Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

Mechanistic study of α-mangostin derivatives as potent α-glucosidase inhibitors

α-Glucosidase inhibitors (AGIs) are pharmacological agents commonly used to manage postprandial hyperglycemia associated with type 2 diabetes mellitus (T2DM). Developing novel, potent AGIs remains a significant area of research. In this study, we investigated a series of derivatives of the natural product from α-mangostin as potential AGIs. A combined experimental and computational approach was employed to characterize promising compounds with potent α-glucosidase inhibitory activity. We found that α-mangostin (AM) and its derivatives (AM1 - 3) exhibited micromolar range α-glucosidase inhibition (IC50 ranging from 15.14 to 67.81 µM), surpassing the known drug acarbose (IC50 of 197.09 µM). Among the derivatives, AM1 exhibited the most promising α-glucosidase inhibition, displaying competitive inhibition kinetics with a Ki value of 47.04 µM. Molecular docking and molecular dynamics (MD) simulations provided mechanistic insights into the binding interactions between AM1 and the α-glucosidase active site. AM1 was observed to form hydrogen bonds and hydrophobic interactions with key amino acid residues within the enzyme's active site. The introduction of amine groups in compound AM1 enhanced activity compared to AM, the parent compound. This study highlights the potential of α-mangostin derivatives as potent AGIs. The identified lead compound, AM1, warrants further investigation to assess its efficacy and safety in managing T2DM.

Masked graph transformer for blood-brain barrier permeability prediction

The blood-brain barrier (BBB) is a highly protective structure that strictly regulates the passage of molecules, ensuring the central nervous system remains free from harmful chemicals and maintains brain homeostasis. Since most compounds cannot easily cross the BBB, assessing the blood-brain barrier permeability (BBBP) of drug candidates is critical in drug discovery. While several computational methods have been developed to screen BBBP with promising results, these approaches have limitations that affect their predictive power. In this study, we constructed classification models for screening the BBBP of molecules. Our models were trained with chemical data featurized by a Masked Graph Transformer-based Pretrained (MGTP) encoder. The molecular encoder was designed to generate molecular features for various downstream tasks. The training of the MGTP encoder was guided by masked attention-based learning, improving the model's generalization in encoding molecular structures. The results showed that classification models developed using MGTP features had outperformed those using other representations in 6 out of 8 cases, demonstrating the effectiveness of the proposed encoder. Also, chemical diversity analysis confirmed the encoder's ability to effectively distinguish between different classes of molecules.

Brain targeted lactoferrin coated lipid nanocapsules for the combined effects of apocynin and lavender essential oil in PTZ induced seizures

Apocynin (APO) is a plant derived antioxidant exerting specific NADPH oxidase inhibitory action substantiating its neuroprotective effects in various CNS disorders, including epilepsy. Due to rapid elimination and poor bioavailability, treatment with APO is challenging. Correspondingly, novel APO-loaded lipid nanocapsules (APO-LNC) were formulated and coated with lactoferrin (LF-APO-LNC) to improve br ain targetability and prolong residence time. Lavender oil (LAV) was incorporated into LNC as a bioactive ingredient to act synergistically with APO in alleviating pentylenetetrazol (PTZ)-induced seizures. The optimized LF-APO-LAV/LNC showed a particle size 59.7 ± 4.5 nm with narrow distribution and 6.07 ± 1.6mV zeta potential) with high entrapment efficiency 92 ± 2.4% and sustained release (35% in 72 h). Following subcutaneous administration, LF-APO-LAV/LNC brought about ⁓twofold increase in plasma AUC and MRT compared to APO. A Log BB value of 0.2 ± 0.14 at 90 min reflects increased brain accumulation. In a PTZ-induced seizures rat model, LF-APO-LAV/LNC showed a Modified Racine score of 0.67 ± 0.47 with a significant increase in seizures latency and decrease in duration. Moreover, oxidant/antioxidant capacity and inflammatory markers levels in brain tissue were significantly improved. Histopathological and immunohistochemical assessment of brain tissue sections further supported these findings. The results suggest APO/LAV combination in LF-coated LNC as a promising approach to counteract seizures.

Integrated Approach to Cyclopiazonic Acid Cytotoxicity Using In Vitro (2D and 3D Models) and In Silico Methods

Cyclopiazonic acid (CPA) is an indole-tetramic acid neurotoxin produced by Aspergillus and Penicillium genera present mainly in fruit, cereals and nuts. This study compares the cytotoxicity produced by CPA after 24, 48 and 72 h of exposure using both monolayers and 3D spheroids in human neuroblastoma SH-SY5Y cells. Furthermore, CPA toxicokinetics was evaluated using in silico models. Cytotoxicity increased dose- and time-dependently, as shown by the MTT assay. The lowest CPA IC50 values were found in the monolayer study compared to the 3D spheroids at all exposure times (24 h: 864.01 vs. 1132; 48 h: 437 vs. 1069; 72 h: 392 vs. 567 nM). The CPA exposure on SH-SY5Y spheroid organization and morphology was also studied. Morphological changes, including spheroid disaggregation, were observed after mycotoxin exposure. The in silico methods, SwissADME and admetSAR, were used for short and full ADMEt profiles of CPA. The ADMEt predictive profile shows high gastrointestinal absorption and ability to penetrate the blood-brain barrier. Including in silico studies emphasizes the comprehensive approach to understanding mycotoxin toxicity and risk assessment. By combining in vitro 3D spheroid models with computational simulations, this study aims to provide a holistic perspective on the effects of CPA, enhancing the accuracy and relevance of our findings.

Navigating Ultralarge Virtual Chemical Spaces with Product-of-Experts Chemical Language Models

Ultralarge virtual chemical spaces have emerged as a valuable resource for drug discovery, providing access to billions of make-on-demand compounds with high synthetic success rates. Chemical language models can potentially accelerate the exploration of these vast spaces through direct compound generation. However, existing models are not designed to navigate specific virtual chemical spaces and often overlook synthetic accessibility. To address this gap, we introduce product-of-experts (PoE) chemical language models, a modular and scalable approach to navigating ultralarge virtual chemical spaces. This method allows for controlled compound generation within a desired chemical space by combining a prior model pretrained on the target space with expert and anti-expert models fine-tuned using external property-specific data sets. We demonstrate that the PoE chemical language model can generate compounds with desirable properties, such as those that favorably dock to dopamine receptor D2 (DRD2) and are predicted to cross the blood-brain barrier (BBB), while ensuring that the majority of generated compounds are present within the target chemical space. Our results highlight the potential of chemical language models for navigating ultralarge virtual chemical spaces, and we anticipate that this study will motivate further research in this direction. The source code and data are freely available at https://github.com/shuyana/poeclm.

