Promoting Nanoparticle Delivery Efficiency to Tumors by Locally Increasing Blood Flow There

Hydrogel-based nanoparticles: revolutionizing brain tumor treatment and paving the way for future innovations

Brain tumor treatment remains a significant challenge due to their high mortality and resistance to current therapies. This paper discusses the promising potential of hydrogel-based nanoparticles as innovative drug delivery systems for brain tumor therapy. Extensive characterization techniques reveal the ability of these Nano-systems to demonstrate prolonged blood circulation and targeted delivery, leading to improved survival rates. Designed with optimized physicochemical characteristics, these nanoparticles effectively cross the blood-brain barrier, circumventing a major impediment to drug delivery to the brain. By delivering drugs directly to the tumor bed, these nanoparticles enhance therapeutic outcomes and minimize adverse effects. In addition, this review investigates the techniques for characterizing, visualizing, and modifying these nanoparticles, as well as the standing challenges and promising research avenues for their clinical application. Further investigations are encouraged by this review to investigate potential advancements in hydrogel-based nanoparticle therapeutic approaches for brain tumors. This includes investigating tailored hydrogels, hybrid systems, computational modeling, and the integration of gene therapy and immunotherapy techniques. The study also addresses the need for enhanced synthesis techniques, stability, scalability, and cost-cutting measures to overcome obstacles and advance the clinical use of hydrogel-based nanoparticles in treating brain tumors.

Effects of nanoparticle deformability on multiscale biotransport

Deformability is one of the critical attributes of nanoparticle (NP) drug carriers, along with size, shape, and surface properties. It affects various aspects of NP biotransport, ranging from circulation and biodistribution to interactions with biological barriers and target cells. Recent studies report additional roles of NP deformability in biotransport processes, including protein corona formation, intracellular trafficking, and organelle distribution. This review focuses on the literature published in the past five years to update our understanding of NP deformability and its effect on NP biotransport. We introduce different methods of modulating and evaluating NP deformability and showcase recent studies that compare a series of NPs in their performance in biotransport events at all levels, highlighting the consensus and disagreement of the findings. It concludes with a perspective on the intricacy of systematic investigation of NP deformability and future opportunities to advance its control toward optimal drug delivery.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

Heating Induced Nanoparticle Migration and Enhanced Delivery in Tumor Treatment Using Nanotechnology

Nanoparticles have been developed as imaging contrast agents, heat absorbers to confine energy into targeted tumors, and drug carriers in advanced cancer treatment. It is crucial to achieve a minimal concentration of drug-carrying nanostructures or to induce an optimized nanoparticle distribution in tumors. This review is focused on understanding how local or whole-body heating alters transport properties in tumors, therefore leading to enhanced nanoparticle delivery or optimized nanoparticle distributions in tumors. First, an overview of cancer treatment and the development of nanotechnology in cancer therapy is introduced. Second, the importance of particle distribution in one of the hyperthermia approaches using nanoparticles in damaging tumors is discussed. How intensive heating during nanoparticle hyperthermia alters interstitial space structure to induce nanoparticle migration in tumors is evaluated. The next section reviews major obstacles in the systemic delivery of therapeutic agents to targeted tumors due to unique features of tumor microenvironments. Experimental observations on how mild local or whole-body heating boosts systemic nanoparticle delivery to tumors are presented, and possible physiological mechanisms are explored. The end of this review provides the current challenges facing clinicians and researchers in designing effective and safe heating strategies to maximize the delivery of therapeutic agents to tumors.

