Engineered nanomaterials that produce reactive oxygen species on exposure to X‐ and gamma‐rays used in radiation therapy offer promise of novel cancer treatment strategies. Similar to photodynamic therapy but suitable for large and deep tumors, this new approach where nanomaterials acting as sensitizing agents are combined with clinical radiation can be effective at well‐tolerated low radiation doses. Suitably engineered nanomaterials can enhance cancer radiotherapy by increasing the tumor selectivity and decreasing side effects. Additionally, the nanomaterial platform offers therapeutically valuable functionalities, including molecular targeting, drug/gene delivery, and adaptive responses to trigger drug release. The potential of such nanomaterials to be combined with radiotherapy is widely recognized. In order for further breakthroughs to be made, and to facilitate clinical translation, the applicable principles and fundamentals should be articulated. This review focuses on mechanisms underpinning rational nanomaterial design to enhance radiation therapy, the understanding of which will enable novel ways to optimize its therapeutic efficacy. A roadmap for designing nanomaterials with optimized anticancer performance is also shown and the potential clinical significance and future translation are discussed.

Radiodynamic therapy (RDT) is a recent extension of conventional photodynamic therapy, in which visible/near infrared light irradiation is replaced by a well-tolerated dose of high-energy X-rays. This enables greater tissue penetration to allow non-invasive treatment of large, deep-seated tumors. We report here the design and testing of a drug delivery system for RDT that is intended to enhance intra- or peri-nuclear localization of the photosensitizer, leading to DNA damage and resulting clonogenic cell kill. This comprises a photosensitizer (Verteporfin, VP) incorporated into poly (lactic-co-glycolic acid) nanoparticles (PLGA NPs) that are surface-functionalized with a cell-penetrating HIV trans-activator of transcription (TAT) peptide. In addition to a series of physical and photophysical characterization studies, cytotoxicity tests in pancreatic (PANC-1) cancer cells in vitro under 4 Gy X-ray exposure from a clinical 6 MV linear accelerator (LINAC) showed that TAT targeting of the nanoparticles markedly enhances the effectiveness of RDT treatment, particularly when assessed by a clonogenic, i.e., DNA damage-mediated, cell kill.

Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses from chest X-rays and have extended this work to prove the feasibility of rapidly diagnosing COVID-19 with high degrees of accuracy. One issue in previous research has been the use of datasets containing only a few hundred images of chest X-rays containing COVID-19, causing CNNs to overfit the image data. This leads to lower accuracy when the model attempts to classify new images, as would be clinically expected. In this work, we present a model trained on the COVID-QU-Ex dataset containing 33,920 chest X-ray images, with an equal share of COVID-19, Non-COVID pneumonia, and Normal images. The model is an ensemble of pre-trained CNNs (ResNet50, VGG19, and VGG16) and GLCM textural features. The model achieved a 98.34% binary classification accuracy (COVID-19/no COVID-19) on a test dataset of 6581 chest X-rays and 94.68% for distinguishing between COVID-19, Non-COVID pneumonia, and normal chest X-rays. The results also demonstrate that a higher 98.82% three-class test accuracy can be achieved using the model if the training dataset only contains a few thousand images. However, the generalizability of the model suffers due to the smaller dataset size. This study highlights the benefits of both ensemble CNN techniques and larger dataset sizes for medical image classification performance.

Radiodynamic therapy (RDT) is an emerging non-invasive anti-cancer treatment based on the generation of the reactive oxygen species (ROS) at the lesion site following the interaction between X-rays and a photosensitizer drug (PS). The broader application of RDT is impeded by the tumor-associated hypoxia that results in low availability of oxygen for the generation of sufficient amounts of ROS. Herein, a novel nanoparticle drug formulation for RDT, which addresses the problem of low oxygen availability, is reported. It consists of poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) co-loaded with a PS drug verteporfin (VP), and the clinically approved oxygen-carrying molecule, perfluorooctylbromide (PFOB). When triggered by X-rays (4 Gy), under both normoxic and hypoxic conditions, PLGA–VP–PFOB nanoconstructs (NCs) induced a significant increase of the ROS production compared with matching PLGA–VP nanoparticles. The RDT with NCs effectively killed ~60% of human pancreatic cancer cells in monolayer cultures, and almost completely suppressed the outgrowth of tumor cells in 2-weeks clonogenic assay. In a 3D engineered model of pancreatic cancer metastasis to the liver, RDT with NCs destroyed ~35% of tumor cells, demonstrating an exceptional efficiency at a tissue level. These results show that PLGA–VP–PFOB is a promising agent for RDT of deep-seated hypoxic tumors.

The estimation of nanoparticle number concentration in colloidal suspensions is a prerequisite in many procedures, and in particular in multi-stage, low-yield reactions. Here, we describe a rapid, non-destructive method based on optical extinction and dynamic light scattering (DLS), which combines measurements using common bench-top instrumentation with a numerical algorithm to calculate the particle size distribution (PSD) and concentration. These quantities were derived from Mie theory applied to measurements of the optical extinction spectrum of homogeneous, non-absorbing nanoparticles, and the relative PSD of a colloidal suspension. The work presents an approach to account for PSDs achieved by DLS which, due to the underlying model, may not be representative of the true sample PSD. The presented approach estimates the absolute particle number concentration of samples with mono-, bi-modal and broad size distributions with <50% precision. This provides a convenient and practical solution for number concentration estimation required during many applications of colloidal nanomaterials.

A droplet-based microfluidic needle-like probe has been designed and applied for on-site hydrogen peroxide (H₂O₂) sampling, reaction and detection using a commercial, single-step fluorescent H₂O₂ assay.