A successful virtual screening application: prediction of anticonvulsant activity in MES test of widely used pharmaceutical and food preservatives methylparaben and propylparaben

Finger pump microfluidic detection system for methylparaben detection in foods

A novel assay platform consisting of a finger pump microchip (FPM) and a WiFi-based analytical detection platform is presented for measuring the concentration of methylparaben (MP) in commercial foods. In the presented approach, a low quantity (5 μL) of distilled food sample is dripped onto the FPM and undergoes a modified Fenton reaction at a temperature of 40 °C to form a green-colored complex. The MP concentration is then determined by measuring the color intensity (RGB) of the reaction complex using APP software (self-written) installed on a smartphone. The color intensity Red(R) + Green(G) value of the reaction complex is found to be linearly related (R² = 0.9944) to the MP concentration for standard samples with different MP concentrations ranging from 100 to 3000 ppm. The proposed method is used to detect the MP concentrations of 12 real-world commercial foods. The MP concentrations measurements are found to deviate by no more than 5.88% from the results obtained using a conventional benchtop method. The presented platform thus offers a feasible and low-cost alternative to existing macroscale techniques for measuring the MP concentration in commercial foods.

New insight into molecular mechanism of P450-Catalyzed metabolism of emerging contaminants and its consequence for human health: A case study of preservative methylparaben

Hydroxylated metabolites in the living body are considered as a potential biomarker of exposure to emerging contaminations (ECs) and breast cancer, but their formation mechanism has not received enough attention. Besides, the adverse impacts of metabolites during the metabolic transformation of ECs largely remain unknown. In this study, we employed a density functional calculation combing with in-vitro incubation of human liver microsomes to explore the bio-transformation of preservative methylparaben (MPB) in human bodies. Our results showed that hydroxylated metabolites of MPB (OH-MPB) were observed experimentally, while a formation mechanism was revealed at the molecular level. That is, hydroxylated metabolite was exclusively formed via the hydrogen abstraction from the phenolic hydroxyl group of MPB followed by the OH-rebound pathway, rather than the direct hydroxylation on the benzene ring. The increasing of hydroxyl groups on ECs could improve the metabolisms. This was confirmed in the metabolism of ECs without hydroxyl group and with multiple-hydroxyl groups, respectively. Furthermore, toxicity assessments show that compared to parent MPB, the hydroxylated metabolites have increased negative impacts on the gastrointestinal system and liver. A semiquinone product exhibits potential damage in the cardiovascular system and epoxides are toxic to the blood and gastrointestinal system. The findings deepen our insight into the biotransformation of parabens in human health, especially by providing health warnings about the potential impacts caused by semiquinone and epoxides.

New anticonvulsant candidates prevent P-glycoprotein (P-gp) overexpression in a pharmacoresistant seizure model in mice

Despite the approval of a considerable number of last generation antiepileptic drugs (AEDs) (only in the last decade, six drugs have gained Food and Drug Administration approval), the global figures of seizure control have seemingly not improved, and available AED can still be regarded as symptomatic treatments. Fresh thinking in AEDs drug discovery, including the development of drugs with novel mechanisms of action, is required to achieve truly innovative antiepileptic medications. The transporter hypothesis proposes that inadequate penetration of AEDs across the blood-brain barrier, caused by increased expression of efflux transporters such as P-glycoprotein (P-gp), contributes to drug-resistant epilepsy. Neuroinflammation due to high levels of glutamate has been identified as one of the causes of P-gp upregulation, and several studies in animal models of epilepsy suggest that antiinflammatory drugs might prevent P-gp overexpression and, thus, avoid the development of refractory epilepsy. We have applied ligand-based in silico screening to select compounds that exert dual anticonvulsant and antiinflammatory effects. Five of the hits were tested in animal models of seizure, with protective effects. Later, two of them (sebacic acid (SA) and gamma-decanolactone) were submitted to the recently described MP23 model of drug-resistant seizures. All in all, SA displayed the best profile, showing activity in the maximal electroshock seizure (MES) and pentylenetetrazol (PTZ) seizure models, and reversing resistance to phenytoin (PHT) and decreasing the P-gp upregulation in the MP23 model. Furthermore, pretreatment with SA in the pilocarpine status epilepticus (SE) model resulted in decreased histamine release in comparison with nontreated animals. This is the first report of the use of the MP23 model to screen for novel anticonvulsant compounds that may avoid the development of P-gp-related drug resistance.

