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

Towards development of new antimalarial compounds through in silico and in vitro assays

Malaria is one of most widespread infectious disease in world. The antimalarial therapy presents a series of limitations, such as toxicity and the emergence of resistance, which makes the search for new drugs urgent. Thus, it becomes necessary to explore essential and exclusive therapeutic targets of the parasite to achieve selective inhibition. Enoyl-ACP reductase is an enzyme of the type II fatty acid biosynthetic pathway and is responsible for the rate-limiting step in the fatty acid elongation cycle. In this work, we use hierarchical virtual screening and drug repositioning strategies to prioritize compounds for phenotypic assays and molecular dynamics studies. The molecules were tested against chloroquine-resistant W2 strain of Plasmodium falciparum (EC50 between 330.05 and 13.92 µM). Nitrofurantoin was the best antimalarial activity at low micromolar range (EC50 = 13.92 µM). However, a hit compound against malaria must have a biological activity value below 1 µM. A large number of molecules present problems with permeability in biological membranes and reaching an effective concentration in their target's microenvironment. Nitrofurantoin derivatives with inclusions of groups which confer increased lipid solubility (methyl groups, halogens and substituted and unsubstituted aromatic rings) have been proposed. These derivatives were pulled through the lipid bilayer in molecular dynamics simulations. Molecules 14, 18 and 21 presented lower free energy values than nitrofurantoin when crossing the lipid bilayer.

Multilayered Computational Framework for Designing Peptide Inhibitors of HVEM-LIGHT Interaction

The herpesvirus entry mediator (HVEM) and its ligand LIGHT play crucial roles in immune system regulation, including T-cell proliferation, B-cell differentiation, and immunoglobulin secretion. However, excessive T-cell activation can lead to chronic inflammation and autoimmune diseases. Thus, inhibiting the HVEM-LIGHT interaction emerges as a promising therapeutic strategy for these conditions and in preventing adverse reactions in organ transplantation. This study focused on designing peptide inhibitors, targeting the HVEM-LIGHT interaction, using molecular dynamics (MD) simulations of 65 peptides derived from HVEM. These peptides varied in length and disulfide-bond configurations, crucial for their interaction with the LIGHT trimer. By simulating 31 HVEM domain variants, including the full-length protein, we assessed conformational changes upon LIGHT binding to understand the influence of HVEM segments and disulfide bonds on the binding mechanism. Employing multitrajectory microsecond-scale, all-atom MD simulations and molecular mechanics with generalized Born and surface area (MM-GBSA) binding energy estimation, we identified promising CRD2 domain variants with high LIGHT affinity. Notably, point mutations in these variants led to a peptide with a single disulfide bond (C58-C73) and a K54E substitution, exhibiting the highest binding affinity. The importance of the CRD2 domain and Cys58-Cys73 disulfide bond for interrupting HVEM-LIGHT interaction was further supported by analyzing truncated CRD2 variants, demonstrating similar binding strengths and mechanisms. Further investigations into the binding mechanism utilized steered MD simulations at various pulling speeds and umbrella sampling to estimate the energy profile of HVEM-based inhibitors with LIGHT. These comprehensive analyses revealed key interactions and different binding mechanisms, highlighting the increased binding affinity of selected peptide variants. Experimental circular dichroism techniques confirmed the structural properties of these variants. This study not only advances our understanding of the molecular basis of HVEM-LIGHT interactions but also provides a foundation for developing novel therapeutic strategies for immune-related disorders. Furthermore, it sets a gold standard for peptide inhibitor design in drug development due to its systematic approach.

