Implantable Devices for the Treatment of Breast Cancer

Measuring Erosion of Biodegradable Polymers in Brimonidine Drug Delivery Implants by Quantitative Proton NMR Spectroscopy (q-HNMR)

Erosion of biodegradable polymeric excipients, such as polylactic acid (PLA) and polylactic-co-glycolic acid (PLGA), is generally characterized by microbalance for the remaining mass of PLA and/or PLGA and Gel Permeation Chromatography (GPC) for molecular weight (MW) decrease. For polymer erosion studies of intravitreal sustained release brimonidine implants, however, both microbalance and GPC present several challenges. Mass loss measurement by microbalance does not have specificity for excipient polymers and drug substances. Accuracy of the remaining mass by weighing could also be low due to sample mass loss through retrieval-drying steps, especially at later drug release (DR) time points. When measuring the decrease of polymer MW by GPC, trace amounts of polymeric degradants (oligomers and/or monomers) trapped inside the implants during DR tests may not be measurable due to sensitivity limitations of the GPC detector and column MW range. Previous efforts to measure remained PLGA weight of dexamethasone micro-implants using qNMR with external calibration have been performed, however, these measurements do not account for chemical structure changes (i.e. LA to GA ratio changes from time zero) of PLGA implants during drug release tests. Here, a qNMR method with an internal standard was developed to monitor the following changes in micro-implants during drug release tests: 1. The remaining overall PLA/PLGA mass. 2. The remaining lactic acid (LA), glycolic acid (GA) unit and PLGA's lauryl ester end group percentages. 3. The trace content of PLA/PLGA oligomers as degradants retained in the implants. Unlike microbalance analysis, qNMR has both specificity for drug substance, excipient polymer, and accuracy due to minimal implant loss during sample preparation. Compared to the overall PLA/PLGA remaining mass generally monitored in erosion studies, the percentage of remaining LA, GA, and the ester end group provide more information about the microstructure change (such as hydrophobicity) of PLA/PLGA. Additionally, the qNMR method can complement GPC methods by measuring the change of remaining PLA and PLGA oligomer concentrations in brimonidine implants, with tenfold less sample and no MW cutoff. The qNMR method can be used as a sensitive tool for both polymer excipient characterization and kinetics studies of brimonidine implant erosion.

Localized radiotherapy of solid tumors using radiopharmaceutical loaded implantable system: insights from a mathematical model

Introduction

Computational models yield valuable insights into biological interactions not fully elucidated by experimental approaches. This study investigates an innovative spatiotemporal model for simulating the controlled release and dispersion of radiopharmaceutical therapy (RPT) using 177Lu-PSMA, a prostate-specific membrane antigen (PSMA) targeted radiopharmaceutical, within solid tumors via a dual-release implantable delivery system. Local delivery of anticancer agents presents a strategic approach to mitigate adverse effects while optimizing therapeutic outcomes.

Methods

This study evaluates various factors impacting RPT efficacy, including hypoxia region extension, binding affinity, and initial drug dosage, employing a novel 3-dimensional computational model. Analysis gauges the influence of these factors on radiopharmaceutical agent concentration within the tumor microenvironment. Furthermore, spatial and temporal radiopharmaceutical distribution within both the tumor and surrounding tissue is explored.

Results

Analysis indicates a significantly higher total concentration area under the curve within the tumor region compared to surrounding normal tissue. Moreover, drug distribution exhibits notably superior efficacy compared to the radiation source. Additionally, low microvascular density in extended hypoxia regions enhances drug availability, facilitating improved binding to PSMA receptors and enhancing therapeutic effectiveness. Reductions in the dissociation constant (KD) lead to heightened binding affinity and increased internalized drug concentration. Evaluation of initial radioactivities (7.1×107, 7.1×108, and 7.1×109 [Bq]) indicates that an activity of 7.1×108 [Bq] offers a favorable balance between tumor cell elimination and minimal impact on normal tissues.

Discussion

These findings underscore the potential of localized radiopharmaceutical delivery strategies and emphasize the crucial role of released drugs relative to the radiation source (implant) in effective tumor treatment. Decreasing the proximity of the drug to the microvascular network and enhancing its distribution within the tumor promote a more effective therapeutic outcome. The study furnishes valuable insights for future experimental investigations and clinical trials, aiming to refine medication protocols and minimize reliance on in vivo testing.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

Engineering the Tumor Immune Microenvironment through Minimally Invasive Interventions

The tumor microenvironment (TME) is a unique landscape that poses several physical, biochemical, and immune barriers to anti-cancer therapies. The rapidly evolving field of immuno-engineering provides new opportunities to dismantle the tumor immune microenvironment by efficient tumor destruction. Systemic delivery of such treatments can often have limited local effects, leading to unwanted offsite effects such as systemic toxicity and tumor resistance. Interventional radiologists use contemporary image-guided techniques to locally deliver these therapies to modulate the immunosuppressive TME, further accelerating tumor death and invoking a better anti-tumor response. These involve local therapies such as intratumoral drug delivery, nanorobots, nanoparticles, and implantable microdevices. Physical therapies such as photodynamic therapy, electroporation, hyperthermia, hypothermia, ultrasound therapy, histotripsy, and radiotherapy are also available for local tumor destruction. While the interventional radiologist can only locally manipulate the TME, there are systemic offsite recruitments of the immune response. This is known as the abscopal effect, which leads to more significant anti-tumoral downstream effects. Local delivery of modern immunoengineering methods such as locoregional CAR-T therapy combined with immune checkpoint inhibitors efficaciously modulates the immunosuppressive TME. This review highlights the various advances and technologies available now to change the TME and revolutionize oncology from a minimally invasive viewpoint.