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

Assessing the anticholinergic cognitive burden classification of putative anticholinergic drugs using drug properties

AIMS

This study evaluated the use of machine learning to leverage drug absorption, distribution, metabolism and excretion (ADME) data together with physicochemical and pharmacological data to develop a novel anticholinergic burden scale and compare its performance to previously published scales.

METHODS

Experimental and in silico ADME, physicochemical and pharmacological data were collected for antimuscarinic activity, blood-brain barrier penetration, bioavailability, chemical structure and P-glycoprotein (P-gp) substrate profile. These five drug properties were used to train an unsupervised model to assign anticholinergic burden scores to drugs. The model performance was evaluated through 10-fold cross-validation and compared with the clinical Anticholinergic Cognitive Burden (ACB) scale and nonclinical Anticholinergic Toxicity Scores (ATS) scale, which is based primarily on muscarinic binding affinity.

RESULTS

In silico software (ADMET Predictor) used for screening drugs for their blood-brain barrier (BBB) penetration correctly identified some drugs that do not cross the BBB. The mean area under the curve for the unsupervised and ACB scale based on the five selected variables was 0.76 and 0.64, respectively. The unsupervised model agreed with the ACB scale on the classification of more than half of the drugs (49 of 88) agreed on the classification of less than half the drugs in the ATS scale (12 of 25).

CONCLUSIONS

Our findings suggest that the commonly used ACB scale may misclassify certain drugs due to their inability to cross the BBB. By contrast, the ATS scale would misclassify drugs solely depending on muscarinic binding affinity without considering other drug properties. Machine learning models can be trained on these features to build classification models that are easy to update and have greater generalizability.

Quantitative PET imaging and modeling of molecular blood-brain barrier permeability

Blood-brain barrier (BBB) disruption is involved in the pathogenesis and progression of many neurological and systemic diseases. Non-invasive assessment of BBB permeability in humans has mainly been performed with dynamic contrast-enhanced magnetic resonance imaging, evaluating the BBB as a structural barrier. Here, we developed a novel non-invasive positron emission tomography (PET) method in humans to measure the BBB permeability of molecular radiotracers that cross the BBB through different transport mechanisms. Our method uses high-temporal resolution dynamic imaging and kinetic modeling to jointly estimate cerebral blood flow and tracer-specific BBB transport rate from a single dynamic PET scan and measure the molecular permeability-surface area (PS) product of the radiotracer. We show our method can resolve BBB PS across three PET radiotracers with greatly differing permeabilities, measure reductions in BBB PS of 18F-fluorodeoxyglucose (FDG) in healthy aging, and demonstrate a possible brain-body association between decreased FDG BBB PS in patients with metabolic dysfunction-associated steatotic liver inflammation. Our method opens new directions to efficiently study the molecular permeability of the human BBB in vivo using the large catalogue of available molecular PET tracers.

Curcumin's membrane localization and disruptive effects on cellular processes - insights from neuroblastoma, leukemic cells, and Langmuir monolayers

In therapies, curcumin is now commonly formulated in liposomal form, administered through injections or creams. This enhances its concentration at the cellular level compared to its natural form ingestion. Due to its hydrophobic nature, curcumin is situated in the lipid part of the membrane, thereby modifying its properties and influencing processes The aim of the research was to investigate whether the toxicity of specific concentrations of curcumin, assessed through biochemical tests for the SK-N-SH and H-60 cell lines, is related to structural changes in the membranes of these cells, caused by the localization of curcumin in their hydrophobic regions. Biochemical tests were performed using spectrophotometric methods. Langmuir technique were used to evaluate the interaction of the curcumin with the studied lipids. Direct introduction of curcumin into the membranes alters their physicochemical parameters. The extent of these changes depends on the initial properties of the membrane. In the conducted research, it has been demonstrated that curcumin may exhibit toxicity to human cells. The mechanism of this toxicity is related to its localization in cell membranes, leading to their dysfunction. The sensitivity of cells to curcumin presence depends on the saturation level of their membranes; the more rigid the membrane, the lower the concentration of curcumin causes its disruption.

α-Glucosidase Inhibitory Activity of Prenylated Pyranocoumarins from Clausena excavata: Mechanism of Action, ADMET and Molecular Docking

Three naturally occurring prenylated pyranocoumarins, nordentatin (1), dentatin (2), and clausarin (3), isolated from the roots of Clausena excavata (Family Rutaceae), and O-methylclausarin (4) which was obtained by methylation of 3, were investigated for their α-glucosidase inhibitory activity. The mechanism of action and the in silico prediction of their physicochemical and ADMET properties as well as the molecular docking were also studied. Compounds 1-4 exhibited stronger α-glucosidase inhibitory activity than the positive control, acarbose, through a non-competitive mechanism. Among them, 3 exhibited the highest activity, with an IC50 of 8.36 μM, which is significantly stronger than that of acarbose (IC50=430.35 μM). The prenyl group on C-3 and the hydroxyl group on C-5 in 3 may play important roles in enhancing the activity. Calculated physicochemical and ADMET parameters of 1-4 satisfied the Lipinski's and Veber's rules. Molecular simulation analysis indicated they are promising drug candidates with no hepatotoxicity. Compound 3 exhibited potent activity in the experiment and demonstrated good drug properties based on the calculations. A molecular docking study revealed that 3 showed H-bonding and π-π stacking interactions with selective Phe321, as well as interactions with thirteen other amino acid residues of the α-glucosidase.

Prediction of blood-brain barrier permeability using machine learning approaches based on various molecular representation

The assessment of compound blood-brain barrier (BBB) permeability poses a significant challenge in the discovery of drugs targeting the central nervous system. Conventional experimental approaches to measure BBB permeability are labor-intensive, cost-ineffective, and time-consuming. In this study, we constructed six machine learning classification models by combining various machine learning algorithms and molecular representations. The model based on ExtraTree algorithm and random partitioning strategy obtains the best prediction result, with AUC value of 0.932±0.004 and balanced accuracy (BA) of 0.837±0.010 for the test set. We employed the SHAP method to identify important features associated with BBB permeability. In addition, matched molecular pair (MMP) analysis and representative substructure derivation method were utilized to uncover the transformation rules and distinctive structural features of BBB permeable compounds. The machine learning models proposed in this work can serve as an effective tool for assessing BBB permeability in the drug discovery for central nervous system disease.

Estimating Position-Dependent and Anisotropic Diffusivity Tensors from Molecular Dynamics Trajectories: Existing Methods and Future Outlook

Confinement can substantially alter the physicochemical properties of materials by breaking translational isotropy and rendering all physical properties position-dependent. Molecular dynamics (MD) simulations have proven instrumental in characterizing such spatial heterogeneities and probing the impact of confinement on materials' properties. For static properties, this is a straightforward task and can be achieved via simple spatial binning. Such an approach, however, cannot be readily applied to transport coefficients due to lack of natural extensions of autocorrelations used for their calculation in the bulk. The prime example of this challenge is diffusivity, which, in the bulk, can be readily estimated from the particles' mobility statistics, which satisfy the Fokker-Planck equation. Under confinement, however, such statistics will follow the Smoluchowski equation, which lacks a closed-form analytical solution. This brief review explores the rich history of estimating profiles of the diffusivity tensor from MD simulations and discusses various approximate methods and algorithms developed for this purpose. Besides discussing heuristic extensions of bulk methods, we overview more rigorous algorithms, including kernel-based methods, Bayesian approaches, and operator discretization techniques. Additionally, we outline methods based on applying biasing potentials or imposing constraints on tracer particles. Finally, we discuss approaches that estimate diffusivity from mean first passage time or committor probability profiles, a conceptual framework originally developed in the context of collective variable spaces describing rare events in computational chemistry and biology. In summary, this paper offers a concise survey of diverse approaches for estimating diffusivity from MD trajectories, highlighting challenges and opportunities in this area.