Development of a new experimental NMR strategy for covalent cysteine protease inhibitors screening: toward enhanced drug discovery

In the development of antiviral drugs, proteases and polymerases are among the most important targets. Cysteine proteases, also known as thiol proteases, catalyze the degradation of proteins by cleaving peptide bonds using the nucleophilic thiol group of cysteine. As part of our research, we are examining how cysteine, an essential amino acid found in the active site of the main protease (Mpro) enzyme in SARS-CoV-2, interacts with electrophilic groups present in ethacrynic acid (EA) and compounds 4, 6, and 8 to form sulfur-carbon bonds. Nuclear magnetic resonance (NMR) spectroscopy was used to monitor the reaction rate between cysteine and Michael acceptors. We found that the inhibitory activity of these compounds towards Mpro is correlated to their chemical reactivity toward cysteine. This approach may serve as a valuable tool in drug development for detecting potential covalent inhibitors of Mpro and other cysteine proteases.

Synthesis, Pharmacokinetic Profile, Anticancer Activity and Toxicity of the New Amides of Betulonic Acid-In Silico and In Vitro Study

Betulonic acid (B(O)A) is a pentacyclic lupane-type triterpenoid that widely exists in plants. There are scientific reports indicating anticancer activity of B(O)A, as well as the amides and esters of this triterpenoid. In the first step of the study, the synthesis of novel amide derivatives of B(O)A containing an acetylenic moiety was developed. Subsequently, the medium-soluble compounds (EB171 and EB173) and the parent compound, i.e., B(O)A, were investigated for potential cytotoxic activity against breast cancer (MCF-7 and MDA-MB-231) and melanoma (C32, COLO 829 and A375) cell lines, as well as normal human fibroblasts. Screening analysis using the WST-1 test was applied. Moreover, the lipophilicity and ADME parameters of the obtained derivatives were determined using experimental and in silico methods. The toxicity assay using zebrafish embryos and larvae was also performed. The study showed that the compound EB171 exhibited a significant cytotoxic effect on cancer cell lines: MCF-7, A-375 and COLO 829, while it did not affect the survival of normal cells. Moreover, studies on embryos and larvae showed no toxicity of EB171 in an animal model. Compared to EB171, the compound EB173 had a weaker effect on all tested cancer cell lines and produced less desirable effects against normal cells. The results of the WST-1 assay obtained for B(O)A revealed its strong cytotoxic activity on the examined cancer cell lines, but also on normal cells. In conclusion, this article describes new derivatives of betulonic acid-from synthesis to biological properties. The results allowed to indicate a promising direction for the functionalization of B(O)A to obtain derivatives with selective anticancer activity and low toxicity.

Analysing the effect caused by increasing the molecular volume in M1-AChR receptor agonists and antagonists: a structural and computational study

M1 muscarinic acetylcholine receptor (M1-AChR), a member of the G protein-coupled receptors (GPCR) family, plays a crucial role in learning and memory, making it an important drug target for Alzheimer's disease (AD) and schizophrenia. M1-AChR activation and deactivation have shown modifying effects in AD and PD preclinical models, respectively. However, understanding the pharmacology associated with M1-AChR activation or deactivation is complex, because of the low selectivity among muscarinic subtypes, hampering their therapeutic applications. In this regard, we constructed two quantitative structure-activity relationship (QSAR) models, one for M1-AChR agonists (total and partial), and the other for the antagonists. The binding mode of 59 structurally different compounds, including agonists and antagonists with experimental binding affinity values (pKi), were analyzed employing computational molecular docking over different structures of M1-AChR. Furthermore, we considered the interaction energy (Einter), the number of rotatable bonds (NRB), and lipophilicity (ilogP) for the construction of the QSAR model for agonists (R2 = 89.64, QLMO2 = 78, and Qext2 = 79.1). For the QSAR model of antagonists (R2 = 88.44, QLMO2 = 82, and Qext2 = 78.1) we considered the Einter, the fraction of sp3 carbons fCsp3, and lipophilicity (MlogP). Our results suggest that the ligand volume is a determinant to establish its biological activity (agonist or antagonist), causing changes in binding energy, and determining the affinity for M1-AChR.

In silico evaluation of a new compound incorporating 4(3H)-quinazolinone and sulfonamide as a potential inhibitor of a human carbonic anhydrase

The present study investigates the potential of a new compound containing sulfonamide and 4(3H)-quinazolinone to inhibit the hCA-IIX enzyme using in silico methods. Density functional theory-based calculations of electronic properties have been addressed through the analysis of frontier molecular orbitals, molecule electrostatic potential, and IR and UV-vis spectroscopy data. A molecular electrostatic potential analysis predicts that the target protein will be most inhibited by the sulfonamide groups since it has the highest potential spots for electrophile and nucleophile attack. The investigated compound exhibited good ADMET properties and satisfied the Lipinski rule of drug likeness. The hCA-IIX protein binding affinity with the proposed compound was determined by molecular docking analysis, which revealed a stable conformation with more negative binding energy (-12.19 kcal/mol) than the standard AZA drug (-7.36 kcal/mol). Moreover, a molecular dynamics study confirmed the docking results through trajectory analysis. The RMSD and RMSF both showed convergence and no significant fluctuations during the simulation time, which revealed a stable interaction within the active domain of the target protein. According to these findings, the proposed compound has a good pharmacological nature and could potentially be an efficient drug against hCAIX enzymes.

Modeling the Blood-Brain Barrier Permeability of Potential Heterocyclic Drugs via Biomimetic IAM Chromatography Technique Combined with QSAR Methodology

Penetration through the blood-brain barrier (BBB) is desirable in the case of potential pharmaceuticals acting on the central nervous system (CNS), but is undesirable in the case of drug candidates acting on the peripheral nervous system because it may cause CNS side effects. Therefore, modeling of the permeability across the blood-brain barrier (i.e., the logarithm of the brain to blood concentration ratio, log BB) of potential pharmaceuticals should be performed as early as possible in the preclinical phase of drug development. Biomimetic chromatography with immobilized artificial membrane (IAM) and the quantitative structure-activity relationship (QSAR) methodology were successful in modeling the blood-brain barrier permeability of 126 drug candidates, whose experimentally-derived lipophilicity indices and computationally-derived molecular descriptors (such as molecular weight (MW), number of rotatable bonds (NRB), number of hydrogen bond donors (HBD), number of hydrogen bond acceptors (HBA), topological polar surface area (TPSA), and polarizability (α)) varied by class. The QSARs model established by multiple linear regression showed a positive effect of the lipophilicity (log kw, IAM) and molecular weight of the compound, and a negative effect of the number of hydrogen bond donors and acceptors, on the log BB values. The model has been cross-validated, and all statistics indicate that it is very good and has high predictive ability. The simplicity of the developed model, and its usefulness in screening studies of novel drug candidates that are able to cross the BBB by passive diffusion, are emphasized.

