A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations

Application of in vitro PAMPA technique and in silico computational methods for blood-brain barrier permeability prediction of novel CNS drug candidates

Permeability assessment of small molecules through the blood-brain barrier (BBB) plays a significant role in the development of effective central nervous system (CNS) drug candidates. Since in vivo methods for BBB permeability estimation require a lot of time and resources, in silico and in vitro approaches are becoming increasingly popular nowadays for faster and more economical predictions in early phases of drug discovery. In this work, through application of in vitro parallel artificial membrane permeability assay (PAMPA-BBB) and in silico computational methods we aimed to examine the passive permeability of eighteen compounds, which affect serotonin and dopamine levels in the CNS. The data set was consisted of novel six human dopamine transporter (hDAT) substrates that were previously identified as the most promising lead compounds for further optimisation to achieve neuroprotective effect, twelve approved CNS drugs, and their related compounds. Firstly, PAMPA methods was used to experimentally determine effective BBB permeability (P_e) for all studied compounds and obtained results were further submitted for quantitative structure permeability relationship (QSPR) analysis. QSPR models were built by using three different statistical methods: stepwise multiple linear regression (MLR), partial least square (PLS), and support-vector machine (SVM), while their predictive capability was tested through internal and external validation. Obtained statistical parameters (MLR- R²_pred=-0.10; PLS- R²_pred=0.64, r²_m=0.69, r^/2_m=0.44; SVM- R²_pred=0.57, r²_m=0.72, r^/2_m=0.55) indicated that the SVM model is superior over others. The most important molecular descriptors (H₀p and SolvEMt_3D) were identified and used to propose structural modifications of the examined compounds in order to improve their BBB permeability. Moreover, steered molecular dynamics (SMD) simulation was employed to comprehensively investigate the permeability pathway of compounds through a lipid bilayer. Taken together, the created QSPR model could be used as a reliable and fast pre-screening tool for BBB permeability prediction of structurally related CNS compounds, while performed MD simulations provide a good foundation for future in silico examination.

Ultrathin 2D Titanium Carbide MXene (Ti3 C2 Tx ) Nanoflakes Activate WNT/HIF-1α-Mediated Metabolism Reprogramming for Periodontal Regeneration

Periodontal defect regeneration in severe periodontitis relies on the differentiation and proliferation of periodontal ligament cells (PDLCs). Recently, an emerging 2D nanomaterial, MXene (Ti₃ C₂ T_x ), has gained more and more attention due to the extensive antibacterial and anticancer activity, while its potential biomedical application on tissue regeneration remains unclear. Through a combination of experimental and multiscale simulation schemes, Ti₃ C₂ T_x has exhibited satisfactory biocompatibility and induced distinguish osteogenic differentiation of human PDLCs (hPDLCs), with upregulated osteogenesis-related genes. Ti₃ C₂ T_x manages to activate the Wnt/β-catenin signaling pathway by enhancing the Wnt-Frizzled complex binding, thus stabilizing HIF-1α and altering metabolic reprogramming into glycolysis. In vivo, hPDLCs pretreated by Ti₃ C₂ T_x display excellent performance in new bone formation and osteoclast inhibition with enhanced RUNX2, HIF-1α, and β-catenin in an experimental rat model of periodontal fenestration defects, indicating that this material has high efficiency of periodontal regeneration promotion. It is demonstrated in this work that Ti₃ C₂ T_x has highly efficient therapeutic effects in osteogenic differentiation and periodontal defect repairment.

A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limited chemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical log BB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB and https://doi.org/10.6084/m9.figshare.15634230.v3 (version 3). We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.

Scaffold Searching of FDA and EMA-Approved Drugs Identifies Lead Candidates for Drug Repurposing in Alzheimer's Disease

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a significant amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA), European Medicines Agency (EMA), or Worldwide for another indication is a more rapid and less expensive option. Therefore, we apply the scaffold searching approach based on known amyloid-beta (Aβ) inhibitor tramiprosate to screen the DrugCentral database (n = 4,642) of clinically tested drugs. As a result, menadione bisulfite and camphotamide substances with protrombogenic and neurostimulation/cardioprotection effects were identified as promising Aβ inhibitors with an improved binding affinity (ΔGbind) and blood-brain barrier permeation (logBB). Finally, the data was also confirmed by molecular dynamics simulations using implicit solvation, in particular as Molecular Mechanics Generalized Born Surface Area (MM-GBSA) model. Overall, the proposed in silico pipeline can be implemented through the early stage rational drug design to nominate some lead candidates for AD, which will be further validated in vitro and in vivo, and, finally, in a clinical trial.

Advances in Non-Animal Testing Approaches towards Accelerated Clinical Translation of Novel Nanotheranostic Therapeutics for Central Nervous System Disorders

Nanotheranostics constitute a novel drug delivery system approach to improving systemic, brain-targeted delivery of diagnostic imaging agents and pharmacological moieties in one rational carrier platform. While there have been notable successes in this field, currently, the clinical translation of such delivery systems for the treatment of neurological disorders has been limited by the inadequacy of correlating in vitro and in vivo data on blood-brain barrier (BBB) permeation and biocompatibility of nanomaterials. This review aims to identify the most contemporary non-invasive approaches for BBB crossing using nanotheranostics as a novel drug delivery strategy and current non-animal-based models for assessing the safety and efficiency of such formulations. This review will also address current and future directions of select in vitro models for reducing the cumbersome and laborious mandate for testing exclusively in animals. It is hoped these non-animal-based modelling approaches will facilitate researchers in optimising promising multifunctional nanocarriers with a view to accelerating clinical testing and authorisation applications. By rational design and appropriate selection of characterised and validated models, ranging from monolayer cell cultures to organ-on-chip microfluidics, promising nanotheranostic particles with modular and rational design can be screened in high-throughput models with robust predictive power. Thus, this article serves to highlight abbreviated research and development possibilities with clinical translational relevance for developing novel nanomaterial-based neuropharmaceuticals for therapy in CNS disorders. By generating predictive data for prospective nanomedicines using validated in vitro models for supporting clinical applications in lieu of requiring extensive use of in vivo animal models that have notable limitations, it is hoped that there will be a burgeoning in the nanotherapy of CNS disorders by virtue of accelerated lead identification through screening, optimisation through rational design for brain-targeted delivery across the BBB and clinical testing and approval using fewer animals. Additionally, by using models with tissue of human origin, reproducible therapeutically relevant nanomedicine delivery and individualised therapy can be realised.

Porphyrin-Based Tumor-Targeting Theranostic Agent: Gd-TDAP

The aim of this work was to evaluate a tumor-targeting porphyrin-based gadolinium complex (Gd-TDAP) for use as an MR/optical imaging agent and potential therapeutic agent. Gd-TDAP had higher longitudinal relaxivity (11.8 mM-1 s-1) than a commercial MRI contrast agent (Omniscan; 3.7 mM-1 s-1) in HSA solution (0.67 mM) at 3 T. The tumor-targeting characteristics were confirmed by T1-weighted MR imaging and optical imaging using an orthotopic brain tumor mouse model, which showed 1.3-fold higher uptake in tumor compared to normal brain tissues. The cell fraction data using U87MG glioblastoma cells indicated the potential for gadolinium neutron capture therapy (Gd-NCT), which requires gadolinium to be inside the cell nucleus. In addition, porphyrin derivatives can be used for photodynamic therapy (PDT), and the results demonstrated that Gd-TDAP has great potential not only as a bimodal imaging agent but also for treatment.