Novel 6-hydroxybenzothiazol-2-carboxamides as potent and selective monoamine oxidase B inhibitors endowed with neuroprotective activity

In neurodegenerative diseases, using a single molecule that can exert multiple effects to modify the disease may have superior activity over the classical "one molecule-one target" approach. Herein, we describe the discovery of 6-hydroxybenzothiazol-2-carboxamides as highly potent and selective MAO-B inhibitors. Variation of the amide substituent led to several potent compounds having diverse side chains with cyclohexylamide 40 displaying the highest potency towards MAO-B (IC50 = 11 nM). To discover new compounds with extended efficacy against neurotoxic mechanisms in neurodegenerative diseases, MAO-B inhibitors were screened against PHF6, R3 tau, cellular tau and α-synuclein (α-syn) aggregation. We identified the phenethylamide 30 as a multipotent inhibitor of MAO-B (IC50 = 41 nM) and α-syn and tau aggregation. It showed no cytotoxic effects on SH-SY5Y neuroblastoma cells, while also providing neuroprotection against toxicities induced by α-syn and tau. The evaluation of key physicochemical and in vitro-ADME properties revealed a great potential as drug-like small molecules with multitarget neuroprotective activity.

Application of temperature-dependent and steered molecular dynamics simulation to screen anti-dengue compounds against Marburg virus

Marburg virus infections are extremely fatal with a fatality range of 23% to 90%, therefore there is an urgent requirement to design and develop efficient therapeutic molecules. Here, a comprehensive temperature-dependent molecular dynamics (MD) simulation method was implemented to identify the potential molecule from the anti-dengue compound library that can inhibit the function of the VP24 protein of Marburg. Virtual high throughput screening identified five effective binders of VP24 after screening 484 anti-dengue compounds. These compounds were treated in MD simulation at four different temperatures: 300, 340, 380, and 420 K. Higher temperatures showed dissociation of hit compounds from the protein. Further, triplicates of 100 ns MD simulation were conducted which showed that compounds ID = 118717693, and ID = 5361 showed strong stability with the protein molecule. These compounds were further validated using Δ G binding free energies and they showed: -30.38 kcal/mol, and -67.83 kcal/mol binding free energies, respectively. Later, these two compounds were used in steered MD simulation to detect its dissociation. Compound ID = 5361 showed the maximum pulling force of 199.02 kcal/mol/nm to dissociate the protein-ligand complex while ID = 118717693 had a pulling force of 101.11 kcal/mol/nm, respectively. This ligand highest number of hydrogen bonds with varying occupancies at 89.93%, 69.80%, 57.93%, 52.33%, and 50.63%. This study showed that ID = 5361 can bind with the VP24 strongly and has the potential to inhibit its function which can be validated in the in-vitro experiment.Communicated by Ramaswamy H. Sarma.

Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis

Leishmaniasis affects more than 12 million humans globally and a further 1 billion people are at risk in leishmaniasis endemic areas. The lack of a vaccine for leishmaniasis coupled with the limitations of existing anti-leishmanial therapies prompted this study. Cheminformatic techniques are widely used in screening large libraries of compounds, studying protein-ligand interactions, analysing pharmacokinetic properties, and designing new drug molecules with great speed, accuracy, and precision. This study was undertaken to evaluate the anti-leishmanial potential of some organoselenium compounds by quantitative structure-activity relationship (QSAR) modeling, molecular docking, pharmacokinetic analysis, and molecular dynamic (MD) simulation. The built QSAR model was validated (R2train = 0.8646, R2test = 0.8864, Q2 = 0.5773) and the predicted inhibitory activity (pIC50) values of the newly designed compounds were higher than that of the template (Compound 6). The new analogues (6a, 6b, and 6c) showed good binding interactions with the target protein (Pyridoxal kinase, PdxK) while also presenting excellent drug-likeness and pharmacokinetic profiles. The results of density functional theory, MD simulation, and molecular mechanics generalized Born surface area (MM/GBSA) analyses suggest the favourability and stability of protein-ligand interactions of the new analogues with PdxK, comparing favourably well with the reference drug (Pentamidine). Conclusively, the newly designed compounds could be synthesized and tested experimentally as potential anti-leishmanial drug molecules.Communicated by Ramaswamy H. Sarma.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Novel Lipids to Regulate Obesity and Brain Function: Comparing Available Evidence and Insights from QSAR In Silico Models