Multi-target Phenylpropanoids Against Epilepsy

Epilepsy is a neurological disease with no defined cause, characterized by recurrent epileptic seizures. These occur due to the dysregulation of excitatory and inhibitory neurotransmitters in the central nervous system (CNS). Psychopharmaceuticals have undesirable side effects; many patients require more than one pharmacotherapy to control crises. With this in mind, this work emphasizes the discovery of new substances from natural products that can combat epileptic seizures. Using in silico techniques, this review aims to evaluate the antiepileptic and multi-target activity of phenylpropanoid derivatives. Initially, ligand-based virtual screening models (LBVS) were performed with 468 phenylpropanoid compounds to predict biological activities. The LBVS were developed for the targets alpha- amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), voltage-gated calcium channel Ttype (CaV), gamma-aminobutyric acid A (GABAA), gamma-aminobutyric acid transporter type 1 (GAT-1), voltage-gated potassium channel of the Q family (KCNQ), voltage-gated sodium channel (NaV), and N-methyl D-aspartate (NMDA). The compounds that had good results in the LBVS were analyzed for the absorption, distribution, metabolism, excretion, and toxicity (ADMET) parameters, and later, the best molecules were evaluated in the molecular docking consensus. The TR430 compound showed the best results in pharmacokinetic parameters; its oral absorption was 99.03%, it did not violate any Lipinski rule, it showed good bioavailability, and no cytotoxicity was observed either from the molecule or from the metabolites in the evaluated parameters. TR430 was able to bind with GABAA (activation) and AMPA (inhibition) targets and demonstrated good binding energy and significant interactions with both targets. The studied compound showed to be a promising molecule with a possible multi-target activity in both fundamental pharmacological targets for the treatment of epilepsy.

The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery

Drug discovery and development are aimed at identifying new chemical molecular entities (NCEs) with desirable pharmacokinetic profiles for high therapeutic efficacy. The plasma concentrations of NCEs are a biomarker of their efficacy and are governed by pharmacokinetic processes such as absorption, distribution, metabolism, and excretion (ADME). Poor ADME properties of NCEs are a major cause of attrition in drug development. ADME screening is used to identify and optimize lead compounds in the drug discovery process. Computational models predicting ADME properties have been developed with evolving model-building technologies from a simplified relationship between ADME endpoints and physicochemical properties to machine learning, including support vector machines, random forests, and convolution neural networks. Recently, in the field of in silico ADME research, there has been a shift toward evaluating the in vivo parameters or plasma concentrations of NCEs instead of using predictive results to guide chemical structure design. Another research hotspot is the establishment of a computational prediction platform to strengthen academic drug discovery. Bioinformatics projects have produced a series of in silico ADME models using free software and open-access databases. In this review, we introduce prediction models for various ADME parameters and discuss the currently available academic drug discovery platforms.

Synthesis, Optical Properties, and In Vivo Biodistribution Performance of Polymethine Cyanine Fluorophores

Near-infrared (NIR) cyanine dyes showed enhanced properties for biomedical imaging. A systematic modification within the cyanine skeleton has been made through a facile design and synthetic route for optimal bioimaging. Herein, we report the synthesis of 11 NIR cyanine fluorophores and an investigation of their physicochemical properties, optical characteristics, photostability, and in vivo performance. All synthesized fluorophores absorb and emit within 610-817 nm in various solvents. These dyes also showed high molar extinction coefficients ranging from 27,000 to 270,000 cm-1 M-1, quantum yields 0.01 to 0.33, and molecular brightness 208-79,664 cm-1 M-1 in the tested solvents. Photostability data demonstrate that all tested fluorophores 28, 18, 20, 19, 25, and 24 are more photostable than the FDA-approved indocyanine green. In the biodistribution study, most compounds showed tissue-specific targeting to selectively accumulate in the adrenal glands, lymph nodes, or gallbladder while excreted to the hepatobiliary clearance route. Among the tested, compound 23 showed the best targetability to the bone marrow and lymph nodes. Since the safety of cyanine fluorophores is well established, rationally designed cyanine fluorophores established in the current study will expand an inventory of contrast agents for NIR imaging of not only normal tissues but also cancerous regions originating from these organs/tissues.

Chromatographic Data in Statistical Analysis of BBB Permeability Indices

Blood-brain barrier (BBB) permeability is an essential phenomena when considering the treatment of neurological disorders as well as in the case of central nervous system (CNS) adverse effects caused by peripherally acting drugs. The presented work contains statistical analyses and the correlation assessment of the analyzed group of active pharmaceutical ingredients (APIs) with their BBB-permeability data collected from the literature (such as computational log BB; Kp,uu,brain, and CNS+/- groups). A number of regression models were constructed in order to observe the connections between the APIs' physicochemical properties in combination with their retention data from the chromatographic experiments (TLC and HPLC) and the indices of bioavailability in the CNS. Conducted analyses confirm that descriptors significant in BBB permeability modeling are hydrogen bond acceptors and donors, physiological charge, or energy of the lowest unoccupied molecular orbital. These molecular descriptors were the basis, along with the chromatographic data from the TLC in log BB regression analyses. Normal-phase TLC data showed a significant contribution to the creation of the log BB regression model using the multiple linear regression method. The model using them showed a good predictive value at the level of R² = 0.87. Models for Kp,uu,brain resulted in lower statistics: R² = 0.56 for the group of 23 APIs with the participation of k IAM.

El-Khayatt A. Molecular docking and dynamics simulation of main protease of SARS-CoV-2 with naproxen derivative

Naproxen is a well-known anti-inflammatory drug that is frequently used to relieve inflammation, stiffness, and swelling. Naproxen has previously demonstrated antiviral activity, particularly against the influenza-A virus. There have been previous studies regarding naproxen effect on SARS-CoV-2 infection. Therefore, it is of interest to document the molecular docking and dynamics simulation data of main protease of SARS-CoV-2 with naproxen derivative for further consideration.

Multimodal study of CHI3L1 inhibition and its effect on angiogenesis, migration, immune response and refractive index of cellular structures in glioblastoma

Glioblastoma is one of the most aggressive tumours with a poor response to treatment and a poor prognosis for patients. One of the proteins expressed in glioblastoma tissue is CHI3L1 (YKL-40), which is upregulated and known for its angiogenesis-supporting and pro-tumour immunomodulatory effects in a variety of cancers. In this paper we present the anti-angiogenic, anti-migratory and immunomodulatory effects of the compound G721-0282, an inhibitor of CHI3L1. The inhibitor-induced changes were investigated using conventional techniques as well as the novel label-free digital holographic tomography (DHT), a quantitative phase imaging technique that allows the reconstruction of the refractive index (RI), which is used as an image contrast for 3D visualisation of living cells. DHT allowed digital staining of individual cells and intercellular structures based only on their specific RI. Quantitative spatially resolved analysis of the RI data shows that the concentration of G721-0282 leads to significant changes in the density of cells and their intracellular structures (in particular the cytoplasm and nucleus), in the volume of lipid droplets and in protein concentrations. Studies in the U-87 MG glioblastoma cell line, THP-1 monocytes differentiated into macrophages, human microvascular endothelial cells (HMEC-1) and in the spheroid model of glioblastoma composed of U-87 MG, HMEC-1 and macrophages suggest that inhibition of CHI3L1 may have potential in the antitumour treatment of glioblastoma. In this paper, we also propose a spheroid model for in vitro studies that mimics this type of tumour.