Physiologically Based Pharmacokinetic (PBPK) Modeling for Predicting Brain Levels of Drug in Rat

One of the main obstacles in neurological disease treatment is the presence of the blood-brain barrier. New predictive high-throughput screening tools are essential to avoid costly failures in the advanced phases of development and to contribute to the 3 Rs policy. The objective of this work was to jointly develop a new in vitro system coupled with a physiological-based pharmacokinetic (PBPK) model able to predict brain concentration levels of different drugs in rats. Data from in vitro tests with three different cells lines (MDCK, MDCK-MDR1 and hCMEC/D3) were used together with PK parameters and three scaling factors for adjusting the model predictions to the brain and plasma profiles of six model drugs. Later, preliminary quantitative structure-property relationships (QSPRs) were constructed between the scaling factors and the lipophilicity of drugs. The predictability of the model was evaluated by internal validation. It was concluded that the PBPK model, incorporating the barrier resistance to transport, the disposition within the brain and the drug-brain binding combined with MDCK data, provided the best predictions for passive diffusion and carrier-mediated transported drugs, while in the other cell lines, active transport influence can bias predictions.

Translocation of Endo-Functionalized Molecular Tubes across Different Lipid Bilayers: Atomistic Molecular Dynamics Simulation Study

Various artificial receptors, such as calixarenes, cyclodextrins, cucurbit[n]urils, and their acyclic compounds, pliiar[n]arenes, deep cavitands, and molecular tweezers, can permeate the lipid membranes and they are used as drug carriers to improve the drug solubility, stability, and bioavailability. Inspired by these, we have employed atomistic molecular dynamics simulation to examine the effects of endo-functionalized molecular tubes or naphthotubes (host-1a and host-1b) on seven different types of model lipid bilayers and the permeation properties of these receptors through these model lipid bilayers. Lipid types include six model lipid bilayers (POPC, POPE, DOPC, POPG, DPPE, POPE/POPG) and one realistic membrane (Yeast). We observe that these receptors are spontaneously translocated toward these model lipid bilayer head regions and do not proceed further into these lipid bilayer tail regions (reside at the interface between lipid head and lipid tail region), except for the DPPE-containing systems. In the DPPE model lipid bilayer-containing systems (1a-dppe and 1b-dppe), receptor molecules are only adsorbed on the bilayer surface and reside at the interface between lipid head and water. This finding is also supported by the biased free-energy profiles of these translocation processes. Passive transport of these receptors may be possible through these model lipid bilayers (due to low barrier height), except for DPPE bilayer-containing systems (that have a very high energy barrier at the center). The results from these simulations provide insight into the biocompatibility of host-1a or host-1b in microscopic detail. Based on this work, more research is needed to fully comprehend the role of these synthesized receptors as a prospective drug carrier.

Data-driven equation for drug-membrane permeability across drugs and membranes

Drug efficacy depends on its capacity to permeate across the cell membrane. We consider the prediction of passive drug-membrane permeability coefficients. Beyond the widely recognized correlation with hydrophobicity, we additionally consider the functional relationship between passive permeation and acidity. To discover easily interpretable equations that explain the data well, we use the recently proposed sure-independence screening and sparsifying operator (SISSO), an artificial-intelligence technique that combines symbolic regression with compressed sensing. Our study is based on a large in silico dataset of 0.4 × 10⁶ small molecules extracted from coarse-grained simulations. We rationalize the equation suggested by SISSO via an analysis of the inhomogeneous solubility-diffusion model in several asymptotic acidity regimes. We further extend our analysis to the dependence on lipid-membrane composition. Lipid-tail unsaturation plays a key role but surprisingly contributes stepwise rather than proportionally. Our results are in line with previously observed changes in permeability, suggesting the distinction between liquid-disordered and liquid-ordered permeation. Together, compressed sensing with analytically derived asymptotes establish and validate an accurate, broadly applicable, and interpretable equation for passive permeability across both drug and lipid-tail chemistry.

B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood-Brain Barrier Penetrating Peptides

The blood–brain barrier is a major obstacle in treating brain-related disorders, as it does not allow the delivery of drugs into the brain. We developed a method for predicting blood–brain barrier penetrating peptides to facilitate drug delivery into the brain. These blood–brain barrier penetrating peptides (B3PPs) can act as therapeutics, as well as drug delivery agents. We trained, tested, and evaluated our models on blood–brain barrier peptides obtained from the B3Pdb database. First, we computed a wide range of peptide features. Then, we selected relevant peptide features. Finally, we developed numerous machine-learning-based models for predicting blood–brain barrier peptides using the selected features. The random-forest-based model performed the best with respect to the top 80 selected features and achieved a maximal 85.08% accuracy with an AUROC of 0.93. We also developed a webserver, B3pred, that implements our best models. It has three major modules that allow users to predict/design B3PPs and scan B3PPs in a protein sequence.

Novel diarylamides and diarylureas with N-substitution dependent activity against medulloblastoma

Medulloblastoma - highly aggressive and heterogeneous tumours of the cerebellum - account for 15-20% of all childhood brain tumours, and are the most common high-grade childhood embryonal tumour of the central nervous system. Herein, potent in vitro anticancer activity against two established medulloblastoma cell lines of the sonic hedgehog subgroup, namely DAOY (p53 mutant) and ONS-76 (p53 wild type), has been achieved. A number of first-generation diarylamides and diarylureas were evaluated and activity is likely to be, in-part, conformation-dependent. The most active compound from this first-generation set of compounds, 1-naphthyl derivative 4b, was selected and a second-generation of compounds were optimised and tested for activity against the medulloblastoma cell lines. This process resulted in drug-like compounds with up to sixty times the activity (sub-micromolar) of the first-generation - thus providing potent new leads for further study.

Development of blood brain barrier permeation prediction models for organic and inorganic biocidal active substances

Biocidal products are broadly used in homes and industries. However, the safety of biocidal active substances (BASs) is not yet fully understood. In particular, the neurotoxic action of BASs needs to be studied as diverse epidemiological studies have reported associations between exposure to BASs and neural diseases. In this study, we developed in silico models to predict the blood-brain barrier (BBB) permeation of organic and inorganic BASs. Due to a lack of BBB data for BASs, the chemical space of BASs and BBB dataset were compared in order to select BBB data that were structurally similar to BASs. In silico models to predict log-scaled BBB penetration were developed using support vector regression for organic BASs and multiple linear regression for inorganic BASs. The model for organic BASs was developed with 231 compounds (training set: 153 and test set: 78) and achieved good prediction accuracy on an external test set (R² = 0.64), and the model outperformed the model for pharmaceuticals. The model for inorganic BASs was developed with 11 compounds (R² = 0.51). Applicability domain (AD) analysis of the models clarified molecular structures reliably predicted by the models. Therefore, the models developed in this study can be used for predicting BBB permeable BASs in human. These models were developed according to the Quantitative Structure-Activity Relationship validation principles proposed by the Organization for Economic Cooperation and Development.

Development of a CNS-permeable reactivator for nerve agent exposure: an iterative, multi-disciplinary approach

Nerve agents have experienced a resurgence in recent times with their use against civilian targets during the attacks in Syria (2012), the poisoning of Sergei and Yulia Skripal in the United Kingdom (2018) and Alexei Navalny in Russia (2020), strongly renewing the importance of antidote development against these lethal substances. The current standard treatment against their effects relies on the use of small molecule-based oximes that can efficiently restore acetylcholinesterase (AChE) activity. Despite their efficacy in reactivating AChE, the action of drugs like 2-pralidoxime (2-PAM) is primarily limited to the peripheral nervous system (PNS) and, thus, provides no significant protection to the central nervous system (CNS). This lack of action in the CNS stems from their ionic nature that, on one end makes them very powerful reactivators and on the other renders them ineffective at crossing the Blood Brain Barrier (BBB) to reach the CNS. In this report, we describe the use of an iterative approach composed of parallel chemical and in silico syntheses, computational modeling, and a battery of detailed in vitro and in vivo assays that resulted in the identification of a promising, novel CNS-permeable oxime reactivator. Additional experiments to determine acute and chronic toxicity are ongoing.

