size of nanoparticles in the range of 50-200 nm was found to be high values of pH and sonication time (i.e. approximately 4.9 and 500 s, respectively) and amplitude values of more than ~55.

Chitosan nanoparticles were prepared using ultrasonication methodology at specific amplitudes and times of sonication. Subsequently, small interfering RNA (siRNA) was added to the solution at predetermined values of nitrogen to phosphorous ratio (N/P), and stirring time. Employing response surfaces generated from a statistical model, the effect of sonication time and amplitude, stirring time, and N/P ratio was studied on the particle size, polydispersity, and loading efficiency of prepared siRNA/chitosan nanoparticles. It was found that to obtain the smallest size, amplitude and time of sonication as well as stirring time should be kept at ∼45%, 165 seconds, and 50 minutes, respectively. Minimum polydispersity values were also obtained at similar values of sonication time/amplitude and stirring time in addition to N/P values of ∼28. Also, the maximum proportion of siRNA loading was observed at approximate values of 300 seconds, 80% and 280 for sonication time, amplitude, and N/P ratio, respectively. The optimum conditions (i.e., to prepare a sample with minimum values of particle size and polydispersity index and maximum values of loading efficiency) were determined as 60.6, 30.0 (seconds), 28.0, and 12.5 (minutes) for amplitude, time of sonication, N/P, and stirring time, respectively. In this scrutiny, the predicted values of optimum formulation were 456 nm size, 89.6% loading efficiency, and 0.4 polydispersity index.

Multidrug resistance (MDR) is a main cause of chemotherapy failure in cancer treatment. It is associated with complex cellular and molecular mechanisms including overexpression of drug efflux transporters, increased membrane rigidity, and impaired apoptosis. Numerous efforts have been made to overcome efflux transporter-mediated MDR using nanotechnology-based approaches. However, these approaches fail to surmount plasma membrane rigidity that attenuates drug penetration and nanoparticle endocytosis. Here, a “one-two punch” nanoparticle approach is proposed to coordinate intracellular biointeraction and bioreaction of a nanocarrier material docosahexaenoic acid (DHA) and an anticancer prodrug mitomycin C (MMC) to enhance mitochondrion-targeted toxicity. Incorporation of DHA in solid polymer-lipid nanoparticles first reduces the membrane rigidity in live cancer cells thereby increasing nanoparticle cellular uptake and MMC accumulation. Subsequent intracellular MMC bioreduction produces free radicals that in turn react with adjacent DHA inducing significantly elevated mitochondrial lipid peroxidation, leading to irreversible damage to mitochondria. Preferential tumor accumulation of the nanoparticles and the synergistic anticancer cytotoxicity remarkably inhibit tumor growth and prolonged host survival without any systemic toxicity in an orthotopic MDR breast tumor model. This work suggests that combinatorial use of biophysical and biochemical properties of nanocarrier materials with bioreactive prodrugs is a powerful approach to overcoming multifactorial MDR in cancer.

The aim of this study is to prepare a nanosuspension of budesonide for respiratory delivery using nebuliser by optimising its particle size and characterising its in vitro deposition behaviour. PLA (poly lactic acid)-budesonide nanosuspension (BNS) was prepared using high-pressure emulsification/solvent evaporation method. To optimise particle size, different parameters such as PLA concentration, sonication time, and amplitude were investigated. Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and scanning electron microscope (SEM) analyses were performed to characterise the prepared PLA-budesonide nanoparticles. The in vitro aerodynamic characteristics of the PLA-BNS using a jet nebuliser were estimated and compared with that of commercially available suspension formulation of budesonide. Budesonide-loaded PLA nanoparticles with fine particle size (an average size of 224-360 nm), narrow size distribution, and spherical and smooth surface were prepared. The optimum condition for preparation of fine particle size for aerosolisation was found to be at PLA concentration of 1.2 mg/ml and amplitude of 70 for 75 s sonication time. The in vitro aerosolisation performance of PLA-BNS compared to that of commercial budesonide indicated that it has significantly (p 0.05) smaller mass median aerodynamic diameter (MMAD) value with an enhancement in fine particle fraction (FPF) value. Improving the in vitro deposition of budesonide, PLA-BNS could be considered as a promising alternative suspension formulation for deep lung delivery of the drug using nebuliser.

The purpose of this study was to find an artificial neural networks model for determining major factors impacting the stability of an acetaminophen nanosuspansion that was prepared using nanoprecipitation in microfluidic reactors. Four variables, namely concentration of surfactant, solvent and antisolvent flow rate and solvent temperature were used as input variables and time of sedimentation of nanoparticles was considered as output variable. The particle size of optimized formulation was measured by transmission electron microscope and dynamic light scattering. Comparing the 3D graphs from the model showed that antisolvent flow rate and temperature have direct relation with time of sedimentation, whereas solvent flow rate generally has reverse relation with the time of sedimentation. Concentration of surfactant was found to be the most important factor in determining the stability of nanosuspension.