Molecular docking analysis of novel quercetin derivatives for combating SARS-CoV-2

Quercetin belongs to the flavonoid family, which is one of the most frequent types of plant phenolics. This flavonoid compound is a natural substance having a number of pharmacological effects, including anticancer and antioxidant capabilities, as well as being a strong inhibitor of various toxicologically important enzymes. We discuss the potential of newly recently synthesized quercetin-based derivatives to inhibit SARS-CoV-2 protein. ADMET analysis indicated that all of the studied compounds had low toxicities and good absorption and solubility properties. The molecular docking results revealed that the propensity for binding to SARS-CoV-2 main protease is extraordinary. The results are remarkable not only for the binding energy values, which outperform several compounds in prior studies, but also for the number of hydrogen bonds formed. Compound 7a was capable of forming 10 strong hydrogen bonds as well as interact to the protein receptor with a binding energy of -7.79 kcal/mol. Therefore, these compounds should be highlighted in further experimental studies in the context of treating SARS-CoV-2 infection and its effects.

Design, synthesis, anti-acetylcholinesterase evaluation and molecular modelling studies of novel coumarin-chalcone hybrids

The major enzyme responsible for the hydrolytic breakdown of the neurotransmitter acetylcholine (ACh) is acetylcholinesterase (AChE). Acetylcholinesterase inhibitors (AChEIs) are the most prescribed class of medications for the treatment of Alzheimer's disease (AD) and dementia. The limitations of available therapy, like side effects, drug tolerance, and inefficacy in halting disease progression, drive the need for better, more efficacious, and safer drugs. In this study, a series of fourteen novel chalcone-coumarin derivatives (8a-n) were designed, synthesized and characterized by spectral techniques like FT-IR, NMR, and HR-MS. Subsequently, the synthesized compounds were tested for their ability to inhibit acetylcholinesterase (AChE) activity by Ellman's method. All tested compounds showed AChE inhibition with IC50 value ranging from 0.201 ± 0.008 to 1.047 ± 0.043 μM. Hybrid 8d having chloro substitution on ring-B of the chalcone scaffold showed relatively better potency, with IC50 value of 0.201 ± 0.008 μM compared to other members of the series. The reference drug, galantamine, exhibited an IC50 at 1.142 ± 0.027 μM. Computational studies revealed that designed compounds bind to the peripheral anionic site (PAS), the catalytic active site (CAS), and the mid-gorge site of AChE. Putative binding modes, ligand-enzyme interactions, and stability of the best active compound are studied using molecular docking, followed by molecular dynamics (MD) simulations. The cytotoxicity of the synthesised derivatives was determined using the MTT test at three concentrations (100 g/mL, 500 g/mL, and 1 mg/mL). None of the chemicals had a significant effect on the body at the highest dose of 1 mg/mL.Communicated by Ramaswamy H. Sarma.

Development of QSAR models to predict blood-brain barrier permeability

Assessing drug permeability across the blood-brain barrier (BBB) is important when evaluating the abuse potential of new pharmaceuticals as well as developing novel therapeutics that target central nervous system disorders. One of the gold-standard in vivo methods for determining BBB permeability is rodent log BB; however, like most in vivo methods, it is time-consuming and expensive. In the present study, two statistical-based quantitative structure-activity relationship (QSAR) models were developed to predict BBB permeability of drugs based on their chemical structure. The in vivo BBB permeability data were harvested for 921 compounds from publicly available literature, non-proprietary drug approval packages, and University of Washington's Drug Interaction Database. The cross-validation performance statistics for the BBB models ranged from 82 to 85% in sensitivity and 80-83% in negative predictivity. Additionally, the performance of newly developed models was assessed using an external validation set comprised of 83 chemicals. Overall, performance of individual models ranged from 70 to 75% in sensitivity, 70-72% in negative predictivity, and 78-86% in coverage. The predictive performance was further improved to 93% in coverage by combining predictions across the two software programs. These new models can be rapidly deployed to predict blood brain barrier permeability of pharmaceutical candidates and reduce the use of experimental animals.

Effect of tricyclic 1,2-thiazine derivatives in neuroinflammation induced by preincubation with lipopolysaccharide or coculturing with microglia-like cells

Background

Alzheimer’s disease (AD) is considered the most common cause of dementia among the elderly. One of the modifiable causes of AD is neuroinflammation. The current study aimed to investigate the influence of new tricyclic 1,2-thiazine derivatives on in vitro model of neuroinflammation and their ability to cross the blood–brain barrier (BBB).

Methods

The potential anti-inflammatory effect of new tricyclic 1,2-thiazine derivatives (TP1, TP4, TP5, TP6, TP7, TP8, TP9, TP10) was assessed in SH-SY5Y cells differentiated to the neuron-like phenotype incubated with bacterial lipopolysaccharide (5 or 50 μg/ml) or THP-1 microglial cell culture supernatant using MTT, DCF-DA, Griess, and fast halo (FHA) assays. Additionally, for cultures preincubated with 50 µg/ml lipopolysaccharide (LPS), a cyclooxygenase (COX) activity assay was performed. Finally, the potential ability of tested compounds to cross the BBB was evaluated by computational studies. Molecular docking was performed with the TLR4/MD-2 complex to assess the possibility of binding the tested compounds in the LPS binding pocket. Prediction of ADMET parameters (absorption, distribution, metabolism, excretion and toxicity) was also conducted.

Results

The unfavorable effect of LPS and co-culture with THP-1 cells on neuronal cell viability was counteracted with TP1 and TP4 in all tested concentrations. Tested compounds reduced the oxidative and nitrosative stress induced by both LPS and microglia activation and also reduced DNA damage. Furthermore, new derivatives inhibited total COX activity. Additionally, new compounds would cross the BBB with high probability and reach concentrations in the brain not lower than in the serum. The binding affinity at the TLR4/MD-2 complex binding site of TP4 and TP8 compounds is similar to that of the drug donepezil used in Alzheimer's disease. The ADMET analysis showed that the tested compounds should not be toxic and should show high intestinal absorption.

Conclusions

New tricyclic 1,2-thiazine derivatives exert a neuroregenerative effect in the neuroinflammation model, presumably via their inhibitory influence on COX activity and reduction of oxidative and nitrosative stress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43440-022-00414-8.

deepGraphh: AI-driven web service for graph-based quantitative structure-activity relationship analysis

Artificial intelligence (AI)-based computational techniques allow rapid exploration of the chemical space. However, representation of the compounds into computational-compatible and detailed features is one of the crucial steps for quantitative structure-activity relationship (QSAR) analysis. Recently, graph-based methods are emerging as a powerful alternative to chemistry-restricted fingerprints or descriptors for modeling. Although graph-based modeling offers multiple advantages, its implementation demands in-depth domain knowledge and programming skills. Here we introduce deepGraphh, an end-to-end web service featuring a conglomerate of established graph-based methods for model generation for classification or regression tasks. The graphical user interface of deepGraphh supports highly configurable parameter support for model parameter tuning, model generation, cross-validation and testing of the user-supplied query molecules. deepGraphh supports four widely adopted methods for QSAR analysis, namely, graph convolution network, graph attention network, directed acyclic graph and Attentive FP. Comparative analysis revealed that deepGraphh supported methods are comparable to the descriptors-based machine learning techniques. Finally, we used deepGraphh models to predict the blood-brain barrier permeability of human and microbiome-generated metabolites. In summary, deepGraphh offers a one-stop web service for graph-based methods for chemoinformatics.