Predicting the Membrane Permeability of Fentanyl and Its Analogues by Molecular Dynamics Simulations

The lipid membrane is considered a crucial component of opioid general anesthesia. The main drug used for the induction and maintenance of opioid anesthesia is fentanyl and its various analogues. However, these drugs have different clinical effects, and detailed atomic-level insight into the drug–membrane interactions could lead to a better understanding how these drugs exert their anesthetic properties. In this study, we have used extensive umbrella sampling molecular dynamics simulations to study the permeation process of fentanyl and three of its analogues into a variety of simple phospholipid membrane models. Our simulations show that we can accurately predict the permeability coefficients of these drug molecules, which is an important process in understanding how pharmaceuticals reach their molecular targets. We were also able to show that one phospholipid provides more accurate predictions than other lipids commonly used in these types of permeation studies, which will aid future studies of these types of processes.

Bioengineered microfluidic blood-brain barrier models in oncology research

Metastasis is the major reason for most brain tumors with up to a 50% chance of occurrence in patients with other types of malignancies. Brain metastasis occurs if cancer cells succeed to cross the 'blood-brain barrier' (BBB). Moreover, changes in the structure and function of BBB can lead to the onset and progression of diseases including neurological disorders and brain-metastases. Generating BBB models with structural and functional features of intact BBB is highly important to better understand the molecular mechanism of such ailments and finding novel therapeutic agents targeting them. Hence, researchers are developing novel in vitro BBB platforms that can recapitulate the structural and functional characteristics of BBB. Brain endothelial cells-based in vitro BBB models have thus been developed to investigate the mechanism of brain metastasis through BBB and facilitate the testing of brain targeted anticancer drugs. Bioengineered constructs integrated with microfluidic platforms are vital tools for recapitulating the features of BBB in vitro closely as possible. In this review, we outline the fundamentals of BBB biology, recent developments in the microfluidic BBB platforms, and provide a concise discussion of diverse types of bioengineered BBB models with an emphasis on the application of them in brain metastasis and cancer research in general. We also provide insights into the challenges and prospects of the current bioengineered microfluidic platforms in cancer research.

A deep learning approach to predict blood-brain barrier permeability

The blood-brain barrier plays a crucial role in regulating the passage of 98% of the compounds that enter the central nervous system (CNS). Compounds with high permeability must be identified to enable the synthesis of brain medications for the treatment of various brain diseases, such as Parkinson's, Alzheimer's, and brain tumors. Throughout the years, several models have been developed to solve this problem and have achieved acceptable accuracy scores in predicting compounds that penetrate the blood-brain barrier. However, predicting compounds with "low" permeability has been a challenging task. In this study, we present a deep learning (DL) classification model to predict blood-brain barrier permeability. The proposed model addresses the fundamental issues presented in former models: high dimensionality, class imbalances, and low specificity scores. We address these issues to enhance the high-dimensional, imbalanced dataset before developing the classification model: the imbalanced dataset is addressed using oversampling techniques and the high dimensionality using a non-linear dimensionality reduction technique known as kernel principal component analysis (KPCA). This technique transforms the high-dimensional dataset into a low-dimensional Euclidean space while retaining invaluable information. For the classification task, we developed an enhanced feed-forward deep learning model and a convolutional neural network model. In terms of specificity scores (i.e., predicting compounds with low permeability), the results obtained by the enhanced feed-forward deep learning model outperformed those obtained by other models in the literature that were developed using the same technique. In addition, the proposed convolutional neural network model surpassed models used in other studies in multiple accuracy measures, including overall accuracy and specificity. The proposed approach solves the problem inevitably faced with obtaining low specificity resulting in high false positive rate.

Synthesis and Characterization of Some New Quinoxalin-2(1H)one and 2-Methyl-3H-quinazolin-4-one Derivatives Targeting the Onset and Progression of CRC with SRA, Molecular Docking, and ADMET Analyses

The pathogenesis of colorectal cancer is a multifactorial process. Dysbiosis and the overexpression of COX-2 and LDHA are important effectors in the initiation and development of the disease through chromosomal instability, PGE2 biosynthesis, and induction of the Warburg effect, respectively. Herein, we report the in vitro testing of some new quinoxalinone and quinazolinone Schiff’s bases as: antibacterial, COX-2 and LDHA inhibitors, and anticolorectal agents on HCT-116 and LoVo cells. Moreover, molecular docking and SAR analyses were performed to identify the structural features contributing to the biological activities. Among the synthesized molecules, the most active cytotoxic agent, (6d) was also a COX-2 inhibitor. In silico ADMET studies predicted that (6d) would have high Caco-2 permeability, and %HIA (99.58%), with low BBB permeability, zero hepatotoxicity, and zero risk of sudden cardiac arrest, or mutagenicity. Further, (6d) is not a potential P-gp substrate, instead, it is a possible P-gpI and II inhibitor, therefore, it can prevent or reverse the multidrug resistance of the anticancer drugs. Collectively, (6d) can be considered as a promising lead suitable for further optimization to develop anti-CRC agents or glycoproteins inhibitors.

Functional Group Distributions, Partition Coefficients, and Resistance Factors in Lipid Bilayers Using Site Identification by Ligand Competitive Saturation

Small molecules such as metabolites and drugs must pass through the membrane of the cell, a barrier primarily comprising phospholipid bilayers and embedded proteins. To better understand the process of passive diffusion, knowledge of the ability of various functional groups to partition across bilayers and the associated energetics would be of utility. In the present study, the site identification by ligand competitive saturation (SILCS) methodology has been applied to sample the distributions of a diverse set of chemical solutes representing the functional groups of small molecules across phospholipid bilayers composed of 0.9:0.1 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine/cholesterol and a mixture of 0.52:0.18:0.3 1,2-dioleoyl-sn-glycero-3-phospho-l-serine/1,2-dioleoyl-sn-glycero-3-phosphocholine/cholesterol used in parallel artificial membrane permeability assay experiments. A combination of oscillating chemical potential grand canonical Monte Carlo and molecular dynamics in the SILCS simulations was applied to achieve solute sampling through the bilayers and surrounding aqueous environment from which the distribution of solutes and the functional groups they represent were obtained. Results show differential distribution of aliphatic versus aromatic groups with the former having increased sampling in the center of the bilayers versus in the region of the glycerol linker for the latter. Variations in the distribution of different polar groups are evident, with large differences between negative acetate and positive methylammonium with accumulation of the polar-neutral and acetate solutes above the bilayer head groups. Conversion of the distributions to absolute free energies allows for a detailed understanding of energetics of functional groups in different regions of the bilayers and for calculation of absolute free-energy profiles of multifunctional drug-like molecules across the bilayers from which partition coefficients and resistance factors suitable for insertion into the homogenous solubility-diffusion equation for calculation of permeability were obtained. Comparisons of the calculated bilayer/solution partition coefficients with 1-octanol/water experimental data for both drug-like molecules and the solutes show overall good agreement, validating the calculated distributions and associated absolute free-energy profiles.

