The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability

GCN-BBB: Deep Learning Blood-Brain Barrier (BBB) Permeability PharmacoAnalytics with Graph Convolutional Neural (GCN) Network

The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblastoma-related drug discovery. In addition, more CNS diseases still present significant challenges, for instance, neurological disorders like Alzheimer's Disease (AD) and drug abuse. Conversely, cannabinoid drugs that do not cross the BBB are needed to avoid off-target CNS psychotropic effects. In vitro and in vivo experiments measuring BBB permeability are costly and low throughput. Computational pharmacoanalytics modeling, particularly using deep-learning Graph Neural Networks (GNNs), offers a promising alternative. GNNs excel at capturing intricate relationships in graph-based information, such as small molecular structures. In this study, we developed GNNs model for BBB permeability using the graph representation of drugs. The GNNs were compared with other algorithms using molecular fingerprints or physical-chemical descriptors. With a dataset of 1924 molecules, the best GNNs model, a convolutional graph neural network using a normalized Laplacian matrix (GCN_2), achieved a precision of 0.94, recall of 0.96, F1 score of 0.95, and MCC score of 0.77. This outperformed other machine learning algorithms with molecular fingerprints. The findings indicate that the graphic representation of small molecules combined with GNNs architecture is powerful in predicting BBB permeability with high accuracy and recall. The developed GNNs model can be utilized in the initial screening stage for new drug development.

Interfacing with the Brain: How Nanotechnology Can Contribute

Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.

Advancing CNS Therapeutics: Enhancing Neurological Disorders with Nanoparticle-Based Gene and Enzyme Replacement Therapies

Given the complexity of the central nervous system (CNS) and the diversity of neurological conditions, the increasing prevalence of neurological disorders poses a significant challenge to modern medicine. These disorders, ranging from neurodegenerative diseases to psychiatric conditions, not only impact individuals but also place a substantial burden on healthcare systems and society. A major obstacle in treating these conditions is the blood-brain barrier (BBB), which restricts the passage of therapeutic agents to the brain. Nanotechnology, particularly the use of nanoparticles (NPs), offers a promising solution to this challenge. NPs possess unique properties such as small size, large surface area, and modifiable surface characteristics, enabling them to cross the BBB and deliver drugs directly to the affected brain regions. This review focuses on the application of NPs in gene therapy and enzyme replacement therapy (ERT) for neurological disorders. Gene therapy involves altering or manipulating gene expression and can be enhanced by NPs designed to carry various genetic materials. Similarly, NPs can improve the efficacy of ERT for lysosomal storage disorders (LSDs) by facilitating enzyme delivery to the brain, overcoming issues like immunogenicity and instability. Taken together, this review explores the potential of NPs in revolutionizing treatment options for neurological disorders, highlighting their advantages and the future directions in this rapidly evolving field.

TNFipred: a classification model to predict TNF-α inhibitors

Rheumatoid arthritis (RA), characterized by severe inflammation in the joint lining, is a progressive, chronic, autoimmune disorder with high morbidity and mortality rates. There are several mechanisms responsible for joint damage, but overproduction of TNF-α is a significant mechanism that results in excess swelling and pain. Drugs acting on TNF-α are known to significantly reduce the disease progression and improve the quality of life in many RA patients. Hence, inhibiting TNF-α is considered one of the most effective treatments for RA. Currently, there are only a few FDA-approved TNF-α inhibitors, which are mainly monoclonal antibodies, fusion proteins, or biosimilars with disadvantages such as poor stability, difficulty in route of administration (often given as injection or infusion), cost-prohibitive large-scale production, and increased side effects. There are just a handful of small compounds known to have TNF- inhibitory capabilities. Thus, there is a dire need for new drugs, especially small molecules in the market, such as TNF-α inhibitors. The conventional method of identifying TNF-α inhibitors is expensive, labor, and time intensive. Machine learning (ML) can be used to solve existing drug discovery and development problems. In this study, four classification algorithms-naïve Bayes (NB), random forest (RF), k-nearest neighbor (kNN), and support vector machine (SVM)-were used to train ML models for classifying TNF-α inhibitors based on three sets of features. The performance of the RF model was found to be best when using 1D, 2D, and fingerprints as features, with an accuracy of 87.96 and a sensitivity of 86.17. To our knowledge, this is the first ML model for TNF-α inhibitor prediction. The model is available at http://14.139.57.41/tnfipred/.

Nucleolin-Targeting AS1411 Aptamer-Conjugated Nanospheres for Targeted Treatment of Glioblastoma

Post-operative chemotherapy is still required for the treatment of glioblastoma (GBM), for which nanocarrier-based drug delivery has been identified as one of the most effective methods. However, the blood-brain barrier (BBB) and non-specific delivery to non-tumor tissues can significantly limit drug accumulation in tumor tissues and cause damage to nearby normal tissues. This study describes a targeted cancer therapy approach that uses AS1411 aptamer-conjugated nanospheres (100-300 nm in size) loaded with doxorubicin (Dox) to selectively identify tumor cells overexpressing nucleolin (NCL) proteins. The study demonstrates that the active target model, which employs aptamer-mediated drug delivery, is more effective than non-specific enhanced permeability and maintenance (EPR)-mediated delivery and passive drug delivery in improving drug penetration and maintenance in tumor cells. Additionally, the study reveals the potential for anti-cancer effects through 3D spheroidal and in vivo GBM xenograft models. The DNA-protein hybrid nanospheres utilized in this study offer numerous benefits, such as efficient synthesis, structural stability, high drug loading, dye labeling, biocompatibility, and biodegradability. When combined with nanospheres, the 1411 aptamer has been shown to be an effective drug delivery carrier allowing for the precise targeting of tumors. This combination has the potential to produce anti-tumor effects in the active targeted therapy of GBM.

