Overcoming blood-brain barrier transport: Advances in nanoparticle-based drug delivery strategies.

The Blood-Brain Barrier (BBB), a unique structure in the central nervous system (CNS), protects the brain from bloodborne pathogens by its excellent barrier properties. Nevertheless, this barrier limits therapeutic efficacy and becomes one of the biggest challenges in new drug development for neurodegenerative disease and brain cancer. Recent breakthroughs in nanotechnology have resulted in various nanoparticles (NPs) as drug carriers to cross the BBB by different methods. This review presents the current understanding of advanced NP-mediated non-invasive drug delivery for the treatment of neurological disorders. Herein, the complex compositions and special characteristics of BBB are elucidated exhaustively. Moreover, versatile drug nanocarriers with their recent applications and their pathways on different drug delivery strategies to overcome the formidable BBB obstacle are briefly discussed. In terms of significance, this paper provides a general understanding of how various properties of nanoparticles aid in drug delivery through BBB and usher the development of novel nanotechnology-based nanomaterials for cerebral disease therapies.

Iron transport kinetics through blood-brain barrier endothelial cells.

BACKGROUND: Transferrin and its receptors play an important role during the uptake and transcytosis of iron through blood-brain barrier (BBB) endothelial cells (ECs) to maintain iron homeostasis in BBB endothelium and brain. Any disruptions in the cell environment may change the distribution of transferrin receptors on the cell surface, which eventually alter the homeostasis and initiate neurodegenerative disorders. In this paper, we developed a comprehensive mathematical model that considers the necessary kinetics for holo-transferrin internalization and acidification, apo-transferrin recycling, and exocytosis of free iron and transferrin-bound iron through basolateral side of BBB ECs. METHODS: Ordinary differential equations are formulated based on the first order reaction kinetics to model the iron transport considering their interactions with transferrin and transferrin receptors. Unknown kinetics rate constants are determined from experimental data by applying a non-linear optimization technique. RESULTS: Using the estimated kinetic rate constants, the presented model can effectively reproduce the experimental data of iron transports through BBB ECs for many in-vitro studies. Model results also suggest that the BBB ECs can regulate the extent of the two possible iron transport pathways (free and transferrin-bound iron) by controlling the receptor expression, internalization of holo-transferrin-receptor complexes and acidification of holo-transferrin inside the cell endosomes. CONCLUSION: The comprehensive mathematical model described here can predict the iron transport through BBB ECs considering various possible routes from blood side to brain side. The model can also predict the transferrin and iron transport behavior in iron-enriched and iron-depleted cells, which has not been addressed in previous work.

Fine-tuning-based Transfer Learning for Characterization of Adeno-Associated Virus.

Accurate and precise identification of adeno-associated virus (AAV) vectors play an important role in dose-dependent gene therapy. Although solid-state nanopore techniques can potentially be used to characterize AAV vectors by capturing ionic current, the existing data analysis techniques fall short of identifying them from their ionic current profiles. Recently introduced machine learning methods such as deep convolutional neural network (CNN), developed for image identification tasks, can be applied for such classification. However, with smaller data set for the problem in hand, it is not possible to train a deep neural network from scratch for accurate classification of AAV vectors. To circumvent this, we applied a pre-trained deep CNN (GoogleNet) model to capture the basic features from ionic current signals and subsequently used fine-tuning-based transfer learning to classify AAV vectors. The proposed method is very generic as it requires minimal preprocessing and does not require any handcrafted features. Our results indicate that fine-tuning-based transfer learning can achieve an average classification accuracy between 90 and 99% in three realizations with a very small standard deviation. Results also indicate that the classification accuracy depends on the applied electric field (across nanopore) and the time frame used for data segmentation. We also found that the fine-tuning of the deep network outperforms feature extraction-based classification for the resistive pulse dataset. To expand the usefulness of the fine-tuning-based transfer learning, we have tested two other pre-trained deep networks (ResNet50 and InceptionV3) for the classification of AAVs. Overall, the fine-tuning-based transfer learning from pre-trained deep networks is very effective for classification, though deep networks such as ResNet50 and InceptionV3 take significantly longer training time than GoogleNet.

Bayesian inference for parameter estimation in lactoferrin-mediated iron transport across blood-brain barrier.

