Breast cancer incidence has increased
in recent decades. In the
present study, an optimum formulation of chitosan (CS)-adorned niosome-based
nanocarriers for co-delivery of doxorubicin (DOX) and vincristine
(VIN) was developed for the treatment of breast cancer to reduce drug
doses and overcome multidrug resistance. The three-level Box–Behnken
method was utilized to optimize the particles in terms of size, polydispersity
index (PDI), entrapment efficacy (EE (%)), and percent of drug release
(%). The release rate of two drugs from CS-adorned nanoparticles (DOX+VIN/Nio/CS)
in acidic and physiological pH is less than uncoated niosome (DOX+VIN/Nio).
In addition, acidic pH increases the release rate of drugs from these
formulations. The size, polydispersity index, and entrapment efficacy
of nanoparticles were more stable at 4 °C compared to 25 °C.
MTT assay showed that the IC50 of DOX+VIN/Nio/CS is the
lowest value between all fabricated formulations. We evaluated the
cancer metastasis and migration (MMP2, MMP9) and transcriptional targets
for the tumor suppressor protein (Bax, Bcl2) that induces cell cycle
arrest or apoptosis in response to DNA. Bax gene was highly expressed,
while the Bcl2, MMP2, and MMP9 genes decreased in DOX+VIN/Nio/CS compared
to control, free forms of DOX, VIN, DOX+VIN, and DOX+VIN/Nio. DOX+VIN/Nio/CS
inhibited cell migration and increased apoptosis, cell uptake, and
endocytosis in human SKBR3 breast cancer cells compared to DOX, VIN,
DOX+VIN. These in vitro data are promising to treat breast cancer
with advanced pH-responsive drug release nanoformulations.
Understanding the drug solubility behavior is likely the first essential requirement for designing the supercritical technology for pharmaceutical processing. Therefore, this study utilizes different machine learning scenarios to simulate the solubility of twelve non-steroidal anti-inflammatory drugs (NSAIDs) in the supercritical carbon dioxide (SCCO2). The considered NSAIDs are Fenoprofen, Flurbiprofen, Ibuprofen, Ketoprofen, Loxoprofen, Nabumetone, Naproxen, Nimesulide, Phenylbutazone, Piroxicam, Salicylamide, and Tolmetin. Physical characteristics of the drugs (molecular weight and melting temperature), operating conditions (pressure and temperature), and solvent property (SCCO2 density) are effectively used to estimate the drug solubility. Monitoring and comparing the prediction accuracy of twelve intelligent paradigms from three categories (artificial neural networks, support vector regression, and hybrid neuro-fuzzy) approves that adaptive neuro-fuzzy inference is the best tool for the considered task. The hybrid optimization strategy adjusts the cluster radius of the subtractive clustering membership function to 0.6111. This model estimates 254 laboratory-measured solubility data with the AAPRE = 3.13%, MSE = 2.58 × 10–9, and R2 = 0.99919. The leverage technique confirms that outliers may poison less than four percent of the experimental data. In addition, the proposed hybrid paradigm is more reliable than the equations of state and available correlations in the literature. Experimental measurements, model predictions, and relevancy analyses justified that the drug solubility in SCCO2 increases by increasing temperature and pressure. The results show that Ibuprofen and Naproxen are the most soluble and insoluble drugs in SCCO2, respectively.
Ex vivo engineering of organs that uses decellularized whole organs as a scaffold with autologous stem cells is a potential alternative to traditional transplantation. However, one of the main challenges in this approach is preparing cytocompatible scaffolds. So far, high-precision and specific evaluation methods have not been developed for this purpose. Cell-based biosensors (CBBs) are promising tools to measure analytes with high sensitivity and specificity in a cost-effective and noninvasive manner. In this paper, using the NF-κB inducible promoter we developed a CBB for residual detergent detection. Proximal and core sections of the inducible promoter, containing NF-κB binding sequence, are designed and cloned upstream of the reporter gene (secreted alkaline phosphatase (SEAP)). After transfection into HEK293 cells, stable and reliable clones were selected. After confirmation of induction of this gene construct by sodium dodecyl sulfate (SDS), the stability and function of cells treated by qPCR and SEAP activity were measured. This biosensor was also used to evaluate the cytocompatibility of decellularized tissue. Results showed that the developed biosensor could detect very small amounts of SDS detergent (3.467 pM). It has the best performance 8 h after exposure to detergent, and its stability in high passage numbers was not significantly reduced. Applying this biosensor on decellularized tissues showed that SEAP activity higher than 4.36 (U/L) would lead to a viability reduction of transplanted cells below 70%. This paper presents a novel method to evaluate the cytocompatibility of decellularized tissues. The developed CBB can detect residual detergents (such as SDS) in tissues with high sensitivity and efficiency.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.