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.

The Application of in silico Methods for Prediction of Blood-Brain Barrier Permeability of Small Molecule PET Tracers

Designing positron emission tomography (PET) tracers for targets in the central nervous system (CNS) is challenging. Besides showing high affinity and high selectivity for their intended target, these tracers have to be able to cross the blood-brain barrier (BBB). Since only a small fraction of small molecules is estimated to be able to cross the BBB, tools that can predict permeability at an early stage during the development are of great importance. One such tool is in silico models for predicting BBB-permeability. Thus far, such models have been built based on CNS drugs, with one exception. Herein, we sought to discuss and analyze if in silico predictions that have been built based on CNS drugs can be applied for CNS PET tracers as well, or if dedicated models are needed for the latter. Depending on what is taken into account in the prediction, i.e., passive diffusion or also active influx/efflux, there may be a need for a model build on CNS PET tracers. Following a brief introduction, an overview of a few selected in silico BBB-permeability predictions is provided along with a short historical background to the topic. In addition, a combination of previously reported CNS PET tracer datasets were assessed in a couple of selected models and guidelines for predicting BBB-permeability. The selected models were either predicting only passive diffusion or also the influence of ADME (absorption, distribution, metabolism and excretion) parameters. To conclude, we discuss the potential need of a prediction model dedicated for CNS PET tracers and present the key issues in respect to setting up a such a model.

Anti-glioblastoma effects of phenolic variants of benzoylphenoxyacetamide (BPA) with high potential for blood brain barrier penetration

Glioblastomas are the most aggressive brain tumors for which therapeutic options are limited. Current therapies against glioblastoma include surgical resection, followed by radiotherapy plus concomitant treatment and maintenance with temozolomide (TMZ), however, these standard therapies are often ineffective, and average survival time for glioblastoma patients is between 12 and 18 months. We have previously reported a strong anti-glioblastoma activity of several metabolic compounds, which were synthetized based compounds, which were synthetized based on the chemical structure of a common lipid-lowering drug, fenofibrate, and share a general molecular skeleton of benzoylphenoxyacetamide (BPA). Extensive computational analyses of phenol and naphthol moieties added to the BPA skeleton were performed in this study with the objective of selecting new BPA variants for subsequent compound preparation and anti-glioblastoma testing. Initially, 81 structural variations were considered and their physical properties such as solubility (logS), blood–brain partitioning (logBB), and probability of entering the CNS calculated by the Central Nervous System—Multiparameter Optimization (MPO-CNS) algorithm were evaluated. From this initial list, 18 compounds were further evaluated for anti-glioblastoma activity in vitro. Nine compounds demonstrated desirable glioblastoma cell toxicity in cell culture, and two of them, HR51, and HR59 demonstrated significantly improved capability of crossing the model blood–brain-barrier (BBB) composed of endothelial cells, astrocytes and pericytes.

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.

Part-II- in silico drug design: application and success

In silico tools have indeed reframed the steps involved in traditional drug discovery and development process and the term in silico has become a familiar term in pharmaceutical sector like the terms in vitro and in vivo. The successful design of HIV protease inhibitors, Saquinavir, Indinavir and other important medicinal agents, initiated interest of researchers in structure based drug design approaches (SBDD). The interactions between biomolecules and a ligand, binding energy, free energy and stability of biomolecule-ligand complex can be envisioned and predicted by applying molecular docking studies. Protein-ligand, protein-protein, DNA-ligand interactions etc. aid in elucidating molecular level mechanisms of drug molecules. In the Ligand based drug design (LBDD) approaches, QSAR studies have tremendously contributed to the development of antimicrobial, anticancer, antimalarial agents. In the recent years, multiQSAR (mt-QSAR) approaches have been successfully employed for designing drugs against multifactorial diseases. Output of a research in several instances is rewarding when both SBDD and LBDD approaches are combined. Application of in silico studies for prediction of pharmacokinetics was once a real challenge but one can see unlimited number publications comprising tools, data bases which can accurately predict almost all the pharmacokinetic parameters. Absorption, distribution, metabolism, transporters, blood brain barrier permeability, hERG toxicity, P-gp affinity and several toxicological end points can be accurately predicted for a candidate molecule before its synthesis. In silico approaches are greatly encouraged a result of growing limitations and new legislations related to the animal use for research. The combined use of in vitro data and in silico tools will definitely decrease the use of animal testing in the future.In this chapter, in silico approaches and their applications are reviewed and discussed giving suitable examples.

Discovery of 3,4-dichloro-N-(1H-indol-5-yl)benzamide: A highly potent, selective, and competitive hMAO-B inhibitor with high BBB permeability profile and neuroprotective action

Since there is no disease-modifying treatment discovered yet for Parkinson's disease (PD), there is still a vital need to develop novel selective monoamine oxidase B (MAO-B) inhibitors as promising therapeutically active candidates for PD patients. Herein, we report the design, synthesis, and full characterization of new twenty-six indole derivatives as potential human MAO-B (hMAO-B) selective inhibitors. Six compounds (2i, 3b-e, and 5) exhibited low micromolar to nanomolar inhibitory activities over hMAO-B; compared to our recently reported N-substituted indole-based lead compound VIII (hMAO-B IC₅₀ = 777 nM), compound 5 (3,4-dichloro-N-(1H-indol-5-yl)benzamide) exhibited 18-fold increase in potency (IC₅₀ = 42 nM). A selectivity study over hMAO-A revealed an excellent selectivity index of compound 5 (SI > 2375) with a 47-fold increase compared to rasagiline (II, a well-known MAO-B inhibitor, SI > 50). A further kinetic evaluation of compound 5 over hMAO-B showed a reversible and competitive mode of inhibition with K_i value of 7 nM. Highly effective permeability and high CNS bioavailability of compound 5 with P_e = 54.49 × 10^-6 cm/s were demonstrated. Compound 5 also exhibited a low cytotoxicity profile and a promising neuroprotective effect against the 6-hydroxydopamine-induced neuronal cell damage in PC12 cells, which was more effective than that of rasagiline. Docking simulations on both hMAO-B and hMAO-A supported the in vitro data and served as further molecular evidence. Accordingly, we report the discovery of compound 5 as one of the most potent indole-based MAO-B inhibitors to date which is noteworthy to be further evaluated as a promising agent for PD treatment.