BACKGROUND: In neurodegenerative diseases such as Alzheimer's and Parkinson's, excessive irons as well as lactoferrin (Lf), but not transferrin (Tf), have been found in and around the affected regions of the brain. These evidences suggest that lactoferrin plays a critical role during neurodegenerative diseases, although Lf-mediated iron transport across blood-brain barrier (BBB) is negligible compared to that of transferrin in normal condition. However, the kinetics of lactoferrins and lactoferrin-mediated iron transport are still unknown. METHOD: To determine the kinetic rate constants of lactoferrin-mediated iron transport through BBB, a mass-action based ordinary differential equation model has been presented. A Bayesian framework is developed to estimate the kinetic rate parameters from posterior probability density functions. The iron transport across BBB is studied by considering both Lf- and Tf-mediated pathways for both normal and pathologic conditions. RESULTS: Using the point estimates of kinetic parameters, our model can effectively reproduce the experimental data of iron transport through BBB endothelial cells. The robustness of the model and parameter estimation process are further verified by perturbation of kinetic parameters. Our results show that surge in high-affinity receptor density increases lactoferrin as well as iron in the brain. CONCLUSIONS: Due to the lack of a feedback loop such as iron regulatory proteins (IRPs) for lactoferrin, iron can transport to the brain continuously, which might increase brain iron to pathological levels and can contribute to neurodegeneration. GENERAL SIGNIFICANCE: This study provides an improved understanding of presence of lactoferrin and iron in the brain during neurodegenerative diseases.

Adeno-associated virus characterization for cargo discrimination through nanopore responsiveness.

Solid-state nanopore (SSN)-based analytical methods have found abundant use in genomics and proteomics with fledgling contributions to virology - a clinically critical field with emphasis on both infectious and designer-drug carriers. Here we demonstrate the ability of SSN to successfully discriminate adeno-associated viruses (AAVs) based on their genetic cargo [double-stranded DNA (AAVdsDNA), single-stranded DNA (AAVssDNA) or none (AAVempty)], devoid of digestion steps, through nanopore-induced electro-deformation (characterized by relative current change; ΔI/I0). The deformation order was found to be AAVempty > AAVssDNA > AAVdsDNA. A deep learning algorithm was developed by integrating support vector machine with an existing neural network, which successfully classified AAVs from SSN resistive-pulses (characteristic of genetic cargo) with >95% accuracy - a potential tool for clinical and biomedical applications. Subsequently, the presence of AAVempty in spiked AAVdsDNA was flagged using the ΔI/I0 distribution characteristics of the two types for mixtures composed of ∼75 : 25% and ∼40 : 60% (in concentration) AAVempty : AAVdsDNA.

Analytical Solution of Time-Periodic Electroosmotic Flow through Cylindrical Microchannel with Non-Uniform Surface Potential.

Time-periodic electroosmotic flow (EOF) with heterogeneous surface charges on channel walls can potentially be used to mix species or reagent molecules in microfluidic devices. Although significant research efforts have been placed to understand different aspects of EOF, its role in the mixing process is still poorly understood, especially for non-homogeneous surface charge cases. In this work, dynamic aspects of EOF in a cylindrical capillary are analyzed for heterogeneous surface charges. Closed form analytical solutions for time-periodic EOF are obtained by solving the Navier-Stokes equation. An analytical expression of induced pressure is also obtained from the velocity field solution. The results show that several vortices can be formed inside the microchannel with sinusoidal surface charge distribution. These vortices change their pattern and direction as the electric field change its strength and direction with time. In addition, the structure and strength of the vorticity depend on the frequency of the external electric field and the size of the channel. As the electric field frequency or channel diameter increases, vortices are shifted towards the channel surface and the perturbed flow region becomes smaller, which is not desired for effective mixing. Moreover, the number of vorticities depends on the periodicity of the surface charge.

Quantification of kinetic rate constants for transcytosis of polymeric nanoparticle through blood-brain barrier.

BACKGROUND: Polymeric nanoparticles (PNP) have received significant amount of interests for targeted drug delivery across the blood-brain barrier (BBB). Experimental studies have revealed that PNP can transport drug molecules from microvascular blood vessels to brain parenchyma in an efficient and non-invasive way. Despite that, very little attention has been paid to theoretically quantify the transport of such nanoparticles across BBB. METHODS: In this study, for the first time, we developed a mathematical model for PNP transport through BBB endothelial cells. The mathematical model is developed based on mass-action laws, where kinetic rate parameters are determined by an artificial neural network (ANN) model using experimental data from in-vitro BBB experiments. RESULTS: The presented ANN model provides a much simpler way to solve the parameter estimation problem by avoiding integration scheme for ordinary differential equations associated with the mass-action laws. Furthermore, this method can efficiently deal with both small and large data set and can approximate highly nonlinear functions. Our results show that the mass-action model, constructed with ANN based rate parameters, can successfully predict the characteristics of the polymeric nanoparticle transport across the BBB. CONCLUSIONS: Our model results indicate that exocytosis of nanoparticles is seven fold slower to endocytosis suggesting that future studies should focus on enhancing the exocytosis process. GENERAL SIGNIFICANCE: This mathematical study will assist in designing new drug carriers to overcome the drug delivery problems in brain. Furthermore, we anticipate that this model will form the basis of future comprehensive models for drug transport across BBB.