Propylparaben Reduces the Long-Term Consequences in Hippocampus Induced by Traumatic Brain Injury in Rats: Its Implications as Therapeutic Strategy to Prevent Neurodegenerative Diseases

BACKGROUND

Severe traumatic brain injury (TBI), an important risk factor for Alzheimer's disease, induces long-term hippocampal damage and hyperexcitability. On the other hand, studies support that propylparaben (PPB) induces hippocampal neuroprotection in neurodegenerative diseases.

OBJECTIVE

Experiments were designed to evaluate the effects of subchronic treatment with PPB on TBI-induced changes in the hippocampus of rats.

METHODS

Severe TBI was induced using the lateral fluid percussion model. Subsequently, rats received subchronic administration with PPB (178 mg/kg, TBI+PPB) or vehicle (TBI+PEG) daily for 5 days. The following changes were examined during the experimental procedure: sensorimotor dysfunction, changes in hippocampal excitability, as well as neuronal damage and volume.

RESULTS

TBI+PEG group showed sensorimotor dysfunction (p < 0.001), hyperexcitability (64.2%, p < 0.001), and low neuronal preservation ipsi- and contralateral to the trauma. Magnetic resonance imaging (MRI) analysis revealed lower volume (17.2%; p < 0.01) and great damage to the ipsilateral hippocampus. TBI+PPB group showed sensorimotor dysfunction that was partially reversed 30 days after trauma. This group showed hippocampal excitability and neuronal preservation similar to the control group. However, MRI analysis revealed lower hippocampal volume (p < 0.05) when compared with the control group.

CONCLUSION

The present study confirms that post-TBI subchronic administration with PPB reduces the long-term consequences of trauma in the hippocampus. Implications of PPB as a neuroprotective strategy to prevent the development of Alzheimer's disease as consequence of TBI are discussed.

Parabens inhibit hNaV 1.2 channels

Propylparaben, a commonly used antimicrobial preservative, has been reported as an anticonvulsant agent targeting neuronal Na⁺ channels (Na_V). However, the specific features of the Na_V channel inhibition by this agent have so far not been extensively studied. Moreover, it is still unclear if it shares this pharmacological activity with other parabens. Here, we fully characterized the mechanism of action of the inhibitory effect that propylparaben and benzylparaben induce on human Na_V 1.2 channel isoform (hNa_V1.2). We established a first approach to know the parabens structural determinants for this channel inhibition. The parabens effects on hNa_V1.2 channel mediated currents were recorded using the patch-clamp whole-cell configuration on hNa_V1.2 stably transfected HEK293 cells. Propylparaben induced a typical state-dependent inhibition on hNa_V1.2 channel carried current, characterized by a left-shift in the steady-state inactivation curve, a prolongation in the time needed for recovery from fast inactivation and a frequency-dependent blocking behavior. The state-dependent inhibition is increased for butylparaben and benzylparaben and diminished for methylparaben, ethylparaben and p-hydroxybenzoic acid (the major metabolite of parabens hydrolysis). Particularly, butylparaben and benzylparaben shift the steady-state inactivation curve 2- and 3-times more than propylparaben, respectively. Parabens are blockers of hNa_V1.2 channels, sharing the mechanism of action of most of sodium channel blocking antiseizure drugs. The potency of this inhibition increases with the size of the lipophilic alcoholic residue of the ester group. These results provide a basis for rational drug design directed to generate new potential anticonvulsant agents.