Primary Human Breast Cancer-Associated Endothelial Cells Favor Interactions with Nanomedicines

Cancer nanomedicines predominately rely on transport processes controlled by tumor-associated endothelial cells to deliver therapeutic and diagnostic payloads into solid tumors. While the dominant role of this class of endothelial cells for nanoparticle transport and tumor delivery is established in animal models, the translational potential in human cells needs exploration. Using primary human breast cancer as a model, the differential interactions of normal and tumor-associated endothelial cells with clinically relevant nanomedicine formulations are explored and quantified. Primary human breast cancer-associated endothelial cells exhibit up to ≈2 times higher nanoparticle uptake than normal human mammary microvascular endothelial cells. Super-resolution imaging studies reveal a significantly higher intracellular vesicle number for tumor-associated endothelial cells, indicating a substantial increase in cellular transport activities. RNA sequencing and gene expression analysis indicate the upregulation of transport-related genes, especially motor protein genes, in tumor-associated endothelial cells. Collectively, the results demonstrate that primary human breast cancer-associated endothelial cells exhibit enhanced interactions with nanomedicines, suggesting a potentially significant role for these cells in nanoparticle tumor delivery in human patients. Engineering nanoparticles that leverage the translational potential of tumor-associated endothelial cell-mediated transport into human solid tumors may lead to the development of safer and more effective clinical cancer nanomedicines.

In vivo biodistribution and tumor uptake of [64Cu]-FAU nanozeolite via positron emission tomography Imaging

Nanoparticles have emerged as promising theranostic tools for biomedical applications, notably in the treatment of cancers. However, to fully exploit their potential, a thorough understanding of their biodistribution is imperative. In this context, we prepared radioactive [64Cu]-exchanged faujasite nanosized zeolite ([64Cu]-FAU) to conduct positron emission tomography (PET) imaging tracking in preclinical glioblastoma models. In vivo results revealed a rapid and gradual accumulation over time of intravenously injected [64Cu]-FAU zeolite nanocrystals within the brain tumor, while no uptake in the healthy brain was observed. Although a specific tumor targeting was observed in the brain, the kinetics of uptake into tumor tissue was found to be dependent on the glioblastoma model. Indeed, our results showed a rapid uptake in U87-MG model while in U251-MG glioblastoma model tumor uptake was gradual over the time. Interestingly, a [64Cu] activity, decreasing over time, was also observed in organs of elimination such as kidney and liver without showing a difference in activity between both glioblastoma models. Ex vivo analyses confirmed the presence of zeolite nanocrystals in brain tumor with detection of both Si and Al elements originated from them. This radiolabelling strategy, performed for the first time using nanozeolites, enables precise tracking through PET imaging and confirms their accumulation within the glioblastoma. These findings further bolster the potential use of zeolite nanocrystals as valuable theranostic tools.

Exploring and Analyzing the Systemic Delivery Barriers for Nanoparticles

Most nanomedicines require efficient in vivo delivery to elicit diagnostic and therapeutic effects. However, en route to their intended tissues, systemically administered nanoparticles often encounter delivery barriers. To describe these barriers, we propose the term "nanoparticle blood removal pathways" (NBRP), which summarizes the interactions between nanoparticles and the body's various cell-dependent and cell-independent blood clearance mechanisms. We reviewed nanoparticle design and biological modulation strategies to mitigate nanoparticle-NBRP interactions. As these interactions affect nanoparticle delivery, we studied the preclinical literature from 2011-2021 and analyzed nanoparticle blood circulation and organ biodistribution data. Our findings revealed that nanoparticle surface chemistry affected the in vivo behavior more than other nanoparticle design parameters. Combinatory biological-PEG surface modification improved the blood area under the curve by ~418%, with a decrease in liver accumulation of up to 47%. A greater understanding of nanoparticle-NBRP interactions and associated delivery trends will provide new nanoparticle design and biological modulation strategies for safer, more effective, and more efficient nanomedicines.

Meta-Analysis of Nanoparticle Distribution in Tumors and Major Organs in Tumor-Bearing Mice

