Algae-Based Nanoparticles for Oral Drug Delivery Systems.

Drug administration by oral delivery is the preferred route, regardless of some remaining challenges, such as short resident time and toxicity issues. One strategy to overcome these barriers is utilizing mucoadhesive vectors that can increase intestinal resident time and systemic uptake. In this study, biomimetic nanoparticles (NPs) were produced from 14 types of edible algae and evaluated for usage as oral DDSs by measuring their size, surface charge, morphology, encapsulation efficiency, mucoadhesion force, and cellular uptake into Caco-2 cells. The NPs composed of algal materials (aNPs) exhibited a spherical morphology with a size range of 126-606 nm and a surface charge of -9 to -38 mV. The mucoadhesive forces tested ex vivo against mice, pigs, and sheep intestines revealed significant variation between algae and animal models. Notably, Arthospira platensis (i.e., Spirulina) NPs (126 ± 2 nm, -38 ± 3 mV) consistently exhibited the highest mucoadhesive forces (up to 3127 ± 272 µN/mm²). Moreover, a correlation was found between high mucoadhesive force and high cellular uptake into Caco-2 cells, further supporting the potential of aNPs by indicating their ability to facilitate drug absorption into the human intestinal epithelium. The results presented herein serve as a proof of concept for the possibility of aNPs as oral drug delivery vehicles.

Tracking the cellular immune response from early premalignancy to active multiple myeloma in a new model.

To calibrate a murine model to study premalignant to malignant multiple myeloma, mice were inoculated with different amounts of myeloma cells, and changes in the immune profile were tracked for over 200 days. The model highlights the development of T-cell exhaustion and suppressor before the appearance of clinical symptoms.

Search for Synergistic Drug Combinations to Treat Chronic Lymphocytic Leukemia.

Finding synergistic drug combinations is an important area of cancer research. Here, we sought to rationally design synergistic drug combinations with an inhibitor of BTK kinase, ibrutinib, which is used for the treatment of several types of leukemia. We (a) used a pooled shRNA screen to identify genes that protect cells from the drug, (b) identified protective pathways via bioinformatics analysis of these gene sets, and (c) identified drugs that inhibit these pathways. Based on this analysis, we established that inhibitors of proteasome and mTORC1 could synergize with ibrutinib both in vitro and in vivo. We suggest that FDA-approved inhibitors of these pathways could be effectively combined with ibrutinib for the treatment of chronic lymphocytic leukemia (CLL).

Validation of a Mathematical Model Describing the Dynamics of Chemotherapy for Chronic Lymphocytic Leukemia In Vivo.

In recent years, mathematical models have developed into an important tool for cancer research, combining quantitative analysis and natural processes. We have focused on Chronic Lymphocytic Leukemia (CLL), since it is one of the most common adult leukemias, which remains incurable. As the first step toward the mathematical prediction of in vivo drug efficacy, we first found that logistic growth best described the proliferation of fluorescently labeled murine A20 leukemic cells injected in immunocompetent Balb/c mice. Then, we tested the cytotoxic efficacy of Ibrutinib (Ibr) and Cytarabine (Cyt) in A20-bearing mice. The results afforded calculation of the killing rate of the A20 cells as a function of therapy. The experimental data were compared with the simulation model to validate the latter's applicability. On the basis of these results, we developed a new ordinary differential equations (ODEs) model and provided its sensitivity and stability analysis. There was excellent accordance between numerical simulations of the model and results from in vivo experiments. We found that simulations of our model could predict that the combination of Cyt and Ibr would lead to approximately 95% killing of A20 cells. In its current format, the model can be used as a tool for mathematical prediction of in vivo drug efficacy, and could form the basis of software for prediction of personalized chemotherapy.