CNSMolGen: A Bidirectional Recurrent Neural Network-Based Generative Model for De Novo Central Nervous System Drug Design.

Central nervous system (CNS) drugs have had a significant impact on treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models have shown great potential for accelerating drug discovery and improving efficacy. However, specific applications of these techniques in CNS drug discovery have not been widely reported. In this study, we developed the CNSMolGen model, which uses a framework of bidirectional recurrent neural networks (Bi-RNNs) for de novo molecular design of CNS drugs. Results showed that the pretrained model was able to generate more than 90% of completely new molecular structures, which possessed the properties of CNS drug molecules and were synthesizable. In addition, transfer learning was performed on small data sets with specific biological activities to evaluate the potential application of the model for CNS drug optimization. Here, we used drugs against the classical CNS disease target serotonin transporter (SERT) as a fine-tuned data set and generated a focused database against the target protein. The potential biological activities of the generated molecules were verified by using the physics-based induced-fit docking study. The success of this model demonstrates its potential in CNS drug design and optimization, which provides a new impetus for future CNS drug development.

Machine Learning Exploration of the Relationship Between Drugs and the Blood-Brain Barrier: Guiding Molecular Modification.

OBJECTIVE: This study aimed to improve the efficiency of pharmacotherapy for CNS diseases by optimizing the ability of drug molecules to penetrate the Blood-Brain Barrier (BBB). METHODS: We established qualitative and quantitative databases of the ADME properties of drugs and derived characteristic features of compounds with efficient BBB penetration. Using these insights, we developed four machine learning models to predict a drug's BBB permeability by assessing ADME properties and molecular topology. We then validated the models using the B3DB database. For acyclovir and ceftriaxone, we modified the Hydrogen Bond Donors and Acceptors, and evaluated the BBB permeability using the predictive model. RESULTS: The machine learning models performed well in predicting BBB permeability on both internal and external validation sets. Reducing the number of Hydrogen Bond Donors and Acceptors generally improves BBB permeability. Modification only enhanced BBB penetration in the case of acyclovir and not ceftriaxone. CONCLUSIONS: The machine learning models developed can accurately predict BBB permeability, and many drug molecules are likely to have increased BBB penetration if the number of Hydrogen Bond Donors and Acceptors are reduced. These findings suggest that molecular modifications can enhance the efficacy of CNS drugs and provide practical strategies for drug design and development. This is particularly relevant for improving drug penetration of the BBB.

Empirical Analysis of Drug Targets for Nervous System Disorders.

The discovery and development of drugs to treat diseases of the nervous system remains challenging. There is a higher attrition rate in the clinical stage for nervous system experimental drugs compared to other disease areas. In the preclinical stage, additional challenges arise from the considerable effort required to find molecules that penetrate the blood-brain barrier (BBB) coupled with the poor predictive value of many preclinical models of nervous system diseases. In the era of target-based drug discovery, the critical first step of drug discovery projects is the selection of a therapeutic target which is largely driven by its presumed pathogenic involvement. For nervous system diseases, however, the feasibility of identifying potent molecules within the stringent range of molecular properties necessary for BBB penetration should represent another important factor in target selection. To address the latter, the present review analyzes the distribution of human protein targets of FDA-approved drugs for nervous system disorders and compares it with drugs for other disease areas. We observed a substantial difference in the distribution of therapeutic targets across the two clusters. We expanded on this finding by analyzing the physicochemical properties of nervous and non-nervous system drugs in each target class by using the central nervous system multiparameter optimization (CNS MPO) algorithm. These data may serve as useful guidance in making more informed decisions when selecting therapeutic targets for nervous system disorders.

An In Silico Explainable Multiparameter Optimization Approach for De Novo Drug Design against Proteins from the Central Nervous System.

The aim of drug design and development is to produce a drug that can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multistep process where different parameters such as activity and physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method that can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was applied against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which act as an additional challenge for CNS drug delivery. The contribution of each parameter toward molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design similar inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.

Bioactive properties of the aromatic molecules of spearmint (Mentha spicata L.) essential oil: a review.