Insights on Quercetin Therapeutic Potential for Neurodegenerative Diseases and its Nano-technological Perspectives

The neurodegeneration process begins in conjunction with the aging of the neurons. It manifests in different parts of the brain as Aβ plaques, neurofibrillary tangles, Lewy bodies, Pick bodies, and other structures, which leads to progressive loss or death of neurons. Quercetin (QC) is a flavonoid compound found in fruits, tea, and other edible plants have antioxidant effects that have been studied from subcellular compartments to tissue levels in the brain. Also, quercetin has been reported to possess a neuroprotective role by decreasing oxidative stress-induced neuronal cell damage. The use of QC for neurodegenerative therapy, the existence of the blood-brain barrier (BBB) remains a significant barrier to improving the clinical effectiveness of the drug, so finding an innovative solution to develop simultaneous BBB-crossing ability of drugs for treating neurodegenerative disorders and improving neurological outcomes is crucial. The nanoparticle formulation of QC is considered beneficial and useful for its delivery through this route for the treatment of neurodegenerative diseases seems necessary. Increased QC accumulation in the brain tissue and more significant improvements in tissue and cellular levels are among the benefits of QC-involved nanostructures.

Acupuncture with specific mode electro-stimulation effectively and transiently opens the BBB through Shh signaling pathway

To explore a new method that patients with brain diseases such as stroke sequelae are hindered by blood-brain barrier (BBB) in clinical treatment. Research preliminarily found that acupuncture with specific mode electro-stimulation (EA) to open BBB-assisted drug delivery may be is an effective means to improve the clinical efficacy of brain disease patients. So here we further explore the features and mechanism. Middle cerebral artery occlusion/R recovery rats were employed as the animal model. Laser Doppler monitoring cerebral blood flow decreased to 45 ± 10% of the baseline value as modeling criteria and TTC staining observed infarcted areas of brain tissue. The permeability of FITC-Dextran and EB in the frontal lobe of rats was observed by microscope. After that, Western blot and Immunofluorescence staining for the detection of the shh and Gli1 signal molecule, Claudin-5 Occludin ZO-1 tight junction (TJ) proteins. EA can open the BBB stably and effectively, and has the characteristics of starting to close soon after the end of EA; EA inhibits the Shh-Gli1 signaling pathway, and downregulates Occludin ZO-1 TJ proteins. These results suggest that EA is safe and reversible in opening the BBB, and its mechanism is related to the inhibition of Shh signaling pathway to down-regulate the expression of TJ proteins.

Virtual screening of pyrazole derivatives of usnic acid as new class of anti-hyperglycemic agents against PPARγ agonists

The finest sources of therapeutic agents are natural products, and usnic acid is a secondary metabolite derived from lichen that has a wide range of biological actions, including anti-viral, anti-cancer, anti-bacterial, and anti-diabetic (hyperglycemia). Based on the hyperglycemia activity of UA, this work seeks to identify new anti-hyperglycemia medicines by virtual screening of pyrazole derivatives of UA. Seven hit compounds (Compounds 1, 5, 6, 7, 17, 18 and 33), which finally go through docking-based screening to produce the lead molecule, were identified by the physicochemical attributes, drug-likeliness, and ADMET prediction. The docking score for the chosen compounds containing PPARγ agonists ranged from -7.6 to -9.2 kcal/mol, whereas the docking goal for compounds 5, 6, and 7 was -9.2 kcal/mol. Based on the binding energy and bound amino acid residues as well as compared to the reference compound, compound-6 considered as lead compound. Furthermore, the MD simulation of 3CS8-Compound-6 and 3CS8-Rosiglitazone complexes were performed to verify the stability of these complexes and the binding posture acquired in docking experiments. The compound-6 had strong pharmacological characteristics, bound to the PPARγ agonist active site, and was expected to reduce the activity of the receptor, according to the virtual screening results. It must be justified to conduct both in-vitro and in-vivo experiments to examine the efficacy of this compound.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00176-y.

Machine Learning Techniques Applied to the Study of Drug Transporters

With the advancement of computer technology, machine learning-based artificial intelligence technology has been increasingly integrated and applied in the fields of medicine, biology, and pharmacy, thereby facilitating their development. Transporters have important roles in influencing drug resistance, drug-drug interactions, and tissue-specific drug targeting. The investigation of drug transporter substrates and inhibitors is a crucial aspect of pharmaceutical development. However, long duration and high expenses pose significant challenges in the investigation of drug transporters. In this review, we discuss the present situation and challenges encountered in applying machine learning techniques to investigate drug transporters. The transporters involved include ABC transporters (P-gp, BCRP, MRPs, and BSEP) and SLC transporters (OAT, OATP, OCT, MATE1,2-K, and NET). The aim is to offer a point of reference for and assistance with the progression of drug transporter research, as well as the advancement of more efficient computer technology. Machine learning methods are valuable and attractive for helping with the study of drug transporter substrates and inhibitors, but continuous efforts are still needed to develop more accurate and reliable predictive models and to apply them in the screening process of drug development to improve efficiency and success rates.

Blood-Brain Barrier Dysfunction in Normal Aging and Neurodegeneration: Mechanisms, Impact, and Treatments

Cerebral endothelial cells and their linking tight junctions form a unique, dynamic and multi-functional interface, the blood-brain barrier (BBB). The endothelium is regulated by perivascular cells and components forming the neurovascular unit. This review examines BBB and neurovascular unit changes in normal aging and in neurodegenerative disorders, particularly focusing on Alzheimer disease, cerebral amyloid angiopathy and vascular dementia. Increasing evidence indicates BBB dysfunction contributes to neurodegeneration. Mechanisms underlying BBB dysfunction are outlined (endothelium and neurovascular unit mediated) as is the BBB as a therapeutic target including increasing the uptake of systemically delivered therapeutics across the BBB, enhancing clearance of potential neurotoxic compounds via the BBB, and preventing BBB dysfunction. Finally, a need for novel biomarkers of BBB dysfunction is addressed.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Increased brain uptake of pterostilbene loaded folate modified micellar delivery system