Quantitative structure-activity relationship models for compounds with anticonvulsant activity

Introduction: Third-generation antiepileptic drugs have seemingly failed to improve the global figures of seizure control and can still be regarded as symptomatic treatments. Quantitative structure-activity relationships (QSAR) can be used to guide hit-to-lead and lead optimization projects and applied to the large-scale virtual screening of chemical libraries. Areas covered: In this review, the authors cover reports on QSAR models related to antiepileptic drugs and drug targets in epilepsy, analyzing whether they refer to classic or non-classic QSAR and if they apply QSAR as a descriptive or predictive approach, among other considerations. The article finally focuses on a more detailed discussion of those predictive studies which include some sort of experimental validation, i.e. papers in which the reported models have been used to identify novel active compounds which have been tested in vitro and/or in vivo. Expert opinion: There are significant opportunities to apply the QSAR methodology to assist the discovery of more efficacious antiepileptic drugs. Considering the intrinsic complexity of the disorder, such applications should focus on state-of-the-art approximations (e.g. systemic, multi-target and multi-scale QSAR as well as ensemble and deep learning) and modeling the effects on novel drug targets and modern screening tools.

Neuroprotective effects of levetiracetam, both alone and combined with propylparaben, in the long-term consequences induced by lithium-pilocarpine status epilepticus

Status epilepticus (SE) is a neurological condition that frequently induces severe neuronal injury in the hippocampus, subsequent epileptogenesis and pharmacoresistant spontaneous recurrent seizures (SRS). The repeated administration of LEV (a broad-spectrum antiepileptic drug) during the post-SE period does not prevent the subsequent development of SRS. However, this treatment reduces SE-induced neurodegeneration in the hippocampus. Conversely, propylparaben (PPB) is a widely used antimicrobial that blocks voltage-dependent Na⁺ channels, induces neuroprotection and reduces epileptiform activity in vitro. The present study attempted to determine if the neuroprotective effects induced by LEV are augmented when combined with a sub-effective dose of PPB. Long-term SE-induced consequences (hyperexcitability, high glutamate release, neuronal injury and volume loss) were evaluated in the hippocampus of rats. LEV alone, as well as combined with PPB, did not prevent the occurrence of SRS. However, animals treated with LEV plus PPB showed high prevalence of low frequency oscillations (0.1-4 Hz and 8-90 bands, p < 0.001) and low prevalence of high frequency activity (90-250 bands, p < 0.001) during the interictal period. In addition, these animals presented lower extracellular levels of glutamate, decreased rate of neurodegeneration and a similar hippocampal volume compared to the control conditions. This study's results suggest that LEV associated with PPB could represent a new therapeutic strategy to reduce long-term consequences induced by SE that facilitate pharmacoresistant SRS.

Transductive Joint-Knowledge-Transfer TSK FS for Recognition of Epileptic EEG Signals

Intelligent recognition of electroencephalogram (EEG) signals is an important means to detect seizure. Traditional methods for recognizing epileptic EEG signals are usually based on two assumptions: 1) adequate training examples are available for model training and 2) the training set and the test set are sampled from data sets with the same distribution. Since seizures occur sporadically, training examples of seizures could be limited. Besides, the training and test sets are usually not sampled from the same distribution for generic non-patient-specific recognition of EEG signals. Hence, the two assumptions in traditional recognition methods could hardly be satisfied in practice, which results in degradation of model performance. Transfer learning is a feasible approach to tackle this issue attributed to its ability to effectively learn the knowledge from the related scenes (source domains) for model training in the current scene (target domain). Among the existing transfer learning methods for epileptic EEG recognition, transductive transfer learning fuzzy systems (TTL-FSs) exhibit distinctive advantages-the interpretability that is important for medical diagnosis and the transfer learning ability that is absent from traditional fuzzy systems. Nevertheless, the transfer learning ability of TTL-FSs is restricted to a certain extent since only the discrepancy in marginal distribution between the training data and test data is considered. In this paper, the enhanced transductive transfer learning Takagi-Sugeno-Kang fuzzy system construction method is proposed to overcome the challenge by introducing two novel transfer learning mechanisms: 1) joint knowledge is adopted to reduce the discrepancy between the two domains and 2) an iterative transfer learning procedure is introduced to enhance transfer learning ability. Extensive experiments have been carried out to evaluate the effectiveness of the proposed method in recognizing epileptic EEG signals on the Bonn and CHB-MIT EEG data sets. The results show that the method is superior to or at least competitive with some of the existing state-of-art methods under the scenario of transfer learning.