Spearmint belongs to the genus Mentha in the family Labiatae (Lamiaceae), which is cultivated worldwide for its remarkable aroma and commercial value. The aromatic molecules of spearmint essential oil, including carvone, carveol, dihydrocarvone, dihydrocarveol and dihydrocarvyl acetate, have been widely used in the flavors and fragrances industry. Besides their traditional use, these aromatic molecules have attracted great interest in other application fields (e.g., medicine, agriculture, food, and beverages) especially due to their antimicrobial, antioxidant, insecticidal, antitumor, anti-inflammatory and antidiabetic activities. This review presents the sources, properties, synthesis and application of spearmint aromatic molecules. Furthermore, this review focuses on the biological properties so far described for these compounds, their therapeutic effect on some diseases, and future directions of research. This review will, therefore, contribute to the rational and economic exploration of spearmint aromatic molecules as natural and safe alternative therapeutics.

Injectable and Self-Healing Hydrogels with Double-Dynamic Bond Tunable Mechanical, Gel-Sol Transition and Drug Delivery Properties for Promoting Periodontium Regeneration in Periodontitis.

Injection of a hydrogel loaded with drugs with simultaneous anti-inflammatory and tissue regenerating properties can be an effective treatment for promoting periodontal regeneration in periodontitis. Nevertheless, the design and preparation of an injectable hydrogel with self-healing properties for tunable sustained drug release is still highly desired. In this work, polysaccharide-based hydrogels were formed by a dynamic cross-linked network of dynamic Schiff base bonds and dynamic coordination bonds. The hydrogels showed a quick gelation process, injectability, and excellent self-healing properties. In particular, the hydrogels formed by a double-dynamic network would undergo a gel-sol transition process without external stimuli. And the gel-sol transition time could be tuned by the double-dynamic network structure for in situ stimuli involving a change in its own molecular structure. Moreover, the drug delivery properties were also tunable owing to the gel-sol transition process. Sustained drug release characteristics, which were ascribed to a diffusion process, were observed during the first stage of drug release, and complete drug release owing to the gel-sol transition process was achieved. The sustained drug release time could be tuned according to the double-dynamic bonds in the hydrogel. The CCK-8 assay was used to evaluate the cytotoxicity, and the result showed no cytotoxicity, indicating that the injectable and self-healing hydrogels with double-dynamic bond tunable gel-sol transition could be safely used in controlled drug delivery for periodontal disease therapy. Finally, the promotion of periodontal regeneration in periodontitis in vivo was investigated using hydrogels loaded with ginsenoside Rg1 and amelogenin. Micro-CT and histological analyses indicated that the hydrogels were promising candidates for addressing the practical needs of a tunable drug delivery method for promoting periodontal regeneration in periodontitis.

Prodrug strategy for enhanced therapy of central nervous system disease.

Central nervous system (CNS) disease is one of the most notorious arch-criminals of human health across the world. Although considerable efforts have been devoted to promote the development of CNS drugs, ideal therapeutical effects are yet far from enough. The blood-brain barrier remains a major player that impedes the full potential of CNS therapeutical agents as it blocks the entry of CNS drugs into the brain. The past few decades have witnessed the upspring of prodrug strategies as a promising method to accelerate CNS drug development. The prodrug strategy with the ability to overcome the formidable blood-brain barrier enhances the delivery to the brain and hence improves the effects of the CNS therapeutics. In this Feature Article, we summarize the reported barriers and strategies for CNS therapeutics and spotlight prodrug design strategies to improve the efficiency of crossing the blood-brain barrier.

Sorption of Neuropsychopharmaca in Microfluidic Materials for In Vitro Studies.

Sorption (i.e., adsorption and absorption) of small-molecule compounds to polydimethylsiloxane (PDMS) is a widely acknowledged phenomenon. However, studies to date have largely been conducted under atypical conditions for microfluidic applications (lack of perfusion, lack of biological fluids, etc.), especially considering biological studies such as organs-on-chips where small-molecule sorption poses the largest concern. Here, we present an in-depth study of small-molecule sorption under relevant conditions for microphysiological systems, focusing on a standard geometry for biological barrier studies that find application in pharmacokinetics. We specifically assess the sorption of a broad compound panel including 15 neuropsychopharmaca at in vivo concentration levels. We consider devices constructed from PDMS as well as two material alternatives (off-stoichiometry thiol-ene-epoxy, or tape/polycarbonate laminates). Moreover, we study the much neglected impact of peristaltic pump tubing, an essential component of the recirculating systems required to achieve in vivo-like perfusion shear stresses. We find that the choice of the device material does not have a significant impact on the sorption behavior in our barrier-on-chip-type system. Our PDMS observations in particular suggest that excessive compound sorption observed in prior studies is not sufficiently described by compound hydrophobicity or other suggested predictors. Critically, we show that sorption by peristaltic tubing, including the commonly utilized PharMed BPT, dominates over device sorption even on an area-normalized basis, let alone at the typically much larger tubing surface areas. Our findings highlight the importance of validating compound dosages in organ-on-chip studies, as well as the need for considering tubing materials with equal or higher care than device materials.

