Targeting Strategies for Tissue-Specific Drug Delivery.

Off-target effects of systemically administered drugs have been a major hurdle in designing therapies with desired efficacy and acceptable toxicity. Developing targeting strategies to enable site-specific drug delivery holds promise in reducing off-target effects, decreasing unwanted toxicities, and thereby enhancing a drug's therapeutic efficacy. Over the past three decades, a large body of literature has focused on understanding the biological barriers that hinder tissue-specific drug delivery and strategies to overcome them. These efforts have led to several targeting strategies that modulate drug delivery in both the preclinical and clinical settings, including small molecule-, nucleic acid-, peptide-, antibody-, and cell-based strategies. Here, we discuss key advances and emerging concepts for tissue-specific drug delivery approaches and their clinical translation.

A deep eutectic-based, self-emulsifying subcutaneous depot system for apomorphine therapy in Parkinson's disease.

Apomorphine, a dopamine agonist, is a highly effective therapeutic to prevent intermittent off episodes in advanced Parkinson's disease. However, its short systemic half-life necessitates three injections per day. Such a frequent dosing regimen imposes a significant compliance challenge, especially given the nature of the disease. Here, we report a deep eutectic-based formulation that slows the release of apomorphine after subcutaneous injection and extends its pharmacokinetics to convert the current three-injections-a-day therapy into an every-other-day therapy. The formulation comprises a homogeneous mixture of a deep eutectic solvent choline-geranate, a cosolvent n-methyl-pyrrolidone, a stabilizer polyethylene glycol, and water, which spontaneously emulsifies into a microemulsion upon injection in the subcutaneous space, thereby entrapping apomorphine and significantly slowing its release. Ex vivo studies with gels and rat skin demonstrate this self-emulsification process as the mechanism of action for sustained release. In vivo pharmacokinetics studies in rats and pigs further confirmed the extended release and improvement over the clinical comparator Apokyn. In vivo pharmacokinetics, supported by a pharmacokinetic simulation, demonstrate that the deep eutectic formulation reported here allows the maintenance of the therapeutic drug concentration in plasma in humans with a dosing regimen of approximately three injections per week compared to the current clinical practice of three injections per day.

Phalloidin-PAINT: Enhanced quantitative nanoscale imaging of F-actin.

We present phalloidin-based points accumulation for imaging in nanoscale topography (phalloidin-PAINT), enabling quantitative superresolution imaging of filamentous actin (F-actin) in the cell body and delicate membrane protrusions. We demonstrate that the intrinsic phalloidin dissociation enables PAINT superresolution microscopy in an imaging buffer containing low concentrations of dye-conjugated phalloidin. We further show enhanced single-molecule labeling by chemically promoting phalloidin dissociation. Two benefits of phalloidin-PAINT are its ability to consistently quantify F-actin at the nanoscale throughout the entire cell and its enhanced preservation of fragile cellular structures. In a proof-of-concept study, we employed phalloidin-PAINT to superresolve F-actin structures in U2OS and dendritic cells (DCs). We demonstrate more consistent F-actin quantification in the cell body and structurally delicate membrane protrusions of DCs compared with direct stochastic optical reconstruction microscopy (dSTORM). Using DC2.4 mouse DCs as the model system, we show F-actin redistribution from podosomes to actin filaments and altered prevalence of F-actin-associated membrane protrusions on the culture glass surface after lipopolysaccharide exposure. The concept of our work opens new possibilities for quantitative protein-specific PAINT using commercially available reagents.

Modulation of engineered nanomaterial interactions with organ barriers for enhanced drug transport.

The biomedical use of nanoparticles (NPs) has been the focus of intense research for over a decade. As most NPs are explored as carriers to alter the biodistribution, pharmacokinetics and bioavailability of associated drugs, the delivery of these NPs to the tissues of interest remains an important topic. To date, the majority of NP delivery studies have used tumor models as their tool of interest, and the limitations concerning tumor targeting of systemically administered NPs have been well studied. In recent years, the focus has also shifted to other organs, each presenting their own unique delivery challenges to overcome. In this review, we discuss the recent advances in leveraging NPs to overcome four major biological barriers including the lung mucus, the gastrointestinal mucus, the placental barrier, and the blood-brain barrier. We define the specific properties of these biological barriers, discuss the challenges related to NP transport across them, and provide an overview of recent advances in the field. We discuss the strengths and shortcomings of different strategies to facilitate NP transport across the barriers and highlight some key findings that can stimulate further advances in this field.