Design and synthesis of novel tacrine-indole hybrids as potential multitarget-directed ligands for the treatment of Alzheimer's disease

The authors report on the synthesis and biological evaluation of new compounds whose structure combines tacrine and indole moieties. Tacrine-indole heterodimers were designed to inhibit cholinesterases and β-amyloid formation, and to cross the blood-brain barrier. The most potent new acetylcholinesterase inhibitors were compounds 3c and 4d (IC₅₀ = 25 and 39 nM, respectively). Compound 3c displayed considerably higher selectivity for acetylcholinesterase relative to human plasma butyrylcholinesterase in comparison to compound 4d (selectivity index: IC₅₀ [butyrylcholinesterase]/IC₅₀ [acetylcholinesterase] = 3 and 0.6, respectively). Furthermore, compound 3c inhibited β-amyloid-dependent amyloid nucleation in the yeast-based prion nucleation assay and displayed no dsDNA destabilizing interactions with DNA. Compounds 3c and 4d displayed a high probability of crossing the blood-brain barrier. The results support the potential of 3c for future development as a dual-acting therapeutic agent in the prevention and/or treatment of Alzheimer's disease.

Synthesis of New Biscoumarin Derivatives, In Vitro Cholinesterase Inhibition, Molecular Modelling and Antiproliferative Effect in A549 Human Lung Carcinoma Cells

A series of novel C4-C7-tethered biscoumarin derivatives (12a–e) linked through piperazine moiety was designed, synthesized, and evaluated biological/therapeutic potential. Biscoumarin 12d was found to be the most effective inhibitor of both acetylcholinesterase (AChE, IC₅₀ = 6.30 µM) and butyrylcholinesterase (BChE, IC₅₀ = 49 µM). Detailed molecular modelling studies compared the accommodation of ensaculin (well-established coumarin derivative tested in phase I of clinical trials) and 12d in the human recombinant AChE (hAChE) active site. The ability of novel compounds to cross the blood–brain barrier (BBB) was predicted with a positive outcome for compound 12e. The antiproliferative effects of newly synthesized biscoumarin derivatives were tested in vitro on human lung carcinoma cell line (A549) and normal colon fibroblast cell line (CCD-18Co). The effect of derivatives on cell proliferation was evaluated by MTT assay, quantification of cell numbers and viability, colony-forming assay, analysis of cell cycle distribution and mitotic activity. Intracellular localization of used derivatives in A549 cells was confirmed by confocal microscopy. Derivatives 12d and 12e showed significant antiproliferative activity in A549 cancer cells without a significant effect on normal CCD-18Co cells. The inhibition of hAChE/human recombinant BChE (hBChE), the antiproliferative activity on cancer cells, and the ability to cross the BBB suggest the high potential of biscoumarin derivatives. Beside the treatment of cancer, 12e might be applicable against disorders such as schizophrenia, and 12d could serve future development as therapeutic agents in the prevention and/or treatment of Alzheimer’s disease.

Polymerizable Matrix Metalloproteinases' Inhibitors with Potential Application for Dental Restorations

Collagen cleavage by matrix metalloproteinase (MMP) is considered a major cause of dental resins long term failure. Most MMP inhibitors display significant toxicity and are unsuitable for dental resins’ applications. Here we report a study of a new class of inhibitors that display the unique property of being co-polymerizable with other vinyl compounds present in commercial dental resins, limiting their release and potential toxicity. Computational affinity towards the active site of different MMP-1; -2; -8; -9 and -13 of several compounds showed interesting properties and were synthesized. These free compounds were tested concerning their toxicity upon contact with two different cell types, with no substantial decrease in cell viability at high concentrations. Even so, compound’s safety can be further improved upon copolymerization with commercial dental resins, limiting their release.

Partitioning of Aβ Peptide Fragments into Blood-Brain Barrier Mimetic Bilayer

We used all-atom replica-exchange umbrella sampling molecular dynamics simulations to investigate the partitioning of the charged tetrapeptide KLVF and its neutral apolar counterpart VVIA into the blood-brain barrier (BBB)-mimetic bilayer. Our findings allowed us to reconstruct the partitioning mechanism for these two Aβ peptide fragments. Despite dissimilar sequences, their permeation shares significant common features. Computations of free energies and permeabilities show that partitioning of both peptides is highly unfavorable, ruling out passive transport. The peptides experience multiple rotational transitions within the bilayer and typically cause considerable lipid disorder and bilayer thinning. Near the bilayer midplane, they lose almost entirely their solvation shell and the interactions with the lipid headgroups. The peptides cause complex reorganization within the proximal bilayer region. Upon insertion, they induce striking cholesterol influx reversed by its depletion and the influx of DMPC when the peptides reach the midplane. The differences in partitioning mechanisms are due to the much higher polarity of KLVF peptide, the permeation of which is more unfavorable and which exclusively assumes vertical orientations within the bilayer. In contrast, VVIA positions itself flat between the leaflets, causing minor disorder and even thickening of the BBB-mimetic bilayer. Due to the high density of the cholesterol-rich BBB bilayer, the unfavorable work associated with the peptide insertion provides a significant, but not dominant, contribution to the partition free energy, which is still governed by dehydration and loss of peptide-headgroup interactions. Comparison with experiments indicates that KLVF and VVIA permeation is similar to that of proline tetrapeptide, mannitol, or cimetidine, all of which exhibit no passive transport.

Lipid Nanosystems and Serum Protein as Biomimetic Interfaces: Predicting the Biodistribution of a Caffeic Acid-Based Antioxidant

Purpose

AntiOxCIN₃ is a novel mitochondriotropic antioxidant developed to minimize the effects of oxidative stress on neurodegenerative diseases. Prior to an investment in pre-clinical in vivo studies, it is important to apply in silico and biophysical cell-free in vitro studies to predict AntiOxCIN₃ biodistribution profile, respecting the need to preserve animal health in accordance with the EU principles (Directive 2010/63/EU). Accordingly, we propose an innovative toolbox of biophysical studies and mimetic models of biological interfaces, such as nanosystems with different compositions mimicking distinct membrane barriers and human serum albumin (HSA).

Methods

Intestinal and cell membrane permeation of AntiOxCIN₃ was predicted using derivative spectrophotometry. AntiOxCIN₃ –HSA binding was evaluated by intrinsic fluorescence quenching, synchronous fluorescence, and dynamic/electrophoretic light scattering. Steady-state and time-resolved fluorescence quenching was used to predict AntiOxCIN₃-membrane orientation. Fluorescence anisotropy, synchrotron small- and wide-angle X-ray scattering were used to predict lipid membrane biophysical impairment caused by AntiOxCIN₃ distribution.

Results and Discussion

We found that AntiOxCIN₃ has the potential to permeate the gastrointestinal tract. However, its biodistribution and elimination from the body might be affected by its affinity to HSA (>90%) and by its steady-state volume of distribution (VD_SS=1.89± 0.48 L∙Kg⁻¹). AntiOxCIN₃ is expected to locate parallel to the membrane phospholipids, causing a bilayer stiffness effect. AntiOxCIN₃ is also predicted to permeate through blood-brain barrier and reach its therapeutic target – the brain.

Conclusion

Drug interactions with biological interfaces may be evaluated using membrane model systems and serum proteins. This knowledge is important for the characterization of drug partitioning, positioning and orientation of drugs in membranes, their effect on membrane biophysical properties and the study of serum protein binding. The analysis of these interactions makes it possible to collect valuable knowledge on the transport, distribution, accumulation and, eventually, therapeutic impact of drugs which may aid the drug development process.

Low Molecular Weight (poly)Phenol Metabolites Across the Blood-Brain Barrier: The Underexplored Journey

The world of (poly)phenols arising from dietary sources has been significantly amplified with the discovery of low molecular weight (LMW) (poly)phenol metabolites resulting from phase I and phase II metabolism and microbiota transformations. These metabolites, which are known to reach human circulation have been studied to further explore their interesting properties, especially regarding neuroprotection. Nevertheless, once in circulation, their distribution to target tissues, such as the brain, relies on their ability to cross the blood-brain barrier (BBB), one of the most controlled barriers present in humans. This represents a key step of an underexplored journey towards the brain. Present review highlights the main findings related to the ability of LMW (poly)phenol metabolites to reach the brain, considering different studies: in silico, in vitro, and in vivo. The mechanisms associated with the transport of these LMW (poly)phenol metabolites across the BBB and possible transporters will be discussed. Overall, the transport of these LMW (poly)phenol metabolites is crucial to elucidate which compounds may exert direct neuroprotective effects, so it is imperative to continue dissecting their potential to cross the BBB and the mechanisms behind their permeation.