Effective chemotherapy for clinical treatment of brain diseases is still lacking due to the poor penetration of the blood-brain barrier (BBB). The aim of this study was to construct a folate modified pterostilbene (Pt) loaded polymeric micellar delivery system (F-Pt/M) with mPEG-PCL as carrier material to aim at penetrating the BBB for brain tissue targeting via receptor-mediated endocytosis. In this study, F-Pt/M was prepared using thin-film hydration method and then optimized by response surface methodology (RSM) with the entrapment efficiency (EE), drug loading (DL) and hydrodynamic diameter (HD) as indexes. The average hydrodynamic diameter and zeta potential of optimal F-Pt/M were 133.2 nm and 24.6 mV, respectively. DL (18.3%) and EE (98.6%) made the solubility of Pt in water about 25 times higher than that of crude Pt. Results of DSC evaluation revealed that drugs were successfully encapsulated inside the polymeric micelles. TEM images showed that homogeneous spherical micellar structures with a narrow size distribution were developed. The release result in vitro showed that F-Pt/M presented sustained release behavior compared to control free Pt solution. Compared to non-targeted Pt/M, F-Pt/M had a significantly higher cytotoxicity against FR-overexpressing A172 cells. In vitro cellular uptake tests illustrated that the micellar delivery system could significantly improve the accumulation of drugs in target cells via receptor-mediated endocytosis. BBB penetration value (P) of F-Pt/M was about 4 folds higher than that of free Pt group. In addition, drug targeting index (DTI) was calculated to determine targeting of F-Pt/M to the brain which was found to be 4.89, implying improved brain targeting was achieved. Hence, the developed F-Pt/M exhibited great potential for delivering more drug molecules across the BBB for the treatment of brain diseases.

Transport of nanomedicines across the blood-brain barrier: Challenges and opportunities for imaging and therapy

The blood-brain barrier (BBB) is a protective and semipermeable border of endothelial cells that prevents toxins and foreign bodies to enter and damage the brain. Unfortunately, the BBB also hampers the development of pharmaceuticals targeting receptors, enzymes, or other proteins that lie beyond this barrier. Especially large molecules, such as monoclonal antibodies (mAbs) or nanoparticles, are prevented to enter the brain. The limited passage of these molecules partly explains why nanomedicines - targeting brain diseases - have not made it into the clinic to a great extent. As nanomedicines can target a wide range of targets including protein isoforms and oligomers or potentially deliver cytotoxic drugs safely to their targets, a pathway to smuggle nanomedicines into the brain would allow to treat brain diseases that are currently considered 'undruggable'. In this review, strategies to transport nanomedicines over the BBB will be discussed. Their challenges and opportunities will be highlighted with respect to their use for molecular imaging or therapies. Several strategies have been explored for this thus far. For example, carrier-mediated and receptor-mediated transcytosis (RMT), techniques to disrupt the BBB, nasal drug delivery or administering nanomedicines directly into the brain have been explored. RMT has been the most widely and successfully explored strategy. Recent work on the use of focused ultrasound based BBB opening has shown great promise. For example, successful delivery of mAbs into the brain has been achieved, even in a clinical setting. As nanomedicines bear the potential to treat incurable brain diseases, drug delivery technologies that can deliver nanomedicines into the brain will play an essential role for future treatment options.

Pretargeted Imaging beyond the Blood-Brain Barrier-Utopia or Feasible?

Pretargeting is a promising nuclear imaging technique that allows for the usage of antibodies (Abs) with enhanced imaging contrast and reduced patient radiation burden. It is based on bioorthogonal chemistry with the tetrazine ligation-a reaction between trans-cyclooctenes (TCOs) and tetrazines (Tzs)-currently being the most popular reaction due to its high selectivity and reactivity. As Abs can be designed to bind specifically to currently 'undruggable' targets such as protein isoforms or oligomers, which play a crucial role in neurodegenerative diseases, pretargeted imaging beyond the BBB is highly sought after, but has not been achieved yet. A challenge in this respect is that large molecules such as Abs show poor brain uptake. Uptake can be increased by receptor mediated transcytosis; however, it is largely unknown if the achieved brain concentrations are sufficient for pretargeted imaging. In this study, we investigated whether the required concentrations are feasible to reach. As a model Ab, we used the bispecific anti-amyloid beta (Aβ) anti-transferrin receptor (TfR) Ab 3D6scFv8D3 and conjugated it to a different amount of TCOs per Ab and tested different concentrations in vitro. With this model in hand, we estimated the minimum required TCO concentration to achieve a suitable contrast between the high and low binding regions. The estimation was carried out using pretargeted autoradiography on brain sections of an Alzheimer's disease mouse model. Biodistribution studies in wild-type (WT) mice were used to correlate how different TCO/Ab ratios alter the brain uptake. Pretargeted autoradiography showed that increasing the number of TCOs as well as increasing the TCO-Ab concentration increased the imaging contrast. A minimum brain concentration of TCOs for pretargeting purposes was determined to be 10.7 pmol/g in vitro. Biodistribution studies in WT mice showed a brain uptake of 1.1% ID/g using TCO-3D6scFv8D3 with 6.8 TCO/Ab. According to our estimations using the optimal parameters, pretargeted imaging beyond the BBB is not a utopia. Necessary brain TCO concentrations can be reached and are in the same order of magnitude as required to achieve sufficient contrast. This work gives a first estimate that pretargeted imaging is indeed possible with antibodies. This could allow the imaging of currently 'undruggable' targets and therefore be crucial to monitor (e.g., therapies for intractable neurodegenerative diseases).

The barrier and interface mechanisms of the brain barrier, and brain drug delivery

Three different barriers are formed between the cerebrovascular and the brain parenchyma: the blood-brain barrier (BBB), the blood-cerebrospinal fluid barrier (BCSFB), and the cerebrospinal fluid-brain barrier (CBB). The BBB is the main regulator of blood and central nervous system (CNS) material exchange. The semipermeable nature of the BBB limits the passage of larger molecules and hydrophilic small molecules, Food and Drug Administration (FDA)-approved drugs for the CNS have been generally limited to lipid-soluble small molecules. Although the complexity of the BBB affects CNS drug delivery, understanding the composition and function of the BBB can provide a platform for the development of new methods for CNS drug delivery. This review summarizes the classification of the brain barrier, the composition and role of the basic structures of the BBB, and the transport, barrier, and destruction mechanisms of the BBB; discusses the advantages and disadvantages of different drug delivery methods and prospects for future drug delivery strategies.