Structure-Based Virtual Screening and Molecular Dynamics Simulation to Identify Potential SARS-CoV-2 Spike Receptor Inhibitors from Natural Compound Database

The outbreak of respiratory disease, COVID-19 caused by SARS-CoV-2 has now been spread globally and the number of new infections is rising every moment. There are no specific medications that are currently available to combat the disease. The spike receptor of SARS-CoV-2 facilitates the viral entry into a host cell and initiation of infection. Targeting the viral entry at the initial stage has a better advantage than inhibiting it in later stages of the viral life cycle. This study deals with identification of the potential natural molecule or its derivatives from MolPort Databank as SARS-CoV-2 spike receptor inhibitors using structure-based virtual screening followed by molecular dynamics simulation. On the basis of ADME properties, docking score, MMGBSAbinding energy, 150 ns molecular docking studies, and final molecular dynamics analysis, two natural compounds - 3 (MolPort-002-535-004) docking score -9.10 kcal mol-1 and 4 (MolPort-005-910-183) docking score -8.5 kcal mol-1, are selected as potential in-silico spike receptor inhibitors. Both hits are commercially available and can be further used for in-vitro and in-vivo studies. Findings of this study can facilitate rational drug design against SARS-CoV-2 spike receptor.

Effect of pyrrolo[3,4-d]pyridazinone derivatives in neuroinflammation induced by preincubation with lipopolysaccharide or coculturing with microglia-like cells

Alzheimer's disease is one of the most serious disorders of the 21st century. There is still no effective therapy for this condition. The study investigated the potential regenerative effect of four pyrrolo[3,4-d]pyridazinone derivatives in cultures of SH-SY5Y neuron-like cells preincubated with lipopolysaccharide (LPS) or cocultured with microglia-like cells. In addition to the traditional investigation of the effect on viability, the level of free radicals and nitric oxide, the average length of neurites was also measured. Via in silico studies, the possibility of penetration of the blood-brain barrier (BBB) by the tested compounds was assessed. The administration of LPS to the culture of SH-SY5Y cells as well as coculturing with microglia-like cells had a significant negative effect on the results of all the assays performed. The treatment with the tested derivatives in most cases significantly reduced this negative effect. The obtained results suggest that the compound L2 may have a beneficial impact on neuronal damage caused by LPS or proinflammatory cytokines secreted by microglia-like cells. Importantly, tested compounds can pass through the BBB, which allows them to enter the brain.

Computational Design of Copper Ligands with Controlled Metal Chelating, Pharmacokinetics, and Redox Properties for Alzheimer's Disease

BACKGROUND

Redox active metal cations, such as Cu2 +, have been related to induce amyloid plaques formation and oxidative stress, which are two of the key events in the development of Alzheimer's disease (AD) and others metal promoted neurodegenerative diseases. In these oxidative events, standard reduction potential (SRP) is an important property especially relevant in the reactive oxygen species formation.

OBJECTIVE

The SRP is not usually considered for the selection of drug candidates in anti-AD treatments. In this work, we present a computational protocol for the selection of multifunctional ligands with suitable metal chelating, pharmacokinetics, and redox properties.

METHODS

The filtering process is based on quantum chemical calculations and the use of in silico tools. Calculations of SRP were performed by using the M06-2X density functional and the isodesmic approach. Then, a virtual screening technique (VS) was used for similar structure search.

RESULTS

Protocol application allowed the assessment of chelating, drug likeness, and redox properties of copper ligands. Those molecules showing the best features were selected as molecular scaffolds for a VS procedure in order to obtain related compounds. After applying this process, we present a list of candidates with suitable properties to prevent the redox reactions mediated by copper(II) ion.

CONCLUSION

The protocol incorporates SRP in the filtering stage and can be effectively used to obtain a set of potential drug candidates for AD treatments.

Evaluation of drug-likeness and ADME properties of sunscreens and preservatives using reversed-phase thin layer chromatographic retention data and calculated descriptors

RP-18 TLC chromatography was used to evaluate the pharmacokinetic properties (volume of distribution, V_D; plasma protein binding, %PPB; the ability to cross the blood-brain barrier expressed as log PS and log BB) of several cosmetic raw materials - sunscreen and preservatives. The majority of these compounds are intended for topical use on skin and their drug-likeness and the ability to cross biological barriers are undesired. The retention parameters R_M⁰, S, PC₁ and R_M^{75 %} obtained for mobile phases containing six organic modifiers (methanol, acetonitrile, THF, acetone, dioxane, DMF) were used as the sole descriptors or combined with calculated physicochemical properties (PSA, M_W, V_M) of studied compounds. The chromatographic parameters considered in this study are, generally speaking, good predictors of the compounds' pharmacokinetic properties V_D, %PPB and log PS. R_M^{75 %} and the novel parameters derived from it (R_M^{75 %}/M_W and R_M^{75 %}/V_M) can be considered time- and cost-effective alternatives to the chromatographic parameters obtained by extrapolation or interpolation methods. In the case of some pharmacokinetic properties investigated in this study additional descriptors (PSA) have a significant influence on the quality of correlations.

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

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

The Mitochondrial mitoNEET Ligand NL-1 Is Protective in a Murine Model of Transient Cerebral Ischemic Stroke

PURPOSE

Therapeutic strategies to treat ischemic stroke are limited due to the heterogeneity of cerebral ischemic injury and the mechanisms that contribute to the cell death. Since oxidative stress is one of the primary mechanisms that cause brain injury post-stroke, we hypothesized that therapeutic targets that modulate mitochondrial function could protect against reperfusion-injury after cerebral ischemia, with the focus here on a mitochondrial protein, mitoNEET, that modulates cellular bioenergetics.

METHOD

In this study, we evaluated the pharmacology of the mitoNEET ligand NL-1 in an in vivo therapeutic role for NL-1 in a C57Bl/6 murine model of ischemic stroke.

RESULTS

NL-1 decreased hydrogen peroxide production with an IC50 of 5.95 μM in neuronal cells (N2A). The in vivo activity of NL-1 was evaluated in a murine 1 h transient middle cerebral artery occlusion (t-MCAO) model of ischemic stroke. We found that mice treated with NL-1 (10 mg/kg, i.p.) at time of reperfusion and allowed to recover for 24 h showed a 43% reduction in infarct volume and 68% reduction in edema compared to sham-injured mice. Additionally, we found that when NL-1 was administered 15 min post-t-MCAO, the ischemia volume was reduced by 41%, and stroke-associated edema by 63%.

CONCLUSION

As support of our hypothesis, as expected, NL-1 failed to reduce stroke infarct in a permanent photothrombotic occlusion model of stroke. This report demonstrates the potential therapeutic benefits of using mitoNEET ligands like NL-1 as novel mitoceuticals for treating reperfusion-injury with cerebral stroke.

