A proposed mathematical description of in vivo nanoparticle delivery

Nanoparticulated Bimodal Contrast Agent for Ultra-High-Field Magnetic Resonance Imaging and Spectral X-ray Computed Tomography

Bimodal medical imaging based on magnetic resonance imaging (MRI) and computed tomography (CT) is a well-known strategy to increase the diagnostic accuracy. The most recent advances in MRI and CT instrumentation are related to the use of ultra-high magnetic fields (UHF-MRI) and different working voltages (spectral CT), respectively. Such advances require the parallel development of bimodal contrast agents (CAs) that are efficient under new instrumental conditions. In this work, we have synthesized, through a precipitation reaction from a glycerol solution of the precursors, uniform barium dysprosium fluoride nanospheres with a cubic fluorite structure, whose size was found to depend on the Ba/(Ba + Dy) ratio of the starting solution. Moreover, irrespective of the starting Ba/(Ba + Dy) ratio, the experimental Ba/(Ba + Dy) values were always lower than those used in the starting solutions. This result was assigned to lower precipitation kinetics of barium fluoride compared to dysprosium fluoride, as inferred from the detailed analysis of the effect of reaction time on the chemical composition of the precipitates. A sample composed of 34 nm nanospheres with a Ba0.51Dy0.49F2.49 stoichiometry showed a transversal relaxivity (r2) value of 147.11 mM-1·s-1 at 9.4 T and gave a high negative contrast in the phantom image. Likewise, it produced high X-ray attenuation in a large range of working voltages (from 80 to 140 kVp), which can be attributed to the presence of different K-edge values and high Z elements (Ba and Dy) in the nanospheres. Finally, these nanospheres showed negligible cytotoxicity for different biocompatibility tests. Taken together, these results show that the reported nanoparticles are excellent candidates for UHF-MRI/spectral CT bimodal imaging CAs.

Lipid-based nanoparticles as drug delivery carriers for cancer therapy

Cancer is a severe disease that results in death in all countries of the world. A nano-based drug delivery approach is the best alternative, directly targeting cancer tumor cells with improved drug cellular uptake. Different types of nanoparticle-based drug carriers are advanced for the treatment of cancer, and to increase the therapeutic effectiveness and safety of cancer therapy, many substances have been looked into as drug carriers. Lipid-based nanoparticles (LBNPs) have significantly attracted interest recently. These natural biomolecules that alternate to other polymers are frequently recycled in medicine due to their amphipathic properties. Lipid nanoparticles typically provide a variety of benefits, including biocompatibility and biodegradability. This review covers different classes of LBNPs, including their characterization and different synthesis technologies. This review discusses the most significant advancements in lipid nanoparticle technology and their use in medicine administration. Moreover, the review also emphasized the applications of lipid nanoparticles that are used in different cancer treatment types.

Nanoparticles exhibiting virus-mimic surface topology for enhanced oral delivery

The oral delivery of nano-drug delivery systems (Nano-DDS) remains a challenge. Taking inspirations from viruses, here we construct core-shell mesoporous silica nanoparticles (NPs, ~80 nm) with virus-like nanospikes (VSN) to simulate viral morphology, and further modified VSN with L-alanine (CVSN) to enable chiral recognition for functional bionics. By comparing with the solid silica NPs, mesoporous silica NPs and VSN, we demonstrate the delivery advantages of CVSN on overcoming intestinal sequential barriers in both animals and human via multiple biological processes. Subsequently, we encapsulate indomethacin (IMC) into the nanopores of NPs to mimic gene package, wherein the payloads are isolated from bio-environments and exist in an amorphous form to increase their stability and solubility, while the chiral nanospikes multi-sited anchor and chiral recognize on the intestinal mucosa to enhance the penetrability and ultimately improve the oral adsorption of IMC. Encouragingly, we also prove the versatility of CVSN as oral Nano-DDS.

