Near-infrared (NIR)-activable liposomes containing photosensitizer (PS)-lipid conjugates are emerging as tunable, high-payload, and tumor-selective platforms for photodynamic therapy (PDT)-based theranostics. To date, the impact that the membrane composition of a NIR-activable liposome (the chemical nature and subsequent conformation of PS-lipid conjugates) has on their in vitro and in vivo functionality has not been fully investigated. While their chemical nature is critical, the resultant physical conformation dictates their interactions with the immediate biological environments. Here, we evaluate NIR-activable liposomes containing lipid conjugates of the clinically-used PSs benzoporphyrin derivative (BPD; hydrophobic, membrane-inserting conformation) or IRDye 700DX (hydrophilic, membrane-protruding conformation) and demonstrate that membrane composition is critical for their function as tumor-selective PDT-based platforms. The PS-lipid conformations were primarily dictated by the varying solubilities of the two PSs and assisted by their lipid conjugation sites. Conformation was further validated by photophysical analysis and computational predictions of PS membrane partitioning (topological polar surface area [tPSA], calculated octanol/water partition [cLogP], and apparent biomembrane permeability coefficient [Papp]). Results show that the membrane-protruding lipo-IRDye700DX exhibits 5-fold more efficient photodynamic generation of reactive molecular species (RMS), 12-fold expedited phototriggered burst release of entrap-ped agents, and 15-fold brighter fluorescence intensity as compared to the membrane-inserting lipo-BPD-PC (phosphatidylcholine conjugate). Although the membrane-inserting lipo-BPD-PC exhibits less efficient photo-dynamic generation of RMS, it allows for more sustained phototriggered release, 10-fold greater FaDu cancer cell phototoxicity, and 7.16-fold higher tumor-selective delivery in orthotopic mouse FaDu head and neck tumors. These critical insights pave the path for the rational design of emerging NIR-activable liposomes, whereby functional consequences of membrane composition can be tailored toward a specific therapeutic purpose.

The treatment of glioblastoma has limited clinical progress over the past decade, partly due to the lack of effective drug delivery strategies across the blood-brain-tumor barrier. Moreover, discrepancies between preclinical and clinical outcomes demand a reliable translational platform that can precisely recapitulate the characteristics of human glioblastoma. Here we analyze the intratumoral blood-brain-tumor barrier heterogeneity in human glioblastoma and characterize two genetically engineered models in female mice that recapitulate two important glioma phenotypes, including the diffusely infiltrative tumor margin and angiogenic core. We show that pulsed laser excitation of vascular-targeted gold nanoparticles non-invasively and reversibly modulates the blood-brain-tumor barrier permeability (optoBBTB) and enhances the delivery of paclitaxel in these two models. The treatment reduces the tumor volume by 6 and 2.4-fold and prolongs the survival by 50% and 33%, respectively. Since paclitaxel does not penetrate the blood-brain-tumor barrier and is abandoned for glioblastoma treatment following its failure in early-phase clinical trials, our results raise the possibility of reevaluating a number of potent anticancer drugs by combining them with strategies to increase blood-brain-tumor barrier permeability. Our study reveals that optoBBTB significantly improves therapeutic delivery and has the potential to facilitate future drug evaluation for cancers in the central nervous system.

Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.

3D printing has rapidly become a critical enabling technology in tissue engineering and regenerative medicine for the fabrication of complex engineered tissues. 3D bioprinting, in particular, has advanced greatly to facilitate the incorporation of a broad spectrum of biomaterials along with cells and biomolecules of interest for in vitro tissue generation. The increasing complexity of novel bioink formulations and application-dependent printing conditions poses a significant challenge for replicating or innovating new bioprinting strategies. As the field continues to grow, it is imperative to establish a cohesive, open-source database that enables users to search through existing 3D printing formulations rapidly and efficiently. Because of these challenges, we have developed, to our knowledge, the first bioink database for extrusion-based 3D printing. The database is publicly available and allows users to search through and easily access information on biomaterials and cells specifically used in 3D printing. In order to enable a community-driven database growth, we have established an open-source portal for researchers to enter their publication information for addition into the database. Although the database has a broad range of capabilities, we demonstrate its utility by performing a comprehensive analysis of the printability domains of two well-established biomaterials in the printing world, namely poly(-caprolactone) (PCL) and gelatin methacrylate (GelMA). The database allowed us to rapidly identify combinations of extrusion pressure, temperature, and speed that have been used to print these biomaterials and more importantly, identify domains within which printing was not possible. The data also enabled correlation analysis between all the printing parameters including needle size and type that exhibited compatibility for cell-based 3D printing. Overall, this database is an extremely useful tool for the 3D printing and bioprinting community to advance their research and is an important step towards standardization in the field.

Breast cancer is the leading form of cancer in women, accounting for approximately 41,400 deaths in 2018. While a variety of risk factors have been identified, physical exercise has been linked to reducing both the risk and aggressiveness of breast cancer. Within breast cancer, ductal carcinoma in situ (DCIS) is a common finding. However, less than 25% of DCIS tumors actually progress into invasive breast cancer, resulting in overtreatment. This overtreatment is due to a lack of predictive precursors to assess aggressiveness and development of DCIS. We hypothesize that tissue oxygenation and perfusion measured by photoacoustic and contrast-enhanced ultrasound imaging, respectively, can predict DCIS aggressiveness. To test this, 20 FVB/NJ and 20 SV40Tag mice that genetically develop DCIS-like breast cancers were divided evenly into exercise and control groups and imaged over the course of 6 weeks. Tissue oxygenation was a predictive precursor to invasive breast cancer for FVB/NJ mice ( P = 0.015) in the early stages of tumor development. Meanwhile, perfusion results were inconclusive ( P > 0.2) as a marker for disease progression. Moreover, voluntary physical exercise resulted in lower weekly tumor growth and significantly improved median survival ( P = 0.014).