Discovery of Potent Multikinase Type-II Inhibitors Targeting CDK5 in the DFG-out Inactive State with Promising Potential against Glioblastoma

Kinases have proven valuable targets in successful cancer drug discovery projects, but not yet for malignant brain tumors where type-II inhibition of cyclin-dependent kinase 5 (CDK5) stabilizing the DFG-out inactive state has potential for design of selective and clinically efficient drug candidates. In the absence of crystallographic evidence for a CDK5 DFG-out inactive state protein-ligand complex, for the first time, a model was designed using metadynamics/molecular dynamics simulations. Glide docking of the ZINC15 biogenic database identified [pyrimidin-2-yl]amino-furo[3,2-b]-furyl-urea/amide hit chemical scaffolds. For four selected analogues (4, 27, 36, and 42), potent effects on glioblastoma cell viability in U87-MG, T98G, and U251-MG cell lines and patient-derived cultures were generally observed (IC50s ∼ 10-40 μM at 72 h). Selectivity profiling against 11 homologous kinases revealed multikinase inhibition (CDK2, CDK5, CDK9, and GSK-3α/β), most potent for GSK-3α in the nanomolar range (IC50s ∼ 0.23-0.98 μM). These compounds may therefore have diverse anticancer mechanisms of action and are of considerable interest for lead optimization.

Membrane Permeation of Psychedelic Tryptamines by Dynamic Simulations

Renewed scientific interest in psychedelic compounds represents one of the most promising avenues for addressing the current burden of mental health disorders. Classic psychedelics are a group of compounds that exhibit structural similarities to the naturally occurring neurotransmitter serotonin (5-HT). Acting on the 5-HT type 2A receptors (HT2ARs), psychedelics induce enduring neurophysiological changes that parallel their therapeutic psychological and behavioral effects. Recent preclinical evidence suggests that the ability of psychedelics to exert their action is determined by their ability to permeate the neuronal membrane to target a pool of intracellular 5-HT2ARs. In this computational study, we employ classical molecular dynamics simulations and umbrella sampling techniques to investigate the permeation behavior of 12 selected tryptamines and to characterize the interactions that drive the process. We aim at elucidating the impact of N-alkylation, indole ring substitution and positional modifications, and protonation on their membrane permeability. Dimethylation of the primary amine group and the introduction of a methoxy group at position 5 exhibited an increase in permeability. Moreover, there is a significant influence of positional substitutions on the indole groups, and the protonation of the molecules substantially increases the energy barrier at the center of the bilayer, making the compounds highly impermeable. All the information extracted from the trends predicted by the simulations can be applied in future drug design projects to develop psychedelics with enhanced activity.

Revealing the mechanistic interactions of profenofos and captan pesticides with serum protein via biophysical and computational investigations

Profenofos (PF) and captan (CT) are among the most utilized organophosphorus insecticides and phthalimide fungicides, respectively. To elucidate the physicochemical and influential toxicokinetic factors, the mechanistic interactions of serum albumin and either PF or CT were carried out in the current study using a series of spectroscopy and computational analyses. Both PF and CT could bind to bovine serum albumin (BSA), a representative serum protein, with moderate binding constants in a range of 103-104 M-1. The bindings of PF and CT did not induce noticeable BSA's structural changes. Both pesticides bound preferentially to the site I pocket of BSA, where the hydrophobic interaction was the main binding mode of PF, and the electrostatic interaction drove the binding of CT. As a result, PF and CT may not only induce direct toxicity by themselves, but also compete with therapeutic drugs and essential substances to sit in the Sudlow site I of serum albumin, which may interfere with the pharmacokinetics and equilibrium of drugs and other substances causing consequent adverse effects.

Free Energy Barriers for Passive Drug Transport through the Mycobacterium tuberculosis Outer Membrane: A Molecular Dynamics Study

The emergence of multi-drug-resistant tuberculosis strains poses a significant challenge to modern medicine. The development of new antituberculosis drugs is hindered by the low permeability of many active compounds through the extremely strong bacterial cell wall of mycobacteria. In order to estimate the ability of potential antimycobacterial agents to diffuse through the outer mycolate membrane, the free energy profiles, the corresponding activation barriers, and possible permeability modes of passive transport for a series of known antibiotics, modern antituberculosis drugs, and prospective active drug-like molecules were determined using molecular dynamics simulations with the all-atom force field and potential of mean-force calculations. The membranes of different chemical and conformational compositions, density, thickness, and ionization states were examined. The typical activation barriers for the low-mass molecules penetrating through the most realistic membrane model were 6-13 kcal/mol for isoniazid, pyrazinamide, and etambutol, and 19 and 25 kcal/mol for bedaquilin and rifampicin. The barriers for the ionized molecules are usually in the range of 37-63 kcal/mol. The linear regression models were derived from the obtained data, allowing one to estimate the permeability barriers from simple physicochemical parameters of the diffusing molecules, notably lipophilicity and molecular polarizability.

Pleiotropic Potential of Evernia prunastri Extracts and Their Main Compounds Evernic Acid and Atranorin: In Vitro and In Silico Studies

Evernia prunastri is a lichen widely distributed in the Northern Hemisphere. Its biological properties still need to be discovered. Therefore, our paper focuses on studies of E. prunastri extracts, including its main metabolites evernic acid (EA) or atranorin (ATR). Phytochemical profiles using chromatographic analysis were confirmed. The antioxidant activity was evaluated using in vitro chemical tests and in vitro enzymatic cells-free tests, namely superoxide dismutase (SOD), glutathione peroxidase (GPx), glutathione reductase (GR), and catalase (CAT). The anti-inflammatory potential using cyclooxygenase-2 (COX-2) and hyaluronidase were determined. The neuroprotective potential using acetylcholinesterase, (AChE), butyrylcholinesterase (BChE), and tyrosinase (Tyr) was estimated. The hypoglycemic activity was also confirmed (α-glucosidase). Principal component analysis was performed to determine the relationship between the biological activity of extracts. The inhibitory effect of EA and ATR on COX-2 AChE, BChE, Tyr, and α-glucosidase was evaluated using molecular docking techniques and confirmed for EA and ATR (besides α-glucosidase). The penetration of EA and ATR from extracts through the blood-brain barrier was confirmed using the parallel artificial membrane permeability assay blood-brain barrier test. In conclusion, depending on chemical surroundings and the concentration, the E. prunastri extracts, EA or ATR, showed attractive pleiotropic properties, which should be further investigated.

Exploring novel anticancer pyrazole benzenesulfonamides featuring tail approach strategy as carbonic anhydrase inhibitors

This study aimed to design potent carbonic anhydrase inhibitors (CAIs) based on pyrazole benzenesulfonamide core. Nine series of substituted pyrazole benzenesulfonamide compounds were synthesized with variable groups like sulphamoyl group as in compounds 4a-e, its bioisosteric carboxylic acid as in compounds 5a-e and 8e, ethyl carboxylate ester as in compounds 6a-e and 9a-e, which were designed as potential prodrugs, isothiazole ring as in compound 7, hydrazide derivative 10e, hydroxamic acid derivatives 11a-e and semicarbazide derivatives 12a-c,e. All the synthesized compounds were investigated for their carbonic anhydrase (CA) inhibitory activity against two human CA isoforms hCA IX and hCA XII and compared to acetazolamide (AAZ). Also, the compounds were assessed for their anticancer activity against 60 cancer cell lines according to the US NCI protocol. Compounds 4b, 5b, 5d, 5e, 6b, 9b, 9e and 11b revealed significant inhibitory activity against both isoforms hCA IX and hCA XII, while 6e, 9d, 11d and 11e showed significant inhibitory activity against hCA XII only compared to acetazolamide as a reference. This would highlight these compounds as promising anticancer drugs. Moreover, compound 6e revealed a remarkable cytostatic activity against CNS cancer cell line (SF-539; TGI = 5.58 μM), renal cancer cell line (786-0; TGI = 4.32 μM) and breast cancer cell line (HS 578 T; TGI = 5.43 μM). Accordingly, compound 6e was subjected to cell cycle analysis and apoptotic assay on the abovementioned cell lines at the specified GI50 (0.45, 0.89 and 1.18 μM, respectively). Also, it revealed the increment of total apoptotic cells percentage in 786-0 (53.19%), SF-539 (46.11%) and HS 578 T (43.55%) relative to the control cells (2.07, 2.64 and 2.52%, respectively). In silico prediction of BBB permeability showed that most of the calculations for compound 6e resulted as BBB (+), which is required for a compound targeting CNS. Further, the interaction of the most active compounds with the key amino acids in the active sites of hCA IX and hCA XII was highlighted by molecular docking analysis.