Non-Pore Dependent and MMP-9 Responsive Gelatin/Silk Fibroin Composite Microparticles as Universal Delivery Platform for Inhaled Treatment of Lung Cancer

Developing a drug delivery platform that possesses universal drug loading capacity to meet various requirements of cancer treatment is a challenging yet interesting task. Herein, a self-assembled gelatin/silk fibroin composite (GSC) particle based drug delivery system is developed via microphase separation followed by desolvation process. Thanks to its preassembled microphase stage, this GSC system is suitable for varying types of drugs. The desolvation process fix drugs inside GSC rapidly and densify the GSC structure, thereby achieving efficient drug loading and providing comprehensive protection for loaded drugs. Actually, the size of this brand-new non-pore dependent drug delivery system can be easily adjusted from 100 nm to 20 µm to fit different scenarios. This work selects GSC with 3 µm diameter as the universal inhaled drug delivery platform, which shows an excellent transmucosal penetration and lung retention ability. Additionally, the MMP-9 sensitive degradation property of GSC enhances the targeted efficiency of drugs and reduces side effects. Intestinally, GSC can self-amplify the regulation of innate immunity to reverse the cancerous microenvironment into an antitumor niche, significantly improving the therapeutic effect of drugs. This study of GSC universal drug platform provides a new direction to develop the next-generation of drug delivery system for lung cancer.

Chain-extension in hyperbranched polymers alters tissue distribution and cytotoxicity profiles in orthotopic models of triple negative breast cancers

The therapeutic efficacy of nanomedicines is highly dependent on their access to target sites in the body, and this in turn is markedly affected by their size, shape and transport properties in tissue. Although there have been many studies in this area, the ability to design nanomaterials with optimal physicochemical properties for in vivo efficacy remains a significant challenge. In particular, it is often difficult to quantify the detailed effects of cancer drug delivery systems in vivo as tumour volume reduction, a commonly reported marker of efficacy, does not always correlate with cytotoxicity in tumour tissue. Here, we studied the behaviour in vivo of two specific poly(2-hydroxypropyl methacrylamide) (pHPMA) pro-drugs, with hyperbranched and chain-extended branched architectures, redox-responsive backbone components, and pH-sensitive linkers to the anti-cancer drug doxorubicin. Evaluation of the biodistribution of these polymers following systemic injection indicated differences in the circulation time and organ distribution of the two polymers, despite their very similar hydrodynamic radii (∼10 and 15 nm) and architectures. In addition, both polymers showed improved tumour accumulation in orthotopic triple-negative breast cancers in mice, and decreased accumulation in healthy tissue, as compared to free doxorubicin, even though neither polymer-doxorubicin pro-drug decreased overall tumour volume as much as the free drug under the dosing regimens selected. However, the results of histopathological examinations by haematoxylin and eosin, and TUNEL staining indicated a higher population of apoptotic cells in the tumours for both polymer pro-drug treatments, and in turn a lower population of apoptotic cells in the heart, liver and spleen, as compared to free doxorubicin treatment. These data suggest that the penetration of these polymer pro-drugs was enhanced in tumour tissue relative to free doxorubicin, and that the combination of size, architecture, bioresponsive backbone and drug linker degradation yielded greater efficacy for the polymers as measured by biomarkers than that of tumour volume. We suggest therefore that the effects of nanomedicines may be different at various length scales relative to small molecule free drugs, and that penetration into tumour tissue for some nanomedicines may not be as problematic as prior reports have suggested. Furthermore, the data indicate that dual-responsive crosslinked polymer-prodrugs in this study may be effective nanomedicines for breast cancer chemotherapy, and that endpoints beyond tumour volume reduction can be valuable in selecting candidates for pre-clinical trials.

An artificial intelligence-assisted physiologically-based pharmacokinetic model to predict nanoparticle delivery to tumors in mice

