Combining Gemcitabine-Loaded Macrophage-like Nanoparticles and Erlotinib for Pancreatic Cancer Therapy.

Pancreatic cancer is a lethal malignancy with a dismal prognosis. Gemcitabine is currently used to treat pancreatic cancer, but it is limited by significant toxicity. Clinical trials on the combination of gemcitabine and erlotinib reported unsatisfactory outcomes along with concerns of toxicity. The encapsulation of chemotherapy drugs in polylactic-co-glycolic acid (PLGA) nanoparticles (NPs) can alleviate toxicity through targeted delivery and sustained release. In addition, camouflaging the NPs with a macrophage membrane can evade the immune system and further improve tumor homing. We designed gemcitabine-loaded PLGA NPs with a macrophage membrane coating (MPGNPs) to reduce drug toxicity and increase the accumulation in the tumor. The combination of MPGNPs and erlotinib synergistically inhibited pancreatic cancer cell proliferation in vitro and in vivo by targeting the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways. The MPGNPs were also able to evade phagocytosis and achieve passive targeting to the pancreatic tumors. The combination of MPGNPs and erlotinib showed synergistic anti-tumor efficacy in vitro and in vivo. This study provides a proof-of-concept for treating pancreatic cancer with a combination of MPGNPs and erlotinib.

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-PVA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacrylate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-96.0 wt%) as investigational variables, overlay sampling uniform design (OSUD) was employed to construct a high-quality dataset for model development. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate three-class classification of preparation conditions. Among the four models, the GBRT model exhibited the best predictive performance of the basic properties, with the mean absolute percentage error of 16.04 %, 0.85 %, and 2.44 % for permeability, effective porosity, and height of theoretical plate (1.0 cm/min), respectively. Characterization results of the representative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 × 10-12 m2, and a range of height of theoretical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indicate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropores, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt%) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties. This work represents an attempt to efficiently design and prepare target composite cryogels using machine learning and providing valuable insights for the efficient development of polymers.

Single-Step Assembly of Multimodal Imaging Nanocarriers: MRI and Long-Wavelength Fluorescence Imaging.

Magnetic resonance imaging (MRI)- and near-infrared (NIR)-active, multimodal composite nanocarriers (CNCs) are prepared using a simple one-step process, flash nanoprecipitation (FNP). The FNP process allows for the independent control of the hydrodynamic diameter, co-core excipient and NIR dye loading, and iron oxide-based nanocrystal (IONC) content of the CNCs. In the controlled precipitation process, 10 nm IONCs are encapsulated into poly(ethylene glycol) (PEG) stabilized CNCs to make biocompatible T2 contrast agents. By adjusting the formulation, CNC size is tuned between 80 and 360 nm. Holding the CNC size constant at an intensity weighted average diameter of 99 ± 3 nm (PDI width 28 nm), the particle relaxivity varies linearly with encapsulated IONC content ranging from 66 to 533 × 10(-3) m(-1) s(-1) for CNCs formulated with 4-16 wt% IONC. To demonstrate the use of CNCs as in vivo MRI contrast agents, CNCs are surface functionalized with liver-targeting hydroxyl groups. The CNCs enable the detection of 0.8 mm(3) non-small cell lung cancer metastases in mice livers via MRI. Incorporating the hydrophobic, NIR dye tris-(porphyrinato)zinc(II) into CNCs enables complementary visualization with long-wavelength fluorescence at 800 nm. In vivo imaging demonstrates the ability of CNCs to act both as MRI and fluorescent imaging agents.