Exosomes and Biomaterials: In Search of a New Therapeutic Strategy for Multiple Sclerosis

Current efforts to find novel treatments that counteract multiple sclerosis (MS) have pointed toward immunomodulation and remyelination. Currently, cell therapy has shown promising potential to achieve this purpose. However, disadvantages such as poor survival, differentiation, and integration into the target tissue have limited its application. A series of recent studies have focused on the cell secretome, showing it to provide the most benefits of cell therapy. Exosomes are a key component of the cell secretome, participating in the transfer of bioactive molecules. These nano-sized vesicles offer many therapeutical advantages, such as the capacity to cross the blood-brain barrier, an enrichable cargo, and a customizable membrane. Moreover, integrating of biomaterials into exosome therapy could lead to new tissue-specific therapeutic strategies. In this work, the use of exosomes and their integration with biomaterials is presented as a novel strategy in the treatment of MS.

In Vitro Modeling of the Blood–Brain Barrier for the Study of Physiological Conditions and Alzheimer’s Disease

The blood–brain barrier (BBB) is an essential structure for the maintenance of brain homeostasis. Alterations to the BBB are linked with a myriad of pathological conditions and play a significant role in the onset and evolution of neurodegenerative diseases, including Alzheimer’s disease. Thus, a deeper understanding of the BBB’s structure and function is mandatory for a better knowledge of neurodegenerative disorders and the development of effective therapies. Because studying the BBB in vivo imposes overwhelming difficulties, the in vitro approach remains the main possible way of research. With many in vitro BBB models having been developed over the last years, the main aim of this review is to systematically present the most relevant designs used in neurological research. In the first part of the article, the physiological and structural–functional parameters of the human BBB are detailed. Subsequently, available BBB models are presented in a comparative approach, highlighting their advantages and limitations. Finally, the new perspectives related to the study of Alzheimer’s disease with the help of novel devices that mimic the in vivo human BBB milieu gives the paper significant originality.

Physicochemistry shapes bioactivity landscape of pan-ABC transporter modulators: Anchor point for innovative Alzheimer's disease therapeutics

Alzheimer's disease (AD) is a devastating neurological disorder characterized by the pathological accumulation of macromolecular Aβ and tau leading to neuronal death. Drugs approved to treat AD may ameliorate disease symptoms, however, no curative treatment exists. Aβ peptides were discovered to be substrates of adenosine triphosphate-(ATP)-binding cassette (ABC) transporters. Activators of these membrane-bound efflux proteins that promote binding and/or translocation of Aβ could revolutionize AD medicine. The knowledge about ABC transporter activators is very scarce, however, the few molecules that were reported contain substructural features of multitarget (pan-)ABC transporter inhibitors. A cutting-edge strategy to obtain new drug candidates is to explore and potentially exploit the recently proposed multitarget binding site of pan-ABC transporter inhibitors as anchor point for the development of innovative activators to promote Aβ clearance from the brain. Molecular associations between functional bioactivities and physicochemical properties of small-molecules are key to understand these processes. This study provides an analysis of a recently reported unique multitarget dataset for the correlation between multitarget bioactivity and physicochemistry. Six novel pan-ABC transporter inhibitors were validated containing substructural features of ABC transporter activators, which underpins the relevance of the multitarget binding site for the targeted development of novel AD diagnostics and therapeutics.

Relational graph convolutional networks for predicting blood-brain barrier penetration of drug molecules

MOTIVATION

Evaluating the blood-brain barrier (BBB) permeability of drug molecules is a critical step in brain drug development. Traditional methods for the evaluation require complicated in vitro or in vivo testing. Alternatively, in silico predictions based on machine learning have proved to be a cost-efficient way to complement the in vitro and in vivo methods. However, the performance of the established models has been limited by their incapability of dealing with the interactions between drugs and proteins, which play an important role in the mechanism behind the BBB penetrating behaviors. To address this limitation, we employed the relational graph convolutional network (RGCN) to handle the drug-protein interactions as well as the properties of each individual drug.

RESULTS

The RGCN model achieved an overall accuracy of 0.872, an area under the receiver operating characteristic (AUROC) of 0.919 and an area under the precision-recall curve (AUPRC) of 0.838 for the testing dataset with the drug-protein interactions and the Mordred descriptors as the input. Introducing drug-drug similarity to connect structurally similar drugs in the data graph further improved the testing results, giving an overall accuracy of 0.876, an AUROC of 0.926 and an AUPRC of 0.865. In particular, the RGCN model was found to greatly outperform the LightGBM base model when evaluated with the drugs whose BBB penetration was dependent on drug-protein interactions. Our model is expected to provide high-confidence predictions of BBB permeability for drug prioritization in the experimental screening of BBB-penetrating drugs.

AVAILABILITY AND IMPLEMENTATION

The data and the codes are freely available at https://github.com/dingyan20/BBB-Penetration-Prediction.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Current Strategies to Enhance Delivery of Drugs across the Blood–Brain Barrier

The blood–brain barrier (BBB) has shown to be a significant obstacle to brain medication delivery. The BBB in a healthy brain is a diffusion barrier that prevents most substances from passing from the blood to the brain; only tiny molecules can pass across the BBB. The BBB is disturbed in specific pathological illnesses such as stroke, diabetes, seizures, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease. The goal of this study is to offer a general overview of current brain medication delivery techniques and associated topics from the last five years. It is anticipated that this review will stimulate readers to look into new ways to deliver medications to the brain. Following an introduction of the construction and function of the BBB in both healthy and pathological conditions, this review revisits certain contested questions, such as whether nanoparticles may cross the BBB on their own and if medications are selectively delivered to the brain by deliberately targeted nanoparticles. Current non-nanoparticle options are also discussed, including drug delivery via the permeable BBB under pathological circumstances and the use of non-invasive approaches to improve brain medication absorption.