A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure-activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug-membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.

New 2-Pyrazoline and Hydrazone Derivatives as Potent and Selective Monoamine Oxidase A Inhibitors

Thirty compounds having 1-[2-(5-substituted-2-benzoxazolinone-3-yl) acetyl]-3,5-disubstitutedphenyl-2-pyrazoline structure and nine compounds having N'-(1,3-disubstitutedphenylallylidene)-2-(5-substituted-2-benzoxazolinone-3-yl)acetohydrazide skeleton were synthesized and evaluated as monoamine oxidase (MAO) inhibitors. All of the compounds exhibited selective MAO-A inhibitor activity in the nanomolar or low micromolar range. The results of the molecular docking for hydrazone derivatives supported the in vitro results. Five compounds, 6 (0.008 μM, Selectivity Index (SI): 9.70 × 10^-4), 7 (0.009 μM, SI: 4.55 × 10^-5), 14 (0.001 μM, SI: 8.00 × 10^-4), 21 (0.009 μM, SI: 1.37 × 10^-5), and 42 (0.010 μM, SI: 5.40 × 10^-6), exhibiting the highest inhibition and selectivity toward hMAO-A and nontoxic to hepatocytes were assessed for antidepressant activity as acute and subchronic in mice. All of these five compounds showed significant antidepressant activity with subchronic administration consistent with the increase in the brain serotonin levels and the compounds crossed the blood-brain barrier according to parallel artificial membrane permeation assay. Compounds 14, 21, and 42 exhibited an ex vivo MAO-A profile, which is highly consistent with the in vitro data.

Multitarget Biological Profiling of New Naphthoquinone and Anthraquinone-Based Derivatives for the Treatment of Alzheimer's Disease

Two series of naphthoquinone and anthraquinone derivatives decorated with an aromatic/heteroaromatic chain have been synthesized and evaluated as potential promiscuous agents capable of targeting different factors playing a key role in Alzheimer’s disease (AD) pathogenesis. On the basis of the in vitro biological profiling, most of them exhibited a significant ability to inhibit amyloid aggregation, PHF6 tau sequence aggregation, acetylcholinesterase (AChE), and monoamine oxidase (MAO) B. In particular, naphthoquinone 2 resulted as one of the best performing multitarget-directed ligand (MTDL) experiencing a high potency profile in inhibiting β-amyloid (Aβ₄₀) aggregation (IC₅₀ = 3.2 μM), PHF6 tau fragment (91% at 10 μM), AChE enzyme (IC₅₀ = 9.2 μM) jointly with a remarkable inhibitory activity against MAO B (IC₅₀ = 7.7 nM). Molecular modeling studies explained the structure–activity relationship (SAR) around the binding modes of representative compound 2 in complex with hMAO B and hAChE enzymes, revealing inhibitor/protein key contacts and the likely molecular rationale for enzyme selectivity. Compound 2 was also demonstrated to be a strong inhibitor of Aβ₄₂ aggregation, with potency comparable to quercetin. Accordingly, atomic force microscopy (AFM) revealed that the most promising naphthoquinones 2 and 5 and anthraquinones 11 and 12 were able to impair Aβ₄₂ fibrillation, deconstructing the morphologies of its fibrillar aggregates. Moreover, the same compounds exerted a moderate neuroprotective effect against Aβ₄₂ toxicity in primary cultures of cerebellar granule cells. Therefore, our findings demonstrate that these molecules may represent valuable chemotypes toward the development of promising candidates for AD therapy.

Effects of lipid heterogeneity on model human brain lipid membranes

Cell membranes naturally contain a heterogeneous lipid distribution. However, homogeneous bilayers are commonly preferred and utilised in computer simulations due to their relative simplicity, and the availability of lipid force field parameters. Recently, experimental lipidomics data for the human brain cell membranes under healthy and Alzheimer's disease (AD) conditions were investigated, since disruption to the lipid composition has been implicated in neurodegenerative disorders, including AD [R. B. Chan et al., J. Biol. Chem., 2012, 287, 2678-2688]. In order to observe the effects of lipid complexity on the various bilayer properties, molecular dynamics simulations were used to study four membranes with increasing heterogeneity: a pure POPC membrane, a POPC and cholesterol membrane in a 1 : 1 ratio (POPC-CHOL), and to our knowledge, the first realistic models of a healthy brain membrane and an Alzheimer's diseased brain membrane. Numerous structural, interfacial, and dynamical properties, including the area per lipid, interdigitation, dipole potential, and lateral diffusion of the two simple models, POPC and POPC-CHOL, were analysed and compared to those of the complex brain models consisting of 27 lipid components. As the membranes gain heterogeneity, a number of alterations were found in the structural and dynamical properties, and more significant differences were observed in the lateral diffusion. Additionally, we observed snorkeling behaviour of the lipid tails that may play a role in the permeation of small molecules across biological membranes. In this work, atomistic description of realistic brain membrane models is provided, which can add insight towards the permeability and transport pathways of small molecules across these membrane barriers.

The importance of intramolecular hydrogen bonds on the translocation of the small drug piracetam through a lipid bilayer

The number of hydrogen bond donors and acceptors is a fundamental molecular descriptor to predict the oral bioavailability of small drug candidates. In fact, the most widely used oral bioavailability rules (such as the Lipinsky's rule-of-five and the Veber rules) make use of this molecular descriptor. It is generally assumed that hydrogen bond donors and acceptors impact on passive diffusion across cell membranes, a fundamental event during drug absorption and distribution. Although the relationship between the number of these motifs and the probability of having good oral bioavailability has been studied and described for more than 20 years, little attention has been given to their spatial distribution in the molecule. In this paper, we used molecular dynamics to describe the effect of intramolecular hydrogen bonding on the passive diffusion of a small drug (piracetam) through a lipid membrane. The results indicated that the formation of an intramolecular hydrogen bond decreases the barrier for translocation by ca. 4 kcal mol-1 and increases the permeability of the tested molecule, partially compensating the desolvation penalty arising from the penetration of the drug into the biological membrane core. This effect was apparent in simulations where the formation of this interaction was prevented with the help of modified potentials, and in simulations with a similar compound to piracetam that was not able to form this intramolecular hydrogen bond due to a larger distance between the hydrogen bond donor and acceptor groups. These results were also supported by coarse-grained methods, which are becoming an important resource for sampling a larger chemical space of molecules, with reduced computational effort. Furthermore, entropy and enthalpy derived profiles were also obtained as the compounds translocated across the membrane, suggesting that, even though the process of formation of internal hydrogen bonds is entropically unfavorable, the enthalpic gain is such that the formation of these interactions is beneficial for the passive diffusion across cell membranes.