The application of molecular topology for ulcerative colitis drug discovery

INTRODUCTION

Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish. Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors. Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a 'topological pattern' that goes beyond a specific mechanism of action.

Propylparaben suppresses epileptiform activity in hippocampal CA1 pyramidal cells in vitro

Epilepsy is a highly prevalent neurological disorder. Additionally, a percentage of patients do not respond to conventional antiepileptic drugs. Therefore, drugs for epilepsy control are still being developed. In the present study, the effect of propylparaben (PPB) in the epileptiform activity induced by 4-aminopyridine in hippocampal CA1 pyramidal neurons was evaluated using individual recordings in current-clamp mode. Results indicated that PPB suppressed the epileptiform activity in registered neurons. This effect disappeared when PPB was removed from the solution of incubation. In contrast, phenytoin only reduced the firing frequency without abolishing epileptiform activity. Our results indicate that PPB exerts an antiepileptic effect on CA1 pyramidal neurons in vitro. Therefore, PPB may represent an effective antiepileptic compound.

Computer-Aided Identification of Anticonvulsant Effect of Natural Nonnutritive Sweeteners Stevioside and Rebaudioside A

Steviol glycosides are natural constituents of Stevia rebaudiana (Bert.) Bert. (Asteraceae) that have recently gained worldwide approval as nonnutritive sweeteners by the Joint Food and Agriculture Organization/World Organization Expert Committee on Food Additives. Cheminformatic tools suggested that the aglycone steviol and several of its phase I metabolites were predicted as potential anticonvulsant agents effective in the seizure animal model maximal electroshock seizure (MES) test. Thus, aqueous infusion from S. rebaudiana was tested in the MES test (mice, intraperitoneal administration), confirming dose-dependent anticonvulsant effect. Afterward, isolated stevioside and rebaudioside A were tested in the MES test, with positive results. Though drug repositioning most often focuses on known therapeutics, this article illustrates the possibilities of this strategy to find new functionalities and therapeutic indications for food constituents and natural products.

Several new diverse anticonvulsant agents discovered in a virtual screening campaign aimed at novel antiepileptic drugs to treat refractory epilepsy

A virtual screening campaign was conducted in order to discover new anticonvulsant drug candidates for the treatment of refractory epilepsy. To this purpose, a topological discriminant function to identify antiMES drugs and a sequential filtering methodology to discriminate P-glycoprotein substrates and nonsubstrates were jointly applied to ZINC 5 and DrugBank databases. The virtual filters combine an ensemble of 2D classifiers and docking simulations. In the light of the results, 10 structurally diverse compounds were acquired and tested in animal models of seizure and the rotorod test. All 10 candidates showed some level of protection against MES test.

Anticonvulsant activity of artificial sweeteners: a structural link between sweet-taste receptor T1R3 and brain glutamate receptors

A virtual screening campaign based on application of a topological discriminant function capable of identifying novel anticonvulsant agents indicated several widely-used artificial sweeteners as potential anticonvulsant candidates. Acesulfame potassium, cyclamate and saccharin were tested in the Maximal Electroshock Seizure model (mice, ip), showing moderate anticonvulsant activity. We hypothesized a probable structural link between the receptor responsible of sweet taste and anticonvulsant molecular targets. Bioinformatic tools confirmed a highly significant sequence-similarity between taste-related protein T1R3 and several metabotropic glutamate receptors from different species, including glutamate receptors upregulated in epileptogenesis and certain types of epilepsy.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.