Advances in Computational Approaches for Estimating Passive Permeability in Drug Discovery

Passive permeation of cellular membranes is a key feature of many therapeutics. The relevance of passive permeability spans all biological systems as they all employ biomembranes for compartmentalization. A variety of computational techniques are currently utilized and under active development to facilitate the characterization of passive permeability. These methods include lipophilicity relations, molecular dynamics simulations, and machine learning, which vary in accuracy, complexity, and computational cost. This review briefly introduces the underlying theories, such as the prominent inhomogeneous solubility diffusion model, and covers a number of recent applications. Various machine-learning applications, which have demonstrated good potential for high-volume, data-driven permeability predictions, are also discussed. Due to the confluence of novel computational methods and next-generation exascale computers, we anticipate an exciting future for computationally driven permeability predictions.

A machine learning-based quantitative model (LogBB_Pred) to predict the blood-brain barrier permeability (logBB value) of drug compounds

MOTIVATION

Efficient assessment of the blood-brain barrier (BBB) penetration ability of a drug compound is one of the major hurdles in central nervous system drug discovery since experimental methods are costly and time-consuming. To advance and elevate the success rate of neurotherapeutic drug discovery, it is essential to develop an accurate computational quantitative model to determine the absolute logBB value (a logarithmic ratio of the concentration of a drug in the brain to its concentration in the blood) of a drug candidate.

RESULTS

Here, we developed a quantitative model (LogBB_Pred) capable of predicting a logBB value of a query compound. The model achieved an R2 of 0.61 on an independent test dataset and outperformed other publicly available quantitative models. When compared with the available qualitative (classification) models that only classified whether a compound is BBB-permeable or not, our model achieved the same accuracy (0.85) with the best qualitative model and far-outperformed other qualitative models (accuracies between 0.64 and 0.70). For further evaluation, our model, quantitative models, and the qualitative models were evaluated on a real-world central nervous system drug screening library. Our model showed an accuracy of 0.97 while the other models showed an accuracy in the range of 0.29-0.83. Consequently, our model can accurately classify BBB-permeable compounds as well as predict the absolute logBB values of drug candidates.

AVAILABILITY AND IMPLEMENTATION

Web server is freely available on the web at http://ssbio.cau.ac.kr/software/logbb_pred/. The data used in this study are available to download at http://ssbio.cau.ac.kr/software/logbb_pred/dataset.zip.

Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules

Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.

Chitosan-Coated MIL-100(Fe) as an Anticancer Drug Carrier: Theoretical and Experimental Investigation

MIL-100(Fe) was synthesized under biofriendly conditions at room temperature and pressure using iron(II) chloride as the source of iron, and it was coated with chitosan (CS), a natural polysaccharide. In this study, we used a computational technique to predict the amount of drug loading in MIL-100(Fe) and MIL-100(Fe)/CS with molecular dynamics software LAMMPS. Powder X-ray diffraction analysis was conducted to characterize the chitosan-coated MIL-100(Fe) loaded with cyclophosphamide (MIL-100(Fe)/CS/CP). The drug loading and release processes were quantified using UV spectroscopy at 193 nm. The toxic effect of MIL-100(Fe)/CS/CP was determined on human breast cancer (MCF-7) cells. In vivo images and H&E analysis show inhibition properties of MIL-100(Fe)/CS/CP on tumor cells. The conducted research indicates that computational calculation provides a unique insight into the drug adsorption since a proper understanding of the atomic interaction of MIL-100(Fe)/CS with anticancer drugs is important for developing experimental investigations. The biocompatibility and anticancer properties of chitosan molecules enhanced the tumor inhibitory effect of the particles compared with the MIL-100(Fe)/CP and free cyclophosphamide treatments.

Transport of Heptane Molecules across Water-Vapor Interfaces Laden with Surfactants

Molecular transport across liquid-vapor interfaces covered by surfactant monolayers plays a key role in applications such as fire suppression by foams. The molecular understanding of such transport, however, remains incomplete. This work uses molecular dynamics simulations to investigate the heptane transport across water-vapor interfaces populated with sodium dodecyl sulfate (SDS) surfactants. Heptane molecules' potential of mean force (PMF) and local diffusivity profiles across SDS monolayers with different SDS densities are calculated to obtain heptane's transport resistance. We show that a heptane molecule experiences a finite resistance as it crosses water-vapor interfaces covered by SDS. Such interfacial transport resistance is contributed significantly by heptane molecules' high PMF in the SDS headgroup region and their slow diffusion there. This resistance increases linearly as the SDS density rises from zero but jumps as the density approaches saturation when its value is equivalent to that afforded by a 5 nm thick layer of bulk water. These results are understood by analyzing the micro-environment experienced by a heptane molecule crossing SDS monolayers and the local perturbation it brings to the monolayers. The implications of these findings for the design of surfactants to suppress heptane transport through water-vapor interfaces are discussed.

Molecular Design of Magnetic Resonance Imaging Agents Binding to Amyloid Deposits

The ability to detect and monitor amyloid deposition in the brain using non-invasive imaging techniques provides valuable insights into the early diagnosis and progression of Alzheimer's disease and helps to evaluate the efficacy of potential treatments. Magnetic resonance imaging (MRI) is a widely available technique offering high-spatial-resolution imaging. It can be used to visualize amyloid deposits with the help of amyloid-binding diagnostic agents injected into the body. In recent years, a number of amyloid-targeted MRI probes have been developed, but none of them has entered clinical practice. We review the advances in the field and deduce the requirements for the molecular structure and properties of a diagnostic probe candidate. These requirements make up the base for the rational design of MRI-active small molecules targeting amyloid deposits. Particular attention is paid to the novel cryo-EM structures of the fibril aggregates and their complexes, with known binders offering the possibility to use computational structure-based design methods. With continued research and development, MRI probes may revolutionize the diagnosis and treatment of neurodegenerative diseases, ultimately improving the lives of millions of people worldwide.

A Novel Inhibitor of DKK1/LRP6 Interactions Against the Alzheimer Disease: An Insilco Approach

The activation of the Wnt signaling pathway is implicated in a neuroprotective mechanism against the Alzheimer disease. When this pathway is blocked, it activates GSK3 beta, leading to tau hyperphosphorylation and the apoptosis of neurons. Dickkopf-related protein 1 (DKK1) is a protein that competes with the Wnt ligand for the low-density lipoprotein receptor-related protein 6 (LRP6) receptor's binding, interrupting the Wnt-induced Fzd-Wnt-LRP6 complex. This counteracts Wnt's neuroprotective effect and contributes to the progression of the Alzheimer disease. The aim of this study was to use in silico approach to develop new agents that can combat the Alzheimer disease by targeting the interaction between DKK1 and LRP6. To achieve this, we conducted a virtual screening (Vsw) of the Asinex-CNS database library (n = 54 513) compounds against a generated grid in LRP6 protein. From this screening, we selected 6 compounds based on their docking score and performed molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations on the selected ligands. Next, we evaluated the Absorption, Distribution, Metabolism, and Excretion (ADME) results of the 6 screened compounds using the Quick prop module of Schrödinger. We then employed several computational techniques, including PCA (Principal Component Analysis), DCCM (Dynamic Cross-Correlation Map), molecular dynamics simulation, and molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA)-based negative binding free energy (BFE) calculation, to further analyze the compounds. Our extensive computational analysis resulted in the identification of 3 potential hits, LAS 29757582, LAS 29984441, and LAS 29757942. These compounds were found to block the interaction of DKK1 with LRP6 (A and B interface) protein, and their potential as therapeutic agents was supported by negative BFE calculation. Therefore, these compounds show potential as possible therapeutic agents for treating the Alzheimer disease through targeting the interaction between DKK1 and LRP6.