Targeting a conserved pocket (n-octyl-β-D-glucoside) on the dengue virus envelope protein by small bioactive molecule inhibitors

Dengue virus enters the cell by receptor-mediated endocytosis followed by a viral envelope (DENVE) protein-mediated membrane fusion. A small detergent molecule n-octyl-β-D-glucoside (βOG) occupies the hydrophobic pocket which is located in the hinge region plays a major role in the rearrangement. It has been reported that mutations occurred in this binding pocket lead to the alterations of pH threshold for fusion. In addition to this event, the protonation of histidine residues present in the hydrophobic pocket would also impart the conformational change of the E protein evidence this pocket as a promising target. The present study identified novel cinnamic acid analogs as significant blockers of the hydrophobic pocket through molecular modeling studies against DENVE. A library of seventy-two analogs of cinnamic acid was undertaken for the discovery process of DENV inhibitors. A Molecular docking study was used to analyze the binding mechanism between these compounds and DENV followed by ADMET prediction. Binding energies were predicted by the MMGBSA study. The Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. The compounds CA and SCA derivatives have been tested against HSV-1 & 2 viruses. From the computational results, the compounds CA1, CA2, SCA 60, SCA 57, SCA 37, SCA 58, and SCA 14 exhibited favorable interaction energy. The compounds have in-vitro antiviral activity; the results clearly indicate that the compounds showed the activity against both the viruses (HSV-1 & HSV-2). Our study provides valuable information on the discovery of small molecules DENVE inhibitors.Communicated by Ramaswamy H. Sarma.

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.

Unassisted N-acetyl-phenylalanine-amide transport across membrane with varying lipid size and composition: kinetic measurements and atomistic molecular dynamics simulation

Biological membranes are essential to preserve structural integrity and regulate functional properties through the permeability of nutrients, pharmaceutical drugs, and neurotransmitters of a living cell. The movement of acetylated and amidated phenylalanine (NAFA) across 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) membrane bilayers is investigated to probe physical transport. The rate of transport is measured experimentally applying parallel artificial membrane permeation assay (PAMPA). At the physiological temperature, 310 K, the measured time constants in the neutral pH were ∼6 h in DOPC and ∼3 h in POPC, while in a more acidic condition, at a pH 4.8, the time constants were ∼8 h in both lipids. Computationally, we have expanded our transport study of three aromatic dipeptides across a bilayer composed of DOPC¹⁸. In this study, we have examined the effects of lipid composition and bilayer size on the passive transport of NAFA by simulating the dipeptide in three different bilayers, with 50 DOPC lipids, 50 POPC lipids, and 40 POPC molecules. Specifically, atomistic molecular dynamics simulations with umbrella sampling were used to calculate the potential of mean force for the passive permeation of NAFA across the bilayers. Diffusion constants were then calculated by numerically solving the Smoluchowski equation. Permeability coefficients and mean first passage times were then calculated. Structural properties - Ramachandran plots, sidechain torsions, peptide insertion angles, radial distribution functions, and proximal peptide water molecules - were also examined to determine the effect of system size and lipid type. In terms of systems size, we observed a small decrease in the highest barrier of the potential of mean force and fewer sampled sidechain dihedral angle conformations with 40 versus 50 POPC lipids due to weaker membrane deformations within a smaller lipid bilayer. In terms of lipid type, DOPC contains two monounsaturated acyl chains compared to only one such acyl chain in POPC; therefore, DOPC bilayers are less ordered and more easily deformed, as seen by a much broader potential of mean force profile. The NAFA in DOPC lipid also transitioned to an internally hydrogen-bonded backbone conformation at lower membrane depths than in POPC. Similarly, as for other aromatic dipeptides, NAFA tends to insert into the membrane sidechain-first, remains mostly desolvated in the membrane center, and exhibits slow reorientations within the bilayer in both DOPC and POPC. With a joint experimental and computational study we have gained a new insight into the rate of transport and the underlying microscopic mechanism in different lipid bilayer conditions of the simplest hydrophobic aromatic dipeptide.Communicated by Ramaswamy H. Sarma.

Membrane permeability of small molecules from unbiased molecular dynamics simulations

Permeation of many small molecules through lipid bilayers can be directly observed in molecular dynamics simulations on the nano- and microsecond timescale. While unbiased simulations provide an unobstructed view of the permeation process, their feasibility for computing permeability coefficients depends on various factors that differ for each permeant. The present work studies three small molecules for which unbiased simulations of permeation are feasible within less than a microsecond, one hydrophobic (oxygen), one hydrophilic (water), and one amphiphilic (ethanol). Permeabilities are computed using two approaches: counting methods and a maximum-likelihood estimation for the inhomogeneous solubility diffusion (ISD) model. Counting methods yield nearly model-free estimates of the permeability for all three permeants. While the ISD-based approach is reasonable for oxygen, it lacks precision for water due to insufficient sampling and results in misleading estimates for ethanol due to invalid model assumptions. It is also demonstrated that simulations using a Langevin thermostat with collision frequencies of 1/ps and 5/ps yield oxygen permeabilities and diffusion constants that are lower than those using Nosé-Hoover by statistically significant margins. In contrast, permeabilities from trajectories generated with Nosé-Hoover and the microcanonical ensemble do not show statistically significant differences. As molecular simulations become more affordable and accurate, calculation of permeability for an expanding range of molecules will be feasible using unbiased simulations. The present work summarizes theoretical underpinnings, identifies pitfalls, and develops best practices for such simulations.

Partitioning of Benzoic Acid into 1,2-Dimyristoyl-sn-glycero-3-phosphocholine and Blood-Brain Barrier Mimetic Bilayers

Using an all-atom explicit water model and replica exchange umbrella sampling simulations, we investigated the molecular mechanisms of benzoic acid partitioning into two model lipid bilayers. The first was formed of 1,2-dimyristoyl-sn-glycero-3-phosphocholine (DMPC) lipids, whereas the second was composed of an equimolar mixture of DMPC, 1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine, palmitoylsphingomyelin, and cholesterol to constitute a blood-brain barrier (BBB) mimetic bilayer. Comparative analysis of benzoic acid partitioning into the two bilayers has revealed qualitative similarities. Partitioning into the DMPC and BBB bilayers is thermodynamically favorable although insertion into the former lowers the free energy of benzoic acid by approximately an additional 1 kcal mol^-1. The partitioning energetics for the two bilayers is also largely similar based on the balance of benzoic acid interactions with apolar fatty acid tails, polar lipid headgroups, and water. In both bilayers, benzoic acid retains a considerable number of residual water molecules until reaching the bilayer midplane where it experiences nearly complete dehydration. Upon insertion into the bilayers, benzoic acid undergoes several rotations primarily determined by the interactions with the lipid headgroups. Nonetheless, in addition to the depth of the free energy minimum, the BBB bilayer differs from the DMPC counterpart by a much deeper location of the free energy minimum and the appearance of a high free energy barrier and positioning of benzoic acid near the midplane. Furthermore, DMPC and BBB bilayers exhibit different structural responses to benzoic acid insertion. Taken together, the BBB mimetic bilayer is preferable for an accurate description of benzoic acid partitioning.

Molecular Dynamics Simulation of Small Molecules Interacting with Biological Membranes

Cell membranes protect and compartmentalise cells and their organelles. The semi-permeable nature of these membranes controls the exchange of solutes across their structure. Characterising the interaction of small molecules with biological membranes is critical to understanding of physiological processes, drug action and permeation, and many biotechnological applications. This review provides an overview of how molecular simulations are used to study the interaction of small molecules with biological membranes, with a particular focus on the interactions of water, organic compounds, drugs and short peptides with models of plasma cell membrane and stratum corneum lipid bilayers. This review will not delve on other types of membranes which might have different composition and arrangement, such as thylakoid or mitochondrial membranes. The application of unbiased molecular dynamics simulations and enhanced sampling methods such as umbrella sampling, metadynamics and replica exchange are described using key examples. This review demonstrates how state-of-the-art molecular simulations have been used successfully to describe the mechanism of binding and permeation of small molecules with biological membranes, as well as associated changes to the structure and dynamics of these membranes. The review concludes with an outlook on future directions in this field.