Screening of potential drug for Alzheimer's disease: a computational study with GSK-3 β inhibition through virtual screening, docking, and molecular dynamics simulation

The global impact of Alzheimer's disease (AD) necessitates intensive research to find appropriate and effective drugs. Many studies in AD suggested beta-amyloid plaques and neurofibrillary tangles-associated tau protein as the key targets for drug development. On the other hand, it is proved that triggering of Glycogen Synthase Kinase-3β (GSK-3β) also cause AD, therefore, GSK-3β is a potential drug target to combat AD. We, in this study, investigated the ability of small molecules to inhibit GSK-3β through virtual screening, Absorption, Distribution, Metabolism, and Excretion (ADME), induced-fit docking (IFD), molecular dynamics simulation, and binding free energy calculation. Besides, molecular docking was performed to reveal the binding and interaction of the ligand at the active site of GSK-3β. We found two compounds such as 6961 and 6966, which exhibited steady-state interaction with GSK-3β for 30 ns in molecular dynamics simulation. The compounds (6961 and 6966) also achieved a docking score of -9.05 kcal/mol and -8.11 kcal/mol, respectively, which is relatively higher than the GSK-3β II inhibitor (-6.73 kcal/mol). The molecular dynamics simulation revealed that the compounds have a stable state during overall simulation time, and lesser root-mean-square deviation (RMSD) and root-mean-square fluctuation (RMSF) values compared with co-crystal. In conclusion, we suggest the two compounds (6966 and 6961) as potential leads that could be utilized as effective inhibitors of GSK-3β to combat AD.

In vivo and in silico characterization of apocynin in reducing organ oxidative stress: A pharmacokinetic and pharmacodynamic study

Apocynin has been widely used in vivo as a Nox2-contaninig nicotinamide adenine dinucleotide phosphate oxidase inhibitor. However, its time-dependent tissue distribution and inhibition on organ reactive oxygen species (ROS) production remained unclear. In this study, we examined apocynin pharmacokinetics and pharmacodynamics (PKPD) after intravenous (iv) injection (bolus, 5 mg/kg) of mice (CD1, 12-week). Apocynin was detected using a HPLC coupled to a linear ion-trap tandem mass spectrometer. Apocynin peak concentrations were detected in plasma at 1 minute (5494 ± 400 ng/mL) (t1/2  = 0.05 hours, clearance = 7.76 L/h/kg), in urine at 15 minutes (14 942 ± 5977 ng/mL), in liver at 5 minutes (2853 ± 35 ng/g), in heart at 5 minutes (3161 ± 309 ng/g) and in brain at 1 minute (4603 ± 208 ng/g) after iv injection. These were accompanied with reduction of ROS production in the liver, heart and brain homogenates. Diapocynin was not detected in these samples. Therapeutic effect of apocynin was examined using a mouse model (C57BL/6J) of high-fat diet (HFD, 16 weeks)-induced obesity and accelerated aging. Apocynin (5 mmol/L) was supplied in drinking water during the HFD period and was detected at the end of treatment in the brain (5369 ± 1612 ng/g), liver (4818 ± 1340 ng/g) and heart (1795 ± 1487 ng/g) along with significant reductions of ROS production in these organs. In conclusion, apocynin PKPD is characterized by a short half-life, rapid clearance, good distribution and inhibition of ROS production in major organs. Diapocynin is not a metabolite of apocynin in vivo. Apocynin crosses easily the blood-brain barrier and reduces brain oxidative stress associated with metabolic disorders and aging.

Comparison of statistical methods for predicting penetration capacity of drugs into human breast milk using physicochemical, pharmacokinetic and chromatographic descriptors

In silico methods are often used for predicting pharmacokinetic properties of drugs due to their simplicity and cost-effectiveness. This study evaluates the penetration of 83 active pharmaceutical ingredients into human breast milk with an experimental milk-to-plasma ratio (M/P) obtained from the literature. Multiple linear regression (MLR), partial least squares (PLS) and random forest (RF) regression methods were compared to uncover the relationship between physicochemical, pharmacokinetic and membrane crossing properties of active pharmaceutical ingredients (APIs) using their rapid reference measurement value (R_f values), thin-layer chromatography (TLC) data from albumin-impregnated plates. Molecular descriptors of APIs proven to be important for their crossing into breast milk, including protein binding, ionisation state and lipophilicity and TLC data, have been included in the development of the prediction models. The best regression results were achieved by MLR (r ² = 0.83 and r ² = 0.86, n = 28) and RF (r ² = 0.85, n = 58). In addition, the discriminant function analysis (DFA) was performed on acidic, basic and neutral drugs separately and showed a prediction accuracy of 93%, with M/P included as the discriminating variable.

APP Maturation and Intracellular Localization Are Controlled by a Specific Inhibitor of 37/67 kDa Laminin-1 Receptor in Neuronal Cells

Amyloid precursor protein (APP) is processed along both the nonamyloidogenic pathway preventing amyloid beta peptide (Aβ) production and the amyloidogenic pathway, generating Aβ, whose accumulation characterizes Alzheimer’s disease. Items of evidence report that the intracellular trafficking plays a key role in the generation of Aβ and that the 37/67 kDa LR (laminin receptor), acting as a receptor for Aβ, may mediate Aβ-pathogenicity. Moreover, findings indicating interaction between the receptor and the key enzymes involved in the amyloidogenic pathway suggest a strong link between 37/67 kDa LR and APP processing. We show herein that the specific 37/67 kDa LR inhibitor, NSC48478, is able to reversibly affect the maturation of APP in a pH-dependent manner, resulting in the partial accumulation of the immature APP isoforms (unglycosylated/acetylated forms) in the endoplasmic reticulum (ER) and in transferrin-positive recycling endosomes, indicating alteration of the APP intracellular trafficking. These effects reveal NSC48478 inhibitor as a novel small molecule to be tested in disease conditions, mediated by the 37/67 kDa LR and accompanied by inactivation of ERK1/2 (extracellular signal-regulated kinases) signalling and activation of Akt (serine/threonine protein kinase) with consequent inhibition of GSK3β.

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.

Application of a hemodialysis clearance prediction model using quantitative structure-pharmacokinetic relationship analysis

Hemodialysis (HD) is a method used to remove biogenic substances or blood components that cause disease and some drugs used by patients to treat their diseases. Therefore, dosing schedule must be planned according to HD clearance (CL_HD ) when medical treatment is provided to patients receiving HD. We aimed to clarify the physical properties (eg, octanol-water partition coefficient and molecular electronegativity) or pharmacokinetic parameters (eg, volume of distribution) of compounds affecting CL_HD and to construct a mathematical model to predict CL_HD . The analysis covered individual CL_HD data for nine compounds from the literature. The molecular descriptors which are physical properties or pharmacokinetic parameters were calculated using the structural formula of each compound, and searched for factors related to CL_HD among the calculated 148 molecular descriptors. Nonlinear mixed-effects model analysis with CL_HD as objective variable and molecular descriptors as explanatory variable was conducted to examine the factor affecting CL_HD and develop a model for predicting CL_HD . The logarithm of the brain/blood partition coefficient was detected as a factor affecting CL_HD . The predictive accuracy of CL_HD using the constructed mathematical model with the logarithm of the brain/blood partition coefficient as explanatory variable was adequate.