Nanotechnology-based drug delivery for the treatment of CNS disorders

Approximately 6.8 million people die annually because of problems related to the central nervous system (CNS), and out of them, approximately 1 million people are affected by neurodegenerative diseases that include Alzheimer's disease, multiple sclerosis, epilepsy, and Parkinson's disease. CNS problems are a primary concern because of the complexity of the brain. There are various drugs available to treat CNS disorders and overcome problems with toxicity, specificity, and delivery. Barriers like the blood-brain barrier (BBB) are a challenge, as they do not allow therapeutic drugs to cross and reach their target. Researchers have been searching for ways to allow drugs to pass through the BBB and reach the target sites. These problems highlight the need of nanotechnology to alter or manipulate various processes at the cellular level to achieve the desired attributes. Due to their nanosize, nanoparticles are able to pass through the BBB and are an effective alternative to drug administration and other approaches. Nanotechnology has the potential to improve treatment and diagnostic techniques for CNS disorders and facilitate effective drug transfer. With the aid of nanoengineering, drugs could be modified to perform functions like transference across the BBB, altering signaling pathways, targeting specific cells, effective gene transfer, and promoting regeneration and preservation of nerve cells. The involvement of a nanocarrier framework inside the delivery of several neurotherapeutic agents used in the treatment of neurological diseases is reviewed in this study.

Therapeutic Potential of Ferulic Acid in Alzheimer's Disease

Alzheimer's Disease (AD) is one of the most important neurodegenerative diseases and it covers 60% of whole dementia cases. AD is a constantly progressing neurodegenerative disease as a result of the production of β-amyloid (Aβ) protein and the accumulation of hyper-phosphorylated Tau protein; it causes breakages in the synaptic bonds and neuronal deaths to a large extent. Millions of people worldwide suffer from AD because there is no definitive drug for disease prevention, treatment or slowdown. Over the last decade, multiple target applications have been developed for AD treatments. These targets include Aβ accumulations, hyper-phosphorylated Tau proteins, mitochondrial dysfunction, and oxidative stress resulting in toxicity. Various natural or semisynthetic antioxidant formulations have been shown to protect brain cells from Aβ induced toxicity and provide promising potentials for AD treatment. Ferulic acid (FA), a high-capacity antioxidant molecule, is naturally synthesized from certain plants. FA has been shown to have different substantial biological properties, such as anticancer, antidiabetic, antimicrobial, anti-inflammatory, hepatoprotective, and cardioprotective actions, etc. Furthermore, FA exerted neuroprotection via preventing Aβ-fibril formation, acting as an anti-inflammatory agent, and inhibiting free radical generation and acetylcholinesterase (AChE) enzyme activity. In this review, we present key biological roles of FA and several FA derivatives in Aβ-induced neurotoxicity, protection against free radical attacks, and enzyme inhibitions and describe them as possible therapeutic agents for the treatment of AD.

Recent Advancements in Nanodiamond Mediated Brain Targeted Drug Delivery and Bioimaging of Brain Ailments: A Holistic Review

BACKGROUND

The brain is a vital and composite organ. By nature, the innate make-up of the brain is such that in anatomical parlance, it is highly protected by the "Blood-Brain Barrier", which is a nexus of capillary endothelial cells, basement membrane, neuroglial membrane and glialpodocytes. The same barrier, which protects and isolates the interstitial fluid of the brain from capillary circulation, also restricts the therapeutic intervention. Many standing pharmaceutical formulations are ineffective in the treatment of inimical brain ailments because of the inability of the API to surpass and subsist inside the Blood Brain Barrier.

OBJECTIVE

This is an integrated review that emphasizes on the recent advancements in brain-targeted drug delivery utilizing nanodiamonds (NDs) as a carrier of therapeutic agents. NDs are a novel nanoparticulate drug delivery system, having carbon moieties as their building blocks and their surface tenability is remarkable. These neoteric carbon-based carriers have exceptional, mechanical, electrical, chemical, optical, and biological properties, which can be further rationally modified and augmented.

CONCLUSION

NDs could be the next"revolution "in the field of nanoscience for the treatment of neurodegenerative disorders, brain tumors, and other pernicious brain ailments. What sets them apart from other nanocarriers is their versatile properties like diverse size range and surface modification potential, which makes them efficient enough to move across certain biological barriers and offer a plethora of brain targeting and bioimaging abilities. Lay Summary: The blood-brain barrier (BBB) poses a major hurdle in the way of treating many serious brain ailments. A range of nanoparticle based drug delivering systems have been formulated, including solid lipid nanoparticles, liposomes, dendrimers, nanogels, polymeric NPs, metallic NPs (gold, platinum, andironoxide) and diamondoids (carbonnanotubes). Despite this development, only a few of these formulations have shown the ability to cross the BBB. Nanodiamonds, because of their small size, shape, and surface characteristics, have a potential in moving beyond the diverse and intricate BBB, and offer a plethora of brain targeting capabilities.

Engineered Solid Lipid Nanoparticles and Nanostructured Lipid Carriers as New Generations of Blood-Brain Barrier Transmitters

The blood-brain barrier (BBB) is considered as the most challenging barrier in brain drug delivery. Indeed, there is a definite link between the BBB integrity defects and central nervous systems (CNS) disorders, such as neurodegenerative diseases and brain cancers, increasing concerns in the contemporary era because of the inability of most therapeutic approaches. Solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have already been identified as having several advantages in facilitating the transportation of hydrophilic and hydrophobic agents across the BBB. This review first explains BBB functions and its challenges in brain drug delivery, followed by a brief description of nanoparticle-based drug delivery for brain diseases. A detailed presentation of recent progressions in optimizing SLNs and NLCs for controlled release drug delivery, gene therapy, targeted drug delivery, and diagnosis of neurodegenerative diseases and brain cancers is approached. Finally, the problems, challenges, and future perspectives in optimizing these carriers for potential clinical application were described briefly.