Lipid molecules, such as policosanol, ergosterol, sphingomyelin, omega 3 rich phosphatidylcholine, α-tocopherol, and sodium butyrate, have emerged as novel additions to the portfolio of bioactive lipids. In this state-of-the-art review, we discuss these lipids, and their activity against obesity and mental or neurological disorders, with a focus on their proposed cellular targets and the ways in which they produce their beneficial effects. Furthermore, this available information is compared with that provided by in silico Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) models in order to understand the usefulness of these tools for the discovery of new bioactive compounds. Accordingly, it was possible to highlight how these lipids interact with various cellular targets related to the molecule transportation and absorption (e.g., α-tocopherol transfer protein for α-Tocopherol, ATP-binding cassette ABC transporters or Apolipoprotein E for sphingomyelins and phospholipids) or other processes, such as the regulation of gene expression (involving Sterol Regulatory Element-Binding Proteins for ergosterol or Peroxisome Proliferator-Activated Receptors in the case of policosanol) and inflammation (the regulation of interleukins by sodium butyrate). When comparing the literature with in silico Quantitative Structure-Activity Relationship (QSAR) models, it was observed that although they are useful for selecting bioactive molecules when compared in batch, the information they provide does not coincide when assessed individually. Our review highlights the importance of considering a broad range of lipids as potential bioactives and the need for accurate prediction of ADMET parameters in the discovery of new biomolecules. The information presented here provides a useful resource for researchers interested in developing new strategies for the treatment of obesity and mental or neurological disorders.

In silico molecular modeling, neuro-behavioral profile, and toxicity assessment of the essential oil of Ferula gummosa Boiss. as an anti-seizure agent

ETHNOPHARMACOLOGICAL RELEVANCE

Ferula gummosa Boiss., known in Persian as "Baridje," belongs to the Apiaceae family. All parts of this plant, especially the root, contain galbanum. Galbanum, the oleo-gum resin of F. gummosa, is one of the essential traditional herbal medicines in Iran, which is used as a tonic for epilepsy and chorea, memory enhancement, gastrointestinal diseases, and wound healing.

AIM OF THE STUDY

We investigated the toxicity, anticonvulsant effects, and molecular modeling of the essential oil (EO) distilled from the oleo-gum resin of F. gummosa.

MATERIALS AND METHODS

Gas chromatography-mass spectrometry was used to identify the EO components. The cytotoxicity of EO on HepG2 cell lines was assessed by the MTT method. Male mice were arranged as follows: negative control groups (sunflower oil (10 ml/kg, i.p.) or saline (10 ml/kg, p.o.)), EO groups (0.5, 1, 1.5, and 2.5 ml/kg, p.o.), and positive control groups (ethosuximide (150 mg/kg, p.o.) or diazepam (1.0 or 2 mg/kg, i.p.)). The motor coordination and neurotoxicity of EO were studied using the rota-rod test. Open-field, novel object recognition, and passive avoidance learning tests were used to investigate the effect of EO on locomotor activity and memory function. An acute pentylenetetrazole-induced seizure model was utilized to evaluate the anticonvulsant properties of the EO. The interaction of the EO main components with the GABA_A receptor was investigated by coarse-grained molecular dynamics simulations.

RESULTS

β-pinene, sabinene, α-pinene, and ρ-cymene were the main components of EO. The IC₅₀ of the EO at 24, 48, and 72 h was found to be 59.90, 12.96, and 3.93 μl/ml, respectively. No adverse effects were observed in memory, motor coordination, and locomotor activity in mice treated with EO. Administration of EO (1, 1.5, and 2.5 ml/kg) improved survival rates in mice receiving pentylenetetrazole (PTZ; to induce an epileptic seizure). Sabinene was able to bind to the binding site of benzodiazepines at the GABA_A receptor.

CONCLUSIONS

Acute treatment with the EO of F. gummosa caused antiepileptic effects and could effectively increase the survival rate in PTZ-treated mice with no significant toxicity.