A Crowd Movement Analysis Method Based on Radar Particle Flow.

Crowd movement analysis (CMA) is a key technology in the field of public safety. This technology provides reference for identifying potential hazards in public places by analyzing crowd aggregation and dispersion behavior. Traditional video processing techniques are susceptible to factors such as environmental lighting and depth of field when analyzing crowd movements, so cannot accurately locate the source of events. Radar, on the other hand, offers all-weather distance and angle measurements, effectively compensating for the shortcomings of video surveillance. This paper proposes a crowd motion analysis method based on radar particle flow (RPF). Firstly, radar particle flow is extracted from adjacent frames of millimeter-wave radar point sets by utilizing the optical flow method. Then, a new concept of micro-source is defined to describe whether any two RPF vectors originated from or reach the same location. Finally, in each local area, the internal micro-sources are counted to form a local diffusion potential, which characterizes the movement state of the crowd. The proposed algorithm is validated in real scenarios. By analyzing and processing radar data on aggregation, dispersion, and normal movements, the algorithm is able to effectively identify these movements with an accuracy rate of no less than 88%.

Erythrocyte-driven immunization via biomimicry of their natural antigen-presenting function.

Erythrocytes naturally capture certain bacterial pathogens in circulation, kill them through oxidative stress, and present them to the antigen-presenting cells (APCs) in the spleen. By leveraging this innate immune function of erythrocytes, we developed erythrocyte-driven immune targeting (EDIT), which presents nanoparticles from the surface of erythrocytes to the APCs in the spleen. Antigenic nanoparticles were adsorbed on the erythrocyte surface. By engineering the number density of adsorbed nanoparticles, (i.e., the number of nanoparticles loaded per erythrocyte), they were predominantly delivered to the spleen rather than lungs, which is conventionally the target of erythrocyte-mediated delivery systems. Presentation of erythrocyte-delivered nanoparticles to the spleen led to improved antibody response against the antigen, higher central memory T cell response, and lower regulatory T cell response, compared with controls. Enhanced immune response slowed down tumor progression in a prophylaxis model. These findings suggest that EDIT is an effective strategy to enhance systemic immunity.

Fast DOA Estimation Algorithms via Positive Incremental Modified Cholesky Decomposition for Augmented Coprime Array Sensors.

This paper proposes a fast direction of arrival (DOA) estimation method based on positive incremental modified Cholesky decomposition atomic norm minimization (PI-CANM) for augmented coprime array sensors. The approach incorporates coprime sampling on the augmented array to generate a non-uniform, discontinuous virtual array. It then utilizes interpolation to convert this into a uniform, continuous virtual array. Based on this, the problem of DOA estimation is equivalently formulated as a gridless optimization problem, which is solved via atomic norm minimization to reconstruct a Hermitian Toeplitz covariance matrix. Furthermore, by positive incremental modified Cholesky decomposition, the covariance matrix is transformed from positive semi-definite to positive definite, which simplifies the constraint of optimization problem and reduces the complexity of the solution. Finally, the Multiple Signal Classification method is utilized to carry out statistical signal processing on the reconstructed covariance matrix, yielding initial DOA angle estimates. Experimental outcomes highlight that the PI-CANM algorithm surpasses other algorithms in estimation accuracy, demonstrating stability in difficult circumstances such as low signal-to-noise ratios and limited snapshots. Additionally, it boasts an impressive computational speed. This method enhances both the accuracy and computational efficiency of DOA estimation, showing potential for broad applicability.

Human Activity Recognition Method Based on FMCW Radar Sensor with Multi-Domain Feature Attention Fusion Network.

This paper proposes a human activity recognition (HAR) method for frequency-modulated continuous wave (FMCW) radar sensors. The method utilizes a multi-domain feature attention fusion network (MFAFN) model that addresses the limitation of relying on a single range or velocity feature to describe human activity. Specifically, the network fuses time-Doppler (TD) and time-range (TR) maps of human activities, resulting in a more comprehensive representation of the activities being performed. In the feature fusion phase, the multi-feature attention fusion module (MAFM) combines features of different depth levels by introducing a channel attention mechanism. Additionally, a multi-classification focus loss (MFL) function is applied to classify confusable samples. The experimental results demonstrate that the proposed method achieves 97.58% recognition accuracy on the dataset provided by the University of Glasgow, UK. Compared to existing HAR methods for the same dataset, the proposed method showed an improvement of about 0.9-5.5%, especially in the classification of confusable activities, showing an improvement of up to 18.33%.