Explaining Blood-Brain Barrier Permeability of Small Molecules by Integrated Analysis of Different Transport Mechanisms

The blood-brain barrier (BBB) represents a major obstacle to delivering drugs to the central nervous system (CNS), resulting in the lack of effective treatment for many CNS diseases including brain cancer. To accelerate CNS drug development, computational prediction models could save the time and effort needed for experimental evaluation. Here, we studied BBB permeability focusing on active transport (influx and efflux) as well as passive diffusion using previously published and self-curated data sets. We created prediction models based on physicochemical properties, molecular substructures, or their combination to understand which mechanisms contribute to BBB permeability. Our results show that features that predicted passive diffusion over membranes overlap with features that explain endothelial permeation of approved CNS-active drugs. We also identified physical properties and molecular substructures that positively or negatively predicted BBB transport. These findings provide guidance toward identifying BBB-permeable compounds by optimally matching physicochemical and molecular properties to BBB transport mechanisms.

In Silico-Motivated Discovery of Novel Potent Glycogen Synthase-3 Inhibitors: 1-(Alkyl/arylamino)-3H-naphtho[1,2,3-de]quinoline-2,7-dione Identified as a Scaffold for Kinase Inhibitor Development

Glycogen synthase kinase-3 (GSK-3) isoforms α and β have diverse roles within cell biology, and have been linked with multiple diseases that include prominent CNS conditions such as Alzheimer's disease and several psychiatric disorders. In this study, motivated by computation, we aimed to identify novel ATP-binding site inhibitors of GSK-3 with CNS-active potential. A ligand screening (docking) protocol against GSK-3β was first optimized, employing an active/decoy benchmarking set, with the final protocol selected based on statistical performance analysis. The optimized protocol involved pre-filtering of ligands using a three-point 3D-pharmacophore, followed by Glide-SP docking applying hinge region hydrogen bonding constraints. Using this approach, the Biogenic subset of the ZINC15 compound database was screened, focused on compounds with potential for CNS-activity. Twelve compounds (generation I) were selected for experimental validation using in vitro GSK-3β binding assays. Two hit compounds, 1 and 2, with 6-amino-7H-benzo[e]perimidin-7-one and 1-(phenylamino)-3H-naphtho[1,2,3-de]quinoline-2,7-dione type scaffolds were identified with IC₅₀ values of 1.63 µM and 20.55 µM, respectively. Ten analogues of 2 (generation II) were selected for structure activity relationship (SAR) analysis and revealed four low micromolar inhibitors (<10 µM), with 19 (IC₅₀ = 4.1 µM)~five times more potent than initial hit compound 2. Selectivity screening of low micromolar inhibitors 14 and 19 (comparing aryl- and alkyl-substituents) against 10 homologous kinases revealed unique selectivity profiles, with both compounds more potent against the GSK-3α isoform (IC₅₀s~2 µM) and, additionally, inhibitors of PKBβ (IC₅₀s < 25 µM). Compound 14 also inhibited ERK2 and 19, PKCγ, but generally good selectivity for GSK-3 isoforms over the other kinases was observed. The compounds had excellent predicted oral bioavailability and CNS-activity profiles, presenting promising candidates for future testing in cellular models of disease.

Micro- and Nanoplastics Breach the Blood-Brain Barrier (BBB): Biomolecular Corona's Role Revealed

Humans are continuously exposed to polymeric materials such as in textiles, car tires and packaging. Unfortunately, their break down products pollute our environment, leading to widespread contamination with micro- and nanoplastics (MNPs). The blood-brain barrier (BBB) is an important biological barrier that protects the brain from harmful substances. In our study we performed short term uptake studies in mice with orally administered polystyrene micro-/nanoparticles (9.55 µm, 1.14 µm, 0.293 µm). We show that nanometer sized particles-but not bigger particles-reach the brain within only 2 h after gavage. To understand the transport mechanism, we performed coarse-grained molecular dynamics simulations on the interaction of DOPC bilayers with a polystyrene nanoparticle in the presence and absence of various coronae. We found that the composition of the biomolecular corona surrounding the plastic particles was critical for passage through the BBB. Cholesterol molecules enhanced the uptake of these contaminants into the membrane of the BBB, whereas the protein model inhibited it. These opposing effects could explain the passive transport of the particles into the brain.

In-silico targeting TMPK from monkey pox virus: Molecular docking analysis, density functional theory studies and molecular dynamic simulation analysis

The World Health Organization (WHO) proclaimed the monkeypox epidemic a "public health emergency of worldwide significance" recently. The monkeypox virus is a member of the same Orthopoxvirus genus as the smallpox virus. Although smallpox medications are advised against monkeypox, no monkeypox-specific drugs are currently available. In the event of such an outbreak, in-silico medication identification is a practical and efficient strategy. As a result, we report a computational drug repurposing analysis to discover medicines that may be potential inhibitors of thymidylate kinase, a critical monkeypox viral enzyme. The target protein structure of the monkeypox virus was modeled using the vaccinia virus's homologous protein structure. Using molecular docking and density functional theory, we found 11 possible inhibitors of the monkeypox virus from an Asinex library of 261120 chemicals. The primary purpose of this in silico work is to find possible inhibitors of monkeypox viral proteins that can then be experimentally tested in order to develop innovative therapeutic medicines for monkeypox infection.Communicated by Ramaswamy H. Sarma.

Novel 7-Chloro-4-aminoquinoline-benzimidazole Hybrids as Inhibitors of Cancer Cells Growth: Synthesis, Antiproliferative Activity, in Silico ADME Predictions, and Docking

In this study, new 7-chloro-4-aminoquinoline-benzimidazole compounds were synthesized and characterized by NMR, MS, and elemental analysis. These novel hybrids differ in the type of linker and in the substituent on the benzimidazole moiety. Their antiproliferative activities were evaluated on one non-tumor (MDCK1) and seven selected tumor (CaCo-2, MCF-7, CCRF-CEM, Hut78, THP-1, and Raji) cell lines by MTT test and flow cytometry analysis. The compounds with different types of linkers and an unsubstituted benzimidazole ring, 5d, 8d, and 12d, showed strong cytotoxic activity (the GI50 ranged from 0.4 to 8 µM) and effectively suppressed the cell cycle progression in the leukemia and lymphoma cells. After 24 h of treatment, compounds 5d and 12d induced the disruption of the mitochondrial membrane potential as well as apoptosis in HuT78 cells. The drug-like properties and bioavailability of the compounds were calculated using the Swiss ADME web tool, and a molecular docking study was performed on tyrosine-protein kinase c-Src (PDB: 3G6H). Compound 12d showed good solubility and permeability and bound to c-Src with an energy of -119.99 kcal/mol, forming hydrogen bonds with Glu310 and Asp404 in the active site and other residues with van der Waals interactions. The results suggest that compound 12d could be a leading compound in the further design of effective antitumor drugs.

A study on attempt for determination of permeation kinetics of coumarin at lipid bilayer of liposomes by using capillary electrophoresis with moment analysis theory

It was tried to develop a moment analysis method for the determination of lipid membrane permeability. The first absolute and second central moments of elution peaks measured by liposome electrokinetic chromatography (LEKC) are analyzed by using moment equations. As a concrete example, elution peak profiles of coumarin in a LEKC system, in which liposomes consisting of 1-palmitoyl-2-oleoyl-sn‑glycero-3-phosphocholine (POPC) and phosphatidylserine (PS) are used as a pseudo-stationary phase, were analyzed. It seems that lipid membrane permeability of coumarin across the lipid bilayer of POPC/PS liposomes was measured by the moment analysis method because previous permeability measurements using parallel artificial membrane permeability assay (PAMPA) and Caco-2 cells indicated that coumarin is permeable across lipid bilayer. However, it was also pointed out that the moment analysis method with LEKC is not effective for the determination of lipid membrane permeability and that it provides information about adsorption/desorption kinetics at lipid bilayer of liposomes. Therefore, different moment equations were also developed for the determination of adsorption/desorption rate constants of coumarin from the LEKC data. It was demonstrated that permeation rate constants at lipid bilayer or adsorption/desorption rate constants can be determined from the LEKC data on the basis of moment analysis theory for the mass transfer phenomena of coumarin at the lipid bilayer of POPC/PS liposomes. Mass transfer kinetics of solutes at lipid bilayer should be determined under the conditions that liposomes originally be because they are self-assembling and dynamic systems formed through weak interactions between phospholipid monomers. The moment analysis method using LEKC is effective for the experimental determination of the mass transfer rate constants at the lipid bilayer of liposomes because neither immobilization nor chemical modification of liposomes is necessary when LEKC data are measured. It is expected that the results of this study contribute to the dissemination of an opportunity for the determination of permeation rate constants or adsorption/desorption rate constants at the lipid bilayer of liposomes to many researchers because capillary electrophoresis is widespread.