Mast cell activation mediates blood-brain barrier impairment and cognitive dysfunction in septic mice in a histamine-dependent pathway

Introduction

Sepsis-associated encephalopathy (SAE) is a diffuse cerebral dysfunction resulting from a systemic inflammatory response to infection; however, its pathophysiology remains unclear. Sepsis-induced neuroinflammation and blood-brain barrier (BBB) disruption are crucial factors in brain function disturbance in SAE. Mast cells (MCs) activation plays an important role in several neuroinflammation models; however, its role in SAE has not been comprehensively investigated.

Methods

We first established a SAE model by cecal ligation puncture (CLP) surgery and checked the activation of MCs. MCs activation was checked using immumohistochemical staining and Toluidine Blue staining. We administrated cromolyn (10mg/ml), a MC stabilizer, to rescue the septic mice. Brain cytokines levels were measured using biochemical assays. BBB disruption was assessed by measuring levels of key tight-junction (TJ) proteins. Cognitive function of mice was analyzed by Y maze and open field test. Transwell cultures of brain microvascular endothelial cells (BMVECs) co-cultured with MCs were used to assess the interaction of BMVECs and MCs.

Results

Results showed that MCs were overactivated in the hippocampus of CLP-induced SAE mice. Cromolyn intracerebroventricular (i.c.v) injection substantially inhibited the MCs activation and neuroinflammation responses, ameliorated BBB impairment, improved the survival rate and alleviated cognitive dysfunction in septic mice. In vitro experiments, we revealed that MCs activation increased the sensitivity of BMVECs against to lipopolysaccharide (LPS) challenge. Furthermore, we found that the histamine/histamine 1 receptor (H1R) mediated the interaction between MCs and BMVECs, and amplifies the LPS-induced inflammatory responses in BMVECs by modulating the TLR2/4-MAPK signaling pathway.

Conclusions

MCs activation could mediate BBB impairment and cognitive dysfunction in septic mice in a histamine-dependent pathway.

Computer aided drug design in the development of proteolysis targeting chimeras

Proteolysis targeting chimeras represent a class of drug molecules with a number of attractive properties, most notably a potential to work for targets that, so far, have been in-accessible for conventional small molecule inhibitors. Due to their different mechanism of action, and physico-chemical properties, many of the methods that have been designed and applied for computer aided design of traditional small molecule drugs are not applicable for proteolysis targeting chimeras. Here we review recent developments in this field focusing on three aspects: de-novo linker-design, estimation of absorption for beyond-rule-of-5 compounds, and the generation and ranking of ternary complex structures. In spite of this field still being young, we find that a good number of models and algorithms are available, with the potential to assist the design of such compounds in-silico, and accelerate applied pharmaceutical research.

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.

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.

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.

Phosphatidylserine Exposed Lipid Bilayer Models for Understanding Cancer Cell Selectivity of Natural Compounds: A Molecular Dynamics Simulation Study

Development of drugs that are selectively toxic to cancer cells and safe to normal cells is crucial in cancer treatment. Evaluation of membrane permeability is a key metric for successful drug development. In this study, we have used in silico molecular models of lipid bilayers to explore the effect of phosphatidylserine (PS) exposure in cancer cells on membrane permeation of natural compounds Withaferin A (Wi-A), Withanone (Wi-N), Caffeic Acid Phenethyl Ester (CAPE) and Artepillin C (ARC). Molecular dynamics simulations were performed to compute permeability coefficients. The results indicated that the exposure of PS in cancer cell membranes facilitated the permeation of Wi-A, Wi-N and CAPE through a cancer cell membrane when compared to a normal cell membrane. In the case of ARC, PS exposure did not have a notable influence on its permeability coefficient. The presented data demonstrated the potential of PS exposure-based models for studying cancer cell selectivity of drugs.

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.