The critical barrier for clinical translation of cancer nanomedicine stems from the inefficient delivery of nanoparticles (NPs) to target solid tumors. Rapid growth of computational power, new machine learning and artificial intelligence (AI) approaches provide new tools to address this challenge. In this study, we established an AI-assisted physiologically based pharmacokinetic (PBPK) model by integrating an AI-based quantitative structure-activity relationship (QSAR) model with a PBPK model to simulate tumor-targeted delivery efficiency (DE) and biodistribution of various NPs. The AI-based QSAR model was developed using machine learning and deep neural network algorithms that were trained with datasets from a published "Nano-Tumor Database" to predict critical input parameters of the PBPK model. The PBPK model with optimized NP cellular uptake kinetic parameters was used to predict the maximum delivery efficiency (DEmax) and DE at 24 (DE24) and 168 h (DE168) of different NPs in the tumor after intravenous injection and achieved a determination coefficient of R2 = 0.83 [root mean squared error (RMSE) = 3.01] for DE24, R2 = 0.56 (RMSE = 2.27) for DE168, and R2 = 0.82 (RMSE = 3.51) for DEmax. The AI-PBPK model predictions correlated well with available experimentally-measured pharmacokinetic profiles of different NPs in tumors after intravenous injection (R2 ≥ 0.70 for 133 out of 288 datasets). This AI-based PBPK model provides an efficient screening tool to rapidly predict delivery efficiency of a NP based on its physicochemical properties without relying on an animal training dataset.

Rational nanoparticle design: Optimization using insights from experiments and mathematical models

Polymeric nanoparticles are highly tunable drug delivery systems that show promise in targeting therapeutics to specific sites within the body. Rational nanoparticle design can make use of mathematical models to organize and extend experimental data, allowing for optimization of nanoparticles for particular drug delivery applications. While rational nanoparticle design is attractive from the standpoint of improving therapy and reducing unnecessary experiments, it has yet to be fully realized. The difficulty lies in the complexity of nanoparticle structure and behavior, which is added to the complexity of the physiological mechanisms involved in nanoparticle distribution throughout the body. In this review, we discuss the most important aspects of rational design of polymeric nanoparticles. Ultimately, we conclude that many experimental datasets are required to fully model polymeric nanoparticle behavior at multiple scales. Further, we suggest ways to consider the limitations and uncertainty of experimental data in creating nanoparticle design optimization schema, which we call quantitative nanoparticle design frameworks.

Principles of Nanoparticle Delivery to Solid Tumors

The effective treatment of patients with cancer hinges on the delivery of therapeutics to a tumor site. Nanoparticles provide an essential transport system. We present 5 principles to consider when designing nanoparticles for cancer targeting: (a) Nanoparticles acquire biological identity in vivo, (b) organs compete for nanoparticles in circulation, (c) nanoparticles must enter solid tumors to target tumor components, (d) nanoparticles must navigate the tumor microenvironment for cellular or organelle targeting, and (e) size, shape, surface chemistry, and other physicochemical properties of nanoparticles influence their transport process to the target. This review article describes these principles and their application for engineering nanoparticle delivery systems to carry therapeutics to tumors or other disease targets.

A Predictive Pharmacokinetic Model for Immune Cell-Mediated Uptake and Retention of Nanoparticles in Tumors

A promise of cancer nanomedicine is the "targeted" delivery of therapeutic agents to tumors by the rational design of nanostructured materials. During the past several decades, a realization that in vitro and in vivo preclinical data are unreliable predictors of successful clinical translation has motivated a reexamination of this approach. Mathematical models of drug pharmacokinetics (PK) and biodistribution (BD) are essential tools for small-molecule drugs development. A key assumption underlying these models is that drug-target binding kinetics dominate blood clearance, hence recognition by host innate immune cells is not explicitly included. Nanoparticles circulating in the blood are conspicuous to phagocytes, and inevitable interactions typically trigger active biological responses to sequester and remove them from circulation. Our recent findings suggest that, instead of referring to nanoparticles as designed for active or passive "tumor targeting", we ought rather to refer to immune cells residing in the tumor microenvironment (TME) as active or passive actors in an essentially "cell-mediated tumor retention" process that competes with active removal by other phagocytes. Indeed, following intravenous injection, nanoparticles induce changes in the immune compartment of the TME because of nanoparticle uptake, irrespective of the nature of tumor targeting moieties. In this study, we propose a 6-compartment PK model as an initial mathematical framework for modeling this tumor-associated immune cell-mediated retention. Published in vivo PK and BD results obtained with bionized nanoferrite® (BNF®) nanoparticles were combined with results from in vitro internalization experiments with murine macrophages to guide simulations. As a preliminary approximation, we assumed that tumor-associated macrophages (TAMs) are solely responsible for active retention in the TME. We model the TAM approximation by relating in vitro macrophage uptake to an effective macrophage avidity term for the BNF® nanoparticles under consideration.