Strategies for Structural Modification of Small Molecules to Improve Blood-Brain Barrier Penetration: A Recent Perspective.

In the development of central nervous system (CNS) drugs, the blood-brain barrier (BBB) restricts many drugs from entering the brain to exert therapeutic effects. Although many novel delivery methods of large molecule drugs have been designed to assist transport, small molecule drugs account for the vast majority of the CNS drugs used clinically. From this perspective, we review studies from the past five years that have sought to modify small molecules to increase brain exposure. Medicinal chemists make it easier for small molecules to cross the BBB by improving diffusion, reducing efflux, and activating carrier transporters. On the basis of their excellent work, we summarize strategies for structural modification of small molecules to improve BBB penetration. These strategies are expected to provide a reference for the future development of small molecule CNS drugs.

Nanotechnology-based drug delivery for central nervous system disorders.

Drug delivery to central nervous system (CNS) diseases is very challenging since the presence of the innate blood-brain barrier (BBB) and the blood-cerebrospinal fluid barrier that impede drug delivery. Among new strategies to overcome these limitations and successfully deliver drugs to the CNS, nanotechnology-based drug delivery platform, offers potential therapeutic approach for the treatment of some common neurological disorders like Alzheimer's disease, frontotemporal dementia, amyotrophic lateral sclerosis, Parkinson's disease, Huntington's disease. This review aimed to highlight advances in research on the development of nano-based therapeutics for their implications in therapy of CNS disorders. The challenges during clinical translation of nanomedicine from bench to bed side is also discussed.

Multifunctional Arylsulfone and Arylsulfonamide-Based Ligands with Prominent Mood-Modulating Activity and Benign Safety Profile, Targeting Neuropsychiatric Symptoms of Dementia.

The current pharmaceutical market lacks therapeutic agents designed to modulate behavioral disturbances associated with dementia. To address this unmet medical need, we designed multifunctional ligands characterized by a nanomolar affinity for clinically relevant targets that are associated with the disease pathology, namely, the 5-HT2A/6/7 and D2 receptors. Compounds that exhibited favorable functional efficacy, water solubility, and metabolic stability were selected for more detailed study. Pharmacological profiling revealed that compound 11 exerted pronounced antidepressant activity (MED 0.1 mg/kg), outperforming commonly available antidepressant drugs, while compound 16 elicited a robust anxiolytic activity (MED 1 mg/kg), exceeding comparator anxiolytics. In contrast to the existing psychotropic agents tested, the novel chemotypes did not negatively impact cognition. At a chronic dose regimen (25 days), 11 did not induce significant metabolic or adverse blood pressure disturbances. These promising therapeutic-like activities and benign safety profiles make the novel chemotypes potential treatment options for dementia patients.

Antinociceptive and Discriminative Stimulus Effects of Six Novel Psychoactive Opioid Substances in Male Rats.