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

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

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

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

Mechanistic Understanding from Molecular Dynamics in Pharmaceutical Research 2: Lipid Membrane in Drug Design

We review the use of molecular dynamics (MD) simulation as a drug design tool in the context of the role that the lipid membrane can play in drug action, i.e., the interaction between candidate drug molecules and lipid membranes. In the standard "lock and key" paradigm, only the interaction between the drug and a specific active site of a specific protein is considered; the environment in which the drug acts is, from a biophysical perspective, far more complex than this. The possible mechanisms though which a drug can be designed to tinker with physiological processes are significantly broader than merely fitting to a single active site of a single protein. In this paper, we focus on the role of the lipid membrane, arguably the most important element outside the proteins themselves, as a case study. We discuss work that has been carried out, using MD simulation, concerning the transfection of drugs through membranes that act as biological barriers in the path of the drugs, the behavior of drug molecules within membranes, how their collective behavior can affect the structure and properties of the membrane and, finally, the role lipid membranes, to which the vast majority of drug target proteins are associated, can play in mediating the interaction between drug and target protein. This review paper is the second in a two-part series covering MD simulation as a tool in pharmaceutical research; both are designed as pedagogical review papers aimed at both pharmaceutical scientists interested in exploring how the tool of MD simulation can be applied to their research and computational scientists interested in exploring the possibility of a pharmaceutical context for their research.

In silico study of levodopa in hydrated lipid bilayers at the atomistic level

This article presents atomistic molecular dynamics and umbrella sampling simulations of levodopa at various concentrations in hydrated cholesterol-free 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and cholesterol-containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Levodopa is the standard medication for Parkinson's disease and is marketed under various trade names; in the context of this article, the levodopa molecule is mostly studied in its zwitterionic form but some results concerning the neutral levodopa are presented as well for comparison purposes. The motivation is to study in detail how levodopa behaves in different hydrated lipid membranes, primarily from the thermodynamic point of view, and reveal aspects of mechanism of its permeation through them. Dependencies of properties on the levodopa concentration are also investigated. Special attention is paid to the calculation of mass density profiles, order parameters and self-diffusion coefficients. Levodopa zwitterions, which form a hydrogen bond network with water and phospholipid molecules, are found to be preferentially located at the water/lipid interface, as well as in the aqueous phase surrounding the cholesterol-free and cholesterol-containing bilayers. This is concluded from the potentials of mean force calculated by umbrella sampling simulations as levodopa is transferred from the lipid to the aqueous phase along an axis perpendicular to the two leaflets of the membranes.

Prediction of the Blood-Brain Barrier (BBB) Permeability of Chemicals Based on Machine-Learning and Ensemble Methods

The ability of chemicals to enter the blood-brain barrier (BBB) is a key factor for central nervous system (CNS) drug development. Although many models for BBB permeability prediction have been developed, they have insufficient accuracy (ACC) and sensitivity (SEN). To improve performance, ensemble models were built to predict the BBB permeability of compounds. In this study, in silico ensemble-learning models were developed using 3 machine-learning algorithms and 9 molecular fingerprints from 1757 chemicals (integrated from 2 published data sets) to predict BBB permeability. The best prediction performance of the base classifier models was achieved by a prediction model based on an random forest (RF) and a MACCS molecular fingerprint with an ACC of 0.910, an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.957, a SEN of 0.927, and a specificity of 0.867 in 5-fold cross-validation. The prediction performance of the ensemble models is better than that of most of the base classifiers. The final ensemble model has also demonstrated good accuracy for an external validation and can be used for the early screening of CNS drugs.

Fullerene translocation through peroxidized lipid membranes

Recent cytotoxicity research suggests that fullerenes can enter the cell and cross the blood-brain barrier. However, the underlying toxicity mechanism behind the penetration of fullerenes through biological membranes is still not well understood. Here we perform coarse-grained molecular dynamics simulations to investigate the interactions of fullerenes and their polar derivatives (Janus) with model regular and peroxidized bilayers. We show that the translocation of fullerenes and their residence time in bulk water vary depending on the bilayer's peroxidation degree and fullerene polarity. The distribution of fullerenes inside the bilayer is mainly determined by the peroxidation degree and the saturation level of lipid acyl chains. The transport of pristine fullerenes through bilayers occurs at nano timescale while the complete diffusion may not be achieved for Janus fullerenes in micro timescale. As for the toxic response of fullerenes in terms of membrane damage, no mechanical disruption of model bilayers is observed throughout the studied simulation times.

Effects of lipid heterogeneity on model human brain lipid membranes

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

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

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