Red Blood Cell Inspired Strategies for Drug Delivery: Emerging Concepts and New Advances.

In the past five decades, red blood cells (RBCs) have been extensively explored as drug delivery systems due to their distinguishing potential in modulating the pharmacokinetic, pharmacodynamics, and biological activity of carried payloads. The extensive interests in RBC-mediated drug delivery technologies are in part derived from RBCs' unique biological features such as long circulation time, wide access to many tissues in the body, and low immunogenicity. Owing to these outstanding properties, a large body of efforts have led to the development of various RBC-inspired strategies to enable precise drug delivery with enhanced therapeutic efficacy and reduced off-target toxicity. In this review, we discuss emerging concepts and new advances in such RBC-inspired strategies, including native RBCs, ghost RBCs, RBC-mimetic nanoparticles, and RBC-derived extracellular vesicles, for drug delivery.

Modularity of RBC hitchhiking with polymeric nanoparticles: testing the limits of non-covalent adsorption.

Red blood cell (RBC) hitchhiking has great potential in enhancing drug therapy, by improving targeting and reducing rapid clearance of nanoparticles (NPs). However, to improve the potential for clinical translation of RBC hitchhiking, a more thorough understanding of the RBC-NP interface is needed. Here, we evaluate the effects of NP surface parameters on the success and biocompatibility of NP adsorption to extracted RBCs from various species. Major differences in RBC characteristics between rabbit, mouse and human were proven to significantly impact NP adsorption outcomes. Additionally, the effects of NP design parameters, including NP hydrophobicity, zeta potential, surfactant concentration and drug encapsulation, on RBC hitchhiking are investigated. Our studies demonstrate the importance of electrostatic interactions in balancing NP adsorption success and biocompatibility. We further investigated the effect of varying the anti-coagulant used for blood storage. The results presented here offer new insights into the parameters that impact NP adsorption on RBCs that will assist researchers in experimental design choices for using RBC hitchhiking as drug delivery strategy.

Toll-like Receptor 9 Agonists as Adjuvants for Nanoparticle-Based Nicotine Vaccine.

Nicotine vaccine was considered a promising therapy against smoking addiction. The level of immune response that a nicotine vaccine can induce is pivotal to its efficacy. In this study, Toll-like receptor 9 agonists, namely, CpG ODN 1555 and CpG ODN 1826, were incorporated into a nanoparticle-based nicotine vaccine (NanoNicVac) to enhance its immunogenicity. The results showed that NanoNicVac containing either CpG ODN 1555 or CpG ODN 1826 could be rapidly internalized by dendritic cells. In mice trials, it was found that NanoNicVac with CpG ODN 1555 and CpG ODN 1826 induced 3.3- and 3.2-fold higher anti-nicotine antibody titer than that by the native NanoNicVac after two injections, respectively. Instead of enhancing the immunogenicity of the vaccine, however, mixtures of the two CpG ODNs were observed to exert an immune-suppressing effect on NanoNicVac. Finally, the histopathological examination on major organs of the mice immunized with the NanoNicVacs proved that NanoNicVac with either CpG ODN 1555 or CpG ODN 1826 as adjuvants did not cause detectable toxicity to the mice.

An Overdispersed Black-Box Variational Bayesian-Kalman Filter with Inaccurate Noise Second-Order Statistics.

Aimed at the problems in which the performance of filters derived from a hypothetical model will decline or the cost of time of the filters derived from a posterior model will increase when prior knowledge and second-order statistics of noise are uncertain, a new filter is proposed. In this paper, a Bayesian robust Kalman filter based on posterior noise statistics (KFPNS) is derived, and the recursive equations of this filter are very similar to that of the classical algorithm. Note that the posterior noise distributions are approximated by overdispersed black-box variational inference (O-BBVI). More precisely, we introduce an overdispersed distribution to push more probability density to the tails of variational distribution and incorporated the idea of importance sampling into two strategies of control variates and Rao-Blackwellization in order to reduce the variance of estimators. As a result, the convergence process will speed up. From the simulations, we can observe that the proposed filter has good performance for the model with uncertain noise. Moreover, we verify the proposed algorithm by using a practical multiple-input multiple-output (MIMO) radar system.

Effect of Adjuvant Release Rate on the Immunogenicity of Nanoparticle-Based Vaccines: A Case Study with a Nanoparticle-Based Nicotine Vaccine.