Characterisation of the Molecular Mechanism of Permeation of the Prodrug Me-5ALA across the Human Stratum Corneum Using Molecular Dynamics Simulations

The barrier imposed by the outer layer of the skin, the stratum corneum, creates an almost impermeable environment for exogenous substances. Few lipophilic drugs with low molecular mass can passively diffuse through this layer, highlighting the need to develop methods to enable the delivery of more drugs via the transdermal route. The prodrug approach involves modifying the structure of a drug molecule to enhance its permeability across the skin, but it is often difficult to predict how exactly changes in chemical structure affect permeation. This study uses molecular dynamics simulations to predict permeability values and adequately characterise the molecular mechanism of permeation of the prodrugs Me-5ALA and its parent compound 5ALA across a molecular model of the lipid bilayers of the human stratum corneum. The influence of increased hydrophobicity in Me-5ALA on its permeation revealed a reduction in hydrogen bonding capability that enables it to interact more favourably with the hydrophobic region of the bilayer and diffuse at a faster rate with less resistance, thus making it a better permeant compared to its more hydrophilic parent compound. This molecular simulation approach offers a promising route for the rational design of drug molecules that can permeate effectively across the stratum corneum.

Molecular simulations of fluoxetine in hydrated lipid bilayers, as well as in aqueous solutions containing β-cyclodextrin

Fluoxetine, which is a well-known antidepressant drug, is studied in hydrated cholesterol-free and cholesterol-containing lipid bilayers through unbiased and biased atomistic molecular dynamics simulations. The latter are conducted for the calculation of the potential of mean force (PMF) of fluoxetine along an axis perpendicular to the two leaflets of the bilayer. The PMF indicates that the drug prefers to reside inside the lipid phase and allows us to calculate important thermodynamic properties, such as the Gibbs energy difference of partitioning from the water to the lipid phase and the Gibbs energy barrier for hopping events between the two leaflets of the bilayer. The results from the biased simulations are in accord with the mass density profiles calculated from the unbiased simulations. Moreover, we estimate the effect of fluoxetine mole fraction on the order parameters of the lipid alkyl chains and on the area per lipid. It is also found that fluoxetine forms a hydrogen bond network with lipids and water molecules penetrating into the lipid phase. In addition, fluoxoetine is studied in detail in aqueous solutions containing β-cyclodextrin. It is observed from unbiased molecular dynamics simulations that the two aforementioned molecules form a noncovalent complex spontaneously and the calculated binding free energy is in agreement with the literature.

Privileged multi-target directed propargyl-tacrines combining cholinesterase and monoamine oxidase inhibition activities

Twenty-four novel compounds bearing tetrahydroacridine and N-propargyl moieties have been designed, synthesised, and evaluated in vitro for their anti-cholinesterase and anti-monoamine oxidase activities. Propargyltacrine 23 (IC₅₀ = 21 nM) was the most potent acetylcholinesterase (AChE) inhibitor, compound 20 (IC₅₀ = 78 nM) showed the best inhibitory human butyrylcholinesterase (hBChE) profile, and ligand 21 afforded equipotent and significant values on both ChEs (human AChE [hAChE]: IC₅₀ = 0.095 ± 0.001 µM; hBChE: IC₅₀ = 0.093 ± 0.003 µM). Regarding MAO inhibition, compounds 7, 15, and 25 demonstrated the highest inhibitory potential towards hMAO-B (IC₅₀ = 163, 40, and 170 nM, respectively). In all, compounds 7, 15, 20, 21, 23, and 25 exhibiting the most balanced pharmacological profile, were submitted to permeability and cell viability tests. As a result, 7-phenoxy-N-(prop-2-yn-1-yl)-1,2,3,4-tetrahydroacridin-9-amine hydrochloride (15) has been identified as a permeable agent that shows a balanced pharmacological profile [IC₅₀ (hAChE) = 1.472 ± 0.024 µM; IC₅₀ (hBChE) = 0.659 ± 0.077 µM; IC₅₀ (hMAO-B) = 40.39 ± 5.98 nM], and consequently, as a new hit-ligand that deserves further investigation, in particular in vivo analyses, as the preliminary cell viability test results reported here suggest that this is a relatively safe therapeutic agent.

Predicting Blood–Brain Barrier Permeation of Erlotinib and JCN037 by Molecular Simulation

Glioblastoma (GBM) is a highly malignant primary brain tumor, and epidermal growth factor receptor (EGFR) is a well characterized biomaker on GBM. Treatment of GBM with EGFR inhibitors achieved limited efficacy due to low blood-brain barrier (BBB) permeability, and BBB-penetrant drugs are required. In this study, the BBB penetration of erlotinib and JN037 were studied using molecular dynamics method with explicit membrane model. The free energy profiles indicate that JCN037 has a lower central energy barrier than erlotinib, and it has a local minimum at lipid-water interface while erlotinib has not. Unconstrained MD simulations found that erlotinib prefers staying in water while JCN037 tends to interact with lipid molecules. Further analysis reveals that the Br atom of JCN037 plays an important role in its interaction with lipid molecules, and the adjacent F atom enhances the interaction of Br. The two flexible methoxyethoxy chains of erlotinib are responsible for its poor penetration. Our computational results agree well with the experimental results, providing useful information in the design and improvement of drugs with good BBB permeation.

Consensus docking and MM-PBSA computations identify putative furin protease inhibitors for developing potential therapeutics against COVID-19

The coronavirus disease 2019 (COVID-19) is a pandemic that has severely posed substantial health challenges and claimed millions of lives. Though vaccines have been produced to stem the spread of this disease, the death rate remains high since drugs used for treatment have therapeutic challenges. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes the disease, has a slew of potential therapeutic targets. Among them is the furin protease, which has a cleavage site on the virus’s spike protein. The cleavage site facilitates the entry of the virus into human cells via cell–cell fusion. This critical involvement of furin in the disease pathogenicity has made it a viable therapeutic strategy against the virus. This study employs the consensus docking approach using HYBRID and AutoDock Vina to virtually screen a pre-filtered library of 3942 natural product compounds of African origin against the human furin protease (PDB: 4RYD). Twenty of these compounds were selected as hits after meeting molecular docking cut-off of − 7 kcal.mol⁻¹, pose alignment inspection, and having favorable furin-ligand interactions. An area under the curve (AUC) value of 0.72 was computed from the receiver operator characteristic (ROC) curve, and Boltzmann-enhanced discrimination of the ROC curve (BEDROC) value of 0.65 showed that AutoDock Vina was a reasonable tool for selecting actives for this target. Seven of these hits were proposed as potential leads having had bonding interactions with catalytic triad residues Ser368, His194, and Asp153, and other essential residues in the active site with plausible binding free energies between − 189 and − 95 kJ/mol from the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations as well as favorable ADME/Tox properties. The molecules were also predicted as antiviral, anti-inflammatory, membrane permeability inhibitors, RNA synthesis inhibitors, cytoprotective, and hepatoprotective with probable activity (Pa) above 0.5 and probable inactivity values below 0.1. Some of them also have anti-influenza activity. Influenza virus has many similarities with SARS-CoV-2 in their mode of entry into human cells as both are facilitated by the furin protease. Pinobanksin 3-(E)-caffeate, one of the potential leads is a propolis compound. Propolis compounds have shown inhibitory effects against ACE2, TMPRSS2, and PAK1 signaling pathways of SARS-CoV-2 in previous studies. Likewise, quercitrin is structurally similar to isoquercetin, which is currently in clinical trials as possible medication for COVID-19.