Multi-disciplinary Approach for Drug and Gene Delivery Systems to the Brain

Drug delivery into the brain has for long been a huge challenge as the blood–brain barrier (BBB) offers great resistance to entry of foreign substances (with drugs inclusive) into the brain. This barrier in healthy individuals is protective to the brain, disallowing noxious substances present in the blood to get to the brain while allowing for the exchange of small molecules into the brain by diffusion. However, BBB is disrupted under certain disease conditions, such as cerebrovascular diseases including acute ischemic stroke and intracerebral hemorrhage, and neurodegenerative disorders including multiple sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), and cancers. This review aims to provide a broad overview of present-day strategies for brain drug delivery, emphasizing novel delivery systems. Hopefully, this review would inspire scientists and researchers in the field of drug delivery across BBB to uncover new techniques and strategies to optimize drug delivery to the brain. Considering the anatomy, physiology, and pathophysiological functioning of the BBB in health and disease conditions, this review is focused on the controversies drawn from conclusions of recently published studies on issues such as the penetrability of nanoparticles into the brain, and whether active targeted drug delivery into the brain could be achieved with the use of nanoparticles. We also extended the review to cover novel non-nanoparticle strategies such as using viral and peptide vectors and other non-invasive techniques to enhance brain uptake of drugs.

Glycyrrhizin as a Nitric Oxide Regulator in Cancer Chemotherapy

Simple Summary

Glycyrrhizin (GL) has anti-cancer, anti-inflammatory, anti-viral, and anti-oxidant activity. In particular, GL reduces multidrug resistance (MDR) in cancer cells, which is a major obstacle to chemotherapy. Nitric oxide (NO) also plays an important role in MDR, and GL affects NO concentration in the tumor microenvironment. However, the effects of GL and NO interaction on MDR have not been reviewed. Here, we review the role of GL as an NO regulator in cancer cells and its subsequent anti-MDR effect and posit that GL is a promising MDR inhibitor for cancer chemotherapy.

Abstract

Chemotherapy is used widely for cancer treatment; however, the evolution of multidrug resistance (MDR) in many patients limits the therapeutic benefits of chemotherapy. It is important to overcome MDR for enhanced chemotherapy. ATP-dependent efflux of drugs out of cells is the main mechanism of MDR. Recent studies have suggested that nitric oxide (NO) can be used to overcome MDR by inhibiting the ATPase function of ATP-dependent pumps. Several attempts have been made to deliver NO to the tumor microenvironment (TME), however there are limitations in delivery. Glycyrrhizin (GL), an active compound of licorice, has been reported to both reduce the MDR effect by inhibiting ATP-dependent pumps and function as a regulator of NO production in the TME. In this review, we describe the potential role of GL as an NO regulator and MDR inhibitor that efficiently reduces the MDR effect in cancer chemotherapy.

Possible mechanisms of HIV neuro-infection in alcohol use: Interplay of oxidative stress, inflammation, and energy interruption

Alcohol use and HIV-1 infection have a pervasive impact on brain function, which extends to the requirement, distribution, and utilization of energy within the central nervous system. This effect on neuroenergetics may explain, in part, the exacerbation of HIV-1 disease under the influence of alcohol, particularly the persistence of HIV-associated neurological complications. The objective of this review article is to highlight the possible mechanisms of HIV/AIDS progression in alcohol users from the perspective of oxidative stress, neuroinflammation, and interruption of energy metabolism. These include the hallmark of sustained immune cell activation and high metabolic energy demand by HIV-1-infected cells in the central nervous system, with at-risk alcohol use. Here, we discussed the point that the increase in energy supply requirement by HIV-1-infected neuroimmune cells as well as the deterrence of nutrient uptake across the blood-brain barrier significantly depletes the energy source and neuro-environment homeostasis in the CNS. We also described the mechanistic idea that comorbidity of HIV-1 infection and alcohol use can cause a metabolic shift and redistribution of energy usage toward HIV-1-infected neuroimmune cells, as shown in neuropathological evidence. Under such an imbalanced neuro-environment, meaningless energy waste is expected in infected cells, along with unnecessary malnutrition in non-infected neuronal cells, which is likely to accelerate HIV neuro-infection progression in alcohol use. Thus, it will be important to consider the factor of nutrients/energy imbalance in formulating treatment strategies to help impede the progression of HIV-1 disease and associated neurological disorders in alcohol use.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Disease-Induced Modulation of Drug Transporters at the Blood-Brain Barrier Level

The blood–brain barrier (BBB) is a highly selective and restrictive semipermeable network of cells and blood vessel constituents. All components of the neurovascular unit give to the BBB its crucial and protective function, i.e., to regulate homeostasis in the central nervous system (CNS) by removing substances from the endothelial compartment and supplying the brain with nutrients and other endogenous compounds. Many transporters have been identified that play a role in maintaining BBB integrity and homeostasis. As such, the restrictive nature of the BBB provides an obstacle for drug delivery to the CNS. Nevertheless, according to their physicochemical or pharmacological properties, drugs may reach the CNS by passive diffusion or be subjected to putative influx and/or efflux through BBB membrane transporters, allowing or limiting their distribution to the CNS. Drug transporters functionally expressed on various compartments of the BBB involve numerous proteins from either the ATP-binding cassette (ABC) or the solute carrier (SLC) superfamilies. Pathophysiological stressors, age, and age-associated disorders may alter the expression level and functionality of transporter protein elements that modulate drug distribution and accumulation into the brain, namely, drug efficacy and toxicity. This review focuses and sheds light on the influence of inflammatory conditions and diseases such as Alzheimer’s disease, epilepsy, and stroke on the expression and functionality of the BBB drug transporters, the consequential modulation of drug distribution to the brain, and their impact on drug efficacy and toxicity.

68 Ga-Labelled Tropane Analogues for the Visualization of the Dopaminergic System

The development of radiometal-labelled pharmaceuticals for neuroimaging could offer great potential due to easier handling during labelling and availability through radionuclide generator systems. Nonetheless, to date, no such tracers are available for positron emission tomography, primarily owing to the challenge of crossing the blood-brain barrier (BBB) and loss of affinity through chelator attachment. We have prepared a variety of 68 Ga-labelled phenyltropanes showing that, through a simple hydrocarbon-linker, it is possible to introduce a chelator onto the lead structure while maintaining its high affinity for hDAT (human dopamine transporter) and simultaneously achieving adequate lipophilicity. One of the candidates, [68 Ga]Ga-HBED-hexadiyne-tropane, showed an IC50 value of 66 nM, together with a log D7.4 of 0.96. A μPET study in a hemi-parkinsonian rat model showed a fast wash-out of the tracer, and no specific uptake in the brain, thus implying an inability to penetrate the BBB.