Low tumor delivery efficiency is a critical barrier in cancer nanomedicine. This study reports an updated version of "Nano-Tumor Database", which increases the number of time-dependent concentration data sets for different nanoparticles (NPs) in tumors from the previous version of 376 data sets with 1732 data points from 200 studies to the current version of 534 data sets with 2345 data points from 297 studies published from 2005 to 2021. Additionally, the current database includes 1972 data sets for five major organs (i.e., liver, spleen, lung, heart, and kidney) with a total of 8461 concentration data points. Tumor delivery and organ distribution are calculated using three pharmacokinetic parameters, including delivery efficiency, maximum concentration, and distribution coefficient. The median tumor delivery efficiency is 0.67% injected dose (ID), which is low but is consistent with previous studies. Employing the best regression model for tumor delivery efficiency, we generate hypothetical scenarios with different combinations of NP factors that may lead to a higher delivery efficiency of >3%ID, which requires further experimentation to confirm. In healthy organs, the highest NP accumulation is in the liver (10.69%ID/g), followed by the spleen 6.93%ID/g and the kidney 3.22%ID/g. Our perspective on how to facilitate NP design and clinical translation is presented. This study reports a substantially expanded "Nano-Tumor Database" and several statistical models that may help nanomedicine design in the future.

Pharmacokinetics and tumor delivery of nanoparticles

Nanoparticles (NPs) have been widely used in different areas, including consumer products and medicine. In terms of biomedical applications, NPs or NP-based drug formulations have been extensively investigated for cancer diagnostics and therapy in preclinical studies, but the clinical translation rate is low. Therefore, a thorough and comprehensive understanding of the pharmacokinetics of NPs, especially in drug delivery efficiency to the target therapeutic tissue tumor, is important to design more effective nanomedicines and for proper assessment of the safety and risk of NPs. This review article focuses on the pharmacokinetics of both organic and inorganic NPs and their tumor delivery efficiencies, as well as the associated mechanisms involved. We discuss the absorption, distribution, metabolism, and excretion (ADME) processes following different routes of exposure and the mechanisms involved. Many physicochemical properties and experimental factors, including particle type, size, surface charge, zeta potential, surface coating, protein binding, dose, exposure route, species, cancer type, and tumor size can affect NP pharmacokinetics and tumor delivery efficiency. NPs can be absorbed with varying degrees following different exposure routes and mainly accumulate in liver and spleen, but also distribute to other tissues such as heart, lung, kidney and tumor tissues; and subsequently get metabolized and/or excreted mainly through hepatobiliary and renal elimination. Passive and active targeting strategies are the two major mechanisms of tumor delivery, while active targeting tends to have less toxicity and higher delivery efficiency through direct interaction between ligands and receptors. We also discuss challenges and perspectives remaining in the field of pharmacokinetics and tumor delivery efficiency of NPs.

Predicting Nanoparticle Delivery to Tumors Using Machine Learning and Artificial Intelligence Approaches

Background

Low delivery efficiency of nanoparticles (NPs) to the tumor is a critical barrier in the field of cancer nanomedicine. Strategies on how to improve NP tumor delivery efficiency remain to be determined.

Methods

This study analyzed the roles of NP physicochemical properties, tumor models, and cancer types in NP tumor delivery efficiency using multiple machine learning and artificial intelligence methods, using data from a recently published Nano-Tumor Database that contains 376 datasets generated from a physiologically based pharmacokinetic (PBPK) model.

Results

The deep neural network model adequately predicted the delivery efficiency of different NPs to different tumors and it outperformed all other machine learning methods; including random forest, support vector machine, linear regression, and bagged model methods. The adjusted determination coefficients (R2) in the full training dataset were 0.92, 0.77, 0.77 and 0.76 for the maximum delivery efficiency (DEmax), delivery efficiency at 24 h (DE24), at 168 h (DE168), and at the last sampling time (DETlast). The corresponding R2 values in the test dataset were 0.70, 0.46, 0.33 and 0.63, respectively. Also, this study showed that cancer type was an important determinant for the deep neural network model in predicting the tumor delivery efficiency across all endpoints (19-29%). Among all physicochemical properties, the Zeta potential and core material played a greater role than other properties, such as the type, shape, and targeting strategy.

Conclusion

This study provides a quantitative model to improve the design of cancer nanomedicine with greater tumor delivery efficiency. These results help to improve our understanding of the causes of low NP tumor delivery efficiency. This study demonstrates the feasibility of integrating artificial intelligence with PBPK modeling approaches to study cancer nanomedicine.