Adjuvants are a critical component for vaccines, especially for a poorly immunogenic antigen, such as nicotine. However, the impact of adjuvant release rate from a vaccine formulation on its immunogenicity has not been well illustrated. In this study, we fabricated a series of hybrid-nanoparticle-based nicotine vaccines to study the impact of adjuvant release rate on their immunological efficacy. It was found that the nanovaccine with a medium or slow adjuvant release rate induced a significantly higher anti-nicotine antibody titer than that with a fast release rate. Furthermore, the medium and slow adjuvant release rates resulted in a significantly lower brain nicotine concentration than the fast release rate after nicotine challenge. All findings suggest that adjuvant release rate affects the immunological efficacy of nanoparticle-based nicotine vaccines, providing a potential strategy to rationally designing vaccine formulations against psychoactive drugs or even other antigens. The hybrid-nanoparticle-based nicotine vaccine with an optimized adjuvant release rate can be a promising next-generation immunotherapeutic candidate against nicotine.

Alum as an adjuvant for nanoparticle based vaccines: A case study with a hybrid nanoparticle-based nicotine vaccine.

The treatment efficacy of a nicotine vaccine largely relies on its ability to induce high titers of nicotine-specific antibodies. Due to its strong immune-potentiating effects, aluminum salt (Alum) has been commonly used as an adjuvant in various nicotine vaccine formulations. In this study, we attempted to improve the immunological performance of a hybrid nanoparticle-based nicotine vaccine (NanoNicVac) by co-administering it with Alum. It was found that Alum severely restricted the release of NanoNicVac at the site of injection. Moreover, Alum damaged the hybrid structure of the vaccine. In the animal trial, mice immunized with NanoNicVac alone achieved an anti-nicotine IgG titer of 3.5 ±â€¯0.2 × 104 after three injections. Unexpectedly, Alum with quantities of 125, 250, 500, and 1000 µg did not enhance the immunogenicity of NanoNicVac. In addition, Alum did not improve the ability of the vaccine to reduce the entry of nicotine into the brain.

A LS-SVM based Measurement Points Classification Algorithm for Adjacent Targets in WSNs.

In wireless sensor networks (WSNs), the problem of measurement origin uncertainty for observed data has a significant impact on the precision of multi-target tracking. In this paper, a novel algorithm based on least squares support vector machine (LS-SVM) is proposed to classify measurement points for adjacent targets. Extended Kalman filter (EKF) algorithm is firstly adopted to compute the predicted classification line for each sampling period, which will be used to classify sampling points and calculate observed centers of closely moving targets. Then LS-SVM algorithm is utilized to train the classified points and get the best classification line, which will then be the reference classification line for the next sampling period. Finally, the locations of the targets will be precisely estimated by using observed centers based on EKF. A series of simulations validate the feasibility and accuracy of the new algorithm, while the experimental results verify the efficiency and effectiveness of the proposal.

Hybrid nanoparticle-based nicotine nanovaccines: Boosting the immunological efficacy by conjugation of potent carrier proteins.

A series of hybrid nanoparticle-based nicotine nanovaccines (NanoNicVac) were engineered in this work by conjugating potent carrier protein candidates (Keyhole limpet hemocyanin (KLH) multimer, KLH subunit, cross-reactive material 197 (CRM197), or tetanus toxoid (TT)) for enhanced immunological efficacy. NanoNicVac with CRM197 or TT were processed by dendritic cells more efficiently than that with KLH multimer or subunit. NanoNicVac carrying CRM197 or TT exhibited a significantly higher immunogenicity against nicotine and a considerably lower immunogenicity against carrier proteins than NanoNicVac carrying KLH multimer or subunit in mice. The in vivo results revealed that NanoNicVac with CRM197 or TT resulted in lower levels of nicotine in the brain of mice after nicotine challenge. All findings suggest that an enhanced immunological efficacy of NanoNicVac can be achieved by using CRM197 or TT instead of KLH or KLH subunit as carrier proteins, making NanoNicVac a promising next-generation immunotherapeutic candidate against nicotine addiction.

A Nano-in-Nano Polymer-Dendrimer Nanoparticle-Based Nanosystem for Controlled Multidrug Delivery.