Teicoplanin-Modified HPLC Column as a Source of Experimental Parameters for Prediction of the Anticonvulsant Activity of 1,2,4-Triazole-3-Thiones by the Regression Models

The cell membrane is a complex system that consists of lipids, proteins, polysaccharides, and amphiphilic phospholipids. It plays an important role in ADME processes that are responsible for the final pharmaceutical effects of xenobiotics (bioavailability, activity). To study drug-membrane interaction at the molecular level, several high-performance liquid chromatography (HPLC) membrane model systems have been proposed which are mimicking mainly its lipid character. The aim of this work was to study interactions of new synthesized antiepileptic compounds of 4-alkyl-5-(3-chlorophenyl)-2,4-dihydro-3H-1,2,4-triazole-3-thione derivatives with Chirobiotic column containing glycoprotein ligand attached to the silica matrix. The affinity of the analytes to immobilized glycoprotein ligand was examined chromatographically in reversed-phase mode. The thermodynamics of interactions between bioactive compounds and teicoplanin was studied in terms of the van't Hoff linear relationship ln k vs. 1/T in the range of 5-45 °C. Change in enthalpy (ΔH°), change in entropy (ΔS°) and change in Gibbs free energy (ΔG°) were estimated utilizing graphical extrapolation and interpolation methods. The density functional theory (DFT) approach and docking simulations were used to get the molecular interpretation and prove the obtained experimental results. Cross-correlations of chromatographic and thermodynamic parameters with non-empirical topological and quantum chemical indices suggest that the polarizability of analytes appears to be responsible for the interactions of the tested molecules with teicoplanin and, ultimately, their retention on the column. Experimental and theoretical parameters were subjected to statistical analysis using regression models. Partial least squares (PLS) regression model showed the usefulness of the experimentally measured parameter φ0 (MeOH) to discriminate between anticonvulsant active and inactive 1,2,4-triazole-3-thione derivatives. Obtained results point out the usefulness of interaction of potential anticonvulsants with glycoprotein class of compounds to anticipate their activity.

Fast Estimation of the Blood-Brain Barrier Permeability by Pulling a Ligand through a Lipid Membrane

The blood–brain barrier (BBB) is a physical barrier that regulates the homeostasis of the neural microenvironment. A relative estimate of the BBB permeability, which is important for drug design, may be experimentally provided by the logBB (the blood–brain concentration ratio) and the logPS (permeability–surface-area product), while many computational methods aim to identify key properties that correlate well with these quantities. Although currently existing computational methods (e.g., quantitative structure activity relation) have made a significant contribution in screening various compounds that could potentially translocate through the BBB, they are unable to provide a physical explanation of the underlying processes and they can often be computationally demanding. Here, we use steered molecular dynamics simulation to estimate the BBB permeability of various compounds on the basis of simple lipid–membrane models by computing the nonequilibrium work, W_neq, produced by pulling the compounds through the membrane. We found that the values of W_neq correlate remarkably well with logBB and logPS for a range of compounds and different membrane types and pulling speeds, independently of the choice of force field. Moreover, our results provide insight into the role of hydrogen bonds, the energetic barriers, and the forces exerted on the ligands during their pulling. Our method is computationally easy to implement and fast. Therefore, we anticipate that it could provide a reliable prescreening tool for estimating the relative permeability of the BBB to various substances.

Synthesis and characterization of a new series of thiadiazole derivatives as potential anticancer agents

Cancer is one of the most common causes of death in the world. Despite the importance of combating cancer in healthcare systems and research centers, toxicity in normal tissues and the low efficiency of anticancer drugs are major problems in chemotherapy. Nowadays the aim of many medical research projects is to discover new safer and more effective anticancer agents. 1,3,4-Thiadiazole compounds are important fragments in medicinal chemistry because of their wide range of biological activities, including anticancer activities. The aim of this study was to determine the capacity of newly synthesized 1,3,4-thiadiazole compounds as chemotherapeutic agents. The structures of the obtained compounds were elucidated using 1H-NMR, 13C-NMR and mass spectrometry. Although the thiadiazole derivatives did not prove to be significantly cytotoxic to the tumour tissue cultures, compound 4i showed activity against the C6 rat brain cancer cell line (IC50 0.097 mM) at the tested concentrations.

Molecular dynamics trajectories for 630 coarse-grained drug-membrane permeations

The permeation of small-molecule drugs across a phospholipid membrane bears much interest both in the pharmaceutical sciences and in physical chemistry. Connecting the chemistry of the drug and the lipids to the resulting thermodynamic properties remains of immediate importance. Here we report molecular dynamics (MD) simulation trajectories using the coarse-grained (CG) Martini force field. A wide, representative coverage of chemistry is provided: across solutes-exhaustively enumerating all 105 CG dimers-and across six phospholipids. For each combination, umbrella-sampling simulations provide detailed structural information of the solute at all depths from the bilayer midplane to bulk water, allowing a precise reconstruction of the potential of mean force. Overall, the present database contains trajectories from 15,120 MD simulations. This database may serve the further identification of structure-property relationships between compound chemistry and drug permeability.

Predicting the Blood-Brain Barrier Permeability of New Drug-Like Compounds via HPLC with Various Stationary Phases

The permeation of the blood-brain barrier is a very important consideration for new drug candidate molecules. In this research, the reversed-phase liquid chromatography with different columns (Purosphere RP-18e, IAM.PC.DD2 and Cosmosil Cholester) was used to predict the penetration of the blood-brain barrier by 65 newly-synthesized drug-like compounds. The linear free energy relationships (LFERs) model (log BB = c + eE + sS + aA + bB + vV) was established for a training set of 23 congeneric biologically active azole compounds with known experimental log BB (BB = C_blood/C_brain) values (R² = 0.9039). The reliability and predictive potency of the model were confirmed by leave-one-out cross validation as well as leave-50%-out cross validation. Multiple linear regression (MLR) was used to develop the quantitative structure-activity relationships (QSARs) to predict the log BB values of compounds that were tested, taking into account the chromatographic lipophilicity (log k_w), polarizability and topological polar surface area. The excellent statistics of the developed MLR equations (R² > 0.8 for all columns) showed that it is possible to use the HPLC technique and retention data to produce reliable blood-brain barrier permeability models and to predict the log BB values of our pharmaceutically important molecules.

In Silico design of AVP (4-5) peptide and synthesis, characterization and in vitro activity of chitosan nanoparticles

BACKGROUND

Arginine-vasopressin (AVP) is a neuropeptide and provides learning and memory modulation. The AVP (4-5) dipeptide corresponds to the N-terminal fragment of the major vasopressin metabolite AVP (4-9), has a neuroprotective effect and used in the treatment of Alzheimer's and Parkinson's disease.

METHODS

The main objective of the present study is to evaluate the molecular mechanism of AVP (4-5) dipeptide and to develop and synthesize chitosan nanoparticle formulation using modified version of ionic gelation method, to increase drug effectiveness. For peptide loaded chitosan nanoparticles, the synthesized experiment medium was simulated for the first time by molecular dynamics method and used to determine the stability of the peptide, and the binding mechanism to protein (HSP70) was also investigated by molecular docking calculations. A potential pharmacologically features of the peptide was also characterized by ADME (Absorption, Distribution, Metabolism and Excretion) analysis. The characterization, in vitro release study, encapsulation efficiency and loading capacity of the peptide loaded chitosan nanoparticles (CS NPs) were performed by Dynamic Light Scattering (DLS), UV-vis absorption (UV), Scanning Electron Microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy techniques. Additionally, in vitro cytotoxicity of the peptide on human neuroblastoma cells (SH-SY5Y) was examined with XTT assay and the statistical analysis was evaluated.