Towards Deep Neural Network Models for the Prediction of the Blood-Brain Barrier Permeability for Diverse Organic Compounds

Permeation through the blood–brain barrier (BBB) is among the most important processes controlling the pharmacokinetic properties of drugs and other bioactive compounds. Using the fragmental (substructural) descriptors representing the occurrence number of various substructures, as well as the artificial neural network approach and the double cross-validation procedure, we have developed a predictive in silico LogBB model based on an extensive and verified dataset (529 compounds), which is applicable to diverse drugs and drug-like compounds. The model has good predictivity parameters (Q2=0.815, RMSEcv=0.318) that are similar to or better than those of the most reliable models available in the literature. Larger datasets, and perhaps more sophisticated network architectures, are required to realize the full potential of deep neural networks. The analysis of fragment contributions reveals patterns of influence consistent with the known concepts of structural characteristics that affect the BBB permeability of organic compounds. The external validation of the model confirms good agreement between the predicted and experimental LogBB values for most of the compounds. The model enables the evaluation and optimization of the BBB permeability of potential neuroactive agents and other drug compounds.

Modeling blood-brain barrier pathology in cerebrovascular disease in vitro: current and future paradigms

The complexity of the blood-brain barrier (BBB) and neurovascular unit (NVU) was and still is a challenge to bridge. A highly selective, restrictive and dynamic barrier, formed at the interface of blood and brain, the BBB is a "gatekeeper" and guardian of brain homeostasis and it also acts as a "sensor" of pathological events in blood and brain. The majority of brain and cerebrovascular pathologies are associated with BBB dysfunction, where changes at the BBB can lead to or support disease development. Thus, an ultimate goal of BBB research is to develop competent and highly translational models to understand mechanisms of BBB/NVU pathology and enable discovery and development of therapeutic strategies to improve vascular health and for the efficient delivery of drugs. This review article focuses on the progress being made to model BBB injury in cerebrovascular diseases in vitro.

Leveraging the Dynamic Blood-Brain Barrier for Central Nervous System Nanoparticle-based Drug Delivery Applications

Neurological diseases and injuries have profound impact on a patient's lifespan and functional capabilities, but often lack effective intervention strategies to address the underlying neuropathology. The blood-brain barrier (BBB) is a major hurdle in the effective delivery of therapeutics to the brain. Recent discoveries in BBB maintenance reveal a dynamic system where time of day, disease progression, and even biological variables all strongly influence its permeability and flux of molecules. Nanoparticles can be used to improve the efficacy of therapeutics by increasing circulation time, bioavailability, selectivity, and controlling the rate of payload release. Considering these recent findings, the next generation of pharmacological paradigms are evolving to leverage nanotechnology to turn therapeutic intervention to meet the needs of a specific patient (i.e. personalized medicine).

Approaches for encephalic drug delivery using nanomaterials: The current status

Nanotechnology, the investigation of little structures, ranging from the size of 1 nm-100 nm presents a breakthrough in the field of targeted drug delivery. The microvasculature in the human brain along with the blood brain barrier (BBB) offers high resistance to the entry of therapeutics agents and other substances in to the brain. Nanoparticles have certain advantages as high permeability, reactivity, surface area and quantum properties and it also meets various medical challenges which may include poor bioavailability, difficulty in targeting, organ toxicity etc. The use of nanoparticles in pharmaceuticals has been inspired by various natural nanomaterials found in the body, which includes proteins, lipids etc. A brief explanation of different types of pharmaceutical approaches used in brain drug delivery is discussed here. Nanotechnology is used treatment of many illnesses which also include diseases related to the brain such as gliomas, epilepsy, migraine, cerebrovascular disease, Parkinson's disease etc., Different type of nanoparticles are prepared, such as polymer-based nanoparticles, metallic nanoparticles, carbon-based nanoparticles, lipid-based nanoparticles, ceramic nanoparticles semiconductor nanoparticles and are studied for their usefulness in drug delivery. The primary function of nanoparticles is to deliver drug moiety to the desired targeted site by overcoming permeability issues. The shape, size and surface area nanoparticles help in increasing the bioavailability, drug retention and multiple drug delivery. Mechanisms of nanoparticles crossing BBB can be divided into passive and active transport, are briefly explained.

Specific targeting cancer cells with nanoparticles and drug delivery in cancer therapy

Nanotechnology has been the latest approach for diagnosis and treatment for cancer, which opens up a new alternative therapeutic drug delivery option to treat disease. Nanoparticles (NPs) display a broad role in cancer diagnosis and has various advantages over the other conventional chemotherapeutic drug delivery. NPs possess more specific and efficient drug delivery to the targeted tissue, cell, or organs and minimize the risk of side effects. NPs undergo passive and active mode of drug targets to tumor area with less elimination of the drug from the system. Size and surface characteristics of nanoparticles play a crucial role in modulating nanocarrier efficiency and the biodistribution of chemo drugs in the body. Several types of nanocarriers, such as polymers, dendrimers, liposome-based, and carbon-based, are studied widely in cancer therapy. Although FDA approved very few nanotechnology drugs for cancer therapy, a large number of studies are undergoing for the development of novel nanocarriers for potent cancer therapy. In this review, we discuss the details of the nano-based therapeutics and diagnostics strategies, and the potential use of nanomedicines in cancer therapy and cancer drug delivery.