Lipid Composition Is Critical for Accurate Membrane Permeability Prediction of Cyclic Peptides by Molecular Dynamics Simulations

Cyclic peptides have attracted attention as a promising pharmaceutical modality due to their potential to selectively inhibit previously undruggable targets, such as intracellular protein-protein interactions. Poor membrane permeability is the biggest bottleneck hindering successful drug discovery based on cyclic peptides. Therefore, the development of computational methods that can predict membrane permeability and support elucidation of the membrane permeation mechanism of drug candidate peptides is much sought after. In this study, we developed a protocol to simulate the behavior in membrane permeation steps and estimate the membrane permeability of large cyclic peptides with more than or equal to 10 residues. This protocol requires the use of a more realistic membrane model than a single-lipid phospholipid bilayer. To select a membrane model, we first analyzed the effect of cholesterol concentration in the model membrane on the potential of mean force and hydrogen bonding networks along the direction perpendicular to the membrane surface as predicted by molecular dynamics simulations using cyclosporine A. These results suggest that a membrane model with 40 or 50 mol % cholesterol was suitable for predicting the permeation process. Subsequently, two types of membrane models containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and 40 and 50 mol % cholesterol were used. To validate the efficiency of our protocol, the membrane permeability of 18 ten-residue peptides was predicted. Correlation coefficients of R > 0.8 between the experimental and calculated permeability values were obtained with both model membranes. The results of this study demonstrate that the lipid membrane is not just a medium but also among the main factors determining the membrane permeability of molecules. The computational protocol proposed in this study and the findings obtained on the effect of membrane model composition will contribute to building a schematic view of the membrane permeation process. Furthermore, the results of this study will eventually aid the elucidation of design rules for peptide drugs with high membrane permeability.

A merged molecular representation deep learning method for blood-brain barrier permeability prediction

The ability of a compound to permeate across the blood-brain barrier (BBB) is a significant factor for central nervous system drug development. Thus, for speeding up the drug discovery process, it is crucial to perform high-throughput screenings to predict the BBB permeability of the candidate compounds. Although experimental methods are capable of determining BBB permeability, they are still cost-ineffective and time-consuming. To complement the shortcomings of existing methods, we present a deep learning-based multi-model framework model, called Deep-B3, to predict the BBB permeability of candidate compounds. In Deep-B3, the samples are encoded in three kinds of features, namely molecular descriptors and fingerprints, molecular graph and simplified molecular input line entry system (SMILES) text notation. The pre-trained models were built to extract latent features from the molecular graph and SMILES. These features depicted the compounds in terms of tabular data, image and text, respectively. The validation results yielded from the independent dataset demonstrated that the performance of Deep-B3 is superior to that of the state-of-the-art models. Hence, Deep-B3 holds the potential to become a useful tool for drug development. A freely available online web-server for Deep-B3 was established at http://cbcb.cdutcm.edu.cn/deepb3/, and the source code and dataset of Deep-B3 are available at https://github.com/GreatChenLab/Deep-B3.

Combined ligand-based and structure-based design of PDE 9A inhibitors against Alzheimer's disease

PDE9 enzyme hydrolyzes cGMP, which is involved in the regulation of synaptic plasticity through the NMDA pathway (a well-known excitotoxic target for AD) via activation of calcium/calmodulin-dependent neuronal NO synthases in the postsynaptic neurons. The inhibition of PDE9 leads to elevated cGMP levels, causing enhanced NMDA signaling and thus contributing to an increase in synaptic plasticity and stabilization. Therefore, it could be considered a pertinent target for AD drug discovery. PF-04447943 and BI-409306 targeting PDE9 are undergoing clinical trials (Phase II). The present study encompasses a pharmacophoric approach to identify potent PDE9 inhibitors using various computational methods. Pharmacophores generated from the PDB 6A3N yielded 37,554 virtual hits, which underwent drug likeliness and PAINS filtering to arrive at a few virtual leads. The leads were further subjected to extra precision docking, ADMET predictions, and molecular dynamics. The final hits, ZINC000001305675 and ZINC000000377099, showed superior docking scores of - 10.90 and - 10.30 kcal/mol and satisfactory predicted ADMET scores. The hits were subjected to molecular dynamics (MD) studies, wherein they formed stable complexes with PDE9 protein and had ligand RMSDs within acceptable limits. The processes involved in the combined ligand and structure-based strategies.

Drug Repurposing, a Fast-Track Approach to Develop Effective Treatments for Glioblastoma

Glioblastoma (GBM) remains one of the most difficult tumors to treat. The mean overall survival rate of 15 months and the 5-year survival rate of 5% have not significantly changed for almost 2 decades. Despite progress in understanding the pathophysiology of the disease, no new effective treatments to combine with radiation therapy after surgical tumor debulking have become available since the introduction of temozolomide in 1999. One of the main reasons for this is the scarcity of compounds that cross the blood-brain barrier (BBB) and reach the brain tumor tissue in therapeutically effective concentrations. In this review, we focus on the role of the BBB and its importance in developing brain tumor treatments. Moreover, we discuss drug repurposing, a drug discovery approach to identify potential effective candidates with optimal pharmacokinetic profiles for central nervous system (CNS) penetration and that allows rapid implementation in clinical trials. Additionally, we provide an overview of repurposed candidate drug currently being investigated in GBM at the preclinical and clinical levels. Finally, we highlight the importance of phase 0 trials to confirm tumor drug exposure and we discuss emerging drug delivery technologies as an alternative route to maximize therapeutic efficacy of repurposed candidate drug.

Time-dependent effect of clarithromycin on pro-inflammatory cytokines in CRS

Background: The purpose of this study was to assess the time-effect of clarithromycin on the inflammatory response in chronic rhinosinusitis (CRS), to further explore the use of macrolides in cell culture as a model for CRS, and its action on the immune system. Methodology: The time effect of clarithromycin on several cytokines was examined for IL-1β, IL-4, IL-5, IL-8 and GM-CSF. Samples prior and post-incubation were assessed, as well as samples collected 24h following removal of clarithromycin to determine if any immunomodulatory effect persisted. Cytokines were quantified using ProcartaPlexTM assays. Results: Of the 5 cytokines assessed, only IL-1β and IL-8 production were significantly inhibited at 4h. Increased levels of IL-4 were observed at 72 hours of incubation and returned to near baseline levels after its removal. IL-8 showed the most time-dependent relationship with clarithromycin. No differences between the expression of IL-5 and GM-CSF were found. Conclusions: The present work suggests a specific and dose-dependent impact of clarithromycin on the inflammatory response in CRS. Moreover, the immunomodulatory effects of clarithromycin on the cytokines IL-4 and IL-8 varied depending on length of exposure to clarithromycin. Further studies to further establish the relationship between length of exposure and cytokine expression, and with additional “actors” in CRS pathophysiology should be considered. This may enable us in the future to determine appropriate duration of macrolide therapy in patients with CRS.

Characterization of prodigiosin pigment by Serratia marcescens and the evaluation of its bioactivities

The aim of the present study is to discover a bacterial pigment providing protection and prevention of neurological damage and cancer development, which can have a role as a non-synthetic food additive in the food industry as well as an active drug ingredient of anticancer drugs and pharmaceuticals for neural injury. Within this scope, Serratia marcescens MB703 strain was used to produce prodigiosin. Characterization of the prodigiosin was carried out using UV-VIS, and FT-IR. In addition, its inhibitory action on AChE and antioxidant activities were determined. The cytotoxic, genotoxic and antigenotoxic activities of the prodigiosin as well as its antiproliferative activities were detected. It was determined that the maximum production of the prodigiosin (72 mg/L). The prodigiosin was found to cause no significant difference in its inhibitory effect on AChE. The prodigiosin was found effective on all antioxidant parameters tested. The IC₅₀ values of the prodigiosin on SK-MEL-30 and HT-29 cells were calculated as 70 and 47 μM, respectively. This IC₅₀ values of the prodigiosin showed no cytotoxic effect on L929 cells. Prodigiosin did not have genotoxic effect alone and also seem to decrease DNA damage induced by H₂O₂ in L929 cells. The findings of in vitro experimental studies suggest that using the prodigiosin pigment as a drug candidate for cancer and neurodegenerative disease therapy is both effective and safe.