Compounds with novel or fentanyl-like structures continue to appear on the illicit drug market and have been responsible for fatalities, yet there are limited preclinical pharmacological data available to evaluate the risk of these compounds to public health. The purpose of the present study was to examine acetyl fentanyl, butyryl fentanyl, 3,4-dichloro-N-[[1-(dimethylamino)cyclohexyl]methyl]benzamide (AH-7921), 1-cyclohexyl-4-(1,2-diphenylethyl)piperazine (MT-45), 4-chloro-N-[1-(2-phenylethyl)-2-piperidinylidene]-benzenesulfonamide (W-15), and 4-chloro-N-[1-[2-(4-nitrophenyl)ethyl]-2-piperidinylidene]-benzenesulfonamide (W-18) for their relative potency to reference opioids and their susceptibility to naltrexone antagonism using the 55oC warm-water, tail-withdrawal assay of antinociception and a morphine drug discrimination assay in male, Sprague-Dawley rats. In the antinociception assay, groups of 8 rats per drug were placed into restraining tubes, their tails were immersed into 40o or 55oC water, and the latency for tail withdrawal was measured with a cutoff time of 15 seconds. In the drug discrimination assay, rats (n = 11) were trained to discriminate between 3.2 mg/kg morphine and saline, subcutaneously, in a two-choice, drug discrimination procedure under a fixed ratio-5 schedule of sucrose pellet delivery. Morphine, fentanyl, and four of the synthetic opioids dose dependently produced antinociception and fully substituted for morphine in the drug discrimination assay with the following rank order of potency: fentanyl > butyryl fentanyl > acetyl fentanyl > AH-7921 > MT45 > morphine. All drugs that produced antinociception or morphine-like discriminative stimulus effects were blocked by naltrexone. W-15 and W-18 did not show antinociceptive or morphine-like discriminative stimulus effects at the doses tested supporting a lack of opioid activity for these two compounds. These findings suggest that butyryl fentanyl, acetyl fentanyl, AH-7941, and MT-45 have abuse liability like other opioid agonists. SIGNIFICANCE STATEMENT: As novel psychoactive substances appear on the illicit drug market, preclinical pharmacological testing is required to assist law enforcement, medical professionals, and legal regulators with decisions about potential public health risks. In this study, four synthetic opioids, acetyl fentanyl, butyryl fentanyl, AH-7921, and MT-45 produced effects similar to fentanyl and morphine and were blocked by naltrexone. These data suggest the four synthetic opioids possess similar abuse liability risks as typical opioid agonists.

Development of an a priori computational approach for brain uptake of compounds in an insect model system.

Delivery of compounds to the brain is critical for the development of effective treatment therapies of multiple central nervous system diseases. Recently a novel insect-based brain uptake model was published utilizing a locust brain ex vivo system. The goal of our study was to develop a priori, in silico cheminformatic models to describe brain uptake in this insect model, as well as evaluate the predictive ability. The machine learning program Orange® was used to evaluate several machine learning (ML) models on a published data set of 25 known drugs, with in vitro data generated by a single laboratory group to reduce inherent inter-laboratory variability. The ML models included in this study were linear regression (LR), support vector machines (SVN), k-nearest neighbor (kNN) and neural nets (NN). The quantitative structure-property relationship models were able to correlate experimental logCtot (concentration of compound in brain) and predicted brain uptake of r2 > 0.5, with the descriptors log(P*MW-0.5) and hydrogen bond donor used in LR, SVN and KNN, while log(P*MW-0.5) and total polar surface area (TPSA) descriptors used in the NN models. Our results indicate that the locust insect model is amenable to data mining chemoinformatics and in silico model development in CNS drug discovery pipelines.

Computer Assisted Models for Blood Brain Barrier Permeation of 1, 5-Benzodiazepines.

AIM: To generate and validate predictive models for blood-brain permeation (BBB) of CNS molecules using the QSPR approach. BACKGROUND: Prediction of molecules crossing BBB remains a challenge in drug delivery. Predictive models are designed for the evaluation of a set of preclinical drugs which may serve as alternatives for determining BBB permeation by experimentation. OBJECTIVE: The objective of the present study was to generate QSPR models for the permeation of CNS molecules across BBB and its validation using existing in-house leads. METHODS: The present study envisaged the determination of the set of molecular descriptors which are considered significant correlative factors for BBB permeation property. Quantitative Structure- Property Relationship (QSPR) approach was followed to describe the correlation between identified descriptors for 45 molecules and highest, moderate and least BBB permeation data. The molecular descriptors were selected based on drug-likeness, hydrophilicity, hydrophobicity, polar surface area, etc. of molecules that served the highest correlation with BBB permeation. The experimental data in terms of log BB were collected from available literature, subjected to 2D-QSPR model generation using a regression analysis method like Multiple Linear Regression (MLR). RESULTS: The best QSPR model was Model 3, which exhibited regression coefficient as R2= 0.89, F = 36; Q2= 0.7805 and properties such as polar surface area, hydrophobic hydrophilic distance, electronegativity, etc., which were considered key parameters in the determination of the BBB permeability. The developed QSPR models were validated with in-house 1,5-benzodiazepines molecules and correlation studies were conducted between experimental and predicted BBB permeability. CONCLUSION: The QSPR model 3 showed predictive results that were in good agreements with experimental results for blood-brain permeation. Thus, this model was found to be satisfactory in achieving a good correlation between selected descriptors and BBB permeation for benzodiazepines and tricyclic compounds.