STS-NLSP: A Network-Based Label Space Partition Method for Predicting the Specificity of Membrane Transporter Substrates Using a Hybrid Feature of Structural and Semantic Similarity

Membrane transport proteins play crucial roles in the pharmacokinetics of substrate drugs, the drug resistance in cancer and are vital to the process of drug discovery, development and anti-cancer therapeutics. However, experimental methods to profile a substrate drug against a panel of transporters to determine its specificity are labor intensive and time consuming. In this article, we aim to develop an in silico multi-label classification approach to predict whether a substrate can specifically recognize one of the 13 categories of drug transporters ranging from ATP-binding cassette to solute carrier families using both structural fingerprints and chemical ontologies information of substrates. The data-driven network-based label space partition (NLSP) method was utilized to construct the model based on a hybrid of similarity-based feature by the integration of 2D fingerprint and semantic similarity. This method builds predictors for each label cluster (possibly intersecting) detected by community detection algorithms and takes union of label sets for a compound as final prediction. NLSP lies into the ensembles of multi-label classifier category in multi-label learning field. We utilized Cramér's V statistics to quantify the label correlations and depicted them via a heatmap. The jackknife tests and iterative stratification based cross-validation method were adopted on a benchmark dataset to evaluate the prediction performance of the proposed models both in multi-label and label-wise manner. Compared with other powerful multi-label methods, ML-kNN, MTSVM, and RAkELd, our multi-label classification model of NLPS-RF (random forest-based NLSP) has proven to be a feasible and effective model, and performed satisfactorily in the predictive task of transporter-substrate specificity. The idea behind NLSP method is intriguing and the power of NLSP remains to be explored for the multi-label learning problems in bioinformatics. The benchmark dataset, intermediate results and python code which can fully reproduce our experiments and results are available at https://github.com/dqwei-lab/STS.

Barriers to Effective Drug Treatment for Brain Metastases: A Multifactorial Problem in the Delivery of Precision Medicine

The treatment of metastatic lesions in the brain represents a serious unmet medical need in the field of neuro-oncology. Even though many effective compounds have demonstrated success in treating peripheral (non-CNS) tumors with targeted agents, one aspect of this lack of success in the brain may be related to poor delivery of otherwise effective compounds. Many factors can influence the brain delivery of these agents, but one key barrier is a heterogeneously "leaky" BBB that expresses efflux transporters that limit the BBB permeability for many targeted agents. Future success in therapeutics for brain metastases must take into account the adequate delivery of "active, free drug" to the target, and may include combinations of targeted drugs that are appropriate to address each individual patient's tumor type. This review discusses some issues that are pertinent to precision medicine for brain metastases, using specific examples of tumor types that have a high incidence of brain metastases.

Cell Migration Related to MDR-Another Impediment to Effective Chemotherapy?

Multidrug resistance, mediated by members of the ATP-binding cassette (ABC) proteins superfamily, has become one of the biggest obstacles in conquering tumour progression. If the chemotherapy outcome is considered successful, when the primary tumour volume is decreased or completely abolished, modulation of ABC proteins activity is one of the best methods to overcome drug resistance. However, if a positive outcome is represented by no metastasis or, at least, elongation of remission-free time, then the positive effect of ABC proteins inhibition should be compared with the several side effects it causes, which may inflict cancer progression and decrease overall patient health. Clinical trials conducted thus far have shown that the tested ABC modulators add limited or no benefits to cancer patients, as some of them are merely toxic and others induce unwanted drug–drug interactions. Moreover, the inhibition of certain ABC members has been recently indicated as potentially responsible for increased fibroblasts migration. A better understanding of the complex role of ABC proteins in relation to cancer progression may offer novel strategies in cancer therapy.

Current Strategies for Brain Drug Delivery

The blood-brain barrier (BBB) has been a great hurdle for brain drug delivery. The BBB in healthy brain is a diffusion barrier essential for protecting normal brain function by impeding most compounds from transiting from the blood to the brain; only small molecules can cross the BBB. Under certain pathological conditions of diseases such as stroke, diabetes, seizures, multiple sclerosis, Parkinson's disease and Alzheimer disease, the BBB is disrupted. The objective of this review is to provide a broad overview on current strategies for brain drug delivery and related subjects from the past five years. It is hoped that this review could inspire readers to discover possible approaches to deliver drugs into the brain. After an initial overview of the BBB structure and function in both healthy and pathological conditions, this review re-visits, according to recent publications, some questions that are controversial, such as whether nanoparticles by themselves could cross the BBB and whether drugs are specifically transferred to the brain by actively targeted nanoparticles. Current non-nanoparticle strategies are also reviewed, such as delivery of drugs through the permeable BBB under pathological conditions and using non-invasive techniques to enhance brain drug uptake. Finally, one particular area that is often neglected in brain drug delivery is the influence of aging on the BBB, which is captured in this review based on the limited studies in the literature.

Selective Fusion of Heterogeneous Classifiers for Predicting Substrates of Membrane Transporters

Membrane transporters play a crucial role in determining fate of administered drugs in a biological system. Early identification of plausible transporters for a drug molecule can provide insights into its therapeutic, pharmacokinetic, and toxicological profiles. In the present study, predictive models for classifying small molecules into substrates and nonsubstrates of various pharmaceutically important membrane transporters were developed using quantitative structure-activity relationship (QSAR) and proteochemometric (PCM) approaches. For this purpose, 4575 substrate interactions for these transporters were collected from the Metabolism and Transport Database (Metrabase) and the literature. The transporters selected for this study include (i) six efflux transporters, viz., breast cancer resistance protein (BCRP/ABCG2), P-glycoprotein (P-gp/MDR1), and multidrug resistance proteins (MRP1, MRP2, MRP3, and MRP4), and (ii) seven influx transporters, viz., organic cation transporter (OCT1/SO22A1), peptide transporter (PEPT1/SO15A1), apical sodium-bile acid transporter (ASBT/NTCP2), and organic anion transporting peptides (OATP1A2/SO1A2, OATP1B/SO1B1, OATP1B3/SO1B3, and OATP2B1/SO2B1). Various types of descriptors and machine learning methods (classifiers) were evaluated for the development of robust predictive models. Additionally, ensemble models were developed by bagging of homogeneous classifiers and selective fusion of heterogeneous classifiers. It was observed that the latter approach improves the accuracy of substrate/nonsubstrate prediction for transporters (average correct classification rate of more than 0.80 for external validation). Moreover, structural fragments important in determining the substrate specificity across the various transporters were identified. To demonstrate these fragments on the query molecule, contour maps were generated. The prediction efficacy of the developed models was illustrated by a good correlation between the reported logBB value of a molecule and its predicted substrate propensity for blood-brain barrier transporters. Conclusively, this comprehensive modeling analysis can be efficiently employed for the prediction of membrane transporters of a drug, thereby providing insights into its pharmacological profile.