RESULTS

The results showed that; hydrodynamic size, zeta potential and polydispersity index (PdI) of the peptide-loaded CS NPs were 167.6 nm, +13.2 mV, and 0.211, respectively. In vitro release study of the peptide-loaded CS NPs showed that 17.23% of the AVP (4-5)-NH2 peptide was released in the first day, while 61.13% of AVP (4-5)-NH2 peptide was released in the end of the 10th day. The encapsulation efficiency and loading capacity were 99% and 10%, respectively. According to the obtained results from XTT assay, toxicity on SHSY-5Y cells in the concentration from 0.01 μg/μL to 30 μg/μL were evaluated and no toxicity was observed. Also, neuroprotective effect was showed against H₂O₂ treatment.

CONCLUSION

The experimental medium of peptide-loaded chitosan nanoparticles was created for the first time with in silico system and the stability of the peptide in this medium was carried out by molecular dynamics studies. The binding sites of the peptide with the HSP70 protein were determined by molecular docking analysis. The size and morphology of the prepared NPs capable of crossing the blood-brain barrier (BBB) were monitored using DLS and SEM analyses, and the encapsulation efficiency and loading capacity were successfully performed with UV Analysis. In vitro release studies and in vitro cytotoxicity analysis on SHSY-5Y cell lines of the peptide were conducted for the first time. Grapical abstract.

Pre-Treatment with Laminarin Protects Hippocampal CA1 Pyramidal Neurons and Attenuates Reactive Gliosis Following Transient Forebrain Ischemia in Gerbils

Transient brain ischemia triggers selective neuronal death/loss, especially in vulnerable regions of the brain including the hippocampus. Laminarin, a polysaccharide originating from brown seaweed, has various pharmaceutical properties including an antioxidant function. To the best of our knowledge, few studies have been conducted on the protective effects of laminarin against ischemic injury induced by ischemic insults. In this study, we histopathologically investigated the neuroprotective effects of laminarin in the Cornu Ammonis 1 (CA1) field of the hippocampus, which is very vulnerable to ischemia-reperfusion injury, following transient forebrain ischemia (TFI) for five minutes in gerbils. The neuroprotective effect was examined by cresyl violet staining, Fluoro-Jade B histofluorescence staining and immunohistochemistry for neuronal-specific nuclear protein. Additionally, to study gliosis (glial changes), we performed immunohistochemistry for glial fibrillary acidic protein to examine astrocytes, and ionized calcium-binding adaptor molecule 1 to examine microglia. Furthermore, we examined alterations in pro-inflammatory M1 microglia by using double immunofluorescence. Pretreatment with 10 mg/kg laminarin failed to protect neurons in the hippocampal CA1 field and did not attenuate reactive gliosis in the field following TFI. In contrast, pretreatment with 50 or 100 mg/kg laminarin protected neurons, attenuated reactive gliosis and reduced pro-inflammatory M1 microglia in the CA1 field following TFI. Based on these results, we firmly propose that 50 mg/kg laminarin can be strategically applied to develop a preventative against injuries following cerebral ischemic insults.

MolMeDB: Molecules on Membranes Database

Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug–membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule–membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound–membrane interactions including partitioning, penetration and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by in-house calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions.

New phosphazine and phosphazide derivatives as multifunctional ligands targeting acetylcholinesterase and β-Amyloid aggregation for treatment of Alzheimer's disease

Phosphazine and phosphazide derivatives are described herein as a new class of selective and potent acetylcholinesterase (AChE) inhibitors and β-amyloid aggregation inhibitors. Phosphazines (5-7) were synthesized smoothly via a redox-condensation reaction of 1,2-bis(diphenylphosphino)ethane with different amines derivatives in the presence of dialkyl azodicarboxylate (Staudinger reaction) while phosphazides (8) via electrophilic attack of azido derivatives. Structures of the synthesized compounds were justified on the basis of compatible elementary and spectroscopic analyses. All the compounds were evaluated for their acetylcholinesterase inhibitory activity. The most three potent compounds (5b-c and 8b) showing AChE IC₅₀ values (29.85-34.96 nM) comparable to that of donepezil (34.42 nM) were subjected to further investigation by testing their butyrylcholinesterase, MMP-2 and self-induced Aβ aggregation inhibition activity. Especially, the coumarin phosphazide derivative (8b) presented the best AChE inhibition selectivity index (IC₅₀ = 34.96 nM, AChE/BuChE; 3.81) together with good inhibition ability against MMP-2 (IC₅₀ = 441.33 nM) and self-induced Aβ_1-42 aggregation (IC₅₀ = 337.77 nM). In addition, the inhibition of metal-induced Aβ aggregation by 8b was confirmed by thioflavine T fluorescence. The most potent effect of 8b was observed on the Zn²⁺-induced Aβ₄₂ aggregation. Kinetic study of compound 8b suggested it to be a competitive AChE inhibitor. Also, it specifically chelates metal and is predicted to be permeable to BBB. It also possesses low toxicity on SH-SY5Y neuroblastoma cells with a safety index of 15.37. In addition, it was demonstrated that compound 8b can improve the cognitive impairment of scopolamine-induced model in mice with % alternations and transfer latency time comparable to that of donepezil. Also, a docking study was carried out and it was in accordance with the in vitro results. These promising in vitro and in vivo findings highlight compound 8b as a possible drug candidate in searching for new multifunctional AD drugs.

Computational Nanoscopy of Tight Junctions at the Blood-Brain Barrier Interface

The selectivity of the blood-brain barrier (BBB) is primarily maintained by tight junctions (TJs), which act as gatekeepers of the paracellular space by blocking blood-borne toxins, drugs, and pathogens from entering the brain. The BBB presents a significant challenge in designing neurotherapeutics, so a comprehensive understanding of the TJ architecture can aid in the design of novel therapeutics. Unraveling the intricacies of TJs with conventional experimental techniques alone is challenging, but recently developed computational tools can provide a valuable molecular-level understanding of TJ architecture. We employed the computational methods toolkit to investigate claudin-5, a highly expressed TJ protein at the BBB interface. Our approach started with the prediction of claudin-5 structure, evaluation of stable dimer conformations and nanoscale assemblies, followed by the impact of lipid environments, and posttranslational modifications on these claudin-5 assemblies. These led to the study of TJ pores and barriers and finally understanding of ion and small molecule transport through the TJs. Some of these in silico, molecular-level findings, will need to be corroborated by future experiments. The resulting understanding can be advantageous towards the eventual goal of drug delivery across the BBB. This review provides key insights gleaned from a series of state-of-the-art nanoscale simulations (or computational nanoscopy studies) performed on the TJ architecture.

Design of novel monoamine oxidase-B inhibitors based on piperine scaffold: Structure-activity-toxicity, drug-likeness and efflux transport studies

Piperine has been associated with neuroprotective effects and monoamine oxidase (MAO) inhibition, thus being an attractive scaffold to develop new antiparkinsonian agents. Accordingly, we prepared a small library of piperine derivatives and screened the inhibitory activities towards human MAO isoforms (hMAO-A and hMAO-B). Structure-activity relationship (SAR) studies pointed out that the combination of α-cyano and benzyl ester groups increased both potency and selectivity towards hMAO-B. Kinetic experiments with compounds 7, 10 and 15 indicated a competitive hMAO-B inhibition mechanism. Compounds 15 and 16, at 10 μM, caused a small but significant decrease in P-gp efflux activity in Caco-2 cells. Compound 15 stands out as the most potent piperine-based hMAO-B inhibitor (IC₅₀ = 47.4 nM), displaying favourable drug-like properties and a broad safety window. Compound 15 is thus a suitable candidate for lead optimization and the development of multitarget-directed ligands.