Neuropharmacological potentials of ß-carboline alkaloids for neuropsychiatric disorders.

Neuropsychiatric disorders are diseases of the central nervous system (CNS) which are characterised by complex pathomechanisms that including homeostatic failure, malfunction, atrophy, pathology remodelling and reactivity anomaly of the neuronal system where treatment options remain challenging. ß-Carboline (ßC) alkaloids are scaffolds of structurally diverse tricyclic pyrido[3,4-b]indole alkaloid with vast occurrence in nature. Their unique structural features which favour interactions with enzymes and protein receptor targets account for their potent neuropharmacological properties. However, our current understanding of their biological mechanisms for these beneficial effects, especially for neuropsychiatric disorders is sparse. Therefore, we present a comprehensive review of the scientific progress in the last two decades on the prospective pharmacology and physiology of the ßC alkaloids in the treatment of some neuropsychiatric conditions such as depression, anxiety, Alzheimer's disease, Parkinson's disease, brain tumour, essential tremor, epilepsy and seizure, licking behaviour, dystonia, agnosia, spasm, positive ingestive response as demonstrated in non-clinical models. The current evidence supports that ßC alkaloids offer potential therapeutic agents against most of these disorders and amenable for further drug design.

How to perform spectrum-based LC-HR-MS screening for more than 1,000 NPS with HighResNPS consensus fragment ions.

INTRODUCTION: The ever-changing market of new psychoactive substances (NPS) poses challenges for laboratories worldwide. Analytical toxicologists are constantly working to keep high-resolution mass spectrometry (HR-MS) screening libraries updated for NPS. This study sought to use the online crowd-sourced HighResNPS database for spectrum comparison screening, thereby broadening its utility to all HR-MS instruments. METHOD: HighResNPS allows formation of a set of consensus fragment ions for a NPS and prioritises among multiple entries of collision-induced fragment ions. A subset of 42 NPS samples was analysed in data-independent acquisition (DIA) and data-dependent acquisition (DDA) modes on two different instruments. HighResNPS-computed spectra were generated with either Absolute (all fragment ions set to 100%) or Fractional (50% intensity reduction of former fragment ion) intensity. The acquired NPS data were analysed using the consensus library with computed ion intensities and evaluated with vendor-neutral screening software. RESULTS: Overall, of the 42 samples, 100% were identified, with 88% identified as the top candidate. Three samples had the correct candidate proposed as the second highest ranking NPS. In all three of those samples, the top proposed candidate was a positional isomer or closely related compound. Absolute intensity assignment provided identical scoring between the top two proposed compounds in two samples with DIA. DDA had a slightly higher identification rate in the spectra comparison screening with fractional intensity assignment, but no major differences were observed. CONCLUSION: The fractional intensity assignment was slightly more advantageous than the absolute assignment. It was selective between proposed candidates, showed a high identification rate and had an overall higher fragmentation scoring. The candidates proposed by the HighResNPS library spectra comparison simplify the determination of NPS for researchers and toxicologists. The database provides free monthly updates of consensus spectra, thereby enabling laboratories to stay at the forefront of NPS screening by LC-HR-MS with spectra screening software.

Neurotensins and their therapeutic potential: research field study.

The natural tridecapeptide neurotensin has been emerged as a promising therapeutic scaffold for the treatment of neurological diseases and cancer. In this work, we aimed to identify the top 100 most cited original research papers as well as recent key studies related to neurotensins. The Web of Science Core Collection database was searched and the retrieved research articles were analyzed by using the VOSviewer software. The most cited original articles were published between 1973 and 2013. The top-cited article was by Carraway and Leeman reporting the discovery of neurotensin in 1973. The highly cited terms were associated with hypotension and angiotensin-converting-enzyme. The conducted analysis reveals the therapeutic potentials of neurotensin, and further impactful research toward its clinical development is warrantied.

Novel Tetrafunctional Probes Identify Target Receptors and Binding Sites of Small-Molecule Drugs from Living Systems.