Location and Cell-Type-Specific Bias of Metabotropic Glutamate Receptor, mGlu5, Negative Allosteric Modulators

Emerging data indicate that G-protein coupled receptor (GPCR) signaling is determined by not only the agonist and a given receptor but also a variety of cell-type-specific factors that can influence a receptor's response. For example, the metabotropic glutamate receptor, mGlu₅, which is implicated in a number of neuropsychiatric disorders such as depression, anxiety, and autism, also signals from inside the cell which leads to sustained Ca²⁺ mobilization versus rapid transient responses. Because mGlu₅ is an important drug target, many negative allosteric modulators (NAMs) have been generated to modulate its activity. Here we show that NAMs such as AFQ056, AZD2066, and RG7090 elicit very different end points when tested in postnatal neuronal cultures expressing endogenous mGlu₅ receptors. For example, AFQ056 fails to block intracellular mGlu₅-mediated Ca²⁺ increases whereas RG7090 is very effective. These differences are not due to differential receptor levels, since about the same number of mGlu₅ receptors are present on neurons from the cortex, hippocampus, and striatum based on pharmacological, biochemical, and molecular data. Moreover, biotinylation studies reveal that more than 90% of the receptor is intracellular in these neurons. Taken together, these data indicate that the tested NAMs exhibit both location-dependent and cell type specific bias for mGlu₅-mediated Ca²⁺ mobilization which may affect clinical outcomes.

Exploring anticancer activity of structurally modified benzylphenoxyacetamide (BPA); I: Synthesis strategies and computational analyses of substituted BPA variants with high anti-glioblastoma potential

Structural variations of the benzylphenoxyacetamide (BPA) molecular skeleton were explored as a viable starting point for designing new anti-glioblastoma drug candidates. Hand-to-hand computational evaluation, chemical modifications, and cell viability testing were performed to explore the importance of some of the structural properties in order to generate, retain, and improve desired anti-glioblastoma characteristics. It was demonstrated that several structural features are required to retain the anti-glioblastoma activity, including a carbonyl group of the benzophenone moiety, as well as 4′-chloro and 2,2-dimethy substituents. In addition, the structure of the amide moiety can be modified in such a way that desirable anti-glioblastoma and physical properties can be improved. Via these structural modifications, more than 50 compounds were prepared and tested for anti-glioblastoma activity. Four compounds were identified (HR28, HR32, HR37, and HR46) that in addition to HR40 (PP1) from our previous study, have been determined to have desirable physical and biological properties. These include high glioblastoma cytotoxicity at low μM concentrations, improved water solubility, and the ability to penetrate the blood brain barrier (BBB), which indicate a potential for becoming a new class of anti-glioblastoma drugs.

IVIVC Assessment of Two Mouse Brain Endothelial Cell Models for Drug Screening

Since most preclinical drug permeability assays across the blood-brain barrier (BBB) are still evaluated in rodents, we compared an in vitro mouse primary endothelial cell model to the mouse b.End3 and the acellular parallel artificial membrane permeability assay (PAMPA) models for drug screening purposes. The mRNA expression of key feature membrane proteins of primary and bEnd.3 mouse brain endothelial cells were compared. Transwell^® monolayer models were further characterized in terms of tightness and integrity. The in vitro in vivo correlation (IVIVC) was obtained by the correlation of the in vitro permeability data with log BB values obtained in mice for seven drugs. The mouse primary model showed higher monolayer integrity and levels of mRNA expression of BBB tight junction (TJ) proteins and membrane transporters (MBRT), especially for the efflux transporter Pgp. The IVIVC and drug ranking underlined the superiority of the primary model (r² = 0.765) when compared to the PAMPA-BBB (r² = 0.391) and bEnd.3 cell line (r² = 0.019) models. The primary monolayer mouse model came out as a simple and reliable candidate for the prediction of drug permeability across the BBB. This model encompasses a rapid set-up, a fair reproduction of BBB tissue characteristics, and an accurate drug screening.

RP-18 HPLC Analysis of Drugs’ Ability to Cross the Blood-Brain Barrier

One hundred ten compounds of diverse structures (actives and excipients used in pharmaceutical preparations) were studied by RP-18 HPLC with acetonitrile-pH 7.4 phosphate buffer 1 : 1 (v/v) as the mobile phase. The relationships between the BBB permeation coefficients and the chromatographic parameters log k and (log k)/PSA were compared to those between the blood-brain barrier (BBB) permeation parameters and the RP-18 TLC descriptors Rf and Rf/PSA known from our earlier studies. It was found that the correlations between the BBB permeability and the HPLC data are slightly worse than those achieved for the thin-layer chromatographic data. MLR analysis based upon the physicochemical data confirmed the value of the molecular descriptors, related to the CNS bioavailability. These variables, combined with the HPLC data, made it possible to generate computational models, explaining 70–96% of the total variance of the CNS bioavailability. Contrary to TLC Rf, the advantage of the modification of HPLC log k with PSA (polar surface area) has not been confirmed and the results obtained with log k are superior to those obtained after a novel (log k)/PSA parameter has been introduced. Establishing a firm threshold limit of (log k)/PSA, log k, or even k and k/PSA to distinguish between the CNS+ and CNS− compounds was impossible. On the other hand, discriminant function analyses involving log k and (log k)/PSA as discriminating variables separated the CNS+ and CNS− compounds with the success rate ca. 90%. On the basis of these results, it was concluded that the RP-18 HPLC analytical models are entirely successful in studies and predictions of the BBB permeability.

Evaluation of SYA16263 as a new potential antipsychotic agent without catalepsy

SYA16263 exhibited moderate radioligand binding affinity at the D₂ receptor and produced inhibition of apomorphine-induced climbing behavior in mice with an ED₅₀ value of 3.88 mg/kg IP, predicting potential antipsychotic effects in humans. Analysis of plasma and brains from rats injected IP with SYA16263 over the course of 24 h revealed a log [brain]/[plasma] (log BB) at Cmax observed equal to 1.08, indicating that SYA16263 enters the brain and is predicted to cross the blood brain barrier (BBB) readily. When tested in animal behavior tests for catalepsy, SYA16263 did not produce catalepsy at doses up to 19 times the apomorphine ED₅₀ value predicting little or no extra-pyramidal (EPS) side effects in humans. This is similar to aripiprazole, which is associated with a low incidence of EPS in humans, but unlike haloperidol which is known to cause severe EPS in humans. Functional activities for SYA16263 show that it acts as a D₂ agonist at both the Gi and β-arrestin pathways, similar to, but better than aripiprazole, which could account for the absence of the catalepsy observed. Taken together, the receptor binding profile, the functional status, the animal behavioral tests and the log BB value, all provide evidence for further pre-clinical testing of SYA16263 as a potential antipsychotic agent with an interesting profile and a unique mechanism of action resulting in no EPS even up to 19 times the ED₅₀ value.