Biological Membrane-Penetrating Peptides: Computational Prediction and Applications

Peptides comprise a versatile class of biomolecules that present a unique chemical space with diverse physicochemical and structural properties. Some classes of peptides are able to naturally cross the biological membranes, such as cell membrane and blood-brain barrier (BBB). Cell-penetrating peptides (CPPs) and blood-brain barrier-penetrating peptides (B3PPs) have been explored by the biotechnological and pharmaceutical industries to develop new therapeutic molecules and carrier systems. The computational prediction of peptides’ penetration into biological membranes has been emerged as an interesting strategy due to their high throughput and low-cost screening of large chemical libraries. Structure- and sequence-based information of peptides, as well as atomistic biophysical models, have been explored in computer-assisted discovery strategies to classify and identify new structures with pharmacokinetic properties related to the translocation through biomembranes. Computational strategies to predict the permeability into biomembranes include cheminformatic filters, molecular dynamics simulations, artificial intelligence algorithms, and statistical models, and the choice of the most adequate method depends on the purposes of the computational investigation. Here, we exhibit and discuss some principles and applications of these computational methods widely used to predict the permeability of peptides into biomembranes, exhibiting some of their pharmaceutical and biotechnological applications.

Brain and Retinal Organoids for Disease Modeling: The Importance of In Vitro Blood–Brain and Retinal Barriers Studies

Brain and retinal organoids are functional and dynamic in vitro three-dimensional (3D) structures derived from pluripotent stem cells that spontaneously organize themselves to their in vivo counterparts. Here, we review the main literature data of how these organoids have been developed through different protocols and how they have been technically analyzed. Moreover, this paper reviews recent advances in using organoids to model neurological and retinal diseases, considering their potential for translational applications but also pointing out their limitations. Since the blood–brain barrier (BBB) and blood–retinal barrier (BRB) are understood to play a fundamental role respectively in brain and eye functions, both in health and in disease, we provide an overview of the progress in the development techniques of in vitro models as reliable and predictive screening tools for BBB and BRB-penetrating compounds. Furthermore, we propose potential future directions for brain and retinal organoids, in which dedicated biobanks will represent a novel tool for neuroscience and ophthalmology research.

Advances and Challenges in Intranasal Delivery of Antipsychotic Agents Targeting the Central Nervous System

Treatment of central nervous system (CNS) disorders is challenging using conventional delivery strategies and routes of administration because of the presence of the blood–brain barrier (BBB). This BBB restricts the permeation of most of the therapeutics targeting the brain because of its impervious characteristics. Thus, the challenges of delivering the therapeutic agents across the BBB to the brain overcoming the issue of insufficient entry of neurotherapeutics require immediate attention for recovering from the issues by the use of modern platforms of drug delivery and novel routes of administration. Therefore, the advancement of drug delivery tools and delivering these tools using the intranasal route of drug administration have shown the potential of circumventing the BBB, thereby delivering the therapeutics to the brain at a significant concentration with minimal exposure to systemic circulation. These novel strategies could lead to improved efficacy of antipsychotic agents using several advanced drug delivery tools while delivered via the intranasal route. This review emphasized the present challenges of delivering the neurotherapeutics to the brain using conventional routes of administration and overcoming the issues by exploring the intranasal route of drug administration to deliver the therapeutics circumventing the biological barrier of the brain. An overview of different problems with corresponding solutions in administering therapeutics via the intranasal route with special emphasis on advanced drug delivery systems targeting to deliver CNS therapeutics has been focused. Furthermore, preclinical and clinical advancements on the delivery of antipsychotics using this intranasal route have also been emphasized.

Design, Synthesis, and Biological Evaluation of 4,4’-Difluorobenzhydrol Carbamates as Selective M1 Antagonists

Due to their important role in mediating a broad range of physiological functions, muscarinic acetylcholine receptors (mAChRs) have been a promising target for therapeutic and diagnostic applications alike; however, the list of truly subtype-selective ligands is scarce. Within this work, we have identified a series of twelve 4,4’-difluorobenzhydrol carbamates through a rigorous docking campaign leveraging commercially available amine databases. After synthesis, these compounds have been evaluated for their physico–chemical property profiles, including characteristics such as HPLC-logD, tPSA, logBB, and logPS. For all the synthesized carbamates, these characteristics indicate the potential for BBB permeation. In competitive radioligand binding experiments using Chinese hamster ovary cell membranes expressing the individual human mAChR subtype hM1-hM5, the most promising compound 2 displayed a high binding affinitiy towards hM1R (1.2 nM) while exhibiting modest-to-excellent selectivity versus the hM2-5R (4–189-fold). All 12 compounds were shown to act in an antagonistic fashion towards hM1R using a dose-dependent calcium mobilization assay. The structural eligibility for radiolabeling and their pharmacological and physico–chemical property profiles render compounds 2, 5, and 7 promising candidates for future position emission tomography (PET) tracer development.

Neuroprotective effects of curcumin via autophagy induction in 6-hydroxydopamine Parkinson's models

Curcumin, a polyphenolic compound extracted from curcuma longa, acts as a nontoxic matter with anti-oxidant and anti-inflammatory effects as well as antiproliferative activities. Here, our research aimed to explore the neuroprotective effects of curcumin both in the 6-hydroxydopamine (6-OHDA)-lesioned rat model of Parkinson's disease (PD) in vivo and 6-OHDA-lesioned PC12 cells in vitro. In vitro, 6-OHDA caused a distinct decrease in cell viability of PC12 cells (150 μM). With the incubation of curcumin (1 μM), 6-OHDA-induced apoptosis was suppressed, increasing the autophagy markers (LC3-II/LC3-I, Beclin-1) and inhibiting phosphor-AKT/AKT, phosphor-mTOR/mTOR. In vivo, curcumin (50 mg/kg) reduced the accumulation of a-synuclein and led to higher parkinsonian disability scores in 6-OHDA-lesioned PD rats, contributing to induction of autophagy through inhibiting AKT/mTOR signal pathway. Moreover, treatment with autophagy inhibitors, such as 3-MA and chloroquine, abolished the neuroprotective effects of curcumin as evidence by compromised autophagy and declined motor behavior in PD rats. In conclusion, the present study demonstrated that curcumin repressed PC12 cell death in vitro and improved parkinsonian disability scores in vivo by inhibiting AKT/mTOR signaling pathway which mediated by autophagy, indicating a potential value of curcumin in the therapeutic intervention of Parkinson's disease.

In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as the structural complexity of the brain and specifically the blood-brain barrier (BBB) poses tremendous challenges. Several in vitro brain systems have been evaluated, but the ultimate use of these data in terms of translation to human brain concentration profiles remains to be fully developed. Thus, linking up in vitro-to-in vivo extrapolation (IVIVE) strategies to physiologically based pharmacokinetic (PBPK) models of brain is a useful effort that allows better prediction of drug concentrations in CNS components. Such models may overcome some known aspects of inter-species differences in CNS drug disposition. Required physiological (i.e. systems) parameters in the model are derived from quantitative values in each organ. However, due to the inability to directly measure brain concentrations in humans, compound-specific (drug) parameters are often obtained from in silico or in vitro studies. Such data are translated through IVIVE which could be also applied to preclinical in vivo observations. In such exercises, the limitations of the assays and inter-species differences should be adequately understood in order to verify these predictions with the observed concentration data. This report summarizes the state of IVIVE-PBPK-linked models and discusses shortcomings and areas of further research for better prediction of CNS drug disposition.

Use of AD Informer Set compounds to explore validity of novel targets in Alzheimer's disease pathology

Introduction

A chemogenomic set of small molecules with annotated activities and implicated roles in Alzheimer's disease (AD) called the AD Informer Set was recently developed and made available to the AD research community: https://treatad.org/data‐tools/ad‐informer‐set/.

Methods

Small subsets of AD Informer Set compounds were selected for AD‐relevant profiling. Nine compounds targeting proteins expressed by six AD‐implicated genes prioritized for study by Target Enablement to Accelerate Therapy Development for Alzheimer's Disease (TREAT‐AD) teams were selected for G‐protein coupled receptor (GPCR), amyloid beta (Aβ) and tau, and pharmacokinetic (PK) studies. Four non‐overlapping compounds were analyzed in microglial cytotoxicity and phagocytosis assays.

Results

The nine compounds targeting CAPN2, EPHX2, MDK, MerTK/FLT3, or SYK proteins were profiled in 46 to 47 primary GPCR binding assays. Human induced pluripotent stem cell (iPSC)‐derived neurons were treated with the same nine compounds and secretion of Aβ peptides (Aβ40 and Aβ42) as well as levels of phosphophorylated tau (p‐tau, Thr231) and total tau (t‐tau) peptides measured at two concentrations and two timepoints. Finally, CD1 mice were dosed intravenously to determine preliminary PK and/or brain‐specific penetrance values for these compounds. As a final cell‐based study, a non‐overlapping subset of four compounds was selected based on single‐concentration screening for analysis of both cytotoxicity and phagocytosis in murine and human microglia cells.