Significant advancement of chemoproteomics has contributed to uncovering the mechanism of action (MoA) of small-molecule drugs by characterizing drug-protein interactions in living systems. However, cell-membrane proteins such as G protein-coupled receptors (GPCRs) and ion channels, due to their low abundance and unique biophysical properties associated with multiple transmembrane domains, can present challenges for proteome-wide mapping of drug-receptor interactions. Herein, we describe the development of novel tetrafunctional probes, consisting of (1) a ligand of interest, (2) 2-aryl-5-carboxytetrazole (ACT) as a photoreactive group, (3) a hydrazine-labile cleavable linker, and (4) biotin for enrichment. In live cell labeling studies, we demonstrated that the ACT-based probe showed superior reactivity and selectivity for labeling on-target GPCR by mass spectrometry analysis compared with control probes including diazirine-based probes. By leveraging ACT-based cleavable probes, we further identified a set of representative ionotropic receptors, targeted by CNS drugs, with remarkable selectivity and precise binding site information from mouse brain slices. We anticipate that the robust chemoproteomic platform using the ACT-based cleavable probe coupled with phenotypic screening should promote identification of pharmacologically relevant target receptors of drug candidates and ultimately development of first-in-class drugs with novel MoA.

Design, synthesis and neuropharmacological evaluation of new 2,4-disubstituted-1,5-benzodiazepines as CNS active agents.

Benzodiazepines (BZDs) represent a class of privilege scaffold in the modern era of medicinal chemistry as CNS active agents and BZD based drugs are used to treat different psychotic disorders. Inspired from the therapeutic potential of BZDs as promising CNS active agents, in the present work three different series of 1,5-benzodiazepines bearing various substitutions at position 2 and 4 of the benzodiazepine core were synthesized by condensing different substituted chalcones with o-phenylenediamine in the presence of piperidine as a base catalyst. Structural characterization of title compounds was done by using various analytical techniques such as IR, NMR, elemental analysis and mass spectral data. All the synthesized compounds (9a-d, 10a-e and 11a-c) were subjected to in vivo neuropharmacological studies to evaluate their CNS depressant and antiepileptic activity. Results of in vivo evaluation data showed that analogue 11b exhibited potent CNS depressant activity which was comparable to the standard drug diazepam. Compounds 10b and 10c displayed significant antiepileptic activity however they were less potent than the standard drug phenobarbitone. Molecular docking studies were performed using MOE software to find the interaction pattern and binding mode at the GABAA receptor (PDB Id: 6HUP). The results of the docking studies were in good agreement with the observed in vivo activity and revealed the satisfactory binding mode of the compounds within the binding site of the protein. The docking scores for the most promising candidates 10c, 11b and Diazepam were found to be -9.18, -9.46 and -9.88, respectively. Further, the compounds showed compliance with the Lipinski's 'rule of five' and exhibited favourable drug-likeness scores. The identified leads can be explored further for the design and development of new BZD based psychotropic agents.

Smart Strategies for Therapeutic Agent Delivery into Brain across the Blood-Brain Barrier Using Receptor-Mediated Transcytosis.

Discriminatory drug delivery into target cells is essential to effectively elicit the drug activity and to avoid off-target side effects; however, transporting drugs across the cell membrane is difficult due to factors such as molecular size, hydrophilicity, intercellular adhesiveness, and efflux transporters, particularly, in the brain capillary endothelial cells. Drug delivery into the brain is blocked by the blood-brain barrier (BBB). Thus, developing drugs for the central nervous system (CNS) diseases remains a challenge. The approach based on receptor-mediated transcytosis (RMT) can overcome this impassable problem at the BBB. Well-designed molecules for RMT form conjugates with the ligand and drugs via linkers or nanoparticles. Cell penetrating peptides (CPPs), receptor-targeting peptides, and monoclonal antibodies (mAbs) are often used as ligands. The binding of ligand to the receptor on the endothelial cell surface induces endocytosis. Existing exosomes comprising the conjugates move in the cytoplasm and fuse with the opposite plasma membrane to release them. Subsequently, the transcytosed conjugate-loaded drugs or released drugs from the conjugates elicit activity in the brain. As receptors, transferrin receptor (TfR), low-density lipoprotein receptor (LDLR), and insulin receptor (InsR) have been used to intendedly induce transcytosis. Presently, several clinical trials on CNS drugs for Alzheimer's and Parkinson disease are hindered due to poor drug distribution into the brain. Therefore, this strategy based on RMT is a promising method for CNS drugs to be transported into the brain. In this review, I introduce the practicality and possibility of drug delivery into brain across the BBB using RMT.