Codelivery of multiple chemotherapeutics with different action mechanisms is a promising strategy for cancer treatment. In this study, we developed a novel polymer-dendrimer hybrid nanoparticle-based nanosystem for efficient and controlled codelivery of two model chemotherapeutics, doxorubicin (DOX) and paclitaxel (PTX). The nanosystem was characterized to have a nano-in-nano structure with a size of around 150 nm. The model drugs could feasibly be loaded into the nanosystem ratiometrically with high drug-loading contents by controlling the feeding drug ratios. Also, the model drugs could be released from the nanosystem following a sequential release manner-specifically, quick PTX release and sustained DOX release. Acidic pH was found to enhance the release of both drugs. Moreover, the nanosystem was taken up by cancer cells rapidly and efficiently, and the delivered drugs could release sustainably and efficiently in cells to reach their action targets. In vitro cytotoxicity results demonstrated that, by optimizing drug ratios, the dual-drug-loaded nanosystem could result in better antitumor efficacy than the single-drug-loaded nanosystem or free dual-drug combination. Furthermore, the dual-drug-loaded nanosystem could induce significant changes in both the nucleus and tubulin patterns synergistically. All data suggest that the nano-in-nano polymer-dendrimer hybrid nanoparticle-based nanosystem is a promising candidate to achieve controlled multidrug delivery for effective combination cancer therapy.

A nanoparticle-based nicotine vaccine and the influence of particle size on its immunogenicity and efficacy.

Traditional hapten-protein conjugate nicotine vaccines have shown less than desired immunological efficacy due to their poor recognition and internalization by immune cells. We developed a novel lipid-polymeric hybrid nanoparticle-based nicotine vaccine to enhance the immunogenicity of the conjugate vaccine, and studied the influence of particle size on its immunogenicity and pharmacokinetic efficacy. The results demonstrated that the nanovaccines, regardless of size, could induce a significantly stronger immune response against nicotine compared to the conjugate vaccine. Particularly, a significantly higher anti-nicotine antibody titer was achieved by the 100 compared to the 500nm nanovaccine. In addition, both the 100 and 500nm nanovaccines reduced the distribution of nicotine into the brain significantly. The 100nm nanovaccine exhibited better pharmacokinetic efficacy than the 500nm nanovaccine in the presence of alum adjuvant. These results suggest that a lipid-polymeric nanoparticle-based nicotine vaccine is a promising candidate to treat nicotine dependence.

In vitro controlled release of antigen in dendritic cells using pH-sensitive liposome-polymeric hybrid nanoparticles.

A hybrid nanoparticle (NP) consisting of a pH sensitive lipid shell and a poly(lactic-co-glycolic) acid (PLGA) core was constructed. This hybrid NP has a mean size of 120.1 ± 8.8 nm and positively charged surface (zeta potential of 14.2 ± 1.4 mV). The lipid shell of the hybrid NP was quickly disintegrated in buffer with a pH of 5.5, which resembles the acidic environment of endosomes in dendritic cell (DC). Less than 20% of the antigen enclosed in pH-sensitive hybrid NP was released into human serum at physiological pH within 24 h, but more than 40% of the enclosed antigen was released within 8 h after pH was adjusted to 5.5. Fast uptake of the pH sensitive hybrid NP by DC was also observed. It was found that pH sensitive hybrid NP displayed faster degradation and antigen release compared to regular hybrid NPs after uptake by DC.

In situ cellular hitchhiking of nanoparticles for drug delivery.

Since the inception of the concept of "magic bullet", nanoparticles have evolved to be one of the most effective carriers in drug delivery. Nanoparticles improve the therapeutic efficacy of drugs offering benefits to treating various diseases. Unlike free drugs which freely diffuse and distribute through the body, nanoparticles protect the body from the drug by reducing non-specific interactions while also improving the drug's pharmacokinetics. Despite acquiring some FDA approvals, further clinical application of nanoparticles is majorly hindered by its limited ability to overcome biological barriers resulting in uncontrolled biodistribution and high clearance. The use of cell-inspired systems has emerged as a promising approach to overcome this challenge as cells are biocompatible and have improved access to tissues and organs. One of such is the hitchhiking of nanoparticles to circulating cells such that they are recognized as 'self' components evading clearance and resulting in site-specific drug delivery. In this review, we discuss the concept of nanoparticle cellular hitchhiking, highlighting its advantages, the principles governing the process and the challenges currently limiting its clinical translation. We also discuss in situ hitchhiking as a tool for overcoming these challenges and the considerations to be taken to guide research efforts in advancing this promising technology.