Discussion

We have demonstrated the utility of the AD Informer Set in the validation of novel AD hypotheses using biochemical, cellular (primary and immortalized), and in vivo studies. The selectivity for their primary targets versus essential GPCRs in the brain was established for our compounds. Statistical changes in tau, p‐tau, Aβ40, and/or Aβ42 and blood–brain barrier penetrance were observed, solidifying the utility of specific compounds for AD. Single‐concentration phagocytosis results were validated as predictive of dose–response findings. These studies established workflows, validated assays, and illuminated next steps for protein targets and compounds.

cΔlog kw IAM: can we afford estimation of small molecules' blood-brain barrier passage based upon in silico phospholipophilicity?

56 compounds, whose log BB values were known from the scientific literature, were considered and their phospholipophilicity values were calculated in silico. These values, along with either experimentally determined or calculated lipophilicity values, were used to extract cΔ/Δ'log k w IAM parameters. cΔ/Δ'log k w IAM values were found inversely related to data of blood-brain barrier passage, especially in the < -0.20 log BB range and on the IAM.PC.DD2 phase (r2 = 0.79). In multiple linear regression, satisfactory statistic models (r2 (n-1) = 0.76), based on cΔ/Δ'log k w IAM.MG along with other in silico calculated descriptors, were achieved. This method brings the potential to be applied, along with other methodologies, to filter out solutes whose BBB permeation is foreseen to be substandard, thus allowing pharmaceutical companies/research institutes to focus on candidates that are more likely to concentrate in the brain.

Mechanism of the enhancing effect of glycyrrhizin on nifedipine penetration through a lipid membrane

The saponin glycyrrhizin from liquorice root shows the ability to enhance the therapeutic activity of other drugs when used as a drug delivery system. Due to its amphiphilic properties, glycyrrhizin can form self-associates (dimers, micelles) and supramolecular complexes with a wide range of hydrophobic drugs, which leads to an increase in their solubility, stability and bioavailability. That is why the mechanism of the biological activity of glycyrrhizin is of considerable interest and has been the subject of intensive physical and chemical research in the last decade. Two mechanisms have been proposed to explain the effect of glycyrrhizin on drug bioavailability, namely, the increase in drug solubility in water and enhancement of the membrane permeability. Interest in the membrane-modifying ability of glycyrrhizic acid (GA) is also growing at present due to its recently discovered antiviral activity against SARS-CoV-2 Bailly and Vergoten (2020) [1]. In the present study, the passive permeability of the DOPC lipid membrane for the calcium channel blocker nifedipine was elucidated by parallel artificial membrane permeability assay (PAMPA) and full atomistic molecular dynamics (MD) simulation with free energy calculations. PAMPA experiments show a remarkable increase in the amount of nifedipine (NF) permeated with glycyrrhizin compared to free NF. In previous studies, we have shown using MD techniques that glycyrrhizin molecules can integrate into the lipid bilayer. In this study, MD simulation demonstrates a significant decrease in the energy barrier of NF penetration through the lipid bilayer in the presence of glycyrrhizin both in the pure DOPC membrane and in the membrane with cholesterol. This effect can be explained by the formation of hydrogen bonds between NF and GA in the middle of the bilayer.

New In Vitro Methodology for Kinetics Distribution Prediction in the Brain. An Additional Step towards an Animal-Free Approach

Simple Summary

The prevalence of neurological disorders in humans is rising year after year. This fact necessitates the development of new drugs for treating these pathologies. Traditionally, drugs have been tested in animals prior to use in human experiments; however, the use of animals in experimentation must be controlled and as low as possible. Because of that, here we proposed a new in vitro approach with which the access and distribution of drugs into the brain can be evaluated without using/killing any animals.

Abstract

The development of new drugs or formulations for central nervous system (CNS) diseases is a complex pharmacologic and pharmacokinetic process; it is important to evaluate their access to the CNS through the blood–brain barrier (BBB) and their distribution once they have acceded to the brain. The gold standard tool for obtaining this information is the animal microdialysis technique; however, according to 3Rs principles, it would be better to have an “animal-free” alternative technique. Because of that, the purpose of this work was to develop a new formulation to substitute the brain homogenate in the in vitro tests used for the prediction of a drug’s distribution in the brain. Fresh eggs have been used to prepare an emulsion with the same proportion in proteins and lipids as a human brain; this emulsion has proved to be able to predict both the unbound fraction of drug in the brain (f_u,brain) and the apparent volume of distribution in the brain (V_u,brain) when tested in in vitro permeability tests. The new formulation could be used as a screening tool; only the drugs with a proper in vitro distribution would pass to microdialysis studies, contributing to the refinement, reduction and replacement of animals in research.

Absorption, distribution, and toxicity of per- and polyfluoroalkyl substances (PFAS) in the brain: a review

Per- and polyfluoroalkyl substances (PFAS) are a class of synthetic chemicals colloquially known as "forever chemicals" because of their high persistence. PFAS have been detected in the blood, liver, kidney, heart, muscle and brain of various species. Although brain is not a dominant tissue for PFAS accumulation compared to blood and liver, adverse effects of PFAS on brain functions have been identified. Here, we review studies related to the absorption, accumulation, distribution and toxicity of PFAS in the brain. We summarize evidence on two potential mechanisms of PFAS entering the brain: initiating blood-brain barrier (BBB) disassembly through disrupting tight junctions and relying on transporters located at the BBB. PFAS with diverse structures and properties enter and accumulate in the brain with varying efficiencies. Compared to long-chain PFAS, short-chain PFAS may not cross cerebral barriers effectively. According to biomonitoring studies and PFAS exposure experiments, PFAS can accumulate in the brain of humans and wildlife species. With respect to the distribution of PFAS in specific brain regions, the brain stem, hippocampus, hypothalamus, pons/medulla and thalamus are dominant for PFAS accumulation. The accumulation and distribution of PFAS in the brain may lead to toxic effects in the central nervous system (CNS), including PFAS-induced behavioral and cognitive disorders. The specific mechanisms underlying such PFAS-induced neurotoxicity remain to be explored, but two major potential mechanisms based on current understanding are PFAS effects on calcium homeostasis and neurotransmitter alterations in neurons. Based on the information available about PFAS uptake, accumulation, distribution and impacts on the brain, PFAS have the potential to enter and accumulate in the brain at varying levels. The balance of existing studies shows there is some indication of risk in animals, while the human evidence is mixed and warrants further scrutiny.

Advanced Bioinformatics Tools in the Pharmacokinetic Profiles of Natural and Synthetic Compounds with Anti-Diabetic Activity

Diabetes represents a major health problem, involving a severe imbalance of blood sugar levels, which can disturb the nerves, eyes, kidneys, and other organs. Diabes management involves several synthetic drugs focused on improving insulin sensitivity, increasing insulin production, and decreasing blood glucose levels, but with unclear molecular mechanisms and severe side effects. Natural chemicals extracted from several plants such as Gymnema sylvestre, Momordica charantia or Ophiopogon planiscapus Niger have aroused great interest for their anti-diabetes activity, but also their hypolipidemic and anti-obesity activity. Here, we focused on the anti-diabetic activity of a few natural and synthetic compounds, in correlation with their pharmacokinetic/pharmacodynamic profiles, especially with their blood-brain barrier (BBB) permeability. We reviewed studies that used bioinformatics methods such as predicted BBB, molecular docking, molecular dynamics and quantitative structure-activity relationship (QSAR) to elucidate the proper action mechanisms of antidiabetic compounds. Currently, it is evident that BBB damage plays a significant role in diabetes disorders, but the molecular mechanisms are not clear. Here, we presented the efficacy of natural (gymnemic acids, quercetin, resveratrol) and synthetic (TAK-242, propofol, or APX3330) compounds in reducing diabetes symptoms and improving BBB dysfunctions. Bioinformatics tools can be helpful in the quest for chemical compounds with effective anti-diabetic activity that can enhance the druggability of molecular targets and provide a deeper understanding of diabetes mechanisms.